Search results for: violence detection

Authors: Ahmad Akrad, Rabia Sehab, Fadi Alyoussef

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Nowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a fault detection method is suggested using residual errors generated by the root mean square (RMS) of phase currents. The application of this method is based on an asymmetric nonlinear model of Induction Machine considering the winding fault of the three axes frame state space. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults.

Keywords: induction machine, asymmetric nonlinear model, fault diagnosis, inter-turn short-circuit fault, root mean square, current sensor fault, fault detection and isolation

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Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

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This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: F. Sallem, B. Dahhou, A. Kamoun

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In this work, the main problem considered is the detection and the isolation of the actuator fault. A new formulation of the linear system is generated to obtain the conditions of the actuator fault diagnosis. The proposed method is based on the representation of the actuator as a subsystem connected with the process system in cascade manner. The designed formulation is generated to obtain the conditions of the actuator fault detection and isolation. Detectability conditions are expressed in terms of the invertibility notions. An example and a comparative analysis with the classic formulation illustrate the performances of such approach for simple actuator fault diagnosis by using the linear model of nuclear reactor.

Keywords: actuator fault, Fault detection, left invertibility, nuclear reactor, observability, parameter intervals, system inversion

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Authors: Aleksandar Zhelyazkov, Daniele Zonta, Helmut Wenzel, Peter Furtner

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In the current research structural health monitoring is considered for addressing the critical issue of post-earthquake damage detection. A non-standard approach for damage detection via acoustic emission is presented - acoustic emissions are monitored in the low frequency range (up to 120 Hz). Such emissions are termed high-frequency transients. Further a damage indicator defined as the Time-Ratio Damage Indicator is introduced. The indicator relies on time-instance measurements of damage initiation and deformation peaks. Based on the time-instance measurements a procedure for estimation of the maximum drift ratio is proposed. Monitoring data is used from a shaking-table test of a full-scale reinforced concrete bridge pier. Damage of the experimental column is successfully detected and the proposed damage indicator is calculated.

Keywords: acoustic emission, damage detection, shaking table test, structural health monitoring

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Authors: Flavia Kroetz

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From 1964 to 1985, Brazil was ruled by a dictatorial regime that, under the discourse of fight against terrorism and subversion, implemented cruel and atrocious practices against anyone who opposed the State ideology. At the same time, several Latin American countries faced dictatorial periods and experienced State repression through apparatuses of violence institutionalized in the very governmental structure. Despite the correspondence between repressive methods adopted by authoritarian regimes in States such as Argentina, Chile, El Salvador, Peru and Uruguay, the mechanisms of democratic transition adopted with the end of each dictatorship were significantly different. While some States have found ways to deal with past atrocities through serious and transparent investigations of the crimes perpetrated in the name of repression, in others, as in Brazil, a culture of impunity remains rooted in society, manifesting itself in the widespread disbelief of the population in governmental and democratic institutions. While Argentina, Chile, Peru and Uruguay are convincing examples of the possibility and importance of the prosecution of crimes such as torture, forced disappearance and murder committed by the State, El Salvador demonstrates the complete failure to punish or at least remove from power the perpetrators of serious crimes against civilians and political opponents. In a scenario of widespread violations of human rights, State violence becomes entrenched within society as a daily and even necessary practice. In Brazil, a lack of political and judicial will withstands the impunity of those who, during the military regime, committed serious crimes against human rights under the authority of the State. If the reproduction of violence is a direct consequence of the culture of denial and the rejection of everyone considered to be different, ‘the other’, then the adoption of transitional mechanisms that underpin the historical and political contexts of the time seems essential. Such mechanisms must strengthen democracy through the effective implementation of the rights to memory and to truth, the right to justice and reparations for victims and their families, as well as institutional changes in order to remove from power those who, when in power, could not distinguish between legality and authoritarianism. Against this background, this research analyses the importance of transitional justice for the restoration of democracy, considering the adoption of amnesty laws as a strategy to preclude criminal prosecution of offenses committed during dictatorial regimes. The study investigates the scope of Law No 6.683/79, the Brazilian amnesty law, which, according to a 2010 decision of the Brazilian Constitutional Supreme Court, granted amnesty to those responsible for political crimes and related crimes, committed between September 2, 1961 and August 15, 1979. Was the purpose of this Law to grant amnesty to violent crimes committed by the State? If so, is it possible to recognize the legitimacy of a Congress composed of indirectly elected politicians controlled by the dictatorship?

