Reviewerly/exHarmony huggingface dataset:
Interrelaciones socioeducativas, educación en línea y bienestar durante el confinamiento por Covid-19 = Socio-educational interrelationships, on-line education and wellbeing during Covid-19 confinement

In this study, socio-educational interactions of parents, teachers and students are approached regarding distance education in the context of the Programa Aprende en Casa (Learn at Home Program) during the COVID-19 confinement. The distance learning setting is approached as a space for interactions in which emotional, communicative and cognitive factors intervene and revisit factors emanating from a psychosocial line in the role of social interactions to configure wellbeing conditions. Eight hundred and ninety-nine (899) people took part in an electronic survey for the study (parents) from 17 municipalities in the state of Morelos, Mexico. A Multivariate Factor Analysis was applied to analyze relationships between the perception of wellbeing and the different variables dependent on socio-educational interactions as well as an ANOVA test to identify differences between groups. It was detected that wellbeing during distance classes is associated significantly with all socio-educational interactions. Differences were also observed depending on the technology dominance of parents and interactions. Lastly, educational implications were discussed on results regarding the relational character of learning, its impact on wellbeing, and interactions such as the key element in the socio-educational sphere.
Designing a state of art photonic AI accelerator

In this work, we discuss the requirements and challenges of designing a photonic computing chip that can be deployed in the latest commercial AI systems. Silicon Photonics have the potential to revolutionize AI computing by delivering unprecedented improvements in the power consumption and computational throughput of AI computations. Still, there are several challenges to be tackled. Among these are the need to design high-density photonic integrated circuits, designing photonic memory systems for data storage, and solving the bottleneck of the electrical-to-optical conversions. Several innovative photonic technologies have been introduced to address these challenges. The progress on implementing these technologies is discussed.
Erroneous Coordinated Sentences Detection in French Students’ Writings

This paper presents the development stages of an NLP device to be used to improve students’ skills in French academic writing. Among various relevant difficulties, we focus on coordinating constructions that include or not ellipsis. We develop a tool to detect errors automatically in coordinated sentences from a corpus composed of erroneous and correct sentences. We use a deep learning approach based on the French CamemBERT model. To find the best learning environment for the classification task, we show the results obtained from training and testing datasets with different proportions of erroneous and correct sentences.
A coupled 3D finite element model of the sound production by mallet percussion instruments

The sound production in mallet percussion instruments involves mechanical vibrations, acoustical resonance, and radiation inherently coupled. The source of energy is the mallet that excites mechanical oscillations in the sound bar when the player hits one of the notes. The sound bar in itself is an inefficient radiator, therefore acoustical resonators are applied in order to reinforce the radiated sound. There are various challenges in modeling tuned idiophone instruments, including the nonlinear interaction of the mallet and the sound bar, the orthotropic behavior of the material of the bars, and the sound radiation into the half-open acoustical space.In this contribution the coupled finite element model of a xylophone is examined in detail. The three-dimensional model consists of mechanical and acoustical finite elements, and infinite acoustical elements that emulate free field radiation conditions. The coupled system is solved in the time domain with exploiting the benefits of the modal basis for reducing the size of the system of equations. The paper demonstrates the capabilities of the model, emphasizing the effect of two-way vibroacoustic coupling between the sound bar and the resonator. By analyzing the energy balance of the mechanical and acoustical subsystems the effect of tuning the resonator is investigated. The three-dimensional sound bar model is further exploited by studying the effect of the excitation of the torsional modes of the bar.Finally, synthesized sound samples illustrate the applicability of the model for physics-based sound synthesis.
Designing an Online Re-examination System for Graduate Entrance Examination

The conventional on-site re-examination for graduate entrance examination has caused a large number of personnel flow and aggregation, for which the graduate entrance examination could not be implemented safely during the major disease outbreaks, making online re-examination the primary means of graduate entrance examination during the prevention and control epidemic. However, the application of the existing commercial platform for online re-examination has great security risks and instability. This paper arranged the workflow and process settings of each step in the conventional re-examination, analyzed the defects of the traditional software architecture, and first attempted to apply microservices architecture to online re-examination for graduate entrance examination. It took micro service as the core and splited various business functions. And an Online Re-examination System for graduate entrance examination was designed based on microservice architecture by using MySQL database and blockchain technology. Then tracking the system use, an evaluation system for the online re-examination graduate entrance examination is designed through scoring method. The research results show that the design of the online re-examination system for graduate entrance examination is reasonable. It realizes the requirements of the online re-examination for graduate entrance examination, reduces the coupling degree between the services, and solves the problem of data trace in the re-examination. Online Re-examination system can effectively ensure the orderly development of graduate recruitment during the epidemic period, improve the efficiency and fairness of graduate entrance examination, and significantly promote the network process standardization and informatization of graduate re-examination work.
Portrait of College Students and Personalized Teaching Mode Under the Blended Learning Mode Based on Big Data Technology

