Search results for: document processing

Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Abhinay Puvvala, Veerendra Kumar Rai

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A bottleneck situation arises when the outflow is lesser than the inflow in a pipe-like setup. A more practical interpretation of bottlenecks emphasizes on the realization of Service Level Objectives (SLOs) at given workloads. Our approach detects two key aspects of bottlenecks – when and where. To identify ‘when’ we continuously poll on certain key metrics such as resource utilization, processing time, request backlog and throughput at a system level. Further, when the slope of the expected sojourn time at a workload is greater than ‘K’ times the slope of expected sojourn time at the previous step of the workload while the workload is being gradually increased in discrete steps, a bottleneck situation arises. ‘K’ defines the threshold condition and is computed based on the system’s service level objectives. The second aspect of our approach is to identify the location of the bottleneck. In multi-tier systems with a complex network of layers, it is a challenging problem to locate bottleneck that affects the overall system performance. We stage the system by varying workload incrementally to draw a correlation between load increase and system performance to the point where Service Level Objectives are violated. During the staging process, multiple metrics are monitored at hardware and application levels. The correlations are drawn between metrics and the overall system performance. These correlations along with the Service Level Objectives are used to arrive at the threshold conditions for each of these metrics. Subsequently, the same method used to identify when a bottleneck occurs is used on metrics data with threshold conditions to locate bottlenecks.

Keywords: bottleneck, workload, service level objectives (SLOs), throughput, system performance

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: A. Somaya Madkour, B. Abeer sheta, C. Fatma Badr El Dine, D. Yasser Elwakeel, E. Nermine AbdAllah

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Criminal offenders have a fundamental goal not to leave any traces at the crime scene. Some may suppose that items recovered underwater will have no forensic value, therefore, they try to destroy the traces by throwing items in water. These traces are subjected to the destructive environmental effects. This can represent a challenge for Forensic experts investigating finger marks. Accordingly, the present study was conducted to determine the optimal method for latent fingerprints development on non-porous surfaces submerged in aquatic environments at different time interval. The two factors analyzed in this study were the nature of aquatic environment and length of submerged time. In addition, the quality of developed finger marks depending on the used method was also assessed. Therefore, latent fingerprints were deposited on metallic, plastic and glass objects and submerged in fresh or sea water for one, two, and ten days. After recovery, the items were subjected to cyanoacrylate fuming, black powder and small particle reagent processing and the prints were examined. Each print was evaluated according to fingerprint quality assessment scale. The present study demonstrated that the duration of submersion affects the quality of finger marks; the longer the duration, the worse the quality.The best results of visualization were achieved using cyanoacrylate either in fresh or sea water. This study has also revealed that the exposure to sea water had more destructive influence on the quality of detected finger marks.

Keywords: fingerprints, fresh water, sea, non-porous

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Authors: Mehari Kahsay, Krishna Murthy Kyathegowda, Temesgen Berhanu

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Aluminum matrix composites have gained focus on research and industrial use, especially those not requiring extreme loading or thermal conditions, for the last few decades. Their relatively low cost, simple processing and attractive properties are the reasons for the widespread use of aluminum matrix composites in the manufacturing of automobiles, aircraft, military, and sports goods. In this article, the microstructure, mechanical, and corrosion behaviors of the aluminum metal matrix were reviewed, focusing on the stir casting fabrication process and usage of agro/industrial waste reinforcement particles. The results portrayed that mechanical properties like tensile strength, ultimate tensile strength, hardness, percentage of elongation, impact, and fracture toughness are highly dependent on the amount, kind, and size of reinforcing particles. Additionally, uniform distribution, wettability of reinforcement particles, and the porosity level of the resulting composite also affect the mechanical and corrosion behaviors of aluminum matrix composites. The two-step stir-casting process resulted in better wetting characteristics, a lower porosity level, and a uniform distribution of particles with proper handling of process parameters. On the other hand, the inconsistent and contradicting results on corrosion behavior regarding monolithic and hybrid aluminum matrix composites need further study.