Keywords: amnesty law, criminal justice, dictatorship, state violence

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Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim

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Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.

Keywords: building system, time series, diagnosis, outliers, delay, data gap

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

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Authors: Zhongjie Yu, Hancheng Yu

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In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles.

Keywords: concentric circle detection, gradient, contour, edge pre-classification, RANSAC

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Authors: Soledad Carinelli, Iñigo Fernández, José Luis González-Mora, Pedro A. Salazar-Carballo

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Mastitis is the most relevant inflammatory disease in cattle, affecting the animal health and causing important economic losses on dairy farms. This disease takes place in the mammary gland or udder when some opportunistic microorganisms, such as Staphylococcus aureus, Streptococcus agalactiae, Corynebacterium bovis, etc., invade the teat canal. According to the severity of the inflammation, mastitis can be classified as sub-clinical, clinical and chronic. Standard methods for mastitis detection include counts of somatic cells, cell culture, electrical conductivity of the milk, and California test (evaluation of “gel-like” matrix consistency after cell lysed with detergents). However, these assays present some limitations for accurate detection of subclinical mastitis. Currently, haptoglobin, an acute phase protein, has been proposed as novel and effective biomarker for mastitis detection. In this work, an electrochemical biosensor based on polydopamine-modified magnetic nanoparticles (MNPs@pDA) for haptoglobin detection is reported. Thus, MNPs@pDA has been synthesized by our group and functionalized with hemoglobin due to its high affinity to haptoglobin protein. The protein was labeled with specific antibodies modified with alkaline phosphatase enzyme for its electrochemical detection using an electroactive substrate (1-naphthyl phosphate) by differential pulse voltammetry. After the optimization of assay parameters, the haptoglobin determination was evaluated in milk. The strategy presented in this work shows a wide range of detection, achieving a limit of detection of 43 ng/mL. The accuracy of the strategy was determined by recovery assays, being of 84 and 94.5% for two Hp levels around the cut off value. Milk real samples were tested and the prediction capacity of the electrochemical biosensor was compared with a Haptoglobin commercial ELISA kit. The performance of the assay has demonstrated this strategy is an excellent and real alternative as screen method for sub-clinical bovine mastitis detection.

Keywords: bovine mastitis, haptoglobin, electrochemistry, magnetic nanoparticles, polydopamine

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Authors: N. Larbi, F. Debbat

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Wireless communication systems have changed dramatically and shown spectacular evolution over the past two decades. These radio technologies are engaged in a quest endless high-speed transmission coupled to a constant need to improve transmission quality. Various radio communication systems being developed use code division multiple access (CDMA) technique. This work analyses a hybrid honey bees mating optimization algorithm (HBMO) applied to multiuser detection (MuD) in CDMA communication systems. The HBMO is a swarm-based optimization algorithm, which simulates the mating process of real honey bees. We apply a hybridization of HBMO with simulated annealing (SA) in order to improve the solution generated by the HBMO. Simulation results show that the detection based on Hybrid HBMO, in term of bit error rate (BER), is viable option when compared with the classic detectors from literature under Rayleigh flat fading channel.

