Search results for: long short-term memory

Authors: Mohamad Khairi Ishak

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Conventional public key crypto systems such as RSA (Ron Rivest, Adi Shamir and Leonard Adleman), DSA (Digital Signature Algorithm), and Elgamal are no longer efficient to be implemented in the small, memory constrained devices. Elliptic Curve Cryptography (ECC), which allows smaller key length as compared to conventional public key crypto systems, has thus become a very attractive choice for many applications. This paper describes implementation of an elliptic curve cryptography (ECC) encryption engine on a FPGA. The system has been implemented in 2 different key sizes, which are 131 bits and 163 bits. Area and timing analysis are provided for both key sizes for comparison. The crypto system, which has been implemented on Altera’s EPF10K200SBC600-1, has a hardware size of 5945/9984 and 6913/9984 of logic cells for 131 bits implementation and 163 bits implementation respectively. The crypto system operates up to 43 MHz, and performs point multiplication operation in 11.3 ms for 131 bits implementation and 14.9 ms for 163 bits implementation. In terms of speed, our crypto system is about 8 times faster than the software implementation of the same system.

Keywords: elliptic curve cryptography, FPGA, key sizes, memory

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Authors: Dmitriy Kuptsov

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Tree data structures, such as binary or in general k-ary trees, are essential in computer science. The applications of these data structures can range from data search and retrieval to sorting and ranking algorithms. Naive implementations of these data structures can consume prohibitively large volumes of random access memory limiting their applicability in certain solutions. Thus, in these cases, more advanced representation of these data structures is essential. In this paper we present the design of the compact version of ternary tree data structure and demonstrate the results for the experimental evaluation using static dictionary problem. We compare these results with the results for binary and regular ternary trees. The conducted evaluation study shows that our design, in the best case, consumes up to 12 times less memory (for the dictionary used in our experimental evaluation) than a regular ternary tree and in certain configuration shows performance comparable to regular ternary trees. We have evaluated the performance of the algorithms using both 32 and 64 bit operating systems.

Keywords: algorithms, data structures, succinct ternary tree, per- formance evaluation

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Authors: Nikitha Francis, Anjana Hoode, Vinitha George, Jayashree S. Bhat

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The interaction between cognition and language, referred as cognitive-communication, is very intricate, involving several mental processes such as perception, memory, attention, lexical retrieval, decision making, motor planning, self-monitoring and knowledge. Cognitive-communication disorders are difficulties in communicative competencies that result from underlying cognitive impairments of attention, memory, organization, information processing, problem solving, and executive functions. Traumatic brain injury (TBI) is an acquired, non - progressive condition, resulting in distinct deficits of cognitive communication abilities such as naming, word-finding, self-monitoring, auditory recognition, attention, perception and memory. Cognitive-communication intervention in TBI is individualized, in order to enhance the person’s ability to process and interpret information for better functioning in their family and community life. The present case report illustrates the cognitive-communicative behaviors and the intervention outcomes of an adult with TBI, who was brought to the Department of Audiology and Speech Language Pathology, with cognitive and communicative disturbances, consequent to road traffic accident. On a detailed assessment, she showed naming deficits along with perseverations and had severe difficulty in recalling the details of the accident, her house address, places she had visited earlier, names of people known to her, as well as the activities she did each day, leading to severe breakdowns in her communicative abilities. She had difficulty in initiating, maintaining and following a conversation. She also lacked orientation to time and place. On administration of the Manipal Manual of Cognitive Linguistic Abilities (MMCLA), she exhibited poor performance on tasks related to visual and auditory perception, short term memory, working memory and executive functions. She attended 20 sessions of cognitive-communication intervention which followed a domain-general, adaptive training paradigm, with tasks relevant to everyday cognitive-communication skills. Compensatory strategies such as maintaining a dairy with reminders of her daily routine, names of people, date, time and place was also recommended. MMCLA was re-administered and her performance in the tasks showed significant improvements. Occurrence of perseverations and word retrieval difficulties reduced. She developed interests to initiate her day-to-day activities at home independently, as well as involve herself in conversations with her family members. Though she lacked awareness about her deficits, she actively involved herself in all the therapy activities. Rehabilitation of moderate to severe head injury patients can be done effectively through a holistic cognitive retraining with a focus on different cognitive-linguistic domains. Selection of goals and activities should have relevance to the functional needs of each individual with TBI, as highlighted in the present case report.