Keywords: microstructure, mechanical behavior, corrosion, aluminum matrix composite

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: R. Suresh, Pavan Kumar Nimmagadda, Ming Zo Tan, Shane Hart, Sharp Ugwuocha

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Polycrystalline diamond compact (PDC) drill bits are extensively used in the oil and gas industry as well as the mining industry. Industry engineers continually improve upon PDC drill bit designs and hydraulic conditions. Optimized injection nozzles play a key role in improving the drilling performance and efficiency of these ever changing PDC drill bits. In the first part of this study, computational fluid dynamics (CFD) modelling is performed to investigate the hydrodynamic characteristics of drilling fluid flow around the PDC drill bit. An Open-source CFD software – OpenFOAM simulates the flow around the drill bit, based on the field input data. A specifically developed console application integrates the entire CFD process including, domain extraction, meshing, and solving governing equations and post-processing. The results from the OpenFOAM solver are then compared with that of the ANSYS Fluent software. The data from both software programs agree. The second part of the paper describes the parametric study of the PDC drill bit nozzle to determine the effect of parameters such as number of nozzles, nozzle velocity, nozzle radial position and orientations on the flow field characteristics and bit washing patterns. After analyzing a series of nozzle configurations, the best configuration is identified and recommendations are made for modifying the PDC bit design.

Keywords: ANSYS Fluent, computational fluid dynamics, nozzle configuration, OpenFOAM, PDC dill bit

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Authors: A. Ashraff

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The increasing importance of the web as a medium for electronic and business transactions has served as a catalyst or rather a driving force for the introduction and implementation of recommender systems. Recommender Systems play a major role in processing and analyzing thousands of data rows or reviews and help humans make a purchase decision of a product or service. It also has the ability to predict whether a particular user would rate a product or service based on the user’s profile behavioral pattern. At present, Recommender Systems are being used extensively in every domain known to us. They are said to be ubiquitous. However, in the field of recruitment, it’s not being utilized exclusively. Recent statistics show an increase in staff turnover, which has negatively impacted the organization as well as the employee. The reasons being company culture, working flexibility (work from home opportunity), no learning advancements, and pay scale. Further investigations revealed that there are lacking guidance or support, which helps a job seeker find the company that will suit him best, and though there’s information available about companies, job seekers can’t read all the reviews by themselves and get an analytical decision. In this paper, we propose an approach to study the available review data on IT companies (score their reviews based on user review sentiments) and gather information on job seekers, which includes their Psychometric evaluations. Then presents the job seeker with useful information or rather outputs on which company is most suitable for the job seeker. The theoretical approach, Algorithmic approach and the importance of such a system will be discussed in this paper.

Keywords: psychometric tests, recommender systems, sentiment analysis, hybrid recommender systems

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Oriyomi M. Okeyinka, David A. Oloke, Jamal M. Khatib

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Large quantities of solid wastes being generated worldwide from sources such as household, domestic, industrial, commercial and construction demolition activities, leads to environmental concerns. Utilization of these wastes in making building construction materials can reduce the magnitude of the associated problems. When these waste products are used in place of other conventional materials, natural resources and energy are preserved and expensive and/or potentially harmful waste disposal is avoided. Recycling which is regarded as the third most preferred waste disposal option, with its numerous environmental benefits, stand as a viable option to offset the environmental impact associated with the construction industry. This paper reviews the results of laboratory tests and important research findings, and the potential of using these wastes in building construction materials with focus on sustainable development. Research gaps, which includes; the need to develop standard mix design for solid waste based building materials; the need to develop energy efficient method of processing solid waste use in concrete; the need to study the actual behavior or performance of such building materials in practical application and the limited real life application of such building materials have also been identified. Therefore a research is being proposed to develop an environmentally friendly, lightweight building block from recycled waste paper, without the use of cement, and with properties suitable for use as walling unit. This proposed research intends to incorporate, laboratory experimentation and modeling to address the identified research gaps.

Keywords: recycling, solid wastes, construction, building materials

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: Abdul Manan

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The necessity for efficient and cost-effective energy storage devices to intelligently store the inconsistent energy output from modern renewable energy sources is peaked today. The scientific community is struggling to identify the appropriate material system for energy storage applications. Countless contributions by researchers worldwide have now helped us identify the possible snags and limitations associated with each material/method. Energy storage has attracted great attention for its use in portable electronic devices military field. Different devices, such as dielectric capacitors, supercapacitors, and batteries, are used for energy storage. Of these, dielectric capacitors have high energy output, a long life cycle, fast charging and discharging capabilities, work at high temperatures, and excellent fatigue resistance. The energy storage characteristics have been studied to be highly affected by various factors, such as grain size, optimized compositions, grain orientation, energy band gap, processing techniques, defect engineering, core-shell formation, interface engineering, electronegativity difference, the addition of additives, density, secondary phases, the difference of Pmax-Pr, sample thickness, area of the electrode, testing frequency, and AC/DC conditions. The data regarding these parameters/factors are scattered in the literature, and the aim of this study is to gather the data into a single paper that will be beneficial for new researchers in the field of interest. Furthermore, control over and optimizing these parameters will lead to enhancing the energy storage properties.