With the advent of the information age, information technology education has become an inevitable trend in higher education reform. Targeted individualized teaching can help teachers develop more effective teaching models. This article mainly studies the collection and sorting of large amounts of data generated through online learning platforms based on a blended teaching model. The effective data generated in the learning process of students is aggregated and classified, and student portrait tag libraries of different dimensions are developed according to the specific influencing factors reflected by the data. Use portrait labels and statistics to perform variance analysis to understand the association between each label and the result attribute scores, so that the students represented by the portrait labels have clearer problems in the learning process. The design of teaching and the formulation of learning plans for each portrait label help students to learn more efficient professional courses.
Artificial Intelligence and Copyright: Legal Quandary in the Digital Age: Some Musings

This paper examines the legal dilemma that is posed by artificial intelligence to copyright protection
Automatic Attendance Check System Through Multiple Face Recognition

Non-contact biometric identification technology is once again receiving social attention these days when untact technology is required due to the Covid-19. Face recognition technology is being used in a variety of platforms such as airport immigration systems, hospital patient management systems, financial self-authentication systems, criminal suspect detection systems, and personal authentication systems for mobile devices, and is showing its effectiveness. In this paper, multiple face recognition technology is applied to the automatic attendance system. Unlike when only one person is recognized, multiple face recognition results in lower accuracy and slower processing speed. In this paper, a frontal face detection algorithm is proposed. Using the frontal face detection algorithm, only the frontal face was used for recognition, and when 36 people were recognized, 97.2% accuracy was achieved. In addition, we proposed an erasing multiple face recognition method and increased the average number of frames processed per second as the recognition completion rate increased.
Research Guides: ADV 208: The Big Idea in Advertising (Fall 2021): Home

An SU Libraries research starting points guide for students enrolled in ADV 208 (course section taught by Prof Brian Sheehan)
Blockchain and Internet of Things: An Amalgamation of Trending Techniques

The application of computers and automation systems to ease everyday living has exponentially increased in today’s world with a vast variety of technologies being deployed around every aspect of human life. With faster networking as one of the main reasons for this boom, and with the advent of new technologies such that everything around us is getting smarter day by day, we are witness to two of the trending and most fascinating techniques that are elevating the service quality of our life, making it easy and comfortable, along with taking care of our privacy and security: Blockchain and Internet of Things (IoT). This chapter provides a brief literature review pertaining to this field which is motivated by the need to understand the concepts of Blockchain and IoT. On one hand, a detailed description of Blockchain Technology, its structure, advantages, and disadvantages have been provided; on the other, Internet of Things, its structure, and the integration of IoT with Blockchain are covered in detail. While we have discussed the potential area of applications of integration of IoT and Blockchain, we have paid equal attention to the security and the privacy issues arising in the amalgamation of Blockchain technology and IoT that is an area of major concern for users of the technologies. Explored in depth and discussed in thoroughness, it should resolve the issues surrounding the integration and should throw open the doors of curated thought to the researchers who are devoted to meet the challenges faced by integration of these technologies.
Distilling Virtual Examples for Long-Tailed Recognition

We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method. Specifically, by treating the predictions of a teacher model as virtual examples, we prove that distilling from these virtual examples is equivalent to label distribution learning under certain constraints. We show that when the virtual example distribution becomes flatter than the original input distribution, the under-represented tail classes will receive significant improvements, which is crucial in long-tailed recognition. The proposed DiVE method can explicitly tune the virtual example distribution to become flat. Extensive experiments on three benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed DiVE method can significantly outperform state-of-the-art methods. Furthermore, additional analyses and experiments verify the virtual example interpretation, and demonstrate the effectiveness of tailored designs in DiVE for long-tailed problems.
Real-time Anomaly Detection on Financial Data

This work presents an investigation of tailoring Network Representation Learning (NRL) for an application in the Financial Industry. NRL approaches are data-driven models that learn how to encode g ...
“I don’t care what you think and you can’t hurt me”

