Reviewerly/exHarmony huggingface dataset:
Towards a folksonomy graph-based context-aware recommender system of annotated books

Abstract The emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any identifiable thing or item on the system. In education, tagging is considered a powerful meta-cognitive strategy that successfully engages learners in the learning process. Besides, the collaborative tagging gathers learners’ opinions, thus, provides more comprehensible recommendations. Still, the abundant shared contents are mostly unorganized which makes it hard for users to select and discover the appropriate items of their interests. Thus, the use of recommender systems overcomes the distressing search problem by assisting users in their searching and exploring experience, and suggesting relevant items matching their preferences. In this regard, this article presents a folksonomy graphs based context-aware recommender system (CARS) of annotated books. The generated graphs express the semantic relatedness between these resources, i.e. books, by effectively modeling the folksonomy relationship between user-resource-tag and integrating contextual information within a multi-layer graph referring to a Knowledge Graph (KG). To put our proposal into shape, we model a real-world application of Goodbooks-10k dataset to recommend books. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performance results of graph-based book recommendations.
Social aspects of the COVID-19 pandemic in the education system

The COVID-19 pandemic has altered the life of modern society forever. Not only the economy has changed, but also the person himself and his way of life, his communications. The education system and its role in society have also changed. Analysis of public reviews of recent years, as well as national and international programs aimed at compensation of the social consequences of distance learning. The existing stratification of society is changing from an economic to a digital paradigm because of the pandemic and it is impossible to overcome it within the framework of only separate education system. But the simultaneous digital development of the largest possible group of people – students and their parents, their upbringing and development are only possible within the framework of this system. All the social consequences of distance learning during the 2020 pandemic can best be understood in retrospect. There is no doubt that the decline in the quality of education will lead to significant difficulties in offsetting it. Now we can note a significant increase in the inequality among the population in the new area, the area of access to digital technologies and the digital state.
Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications

Eye tracking (ET) has shown to reveal the wearer’s cognitive processes using the measurement of the central point of foveal vision. However, traditional ET evaluation methods have not been able to take into account the wearers’ use of the peripheral field of vision. We propose an algorithmic enhancement to a state-of-the-art ET analysis method, the Object-Gaze Distance (OGD), which additionally allows the quantification of near-peripheral gaze behavior in complex real-world environments. The algorithm uses machine learning for area of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the gaze point, creating a continuous gaze-based time-series. Based on an evaluation of two AOIs in a real surgical procedure, the results show that a considerable increase of interpretable fixation data from 23.8 % to 78.3 % of AOI screw and from 4.5 % to 67.2 % of AOI screwdriver was achieved, when incorporating the near-peripheral field of vision. Additionally, the evaluation of a multi-OGD time series representation has shown the potential to reveal novel gaze patterns, which may provide a more accurate depiction of human gaze behavior in multi-object environments.
Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data

Homomorphic encryption (HE) is one of the representative solutions to privacy-preserving machine learning (PPML) classification enabling the server to classify private data of clients while guaranteeing privacy. This work focuses on PPML using word-wise fully homomorphic encryption (FHE). In order to implement deep learning on word-wise HE, the ReLU and max-pooling functions should be approximated by polynomials for homomorphic operations. Most of the previous studies focus on HE-friendly networks, which approximate the ReLU and max-pooling functions using low-degree polynomials. However, this approximation cannot support deeper neural networks due to large approximation errors in general and can classify only relatively small datasets. Thus, we propose a precise polynomial approximation technique, a composition of minimax approximate polynomials of low degrees for the ReLU and max-pooling functions. If we replace the ReLU and max-pooling functions with the proposed approximate polynomials, standard deep learning models such as ResNet and VGGNet can still be used without further modification for PPML on FHE. Even pre-trained parameters can be used without retraining, which makes the proposed method more practical. We approximate the ReLU and max-pooling functions in the ResNet-152 using the composition of minimax approximate polynomials of degrees 15, 27, and 29. Then, we succeed in classifying the plaintext ImageNet dataset with 77.52% accuracy, which is very close to the original model accuracy of 78.31%. Also, we obtain an accuracy of 87.90% for classifying the encrypted CIFAR-10 dataset in the ResNet-20 without any additional training.
Design (Non) Fiction: Deconstructing/Reconstructing the Definitional Dualism of AI