Keywords: BER, DS-CDMA multiuser detection, genetic algorithm, hybrid HBMO, simulated annealing

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Authors: Mariya Dzhyoyeva

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In the present paper, I examine manifestations of toxic masculinity in the novels by Tomás Eloy Martínez, a post-Boom author, journalist, literary critic, and one of the representatives of the Argentine writing diaspora. I focus on the analysis of Martínez´s characters that display hypermasculine traits to define the relationship between toxic masculinity and power, including the power of authorship and violence as they are represented in his novels. The analysis reveals a complex network in which gender, power, and violence are intertwined and influence and modify each other. As the author exposes toxic masculine behaviors that generate violence, he looks to undermine them. Departing from M. Kimmel´s idea of masculinity as homophobia, I examine how Martínez “outs” his characters by incorporating into the narrative some secret, privileged sources that provide alternative accounts of their otherwise hypermasculine lives. These background stories expose their “weaknesses,” both physical and mental, and thereby feminize them in their own eyes. In a similar way, the toxic masculinity of the fictional male author that wields his power by abusing the written word as he abuses the female character in the story is exposed as a complex of insecurities accumulated by the character due to his childhood trauma. The artistic technique that Martínez uses to condemn the authoritarian male behavior is accessing his subjectivity and subverting it through a multiplicity of identities. Martínez takes over the character’s “I” and turns it into a host of pronouns with a constantly shifting point of reference that distorts not only the notions of gender but also the very notion of identity. In doing so, he takes the character´s affirmation of masculinity to the limit where the very idea of it becomes unsustainable. Viewed in the context of Martínez´s own exilic story, the condemnation of toxic masculine power turns into the condemnation of dictatorship and authoritarianism.

Keywords: gender, masculinity., toxic masculinity, authoritarian, Argentine literature, Martínez

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Authors: Xingyu Pan, Laure Tougne

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For recognizing coins, the graved release date is important information to identify precisely its monetary type. However, reading characters in coins meets much more obstacles than traditional character recognition tasks in the other fields, such as reading scanned documents or license plates. To address this challenging issue in a numismatic context, we propose a training-free approach dedicated to detection and recognition of the release date of the coin. In the first step, the date zone is detected by comparing histogram features; in the second step, a topology-based algorithm is introduced to recognize coin numbers with various font types represented by binary gradient map. Our method obtained a recognition rate of 92% on synthetic data and of 44% on real noised data.

Keywords: coin, detection, character recognition, topology

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Authors: Suzanne Overstreet-Juenke

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Disproportionate minority contact (DMC) is a salient issue that has been found at every stage of the decision-making process in the juvenile justice system. Existing research indicates that DMC influences adjudication for drug, property, and personal crimes. Because intimate partner violence (IPV) is a major public health problem and global concern, the current study examines DMC at adjudication among youth charged for crimes of interpersonal violence. This research uses administrative, Court Designated Worker (CDW) data collected from 2014 to 2016. The results are contextualized using Steffensmeier’s version of focal concerns theory of judicial decision-making. This study assesses race and two seriousness of offense measures to establish whether a link exists between race and adjudication. The results of the study is similar to prior research on the topic. These results are discussed in terms of policy implications, limitations, and future research.

Keywords: race, disproportionate minority contact, focal concerns theory, juvenile