Lessons from a Multispecies Studio - Uncovering Ecological Understanding and Biophilia through Creative Reciprocity; New critical discourse and applied methods based on the author’s original research and interspecies art practice, Animal Lover. Each chapter narrates the creative processes of one project, including the worlds of canines, salmon, birds and forest communities, and how these encounters transformed her outlook on Earth and all life. 50 half-tones with colour plate section
Smart Home Activity Recognition For Ambient Assisted Living(AAL)

With the increasing age of an individual, the chances of being prone to chronic diseases like diabetes or non-curable diseases like Alzheimer’s Syndrome or Parkinson's Syndrome increases. Due to the health issues, elderly must be accompanied by caretakers to monitor their well-being at all times. With growing responsibilities and work pressure, the family members may find it challenging to find a trustworthy caretaker. In such scenarios, an assisted living environment acts as a boon. A normal home embedded with different sensors to monitor an individual’s well-being is called as Ambient Assisted Living(AAL). This living environment detects anomalous behaviour and recognizes human activities. AAL is already being used to observe the wellness of the elderly population across the globe. This paper gives an insight into human activity recognition in a smart home living environment. A smart home activity recognition model is proposed and implemented using four machine learning algorithms. The proposed model is applied to six different publicly available datasets and the accuracy was estimated using different measures. It has been observed that Random Forest machine learning algorithm shows the best accuracy on most of the datasets.
On Initial Pools for Deep Active Learning

Active Learning (AL) techniques aim to minimize the training data required to train a model for a given task. Pool-based AL techniques start with a small initial labeled pool and then iteratively pick batches of the most informative samples for labeling. Generally, the initial pool is sampled randomly and labeled to seed the AL iterations. While recent studies have focused on evaluating the robustness of various query functions in AL, little to no attention has been given to the design of the initial labeled pool for deep active learning. Given the recent successes of learning representations in self-supervised/unsupervised ways, we study if an intelligently sampled initial labeled pool can improve deep AL performance. We investigate the effect of intelligently sampled initial labeled pools, including the use of self-supervised and unsupervised strategies, on deep AL methods. The setup, hypotheses, methodology, and implementation details were evaluated by peer review before experiments were conducted. Experimental results could not conclusively prove that intelligently sampled initial pools are better for AL than random initial pools in the long run, although a Variational Autoencoder-based initial pool sampling strategy showed interesting trends that merit deeper investigation.
Does EBA improve good governance in sub-Saharan Africa?

The contribution analyses the impact of the EU's 'Everything But Arms' (EBA) initiative on good governance in Sub-Saharan Africa (SSA). Good governance is part of the requirements for admission to EBA. The question is whether EBA has created incentives for partner countries to improve their level of good governance. The article shows that few EBA countries have improved their governance structures and that the EU does not threaten sanctions for non-compliance with conditionality. Those countries that have improved their governance have diversified export structures and a middle class that can demand reforms. The design of EBA should therefore be more focused on export diversification and strengthening the middle class.
Professional Training Of Physical Education Teachers In Digital Transformation Of Society

Digital transformation of the society is the reason for changes in the professional training of a physical education teacher in terms of the necessity to master the skills to form pupils’ universal educational actions. The research has made it possible to determine the indicators of pupils’ functional literacy in the field of health and safety, to substantiate the necessity to create organizational and pedagogical conditions in the students’ mastering the following skills: to use the means and methods of enhancing the cognitive interest of schoolchildren; use techniques aimed at determining the goals and personal meaning of a pupil's educational activity; teach pupils to search and analyze information, determine the sequence of actions; define differentiated learning tasks for pupils; keep documentation in digital format, organize the pupils’ activities to master the methods of organizing and self-organizing activities (defining goals taking into account the final result, drawing up a plan and sequence of actions); create conditions for cooperation; use conflict resolution techniques; carry out pedagogical control of universal learning activities using techniques for reflective thinking development; use techniques and methods of developing self-regulation skills doing physical exercises; teach methods of forecasting and adjusting the result; choose educational techniques adequately corresponding to the purpose of the lesson; have a command of measuring health and safety literacy rates.
Guessing Game on Teaching Vocabulary for Intermediate Language Learners