2001: A Space Odyssey (Kubrick, 1968) speculates on humanities technological ascension through the exploration of space and the ultimate transcendence of humanity galvanised by the invention of AI. Every detail of this portrayal was an exercise in World Building, with careful considerations of then state-of-the-art technology and informed predictions. Kubrick’s speculative vision is comparative to the practice of Design Fiction, by suspending disbelief and leveraging a technologies emergence to question the future’s sociotechnical landscape and its ramifications critically. Discovery’s AI system, Hal9000, is a convincing speculation of intelligence with Kubrick’s vision showcasing current and long-term aims in AI research. To this end, Hal9000 uniquely portrays Artificial General Intelligence (AGI) underpinned by visualising ‘narrow’ AI subproblems; thereby, simultaneously highlighting then current research agendas within AI and manifesting them into the aspirational research agenda of human-computer symbiosis. As a result of Kubrick’s mastery in suspending a viewer’s disbelief despite portraying a particular reality for AI, and humanities fascination with artificial life, the term AI simultaneously refers to the grand vision of AGI as well as relating to the contemporary reality of narrow AI. This confusion, along with establishing AI’s ontology, are current challenges that need addressing to create effective and acceptable realisations of AI. This paper responds to the ontological confusion by reviewing and comparing Kubrick’s speculative methodology to the practice of Design Fiction by unpacking Hal9000 as a diegetic prototype while defining the active threads of ‘AI’s Definitional Dualism’. The paper will also present a Design Fiction submerged in the reality of narrow AI and the adoption of a More-Than Human Centred Design approach to address the complexity of AI’s ontology in alternative ways. Finally, this paper will also define the importance of researching the semantics of AI technology and how film and Design Fiction offer a discursive space for design research to transpire.
Prediction of Parameters of Semiconductor Band-pass Filters using Artificial Neural Network

Microwave device design process requires repetitive and time-consuming electromagnetic simulations to extract device parameters. New methods are needed to accelerate the process and enable it for real time parameter calculations. Feed-forward backpropagation multilayer perceptron artificial neural network with 3 hidden layers is presented to predict S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> parameters of 3.5 GHz band-pass filter. Prediction results, received with neural network trained with Resilient Backpropagation, One Step Secant, Scaled Conjugate Gradient and BFGS Quasi-Newton training methods, are compared with each other. The best prediction results are received after training network with BFGS Quasi-Newton training method. Average mean squared error and root mean square error received is 0.0044 and 0.066 respectively. As predictions of neural network are noticeably faster than electromagnetic simulation and prediction errors are low, the method can be used to predict band-pass filter parameters in real time.
Estimation of Hourly Clearness Index and Diffuse Fraction Over Coastal and Sahel Regions of Nigeria Using NCEP/NCAR Satellite Data

In this paper, daily satellite data of global and diffuse irradiances obtained from the archives of National Centre for Environmental Prediction (NCEP) and National Centre for Atmospheric Research (NCAR) covering a period of ten years (2005-2014) have been employed to study clearness index (KT) and diffuse fraction (KD) distributions over six carefully selected meteorological stations in Nigeria. These stations are Port Harcourt, Akure and Enugu in the coastal region; Kano, Maiduguri and Bauchi in the sahelian region. Results have shown that while global irradiance show double peaks in its mean annual daily variation at both regions, diffuse radiation only show this in the sahelian region. In the coastal region, its values are almost uniform throughout the year .In observing the synoptic hourly distribution of clearness index and diffuse fraction, it was found that while clearness index exhibits minima values at hours close to sunrise and sunset, the reverse is the case for diffuse fraction. Also that in the coastal region, clear sky condition is prevalent in Akure and Enugu at both seasons whereas in Port Harcourt, it seldomly occurs. In the sahelian region, on the other hand, clear sky condition is prevalent at both seasons. Using ANOVA method, empirical models were developed for KT. All the models at the stations and regions gave good and significant coefficient of determination R2. During the dry season months, it ranges between 0.98 and 0.99 while during the rainy season, it is 0.89 and 0.95. To further test the efficiencies of the developed models, the Mean Bias Error (MBE) and Root Mean Square Error (RMSE) were computed. Values obtained for both MBE and RMSE for most of the stations are low and positive. These are desirable results for good and significant models. It shows that these models can be used to predict clearness index KTwith high accuracy in the selected stations and proximateones.
Bidirectional Encoder Representations from Transformers (BERT) Language Model for Sentiment Analysis task: Review