Keywords: strategies, ceramics, energy storage, capacitors

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Authors: Benjamin Arazi

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Blockchain is the technology behind cryptocurrencies like Bitcoin. It revolutionizes the meaning of trust in the sense of offering total reliability without relying on any central entity that controls or supervises the system. The Wall Street Journal states: “Blockchain Marks the Next Step in the Internet’s Evolution”. Blockchain was listed as #1 in Linkedin – The Learning Blog “most in-demand hard skills needed in 2020”. As stated there: “Blockchain’s novel way to store, validate, authorize, and move data across the internet has evolved to securely store and send any digital asset”. GSMA, a leading Telco organization of mobile communications operators, declared that “Blockchain has the potential to be for value what the Internet has been for information”. Motivated by these seminal observations, this paper presents the foundations of a Blockchain-based “Internet of Assets” academic program that joins under one roof leading application areas that are characterized by the transfer of assets over communication lines. Two such areas, which are pillars of our economy, are Fintech – Financial Technology and mobile communications services. The next application in line is Healthcare. These challenges are met based on available extensive professional literature. Blockchain-based assets communication is based on extending the principle of Bitcoin, starting with the basic question: If digital money that travels across the universe can ‘prove its own validity’, can this principle be applied to digital content. A groundbreaking positive answer here led to the concept of “smart contract” and consequently to DLT - Distributed Ledger Technology, where the word ‘distributed’ relates to the non-existence of reliable central entities or trusted third parties. The terms Blockchain and DLT are frequently used interchangeably in various application areas. The World Bank Group compiled comprehensive reports, analyzing the contribution of DLT/Blockchain to Fintech. The European Central Bank and Bank of Japan are engaged in Project Stella, “Balancing confidentiality and auditability in a distributed ledger environment”. 130 DLT/Blockchain focused Fintech startups are now operating in Switzerland. Blockchain impact on mobile communications services is treated in detail by leading organizations. The TM Forum is a global industry association in the telecom industry, with over 850 member companies, mainly mobile operators, that generate US$2 trillion in revenue and serve five billion customers across 180 countries. From their perspective: “Blockchain is considered one of the digital economy’s most disruptive technologies”. Samples of Blockchain contributions to Fintech (taken from a World Bank document): Decentralization and disintermediation; Greater transparency and easier auditability; Automation & programmability; Immutability & verifiability; Gains in speed and efficiency; Cost reductions; Enhanced cyber security resilience. Samples of Blockchain contributions to the Telco industry. Establishing identity verification; Record of transactions for easy cost settlement; Automatic triggering of roaming contract which enables near-instantaneous charging and reduction in roaming fraud; Decentralized roaming agreements; Settling accounts per costs incurred in accordance with agreement tariffs. This clearly demonstrates an academic education structure where fundamental technologies are studied in classes together with these two application areas. Advanced courses, treating specific implementations then follow separately. All are under the roof of “Internet of Assets”.

Keywords: blockchain, education, financial technology, mobile telecommunications services

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Authors: Keroles Akram Saed Ghatas