Keywords: cognitive-communication, executive functions, memory, traumatic brain injury

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Authors: Ghayah Alsaleem

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This research project focussed on specific compounds, whereas a literature review was completed on the broader subject of long-lasting phosphorescence. For the review and subsequent laboratory work, long lasting phosphorescence compounds were defined as materials that have an afterglow decay time greater than a few minutes. The decay time is defined as the time between the end of excitation and the moment the light intensity drops below 0.32mcd/m2. This definition is widely used in industry and in most research studies. The experimental work focused on known long-lasting phosphorescence compounds – strontium aluminate (SrAl2O4: Eu2+, Dy3+). At first, preparation was similar to literary methods. Temperature, dopant levels and mixing methods were then varied in order to expose their effects on long-lasting phosphorescence. The effect of temperature was investigated for SrAl2O4: Eu2+, Dy3+, and resulted in the discovery that 1350°C was the only temperature that the compound could be heated to in the Differential scanning calorimetry (DSC) in order to achieve any phosphorescence. However, no temperatures above 1350°C were investigated. The variation of mixing method and co-dopant level in the strontium aluminate compounds resulted in the finding that the dry mixing method using a Turbula mixer resulted in the longest afterglow. It was also found that an increase of europium inclusion, from 1mol% to 2mol% in these compounds, increased the brightest of the phosphorescence. As this increased batch was mixed using sonication, the phosphorescent time was actually reduced which produced green long-lasting phosphorescence for up to 20 minutes following 30 minutes excitation and 50 minutes when the europium content was doubled and mixed using sonication.

Keywords: long lasting, phosphorescence, excitation, europium

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

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This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: Salahud Din, Saima Masood, Hafsa Zaneb, Habib –ur- Rehman, Imad Khan, Muqader Shah

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The objective of this study was to examine the osteomorphological differences between the long bones of adult Chinkara and an adult Beetal goat, using visual observation, which has still not studied. The osseous remains of these small-sized ungulates often encountered, but cannot distinguish, because of the lack of literature. Specimens of the adult Chinkara of known age and sex for osteomorphological studies are collected from the Manglot Wildlife Park and Ungulate Breeding Centre, Nizampur, Pakistan, while the bones of adult Beetal goats are obtained after slaughtering in a slaughterhouse. The research is carried out at the University of Veterinary and Animal Sciences, Lahore, Pakistan. In this research, the main morphological features recorded in the long bones of thoracic limb and pelvic limb of the adult Chinkara, by comparing them to those of the Beetal goat. The most important differences between the two species are noted in the scapula, the humerus, the radius and ulna, the metacarpal, femur, tibia metatarsal and phalanges. In conclusion, the present study suggests that the morphology of the long bones of adult Chinkara has different from the Beetal goat in various points of view. Based on these recorded points, long bones of these two species can easily be differentiated. The study is helpful in zooarcheological, comparative osteometric studies, for forensic specialists and veterinary anatomists.

Keywords: Beetal goat, Chinkara, comparative morphological features, long bones, osteology

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Authors: Md Al Masum Bhuiyan, Maria C. Mariani, Osei K. Tweneboah

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In the present work, we develop a technique for estimating the volatility of financial time series by using stochastic differential equation. Taking the daily closing prices from developed and emergent stock markets as the basis, we argue that the incorporation of stochastic volatility into the time-varying parameter estimation significantly improves the forecasting performance via Maximum Likelihood Estimation. While using the technique, we see the long-memory behavior of data sets and one-step-ahead-predicted log-volatility with ±2 standard errors despite the variation of the observed noise from a Normal mixture distribution, because the financial data studied is not fully Gaussian. Also, the Ornstein-Uhlenbeck process followed in this work simulates well the financial time series, which aligns our estimation algorithm with large data sets due to the fact that this algorithm has good convergence properties.