The latest trend in the direction of sentiment analysis has brought up new demand for understanding the contextual representation of the language. Among the various conventional machine learning and deep learning models, learning the context is the promising candidate for the sentiment classification task.  BERT is a new pre-trained language model for context embedding and attracted more attention due to its deep analyzing capability, valuable linguistic knowledge in the intermediate layer, trained with larger corpus,  and fine-tuned for any NLP task. Many researchers adapted the BERT model for sentiment analysis tasks by influencing the original architecture to get better classification accuracy. This article summarizes and reviews BERT architecture and its performance observed from fine-tuning different layers and attention heads.
Chapter 5. It Depends on the Problem and on the Solver: An Overview of the Working Memory Overload Hypothesis, Its Applicability and Its Limitations

A considerable amount of research has gone into the investigation of the role of information processing and of various associated cognitive (psychometric) factors in problem solving. While the working memory overload hypothesis has been central to many studies, other factors including mental capacity or M-capacity, degree of field dependence/independence, and scientific reasoning have also been examined. This chapter re-examines the Johnstone–El-Banna model, which is based on the working memory overload hypothesis, explores situations where the model is valid and considers its limitations. The role of the aforementioned cognitive factors in problem solving in chemistry is examined. The working memory overload hypothesis is further approached by means of a complexity theory model, which demonstrates nonlinear changes in student performance. Finally, the nature of the working memory is further considered.
Adaptation of First-Year Students to the Conditions of Study in Higher Educational Institutions

The problem of adaptation of first-year students is a topical issue of higher education institutions. Adaptation of students is a rather complex and long process that lasts during all years of study in a higher education institution, but the most important and problematic period is the period of study in the first year. One of the important criteria for the adaptation of freshmen to the conditions of stay and the learning process in higher education is their health. The stronger the health of students, the more successful their adaptation. The article presents the deteriorating health of first-year students; interpretation of the concept of «adaptation»; various forms (types) of adaptation of student's youth are considered; factors influencing the adaptation process; difficulties in adapting freshmen. Independent physical education and sports in extracurricular time contribute to the more successful adaptation of freshmen to the learning process. The article considers the most common forms of independent physical education classes among students. Adaptation takes place during all years of study, but the most important and problematic period is the period of study in the first year. Admission to free economic education is determined by changes in the living conditions of students, leads to the destruction of previously developed habits, skills, values ​​of former high school students, partial or complete loss of relationships with former classmates and more. There are several (types) of forms of adaptation: formal adaptation, refers to the cognitive and informational adaptation of students to the new environment, to the structure of higher education, to the requirements and their obligations; social adaptation – the process of internal integration of groups of first-year students and the integration of these groups with the student environment as a whole; didactic adaptation, concerns the preparation of students for new forms and methods of educational work in higher education.
Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems

Named entity linking (NEL) or mapping "strings" to "things" in a knowledge base is a fundamental preprocessing step in systems that require knowledge of entities such as information extraction and question answering.In this work, we lay out and investigate two challenges faced by individuals or organizations building NEL systems.Can they directly use an off-the-shelf system?If not, how easily can such a system be repurposed for their use case?First, we conduct a study of offthe-shelf commercial and academic NEL systems.We find that most systems struggle to link rare entities, with commercial solutions lagging their academic counterparts by 10%+.Second, for a use case where the NEL model is used in a sports question-answering (QA) system, we investigate how to close the loop in our analysis by repurposing the best off-the-shelf model (BOOTLEG) to correct sport-related errors.We show how tailoring a simple technique for patching models using weak labeling can provide a 25% absolute improvement in accuracy of sport-related errors.
Our Themes on Abduction in Human Reasoning: A Synopsis

Psychological experiments have shown that humans do not reason according to classical logic. Therefore, we might argue that logic-based approaches in general are not suitable for modeling human reasoning. Yet, we take a different view and are convinced that logic can help us as an underlying formalization of a cognitive theory, but claim rather that classical logic is not adequate for this purpose. In this chapter we investigate abduction and its link to human reasoning. In particular we discuss three different variations we have explored and show how they can be adequately modeled within a novel computational and integrated, cognitive theory, the Weak Completion Semantics.
Approaching Listening and Speaking Skills Using Online to Facilitate Interactive Learning from Students’ Perspectives