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The political violence that characterized 1992 continued into 1993, creating a major security crisis for President Hosni Mubarak's government as the death toll and human rights abuses soared. Increasingly sensitive to criticism of 's human rights activities, the government established human rights departments in key ministries, beginning with the Foreign Office in February. Similar offices have been set up in the Justice and Agriculture Ministries, and plans to set up an office in the Home Office have been announced. It turned out that the main task of the law unit was to overturn the conclusions of international human rights organizations.President Mubarak was elected in a national referendum on October 4 for a third six-year term after being appointed on July 21 by the People's Assembly, an elected parliament overwhelmingly dominated by the in-power National Democratic Party will Mr. Mubarak ran unhindered. The Interior Ministry announced that nearly 16 million people cast their votes (84% of eligible voters), of which 96.28%. voted for presidential re-election.In 1993, armed Islamic extremists escalated their attacks on Christian citizens, government officials, police officers and senior security officials, resulting in casualties among the intended victims and bystanders. Sporadic attacks on buses, boats and tourist attractions also occurred throughout the year. From March 1992 to October 28, 1993, a total of 222 people lost their lives in the riots: 36 Coptic Christians and 38 other citizens; If one is a foreigner; sixty-six members of the Security Forces; and seventy-six known or suspected activists who were killed while resisting arrest. The latter was killed in airstrikes and firefights with security forces and at the site of planned attacks. On March 9-10, a series of airstrikes in Cairo, Giza, Qalyubiya province north of the capital and Aswan killed fifteen suspected militants and five members of the security forces.One of the airstrikes in Giza, part of Greater Cairo, killed the wife and son of Khalifa Mahmoud Ramadan, a suspected militant who was himself killed. The government agency Middle East News Agency reported on March 10 that the raids were part of a "broad confrontational plan aimed at ofterrorist elements"The state of emergency declared in October 1981 after the assassination of President Anwar el-Sadat was still in force in Egypt. The law, previously in effect continuously from June 1967 to May 1980, continued to grant the executive branch unique legal powers that effectively overrode the human rights guarantees of the Egyptian constitution. These provisions included wide discretionary powers in arresting and detaining individuals, as well as the ability to try civilians in military courts. The Cairo-based Independent Organization for Human Rights said so in a document sent to the United Nations in July 1993The human rights committee said the continued imposition of the state of emergency had resulted in "another constitution for the country" and "led to widespread misconduct by the security apparatus".

Keywords: constitution, human rights, legal power, president, anwar, el-sadat, assassination, state of emergency, middle east, news, agency, confrontational, arresting, fugitive, leaders, terrorist, elements, armed islamic extremists.

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Authors: Chongtham Jiten, Radhapiyari Laishram, K. Chandramani Singh

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Alkaline niobate (Na_0.5K_0.5)NbO₃ ceramic system has attracted major attention in view of its potential for replacing the highly toxic but superior lead zirconate titanate (PZT) system for piezoelectric applications. Recently, a more detailed study of this system reveals that the ferroelectric and piezoelectric properties are optimized in the Li- and V-modified system having the composition (K_0.485Na_0.5Li_0.015)(Nb_0.98V_0.02)O₃. In the present work, we further study the pyroelectric behaviour of this composition along with another doped with Mn⁴⁺. So, (K_0.485Na_0.5Li_0.015)(Nb_0.98V_0.02)O₃ + x MnO₂ (x = 0, and 0.01 wt. %) ceramic compositions were synthesized by conventional ceramic processing route. X-ray diffraction study reveals that both the undoped and Mn⁴⁺-doped ceramic samples prepared crystallize into a perovskite structure having orthorhombic symmetry. Dielectric study indicates that Mn⁴⁺ doping has little effect on both the Curie temperature (T_c) and tetragonal-orthorhombic phase transition temperature (T_ot). The bulk density, room-temperature dielectric constant (ε_RT), and room-c The room-temperature coercive field (E_c) is observed to be lower in Mn⁴⁺ doped sample. The detailed analysis of the P-E hysteresis loops over the range of temperature from about room temperature to T_ot points out that enhanced ferroelectric properties exist in this temperature range with better thermal stability for the Mn⁴⁺ doped ceramic. The study reveals that small traces of Mn⁴⁺ can modify (K_0.485Na_0.5Li_0.015)(Nb_0.98V_0.02)O₃ system so as to improve its ferroelectric properties with good thermal stability over a wide range of temperature.

Keywords: ceramics, dielectric properties, ferroelectric properties, lead-free, sintering, thermal stability

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Authors: Hao Cheng, Yingzhou Ni, Amr M. Bakry, Li Liang

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Functional foods containing bioactive nutrients offer benefits beyond basic nutrition and hence the possibility of delaying and preventing chronic diseases. However, many bioactive nutrients degrade rapidly under food processing and storage conditions. Encapsulation can be used to overcome these limitations. Food proteins have been widely used as carrier materials for the preparation of nano/micro-particles because of their ability to form gels and emulsions and to interact with polysaccharides. The mechanisms of interaction between bioactive nutrients and proteins must be understood in order to develop protein-based lipid-free delivery systems. Beta-lactoglobulin, a small globular protein in milk whey, exhibits an affinity to a wide range of compounds. Alfa-tocopherol, resveratrol and folic acid were respectively bound to the central cavity, the outer surface near Trp19–Arg124 and the hydrophobic pocket in the groove between the alfa-helix and the beta-barrel of the protein. Beta-lactoglobulin could thus bind the three bioactive nutrients simultaneously to form protein-multi-ligand complexes. Beta-casein, an intrinsically unstructured but major milk protein, could also interact with resveratrol and folic acid to form complexes. These results suggest the potential to develop milk-protein-based complex carrier systems for encapsulation of multiple bioactive nutrients for functional food application and also pharmaceutical and medical uses.