Keywords: financial time series, maximum likelihood estimation, Ornstein-Uhlenbeck type models, stochastic volatility model

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Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

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Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

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Authors: Dan Wang, Qing Ma, Xiaodan Wang, Tianjiao Qi

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The sound of peddling in Beijing, also called “yo-heave-ho” or “cry of one's ware”, is a unique folk culture and usually found in Beijing hutong. For the civilians in Beijing, sound of peddling is part of their childhood. And for those who love the traditional culture of Beijing, it is an old song singing the local conditions and customs of the ancient city. For example, because of his great appreciation, the British poet Osbert Stewart once put sound of peddling which he had heard in Beijing as a street orchestra performance in the article named "Beijing's sound and color".This research aims to collect and integrate the voice/photo resources and historical materials of sound concerning peddling in Beijing by digital technology in order to protect the intangible cultural heritage and pass on the city memory. With the goal in mind, the next stage is to collect and record all the materials and resources based on the historical documents study and interviews with civilians or performers. Then set up a metadata scheme (which refers to the domestic and international standards such as "Audio Data Processing Standards in the National Library", DC, VRA, and CDWA, etc.) to describe, process and organize the sound of peddling into a database. In order to fully show the traditional culture of sound of peddling in Beijing, web design and GIS technology are utilized to establish a website and plan holding offline exhibitions and events for people to simulate and learn the sound of peddling by using VR/AR technology. All resources are opened to the public and civilians can share the digital memory through not only the offline experiential activities, but also the online interaction. With all the attempts, a multi-media narrative platform has been established to multi-dimensionally record the sound of peddling in old Beijing with text, images, audio, video and so on.

Keywords: sound of peddling, GIS, metadata scheme, VR/AR technology

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Authors: Kensho Takahashi, Ko Watanabe, Takashi Kaburagi, Hiroshi Tanaka, Kajiro Watanabe, Yosuke Kurihara

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Near-infrared spectroscopy (NIRS) has been widely used as a non-invasive method to measure brain activity, but it is corrupted by baseline drift noise. Here we present a method to measure regional cerebral blood flow as a derivative of NIRS output. We investigate whether, when listening to languages, blood flow can reasonably localize and represent regional brain activity or not. The prefrontal blood flow distribution pattern when advanced second-language listeners listened to a second language (L2) was most similar to that when listening to their first language (L1) among the patterns of mean and standard deviation. In experiments with 25 healthy subjects, the maximum blood flow was localized to the left BA46 of advanced listeners. The blood flow presented is robust to baseline drift and stably localizes regional brain activity.

Keywords: NIRS, oxy-hemoglobin, baseline drift, blood flow, working memory, BA46, first language, second language

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Sihem Oujdi, Hafida Belbachir

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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining

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Authors: M. Fadlallah, G. Ghibaudo, C. G. Theodorou

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The dynamic variation in memory devices such as the Static Random Access Memory can give errors in read or write operations. In this paper, the effect of low-frequency and random telegraph noise on the dynamic variation of one SRAM cell is detailed. The effect on circuit noise, speed, and length of time of processing is examined, using the Supply Read Retention Voltage and the Read Static Noise Margin. New test run methods are also developed. The obtained results simulation shows the importance of noise caused by dynamic variation, and the impact of Random Telegraph noise on SRAM variability is examined by evaluating the statistical distributions of Random Telegraph noise amplitude in the pull-up, pull-down. The threshold voltage mismatch between neighboring cell transistors due to intrinsic fluctuations typically contributes to larger reductions in static noise margin. Also the contribution of each of the SRAM transistor to total dynamic variation has been identified.

Keywords: low-frequency noise, random telegraph noise, dynamic variation, SRRV

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Authors: J. Urban, Á. S. Botella, L. E. Robaina, A. Bárta, P. Souček, P. Císař, Š. Papáček, L. M. Domínguez

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Skin coloration in fish is of great physiological, behavioral and ecological importance and can be considered as an index of animal welfare in aquaculture as well as an important quality factor in the retail value. Currently, in order to compare color in animals fed on different diets, biochemical analysis, and colorimetry of fished, mildly anesthetized or dead body, are very accurate and meaningful measurements. The noninvasive method using digital images of the fish body was developed as a standalone application. This application deals with the computation burden and memory consumption of large input files, optimizing piece wise processing and analysis with the memory/computation time ratio. For the comparison of color distributions of various experiments and different color spaces (RGB, CIE L*a*b*) the comparable semi-equidistant binning of multi channels representation is introduced. It is derived from the knowledge of quantization levels and Freedman-Diaconis rule. The color calibrations and camera responsivity function were necessary part of the measurement process.