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Wen-Shu Lai, Yi-Ting Wang

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The purpose of this paper is to discuss and clarify what are the essences of Colombian-born sculptor Doris Salcedo’s works. Under the frameworks of Buber’s dialogical philosophy of the “I-Thou relation” and Zurmuehlen’s philosophy of “Art as Presence” within the context of art praxis, Salcedo’s selected works are analyzed and interpreted. Salcedo’s sculptures and installations have expressed her concerns of the collective and personal memories within the context of Colombia’s violent, historical and political conflicts, especially the trauma inscribed onto her fellow people. Salcedo tried to rescue that memory though her work does not directly represent the violent incidents happened in Colombia. They are indirect portraits of the disappeared, the victims, and the lack of identity. What the viewers see is something in between vanishing and emergence, personal and collective. The work, the artist and the viewer are witnesses and also survivors of Columbia’s violent incidents. On the site, the work, the disappeared and the witness-survivors encounter each other, then mourning, memory and dialogue are unfolded, brought to present. Firstly, it is the power of encounter that allows the viewer-witness to recognize the effaced victims, repressive violence, and the profound mourning for the loss, then restore their existence through dialogues and bring them to present. In her sculptures and installations, the displacement of the fragments and the incoherent sites make these daily household objects become unfamiliar, arose feelings of uncanniness of the viewer. The feelings of alienation, confusion, displacement bring the viewer to here and now. The more one studies these objects and sites, the more hidden details begin to appear. And the more one looks at the details, the more absent memories or stories reveals themselves and becomes present. Salcedo’s work is about loss, displacement and alienation caused by violence. She expressed that words are no longer possible when one deals with violence. However, her installation translates the violence, memory, and loss of beloved ones into a place of dialogue, in which the visitors can immerse themselves in a twilight zone between knowing and not knowing, remembering and forgetting. The spaces are the sites or non-sites inhabited by the remains or traces of the victims, the wonders of the survivor-witnesses where they join together through encounter, remain present to others through genuine dialogue. In the moment, the past memory and the ongoing life merge, accept each other, and reconcile. Salcedo reconfigures the silent violence and repressive history in Colombia and transforms them into sites and installations. The victims, the viewer and the artist join together while contemplating and sharing the human situation of silent repression. In the moment of contemplating, a dialogue, spoken or not, occurs in the specific sites. People have become aware and present, and mutual understanding has achieved. This research concludes that encounter, presence and dialogue are the three essences embedded in Salcedo’s works.

Keywords: dialogue, Doris Salcedo, encounter, presence

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Authors: K. T. Kohler

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Typically in war, force involves physical violence, though those who perpetrated the Holocaust expanded manipulation techniques to include mental violence. This qualitative research study was conducted to understand the effects that the music of the Women’s Orchestra of Auschwitz-Birkenau had on women prisoners during World War II. Over 100 testimonies from the USC Shoah Foundation’s Visual History Archive reveal that the orchestra’s music had a profoundly distressing effect on many of the women in the camp. Led by Gustav Mahler’s granddaughter, Alma Rosé, the orchestra rhythmed the life cycle of the camp, from marching to and from work, Sunday concerts, welcoming transports, to the prisoners’ walk to gas chambers. What surfaced from these testimonies was that the more technical the exposure a woman had to music before camp, the more disturbing its effect. The juxtaposition of beauty with the visible horror of the camp thrust them into an impossible state where suicide became a plausible alternative. By exploiting the Women’s Orchestra, the Nazis made music a critical component of manipulation within Auschwitz-Birkenau.

Keywords: Alma Rosé, Auschwitz-Birkenau, camp life, concert, Holocaust, music, Oświęcim, Poland, women’s orchestra

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: Wing Hang Shelley Leung

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With the rise of the recent social movement, namely #MeToo, it shows that a lot of women and children in fact suffered from sexual abuse and some even suffered from child abuse, including in Hong Kong. In view of the ongoing social movements, this paper argues that we have to look beyond their impacts and understand the roots of the problem: what if the underlying cause of the recent social movements was the inherited values that were rooted in us since we were young, or the public’s lack of confidence in the legal system when it comes to this type of personal matters? What if the movements reveal the problematic issue of the lack of protection plans, either in the private or public sphere? If the legal system is presumed to not be able to preemptively protect everyone or effectively punish all perpetrators, can other pillars provide supports to fill in the loopholes of the legal system? This paper takes a theoretical approach to look into current sexuality education, the legal system in Hong Kong and the adoption of Asian values in society to argue that difficulties that are being placed onto victims in disclosing sexual violence they had experienced. Reviews of the current system and recent sexual assaults court cases for case studies allow the research to address the issues of victims’ experience including (a) their reactions to incidents; (b) issues they have in trials; (c) psychological impacts of the incidents; and (d) their understandings of gender equality before and after incidents. The study is significant because it criticises the current legal system in Hong Kong and provides insights to the public by explaining the dynamics between the problem, the legal system and the society. Also, it contributes to the ongoing research about the psychological impacts to victims in Hong Kong, especially how they are placed in a disadvantaged position in the legal system and society and even for their recovery. It contributes to the findings of how family structures, parental responsibilities and gender studies influence a child’s perception of gender equality in Hong Kong and hence their immediate reactions to incidents. To fully address the needs of victims, especially our younger generation, as well as to prevent future harm and to raise awareness, an inclusive framework which recognizes the needs of protecting and safeguarding women and children in the private sphere and a proper education for gender equality are needed.