Keywords: milk protein, bioactive nutrient, interaction, protection

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Authors: Sachin Deshmukh

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Mind and body synchronization occurs through memory and sensation production. Sensations are the guiding language of subconscious mind for conscious mind to take a proper action. Mind-mechanism is based upon memories collected so far since intrauterine life. There are three universal triggers for memory creation; they are persons, situations and objects. Memory is created as sensations experienced by special senses. Based upon experiencing comfort or discomfort, the triggers are categorized as safe or unsafe triggers. A memory comprises of ‘safe or unsafe feeling for triggers, and actions taken for that feeling’. Memories for triggers are created slowly, thoughtfully and consciously by the conscious mind, and archived in the subconscious mind for future references. Later on, similar triggers can come in contact with the individual. Subconscious mind uses these stored feelings to decide whether these triggers are safe or unsafe. It produces comfort or discomfort sensations as emotions accordingly and reacts in the same way as has been recorded in memory. Speed of sensing and processing the triggers, and reacting by subconscious mind is that of the speed of bioelectricity. Hence, formula for human emotions has been designed in this paper as follows: Emotion (Stress or Peace) = Trigger (Person or Situation or object) x Mass of feelings (stressful or peaceful) associated with the Trigger x Speed of Light². We also establish modern medical scientific facts about relationship between reflex activity and memory. This research further develops the ‘Rational Memory Therapy’ focusing on therapeutic feelings conversion techniques, for stress prevention and management.

Keywords: memory, sensations, feelings, emotions, rational memory therapy

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Tal Remez, Or Litany, Alex Bronstein

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The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.

Keywords: binary pixels, maximum likelihood, neural networks, sparse coding

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Authors: K. Paradkar, S. N. Mudliar, A. Sharma, A. B. Pandit, R. A. Pandey

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The study explores the hybrid combination of Hydrodynamic Cavitation (HC) and Subcritical Wet Air Oxidation-based pretreatment of complex industrial effluent to enhance the biodegradability selectively (without major COD destruction) to facilitate subsequent enhanced downstream processing via anaerobic or aerobic biological treatment. Advanced oxidation based techniques can be less efficient as standalone options and a hybrid approach by combining Hydrodynamic Cavitation (HC), and Wet Air Oxidation (WAO) can lead to a synergistic effect since both the options are based on common free radical mechanism. The HC can be used for initial turbulence and generation of hotspots which can begin the free radical attack and this agitating mixture then can be subjected to less intense WAO since initial heat (to raise the activation energy) can be taken care by HC alone. Lab-scale venturi-based hydrodynamic cavitation and wet air oxidation reactor with biomethanated distillery wastewater (BMDWW) as a model effluent was examined for establishing the proof-of-concept. The results indicated that for a desirable biodegradability index (BOD: COD - BI) enhancement (up to 0.4), the Cavitation (standalone) pretreatment condition was: 5 bar and 88 min reaction time with a COD reduction of 36 % and BI enhancement of up to 0.27 (initial BI - 0.17). The optimum WAO condition (standalone) was: 150oC, 6 bar and 30 minutes with 31% COD reduction and 0.33 BI. The hybrid pretreatment (combined Cavitation + WAO) worked out to be 23.18 min HC (at 5 bar) followed by 30 min WAO at 150oC, 6 bar, at which around 50% COD was retained yielding a BI of 0.55. FTIR & NMR analysis of pretreated effluent indicated dissociation and/or reorientation of complex organic compounds in untreated effluent to simpler organic compounds post-pretreatment.