Keywords: color distribution, fish skin color, piecewise transformation, object to background segmentation

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Authors: Palaniraj Rama Raj, Himeesh Kumar, Paul Adler

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Purpose: To investigate the effect of ultrasound energy, expressed as cumulative dissipated energy (CDE), on short and long-term outcomes after uncomplicated cataract surgery by phacoemulsification. Methods: In this single-surgeon, two-center retrospective study, non-glaucomatous participants who underwent uncomplicated cataract surgery were investigated. Best-corrected visual acuity (BCVA) and intraocular pressure (IOP) were measured at 3 separate time points: pre-operative, Day 1 and ≥1 month. Anterior chamber (AC) inflammation and corneal odema (CO) were assessed at 2 separate time points: Pre-operative and Day 1. Short-term changes (Day 1) in BCVA, IOP, AC and CO and long-term changes (≥1 month) in BCVA and IOP were evaluated as a function of CDE using a multivariate multiple linear regression model, adjusting for age, gender, cataract type and grade, preoperative IOP, preoperative BCVA and duration of long-term follow-up. Results: 110 eyes from 97 non-glaucomatous participants were analysed. 60 (54.55%) were female and 50 (45.45%) were male. The mean (±SD) age was 73.40 (±10.96) years. Higher CDE counts were strongly associated with higher grades of sclerotic nuclear cataracts (p <0.001) and posterior subcapsular cataracts (p <0.036). There was no significant association between CDE counts and cortical cataracts. CDE counts also had a positive correlation with Day 1 CO (p <0.001). There was no correlation between CDE counts and Day 1 AC inflammation. Short-term and long-term changes in post-operative IOP did not demonstrate significant associations with CDE counts (all p >0.05). Though there was no significant correlation between CDE counts and short-term changes in BCVA, higher CDE counts were strongly associated with greater improvements in long-term BCVA (p = 0.011). Conclusion: Though higher CDE counts were strongly associated with higher grades of Day 1 postoperative CO, there appeared to be no detriment to long-term BCVA. Correspondingly, the strong positive correlation between CDE counts and long-term BCVA was likely reflective of the greater severity of underlying cataract type and grade. CDE counts were not associated with short-term or long-term postoperative changes in IOP.

Keywords: cataract surgery, phacoemulsification, cumulative dissipated energy, CDE, surgical outcomes

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Authors: Wei Gao

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The current study investigates the change of listener responses in social conversations of the second language (L2) speakers who are staying abroad with Chinese L1 speakers in Australian universities and how their long-term stay abroad impacted their design for L2 recipient actions. There is a limited amount of empirical work on L2 English listener response acquisition, particularly regarding the influence of long-term stay abroad in English-speaking countries. Little is known whether the development of L2 listener responses and the improvement of interactional competence is affected by the prolonged residency in the target L2 country. Forty-eight participants were recruited, and they participated in the designed speaking task through Computer-Mediated Communication. Results showed that long-term stay abroad Chinese L1 speakers demonstrated an English-like pattern of listener responses in communication. Long-term stay abroad experience had a significant impact on L2 English listener responses production and organization in social conversation. Long-term stay abroad L1 Chinese speakers had an active and productive response in listenership than their non-stay abroad counterparts in terms of frequency and placement in producing listener responses. However, the L2 English listener response production only occurred to be partial in response tokens, such as backchannels and reactive expressions, also in resumptive openers' employment. This study shows that L2 English listener responses could be acquired during a long-term stay abroad in English-speaking countries but showed partial acquisition in collaborative finishes production. In addition, the most prominent finding was that Chinese L1 speakers changed their overall listener responses pattern from L1 Chinese to L2 English. The study reveals specific interactional changes in English L2 listener responses acquisition. It generates pedagogical implications for cross-cultural communication and L2 pragmatics acquisition during a long-term stay abroad.

Keywords: listener responses, stay abroad, interactional competence, L2 pragmatics acquisition

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Authors: Tiziana Biasutti, Daniela Rigamonti, Lorenzo Palmiotti, Adelaide Nespoli, Paolo Bettini