Abstract: Teaching and learning process is constantly transforming due to the ever-increasing globalization. In the educational sector the use of information and communication technology (ICT) is constantly changing due to the increasing trend in globalization. This has resulted in the change and transformation the facet of education in this 21st century. The various usage of technologies and other forms of learning materials that have been extensively used has created a medium where students able to be interactive because it is learner centred, and the environment of online learning is more open and flexible. However, several studies showed that students prefer traditional classroom more because they would prefer face-to-face communication with their instructor. Thus, this study intends to explore the perspective of learning listening and speaking using online among L2 learners. A total number of 102 university students at UiTM Perak Branch took part in this study. They were instructed to answer a set of questionnaires which was created using google document. From the findings the students reported that they do see the benefits of online learning. In addition, they found learning through this interactive medium as interesting and provide them space to progress at their own pace in learning. However, this does not portray students’ eagerness in which may be due to several factors. This study is hoped to able to highlight the positive aspects of using online as an alternative to the traditional way of teaching listening and speaking. &#x0D; &#x0D; Keywords: Interactive Learning, Listening Skill, Online Learning, Second language learners, Speaking Skill.
Distributed Second Order Methods with Fast Rates and Compressed Communication

We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inherits its fast local quadratic rate. However, unlike Newton's method, NEWTON-STAR enjoys the same per iteration communication cost as gradient descent. While this method is impractical as it relies on the use of certain unknown parameters characterizing the Hessian of the objective function at the optimum, it serves as the starting point which enables us design practical variants thereof with strong theoretical guarantees. In particular, we design a stochastic sparsification strategy for learning the unknown parameters in an iterative fashion in a communication efficient manner. Applying this strategy to NEWTON-STAR leads to our next method, NEWTON-LEARN, for which we prove local linear and superlinear rates independent of the condition number. When applicable, this method can have dramatically superior convergence behavior when compared to state-of-the-art methods. Finally, we develop a globalization strategy using cubic regularization which leads to our next method, CUBIC-NEWTON-LEARN, for which we prove global sublinear and linear convergence rates, and a fast superlinear rate. Our results are supported with experimental results on real datasets, and show several orders of magnitude improvement on baseline and state-of-the-art methods in terms of communication complexity.
Twitter Sentiment Analysis Using Convolutional Neural Network

Opinion examination via online media stages, for example, Twitter, Facebook, YouTube, E-commerce sites like Amazon, Flipkart and so on has become a vital and testing task. We can discover the supposition about an item or a film or an assessment utilizing conclusion examination, rather than perusing every one of the audits. The presence of tweet length, spelling mistakes, contractions, and extraordinary characters – The opinion investigation task requires a non-customary methodology. Evaluate this feeling with a good or negative value, called extremity. The general feeling is frequently gathered as good, nonpartisan or negative from the indication of the extremity. In opinion examination the extremity of a sentence decides the feeling of a sentence. Assessment of a sentence can be ordered into three classes - positive, negative and unbiased. Most current online media slant order strategies judge the conclusion extremity essentially as per printed substance and disregard other data on these stages. Profound learning has arisen as a solid AI methods that learns various layers of highlights of the information and produces cutting edge expectations results. In opinion investigation, most normally utilized profound learning strategies are convolutional neural organizations (CNN) and intermittent neural organizations (RNN). CNN highlights a convolutional layer to remove data by a greater piece of text. The outcome shows that it accomplishes better exactness in Twitter feeling grouping than some of the typical strategies like SVM and Naive Bayes techniques. Supposition investigation (or assessment mining) might be a tongue preparing strategy wont to decide if information is positive, negative or neutral.
Ecoli Veri Protein Lokalizasyonunda Bulanık ve Olabilirlikli Kümeleme Algoritmalarının Analizi

Clustering is the process of dividing data clusters into fragmented clusters so that the same set of data is similar, but the data of different clusters is different. The basis of the fuzzy clustering algorithms is the c-means families and the strongest algorithm is the fuzzy c-means algorithm. However, the fuzzy c-means algorithm is sensitive to outliers. In this study, on the real data set we examined three different algorithms -possibilistic c-means algorithm (PCM), fuzzy possibilistic c-means (FPCM) and possibilistic fuzzy c- means algorithm (PFCM)- which are developed to overcome the unfavorable side of the FCM algorithm. To compare these algorithms, iteration numbers and completion times were calculated.
Improved Contrastive Divergence Training of Energy Based Models

We propose several different techniques to improve contrastive divergence training of energy-based models (EBMs). We first show that a gradient term neglected in the popular contrastive divergence formulation is both tractable to estimate and is important to avoid training instabilities in previous models. We further highlight how data augmentation, multi-scale processing, and reservoir sampling can be used to improve model robustness and generation quality. Thirdly, we empirically evaluate stability of model architectures and show improved performance on a host of benchmarks and use cases, such as image generation, OOD detection, and compositional generation.