Keywords: hybrid, hydrodynamic cavitation, wet air oxidation, biodegradability index

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Authors: Anthony Townsend, Robyn Fasser

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While complete objectivity is a desirable scientific position from which to conduct a care and contact evaluation (CCE), it is precisely the recognition that we are inherently incapable of operating objectively that is the foundation of ethical practice and skilled assessment. Drawing upon recent research from Daniel Kahneman (2021) on the differences between noise and bias, as well as different inherent biases collectively termed “The Elephant in the Brain” by Kevin Simler and Robin Hanson (2019) from Oxford University, this presentation addresses both the various ways in which our judgments, perceptions and even procedures can be distorted and contaminated while conducting a CCE, but also considers the value of second order cybernetics and the psychodynamic concept of ‘ambivalence’ as a conceptual basis to inform our assessment methodologies to limit such errors or at least better identify them. Both a conceptual framework for ambivalence, our higher-order capacity to allow for the convergence and consideration of multiple emotional experiences and cognitive perceptions to inform our reasoning, and a practical methodology for assessment relying on data triangulation, Bayesian inference and hypothesis testing is presented as a means of promoting ethical practice for health care professionals conducting CCEs. An emphasis on widening awareness and perspective, limiting ‘splitting’, is demonstrated both in how this form of emotional processing plays out in alienating dynamics in families as well as the assessment thereof. In addressing this concept, this presentation aims to illuminate the value of ambivalence as foundational to ethical practice for assessors.

Keywords: ambivalence, forensic, psychology, noise, bias, ethics

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Arum Perwitasari

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Learning L2 sounds is influenced by the first language (L1) sound system. This current study seeks to examine how the listeners with a different L1 vowel system perceive L2 sounds. The fact that English has a bigger number of vowel inventory than Javanese and Sundanese L1 might cause problems for Javanese and Sundanese English language learners perceiving English sounds. To reveal the L2 sound perception over time, we measured the mouse trajectories related to the hand movements made by Javanese and Sundanese language learners, two of Indonesian local languages. Do the Javanese and Sundanese listeners show higher velocity than the English listeners when they perceive English vowels which are similar and new to their L1 system? The study aims to map the patterns of real-time processing through compatible hand movements to reveal any uncertainties when making selections. The results showed that the Javanese listeners exhibited significantly slower velocity values than the English listeners for similar vowels /I, ɛ, ʊ/ in the 826-1200ms post stimulus. Unlike the Javanese, the Sundanese listeners showed slow velocity values except for similar vowel /ʊ/. For the perception of new vowels /i:, æ, ɜ:, ʌ, ɑː, u:, ɔ:/, the Javanese listeners showed slower velocity in making the lexical decision. In contrast, the Sundanese listeners showed slow velocity only for vowels /ɜ:, ɔ:, æ, I/ indicating that these vowels are hard to perceive. Our results fit well with the second language model representing how the L1 vowel system influences the L2 sound perception.

Keywords: velocity profiles, EFL learners, speech perception, experimental linguistics

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Authors: Sfiso Radebe

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The design models dealing with flawless composite structures are in abundance, where the mechanical properties of composite structures are assumed to be known a priori. However, if the worst case scenario is assumed, where material defects combined with processing anomalies in composite structures are expected, a different solution is attained. Furthermore, if the system being designed combines in series hybrid elements, individually affected by material constant variations, it implies that a different approach needs to be taken. In the body of literature, there is a compendium of research that investigates different modes of failure affecting hybrid metal-composite structures. It covers areas pertaining to the failure of the hybrid joints, structural deformation, transverse displacement, the suppression of vibration and noise. In the present study a system employing a combination of two or more hybrid power transmitting elements will be explored for the least favourable dynamic loads as well as weight minimization, subject to uncertain material properties. Elastic constants are assumed to be uncertain-but-bounded quantities varying slightly around their nominal values where the solution is determined using convex models of uncertainty. Convex analysis of the problem leads to the computation of the least favourable solution and ultimately to a robust design. This approach contrasts with a deterministic analysis where the average values of elastic constants are employed in the calculations, neglecting the variations in the material properties.

Keywords: convex modelling, hybrid, metal-composite, robust design

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Authors: Nasreen Basoudan, Andrea Tales, Frederic Boy

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Non-clinical anxiety may be comprised of state anxiety - temporarily experienced anxiety related to a specific situation, and trait anxiety - a longer lasting response or a general disposition to anxiety. While temporary and occasional anxiety whether as a mood state or personality dimension is normal, nonclinical anxiety may influence many more components of information processing than previously recognized. In ageing and dementia-related research, disease characterization now involves attempts to understand a much wider range of brain function such as loss of inhibitory control, as against the more common focus on memory and cognition. However, in many studies, the tendency has been to include individuals with clinical anxiety disorders while excluding persons with lower levels of state or trait anxiety. Loss of inhibitory cognitive control can lead to behaviors such as aggression, reduced sensitivity to others, sociopathic thoughts and actions. Anxiety has also been linked to inhibitory control, with research suggesting that people with anxiety are less capable of inhibiting their emotions than the average person. This study investigates the relationship between anxiety and loss of inhibitory control in younger and older adults, using a variety of questionnaires and computers-based tests. Based on the premise that irrespective of classification, anxiety is associated with a wide range of physical, affective, and cognitive responses, this study explores evidence indicative of the potential influence anxiety per se on loss of inhibitory control, in order to contribute to discussion and appropriate consideration of anxiety-related factors in methodological practice.