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Aerospace industry is based on the continuous development of new technologies and solutions that allows constant improvement of the systems. Shape Memory Alloys are smart materials that can be used as dampers due to their pseudoelastic effect. The purpose of the research was to design a passive damper in Nitinol, manufactured by Selective Laser Melting, for space applications to reduce vibration between different structural parts in space structures. The powder is NiTi (50.2 at.% of Ni). The structure manufactured by additive technology allows us to eliminate the presence of joint and moving parts and to have a compact solution with high structural strength. The designed dampers had single or double cell structures with three different internal angles (30°, 45° and 60°). This particular shape has damping properties also without the pseudoelastic effect. For this reason, the geometries were reproduced in different materials, SS316L and Ti6Al4V, to test the geometry loss factor. The mechanical performances of these specimens were compared to the ones of NiTi structures, pointing out good damping properties of the designed structure and the highest performances of the NiTi pseudoelastic effect. The NiTi damper was mechanically characterized by static and dynamic tests and with DSC and microscope observations. The experimental results were verified with numerical models and with some scaled steel specimens in which optical fibers were embedded. The realized structure presented good mechanical and damping properties. It was observed that the loss factor and the dissipated energy increased with the angles of the cells.

Keywords: additive manufacturing, damper, nitinol, pseudo elastic effect, selective laser melting, shape memory alloys

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Authors: Gaurav Bansod, Swapnil Sutar, Abhijit Patil, Jagdish Patil

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This paper proposes an ultra-lightweight cipher NUX. NUX is a generalized Feistel network. It supports 128/80 bit key length and block length of 64 bit. For 128 bit key length, NUX needs only 1022 GEs which is less as compared to all existing cipher design. NUX design results into less footprint area and minimal memory size. This paper presents security analysis of NUX cipher design which shows cipher’s resistance against basic attacks like Linear and Differential Cryptanalysis. Advanced attacks like Biclique attack is also mounted on NUX cipher design. Two different F function in NUX cipher design results in high diffusion mechanism which generates large number of active S-boxes in minimum number of rounds. NUX cipher has total 31 rounds. NUX design will be best-suited design for critical application like smart grid, IoT, wireless sensor network, where memory size, footprint area and the power dissipation are the major constraints.

Keywords: lightweight cryptography, Feistel cipher, block cipher, IoT, encryption, embedded security, ubiquitous computing

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Authors: Vangmayee. V. Subban, Somashekara H. S, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: Phonological processing is critical in learning to read alphabetical and non-alphabetical languages. However, its role in learning to read Kannada an alphasyllabary is equivocal. The literature has focused on the developmental role of phonological awareness on reading. To the best of authors knowledge, the role of phonological memory and phonological naming has not been addressed in alphasyllabary Kannada language. Therefore, there is a need to evaluate the comprehensive role of the phonological processing skills in Kannada on word decoding skills during the early years of schooling. Aim and Objectives: The present study aimed to explore the phonological processing abilities and their role in learning to decode pseudowords in children learning to read the Kannada language during initial years of formal schooling between 5.6 to 8.6 years. Method: In this cross sectional study, 60 typically developing Kannada speaking children, 20 each from Grade I, Grade II, and Grade III between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. Phonological processing abilities were assessed using an assessment tool specifically developed to address the objectives of the present research. The assessment tool was content validated by subject experts and had good inter and intra-subject reliability. Phonological awareness was assessed at syllable level using syllable segmentation, blending, and syllable stripping at initial, medial and final position. Phonological memory was assessed using pseudoword repetition task and phonological naming was assessed using rapid automatized naming of objects. Both phonological awareneness and phonological memory measures were scored for the accuracy of the response, whereas Rapid Automatized Naming (RAN) was scored for total naming speed. Results: The mean scores comparison using one-way ANOVA revealed a significant difference (p ≤ 0.05) between the groups on all the measures of phonological awareness, pseudoword repetition, rapid automatized naming, and pseudoword reading. Subsequent post-hoc grade wise comparison using Bonferroni test revealed significant differences (p ≤ 0.05) between each of the grades for all the tasks except (p ≥ 0.05) for syllable blending, syllable stripping, and pseudoword repetition between Grade II and Grade III. The Pearson correlations revealed a highly significant positive correlation (p=0.000) between all the variables except phonological naming which had significant negative correlations. However, the correlation co-efficient was higher for phonological awareness measures compared to others. Hence, phonological awareness was chosen a first independent variable to enter in the hierarchical regression equation followed by rapid automatized naming and finally, pseudoword repetition. The regression analysis revealed syllable awareness as a single most significant predictor of pseudoword reading by explaining the unique variance of 74% and there was no significant change in R² when RAN and pseudoword repetition were added subsequently to the regression equation. Conclusion: Present study concluded that syllable awareness matures completely by Grade II, whereas the phonological memory and phonological naming continue to develop beyond Grade III. Amongst phonological processing skills, phonological awareness, especially syllable awareness is crucial for word decoding than phonological memory and naming during initial years of schooling.