Keywords: child abuse, children's rights, domestic violence, gender equality, Hong Kong, Me too, sexual violence, women's rights

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Authors: Gaokai Liu

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Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets.

Keywords: deep learning, defect detection, image segmentation, nanomaterials

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: M. Van 'T Hof, R. V. Pasma, J. T. Bailly, H. W. Hoek, W. A. Ester

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Background: The transition into primary school can be challenging for children with an autism spectrum disorder (ASD). Due to the new demands that are made to children in this period, their limitations in social functioning and school achievements may manifest and appear faster. Detection of possible ASD signals mainly takes place by parents, teachers and during obligatory municipal maternal and child health centre visits. Physicians of municipal maternal and child health centres have limited education and instruments to detect ASD. Further education on detecting ASD is needed to optimally equip these doctors for this task. Most research aims to increase the early detection of ASD in children aged 0-3 years and shows positive results. However, there is a lack of research on educational interventions to detect ASD in children aged 4-6 years by municipal maternal and child health physicians. Aim: The aim of this study is to explore the effect of the online educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health physicians. This educational intervention is developed within The Reach-Aut Academic Centre for Autism; Transitions in education, and will be available throughout The Netherlands. Methods: Ninety-two participants will follow the educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health centre physicians. The educational intervention consists of three, one and a half hour sessions, which are offered through an online interactive classroom. The focus and content of the course has been developed in collaboration with three groups of stakeholders; autism scientists, clinical practitioners (municipal maternal and child health doctors and ASD experts) and parents of children with ASD. The primary outcome measure is knowledge about ASD: signals, early detection, communication with parents and referrals. The secondary outcome measures are the number of ASD related referrals, the attitude towards the mentally ill (CAMI), perceived competency about ASD knowledge and detection skills, and satisfaction about the educational intervention. Results and Conclusion: The study started in January 2016 and data collection will end mid 2017.

Keywords: ASD, child, detection, educational intervention, physicians

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Authors: Samuel A. Muyiwa

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Religious chauvinism has attained an alarming status in Contemporary Nigerian society. Arguably, Nigeria is the largest economy and most populous nation in Africa with over 182 million people, the advantages offer by vibrant economy and high population have been sacrificed on the altar of religion. Tolerance, sacrifice, humility, compassion, love, justice, trustworthiness, dedication to the well-being of others, and unity are the universal spiritual principles that lie at the heart of any religion either Christianity or Islam even traditional. Whereas traditional religious practices foreground the beliefs, norms and ritual that are related to the sacred being God because of its quick and immediate consequence of its effect, the new-found religious sentiments have deviated from the norms, thus undermining cosmic harmony in Nigeria because of its long-time consequence of its effect. Religion, which is expected to accelerate growth and motivate people to develop spiritual nuances for the betterment of their communities, has, however occasioned conflict and violence in Nigeria socio-political cosmo. Therefore, this study examines the content of religion in the promotion of peace and unity and its contextual missing link in the promotion of conflict and violence in Nigeria.