Keywords: anxiety, ageing, dementia, inhibitory control

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Authors: Esther Chiramal, Kelvin Soh Boon Kai

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Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human-interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques, and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally white-box in nature and not practical as they can be easily detected by humans e.g., Changing the word from “Poor” to “Rich”. We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.

Keywords: BERT, explainable AI, Grey-box text attack, transformer

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Authors: N. G. Margiani, I. G. Kvartskhava, G. A. Mumladze, Z. A. Adamia

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Chemical doping with different elements and compounds at various amounts represents the most suitable approach to improve the superconducting properties of bismuth-based superconductors for technological applications. In this paper, the influence of partial substitution of Sr(BO₂)₂ for SrO on the phase formation kinetics and transport properties of (Bi,Pb)-2223 HTS has been studied for the first time. Samples with nominal composition Bi_1.7Pb_0.3Sr_2-xCa₂Cu₃O_y[Sr(BO₂)₂]_x, x=0, 0.0375, 0.075, 0.15, 0.25, were prepared by the standard solid state processing. The appropriate mixtures were calcined at 845 ^oC for 40 h. The resulting materials were pressed into pellets and annealed at 837 ^oC for 30 h in air. Superconducting properties of undoped (reference) and Sr(BO₂)₂-doped (Bi,Pb)-2223 compounds were investigated through X-ray diffraction (XRD), resistivity (ρ) and transport critical current density (J_c) measurements. The surface morphology changes in the prepared samples were examined by scanning electron microscope (SEM). XRD and J_c studies have shown that the low level Sr(BO₂)₂ doping (x=0.0375-0.075) to the Sr-site promotes the formation of high-T_c phase and leads to the enhancement of current carrying capacity in (Bi,Pb)-2223 HTS. The doped sample with x=0.0375 has the best performance compared to other prepared samples. The estimated volume fraction of (Bi,Pb)-2223 phase increases from ~25 % for reference specimen to ~70 % for x=0.0375. Moreover, strong increase in the self-field J_c value was observed for this dopant amount (J_c=340 A/cm²), compared to an undoped sample (J_c=110 A/cm²). Pronounced enhancement of superconducting properties of (Bi,Pb)-2223 superconductor can be attributed to the acceleration of high-T_c phase formation as well as the improvement of inter-grain connectivity by small amounts of Sr(BO₂)₂ dopant.

Keywords: bismuth-based superconductor, critical current density, phase formation, Sr(BO₂)₂ doping

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Authors: Pedro A. S. Bergamo, Emilia S. Streng, Jan Rosenkranz, Yousef Ghorbani

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Within the often-hazardous mineral industry, simulation training has speedily gained appreciation as an important method of increasing site safety and productivity through enhanced operator skill and knowledge. Performance calculations related to froth flotation, one of the most important concentration methods, is probably the hardest topic taught during the training of plant operators. Currently, most training teach those skills by traditional methods like slide presentations and hand-written exercises with a heavy focus on memorization. To optimize certain aspects of these pieces of training, we developed “MinFloat”, which teaches the operation formulas of the froth flotation process with the help of gamification. The simulation core based on a first-principles flotation model was implemented in Unity3D and an instructor tutoring system was developed, which presents didactic content and reviews the selected answers. The game was tested by 25 professionals with extensive experience in the mining industry based on a questionnaire formulated for training evaluations. According to their feedback, the game scored well in terms of quality, didactic efficacy and inspiring character. The feedback of the testers on the main target audience and the outlook of the mentioned solution is presented. This paper aims to provide technical background on the construction of educational games for the mining industry besides showing how feedback from experts can more efficiently be gathered thanks to new technologies such as online forms.

Keywords: training evaluation, simulation based training, modelling, and simulation, froth flotation

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Authors: Jin Fan, M. Ala Saadeghvaziri

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Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: bridge, construction, drones, infrastructure, information

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