Keywords: phonological awareness, phonological memory, phonological naming, phonological processing, pseudo-word decoding

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Authors: Oleg Oborin

Abstract:

This paper is dedicated to the development of the model, which can be used to evaluate the effectiveness of long-term corporate strategies and identify the best strategies. The theoretical model of the relatively homogenous product market (such as iron and steel industry, mobile services or road transport) has been developed. In the model, the market consists of a large number of companies with different internal characteristics and objectives. The companies can perform mergers and acquisitions in order to increase their market share. The model allows the simulation of long-time dynamics of the market (for a period longer than 20 years). Therefore, a large number of simulations on random input data was conducted in the framework of the model. After that, the results of the model were compared with the dynamics of real markets, such as the US steel industry from the beginning of the XX century to the present day, and the market of mobile services in Germany for the period between 1990 and 2015.

Keywords: Economic Modelling, Long-Time Strategy, Mergers and Acquisitions, Simulation

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Authors: Siphat Lim

Abstract:

This study used Autoregressive Distributed Lag (ARDL) approach to cointegration. In the long-run the general price level and exchange rate have a positively significant effect on domestic output. The estimated result further revealed that fiscal stimulus help stimulate domestic output in the long-run, but not in the short-run, while monetary expansion help to stimulate output in both short-run and long-run. The result is complied with the theory which is the macroeconomic policies, fiscal and monetary policy; help to stimulate domestic output in the long-run. The estimated result of the Vector Error Correction Model (VECM) has indicated more clearly that the consumer price index has a positive effect on output with highly statistically significant. Increasing in the general price level would increase the competitiveness among producers than increase in the output. However, the exchange rate also has a positive effect and highly significant on the gross domestic product. The exchange rate depreciation might increase export since the purchasing power of foreigners has increased. More importantly, fiscal stimulus would help stimulate the domestic output in the long-run since the coefficient of government expenditure is positive. In addition, monetary expansion would also help stimulate the output and the result is highly significant. Thus, fiscal stimulus and monetary expansionary would help stimulate the domestic output in the long-run in Cambodia.

Keywords: fiscal policy, monetary policy, ARDL, VECM

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Authors: Ruiyuan Wang, Jing Han, Bruno Di Biase, Mark Antoniou

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The purpose of this study is to explore the relationship between working memory (WM) capacity and Chinese-English consecutive interpreting (CI) performance in interpreting learners with different study abroad experience (SAE). Such relationship is not well understood. This study also examines whether Chinese interpreting learners with SAE in English-speaking countries, demonstrate a better performance in inflectional morphology and agreement, notoriously unstable in Chinese speakers of English L2, in their interpreting output than learners without SAE. Fifty Chinese university students, majoring in Chinese-English Interpreting, were recruited in Australia (n=25) and China (n=25). The two groups matched in age, language proficiency, and interpreting training period. Study abroad (SA) group has been studying in an English-speaking country (Australia) for over 12 months, and none of the students recruited in China (the no study abroad = NSA group) had ever studied or lived in an English-speaking country. Data on language proficiency and training background were collected via a questionnaire. Lexical retrieval performance and working memory (WM) capacity data were collected experimentally, and finally, interpreting data was elicited via a direct CI task. Main results of the study show that WM significantly correlated with participants' CI performance independently of learning context. Moreover, SA outperformed NSA learners in terms of subject-verb number agreement. Apart from that, WM capacity was also found to correlate significantly with their morphosyntactic accuracy. This paper sheds some light on the relationship between study abroad, WM capacity, and CI performance. Exploring the effect of study abroad on interpreting trainees and how various important factors correlate may help interpreting educators bring forward more targeted teaching paradigms for participants with different learning experiences.