This study aims 1) to find out the effectiveness of Guessing Game implementation on teaching vocabulary, and 2) to find out the students’ perceptions of Guessing Game on teaching vocabulary. This study applied pre-experimental research method with one-shot case study design, where in this study the researcher only have one group without any control groups to make a comparison. The participants of this study are the entire students of the second grade in SMK PGRI 1 Pariwisata Cianjur consist of 58 students. Meanwhile in this study the researcher took the sample from the second grade of Banking Class in SMK PGRI 1 Pariwisata Cianjur that consists of 12 students. In collecting the data, the researcher used two instruments; test and close-ended questionnaire. The study conducted two times and ended by giving a test. Then, the result of the test showed that students’ average score is 81. It indicates that Guessing Game is effective on teaching vocabulary because the score is upper than the minimum completeness criteria (KKM). In addition, from the result of questionnaire the researcher categories the questions into three categories related to the implementation of Guessing Game; 1) Guessing Game is a joyful teaching technique, 2) Guessing Game is able to increase students’ confidence, and 3) Guessing Game is able to assist students memorize the vocabulary in longer time. Then, the result showed 100% students agree about that. Therefore, those data indicates that Ha of this study is accepted that Guessing Game is effective to increase students’ vocabulary mastery.
Unsupervised video anomaly detection using 3D CNNs and novelty detection techniques

Unsupervised/semi-Supervised video classification When there are hundreds or thousands of cameras producing video streams all day long it is very useful to have an algorithm that analyzes such streams instead of a human. Today such technology exists and is called convolutional neural networks for video classification. The downside of such neural networks is that we have a fixed number of cases on which the net is trained which is ok for benchmarking our algorithm on a specific dataset but not for real life applications such as security cameras where we don’t know specifically for which scene the algorithm should give an alert signal. So we need to produce an abstract representation of the video scene (embedding) and to classify it in an unsupervised way.
TEKNOLOGI PENGEMASAN UNTUK MENINGKATKAN PEMASARAN PRODUK LOKAL MADU KELULUD DUSUN LIMBUNG BANGKA

Real work learning (KKN) is an academic obligation that must provide benefits for learning for students and provide solutions to problems in society in the form of smart creative ideas that are appropriate for the times. The socialization program for the right packaging technology to increase the marketing of superior products from Limbung, Jada Bahrin Village via online to creative business actors as part of the XV KKN_UBB Program with the theme Creative Learning and Serving in the Covid 19 Period. The activity was attended by 15 students and 1 assistant lecturer. The implementation starts from June 22 to August 9 2020 in Limbung hamlet, Jada Bahrin village, Merawang district, Bangka regency. Activities include, observation activities to better understand social conditions, formulating activity items and real implementation in the form of products. Mentoring activities begin with mentoring in making product packaging designs by means of tutorials and practice, mentoring and teaching online marketing tutorials. The activity succeeded in designing, packaging kelulud honey products and assisting technology-based marketing using online media. The whole stages and program of activities show that after this KKN activity is felt to be beneficial for the community, and the community hopes for the sustainability of the program. Technological improvements in the form of training and mentoring in the management of product innovation, packaging appearance and online marketing strategies are still needed.
A Light Stage on Every Desk

Every time you sit in front of a TV or monitor, your face is actively illuminated by time-varying patterns of light. This paper proposes to use this time-varying illumination for synthetic relighting of your face with any new illumination condition. In doing so, we take inspiration from the light stage work of Debevec et al., who first demonstrated the ability to relight people captured in a controlled lighting environment. Whereas existing light stages require expensive, room-scale spherical capture gantries and exist in only a few labs in the world, we demonstrate how to acquire useful data from a normal TV or desktop monitor. Instead of subjecting the user to uncomfortable rapidly flashing light patterns, we operate on images of the user watching a YouTube video or other standard content. We train a deep network on images plus monitor patterns of a given user and learn to predict images of that user under any target illumination (monitor pattern). Experimental evaluation shows that our method produces realistic relighting results. Video results are available at this http URL.
COMBATING AND ADDRESSING THE MENACE OF CYBERCRIME IN NIGERIA: AN OVERVIEW OF APPLICABLE LAWS

In Nigeria, technological advancement in data processing technology, information communication technology, and the internet pervade all human endeavours. It has also created a climate that allows social outcasts to perpetrate cyber fraud and crime against online users. The emergence of cybercrime in Nigeria has been aided by the fact that it needs little to no resources to begin with and that it can be carried out in countless locations without any geographical constraint. This has caused heated debate on whether or not Nigeria has sufficient laws in place to investigate and prosecute cybercriminals effectively and efficiently. To this end, this paper calls for the passage of successful legislations to eliminate or reduce cybercrime in Nigeria to its barest minimum.