Keywords: religion chauvinism, Nigeria, conflict, conflict resolution

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Authors: Khushboo Verma

Abstract:

The use of rape as a weapon of war has been in existence for as early as the middle ages. Women, during the conflict, have been treated as the spoils of war, a reward for the conquering soldiers granted to them by their superiors which is, arguably, most often overlooked as part of the collateral damage that is unavoidable in conflict zones. Korean-born Therese Park’s first novel, A Gift of the Emperor (1997), describes one such atrocious incidence wherein she highlights the active role the Japanese military played in procuring and condoning trafficking of women, who were euphemistically referred to as ‘comfort women’, for prostitution during World War II. This paper thus aims to look at the remembering and reckonings of these women, which fueled a range of creative gestures in the artistic representations and knowledge production by Korean American artists and writers. The essay divides into three parts wherein first it tries to highlight the relationship of the state and the self in relation to the ‘comfort women’ as to who bears the onus of the exploitation of these women, or the responsibility for the redressal with the present-day notions of human rights as studied through Ueno Chizuko’s ‘The Politics of Memory: Nation, Individual and Self’ (1999). There are several narratological elements of the text that are of interest here which shall be viewed and analysed throughout the paper as well. The second part of the paper talks about the aesthetics of rape and sexual violence as represented or (mis)represented by Park in her novel as she attempts to give voice to the victim and retain her and her suffering as the central focus of the narrative. Finally, the third part of the novel explores as well as places the novel in the context of debates over the highly contested issue of ‘comfort women’ and the actual ‘comfort women’ survivors’ testimonies. For this purpose, the present study focuses on Dori Laub’s ‘Truth and Testimony: The Process and the Struggle’ (1991).

Keywords: Korean comfort women, survivors’ testimonies, sexual slavery, aesthetics of sexual violence, horrible memories

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Authors: Elmongi Elbellili, Ben Lauwens, Daan Huybrechs

Abstract:

The increasing complexity of modern engineering systems presents a challenge to the digital simulation of these systems which usually can be represented by differential equations. The Linearly Implicit Quantized State System (LIQSS) offers an alternative approach to traditional numerical integration techniques for solving Ordinary Differential Equations (ODEs). This method proved effective for handling discontinuous and large stiff systems. However, the inherent discrete nature of LIQSS may introduce oscillations that result in unnecessary computational steps. The current oscillation detection mechanism relies on a condition that checks the significance of the derivatives, but it could be further improved. This paper describes a different cycle detection mechanism and presents the outcomes using LIQSS order one in simulating the Advection Diffusion problem. The efficiency of this new cycle detection mechanism is verified by comparing the performance of the current solver against the new version as well as a reference solution using a Runge-Kutta method of order14.

Keywords: numerical integration, quantized state systems, ordinary differential equations, stiffness, cycle detection, simulation

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Authors: Kunal Das, Priya Sengupta, Abhishek K. Singh

Abstract:

Pin assignment, scheduling, routing and error detection for arbitrary biochemical protocols in Digital Microfluidic Biochip have been reported in this paper. The research work is concentrating on pin assignment for 2 or 3 droplets routing in the arbitrary biochemical protocol, scheduling and routing in m × n biochip. The volumetric error arises due to droplet split in the biochip. The volumetric error detection is also addressed using biochip AND logic gate which is known as microfluidic AND or mAND gate. The algorithm for pin assignment for m × n biochip required m+n-1 numbers of pins. The basic principle of this algorithm is that no same pin will be allowed to be placed in the same column, same row and diagonal and adjacent cells. The same pin should be placed a distance apart such that interference becomes less. A case study also reported in this paper.

Keywords: digital microfludic biochip, cross-contamination, pin assignment, microfluidic AND gate

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Authors: Chun-Wei Huang, Jyh-Cherng Gu, Ming-Ta Yang

Abstract:

Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method.

Keywords: ferroresonance, wavelet transform, intelligent electronic device, transformer

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Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

Abstract:

The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

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