Keywords: study abroad experience, consecutive interpreting, working memory, inflectional agreement

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Authors: Birge Yildirim Okta

Abstract:

This paper aims to narrate the story of Atatürk Cultural Center in Taksim Square, which was demolished in 2018, and discuss its architectonic as a social place of memory and its existence and demolishment as the space of politics. Focusing on the timeline starting from early republican period till today, the paper uses narrative discourse analysis to research Atatürk Cultural Center as a place of memory and a space of politics in its existence. After the establishment of Turkish Republic, one of most important implementation in Taksim Square, reflecting the internationalist style, was the construction of Opera Building in Prost Plan. The first design of the opera building belonged to Aguste Perret, which could not be implemented due to economic hardship during World War II. Later the project was designed by architects Feridun Kip and Rüknettin Güney in 1946 but could not be completed due to 1960 military coup. Later the project was shifted to another architect Hayati Tabanlıoglu, with a change in its function as a cultural center. Eventually, the construction of the building was completed in 1969 in a completely different design. AKM became a symbol of republican modernism not only with its modern architectural style but also with it is function as the first opera building of the republic, reflecting the western, modern cultural heritage by professional groups, artists and the intelligentsia. In 2005, Istanbul’s council for the protection of cultural heritage decided to list AKM as a grade 1 cultural heritage, ending a period of controversy which saw calls for the demolition of the center as it was claimed it ended its useful lifespan. In 2008 the building was announced to be closed for repairs and restoration. Over the following years, the building was demolished piece by piece silently while Taksim mosque has been built just in front of Atatürk Cultural Center. Belonging to the early republican period, AKM was a representation of a cultural production of a modern society for the emergence and westward looking, secular public space in Turkey. Its erasure from Taksim scene under the rule of the conservative government, Justice and Development Party and the construction of Taksim mosque in front of AKM’s parcel is also representational. The question of governing the city through space has always been an important aspect for governments, those holding political power since cities are the chaotic environments that are seen as a threat for the governments, carrying the tensions of proletariat or the contradictory groups. The story of AKM as a dispositive or a regulatory apparatus demonstrates how space itself is becoming a political medium, to transform the socio-political condition. The article aims to discuss the existence and demolishment of Atatürk Cultural Center by discussing the constructed and demolished building as a place of memory and a space of politics.

Keywords: space of politics, place of memory, atatürk cultural center, taksim square

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: long-short-term memory, re-identification, security critical application, spatial-temporal awareness

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Authors: Hamideh Marefat, Eskandar Samadi

Abstract:

This study examined the effect of semantics on processing ambiguous sentences containing Relative Clauses (RCs) preceded by a complex Determiner Phrase (DP) by Persian-speaking learners of L2 English with different proficiency and Working Memory Capacities (WMCs). The semantic relationship studied was one between the subject of the main clause and one of the DPs in the complex DP to see if, as predicted by Spreading Activation Model, priming one of the DPs through this semantic manipulation affects the L2ers’ preference. The results of a task using Rapid Serial Visual Processing (time-controlled paradigm) showed that manipulation of the relationship between the subject of the main clause and one of the DPs in the complex DP preceding RC has no effect on the choice of the antecedent; rather, the L2ers' processing is guided by the phrase structure information. Moreover, while proficiency did not have any effect on the participants’ preferences, WMC brought about a difference in their preferences, with a DP1 preference by those with a low WMC. This finding supports the chunking hypothesis and the predicate proximity principle, which is the strategy also used by monolingual Persian speakers.

Keywords: semantics, relative clause processing, ambiguity resolution, proficiency, working memory capacity

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Authors: Mahsa Namvari, Kari Ullakko

Abstract:

It is well-known that the Co-doping of ferromagnetic shape memory alloys (FSMAs) is a crucial tool to control their multifunctional properties. The present work investigates the use of small quantities of Co to fine-tune the transformation, structure, microstructure, mechanical and magnetic properties of the polycrystalline Ni₄₉.₈Mn₂₈.₅Ga₂₁.₇ (at.%) alloy, At Co concentrations of 1-1.5 at.%, a microstructure with an average grain size of about 2.00 mm was formed with a twin structure, enabling the experimental observation of magnetic-field-induced twin variant rearrangement. At higher levels of Co-doping, the grain size was essentially reduced, and the crystal structure of the martensitic phase became 2M martensite. The decreasing grain size and changing crystal structure are attributed to the progress of γ-phase precipitates. Alongside the academic aspect, the results of the present work point to the commercial advantage of fabricating 10M Co-doped Ni-Mn-Ga actuating elements made from large grains of polycrystalline ingots obtained by a standard melting facility instead of grown single crystals.

Keywords: Ni-Mn-Ga, ferromagnetic shape memory, martensitic phase transformation, grain growth

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