Reviewerly/exHarmony huggingface dataset:
Zero-Shot Knowledge Distillation Using Label-Free Adversarial Perturbation With Taylor Approximation

Knowledge distillation (KD) is one of the most effective neural network light-weighting techniques when training data is available. However, KD is seldom applicable to an environment where it is difficult or impossible to access training data. To solve this problem, a complete zero-shot KD (C-ZSKD) based on adversarial learning has been recently proposed, but the so-called biased sample generation problem limits the performance of C-ZSKD. To overcome this limitation, this paper proposes a novel C-ZSKD algorithm that utilizes a label-free adversarial perturbation. The proposed adversarial perturbation derives a constraint of the squared norm of gradient style by using the convolution of probability distributions and the 2nd order Taylor series approximation. The constraint serves to increase the variance of the adversarial sample distribution, which makes the student model learn the decision boundary of the teacher model more accurately without labeled data. Through analysis of the distribution of adversarial samples on the embedded space, this paper also provides an insight into the characteristics of adversarial samples that are effective for adversarial learning-based C-ZSKD.
Predictive geologic mapping from geophysical data using self-organizing maps: A case study from Baie Verte, Newfoundland, Canada

Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multivariate data sets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We have applied SOMs to magnetic, radiometric, and gravity data sets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geologic maps available for this area and knowledge from numerous geologic studies has enabled the accuracy of SOM-based predictive mapping to be assessed. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geologic units identified by previous traditional geologic mapping. Of the combinations of data sets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or using the analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the data sets. The SOM results could be used as input into k-means clustering because this method requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geologic mapping would be most beneficial, which can be especially useful in parts of the world where access is difficult and expensive.
Comparing the relationship between using educational technology in mathematics and student achievement in South Africa and Germany

(2021). Comparing the relationship between using educational technology in mathematics and student achievement in South Africa and Germany. Journal of Research on Technology in Education. Ahead of Print.
Optimal VM Placement Approach Using Fuzzy Reinforcement Learning for Cloud Data Centers

Ineffective resource management in cloud data centers leads to high energy usage and cost to maintain the resources. A Virtual machine (VM) placement is a part of the VM consolidation problem where each VM should be mapped to the available host. An optimal VM placement is an effective way to enhance both resource usage and energy efficiency. However, minimizing the energy consumption without affecting SLA violation and performance of the application is quite challenging. This paper proposes a fuzzy based State-Action-Reward-State-Action (SARSA) reinforcement learning algorithm to address the VM placement problem. The integration of fuzzy controller with Reinforcement learning (RL) algorithm is capable of optimizing the reallocation of maximal number of VMs into a minimal number of hosts that reduce energy usage and wastage of resource as well. The proposed work is capable of handling the fluctuating workload situations and delivers proper placement of VMs (initial or re-mapping) while ensuring the desired Quality-of-Service (QoS) demands of users to meet the Service Level Agreement (SLA). The experimental results exhibit reduced consumption of energy and less resource wastage as compared with other VM placement algorithms.
Analysis Of Public Sentiment About Covid-19 In Indonesia On Twitter Using Multinomial Naive Bayes And Support Vector Machine

Abstract The Covid-19 case which initially occurred in China until now has spread to all continents, including Indonesia. Indonesia itself was first affected by the Covid-19 virus in February 2020 which made a crowd in cyberspace including in particular Twitter. Everyone is free to give their opini or opinions about Covid-19 so that it raises many opinions, not only positive or neutral opinions but also negative opinions regarding Covid-19. Social media is now not only used as a means of making friends or making friends, but also used for other activities. The purpose of this research is to build a sentiment analysis method with the theme of the Covid-19 Pandemic on Twitter social media with the most optimal and maximum accuracy. Meanwhile, the benefit is to help Indonesians conduct research on public opinion on Twitter which contains positive, neutral, or negative sentiments. In this study, Text Mining has been used using the Support Vector Machine and Multinomial Naive Bayes method, which is one of the methods used to conduct sentiment analysis. The results of the Support Vector Machine and Naive Bayes Multinomial method used for sentiment analysis are useful for obtaining information and knowledge about Covid-19. The results obtained are that the opinion of the Indonesian people regarding Covid-19 is that the Positive and Neutral amount is more than the Negative with an average of 40% Positive and Neutral while the Negative is 20%. For the calculation of the F-1 average value, it can be seen that the Support Vector Machine method is the best model in this study with a value of 93%. little difference with the Naive Bayes Multinomial method of 92%. It is hoped that this research will continue with more data and use other sentiment analysis methods in order to find out the average results of the opinions of the Indonesian people regarding Covid-19.
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces

Abstract We study the computational complexity of (deterministic or randomized) algorithms based on point samples for approximating or integrating functions that can be well approximated by neural networks. Such algorithms (most prominently stochastic gradient descent and its variants) are used extensively in the field of deep learning. One of the most important problems in this field concerns the question of whether it is possible to realize theoretically provable neural network approximation rates by such algorithms. We answer this question in the negative by proving hardness results for the problems of approximation and integration on a novel class of neural network approximation spaces. In particular, our results confirm a conjectured and empirically observed theory-to-practice gap in deep learning. We complement our hardness results by showing that error bounds of a comparable order of convergence are (at least theoretically) achievable.
Towards Dark Jargon Interpretation in Underground Forums

Dark jargons are benign-looking words that have hidden, sinister meanings and are used by participants of underground forums for illicit behavior. For example, the dark term “rat” is often used in lieu of “Remote Access Trojan”. In this work we present a novel method towards automatically identifying and interpreting dark jargons. We formalize the problem as a mapping from dark words to “clean” words with no hidden meaning. Our method makes use of interpretable representations of dark and clean words in the form of probability distributions over a shared vocabulary. In our experiments we show our method to be effective in terms of dark jargon identification, as it outperforms another baseline on simulated data. Using manual evaluation, we show that our method is able to detect dark jargons in a real-world underground forum dataset.
Hybrid grass bee optimization<scp>‐</scp>multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Abstract Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text‐audio‐video consistency‐driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis. A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F‐measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform.
Human home daily living activities recognition based on a LabVIEW implemented hidden Markov model

The recognition of human daily living activities within a house represents an efficient tool to model its power consumption and is also a good indicator for monitoring the health status of the inhabitants. The problematic of activities recognition in smart homes has been extensively addressed in several studies. In this paper, we present an original interactive tool developed under LabVIEW environment with a graphical user interface allowing the modeling of the daily living activities, based on a machine learning Hidden Markov Model. After an overview of the advantage for the consideration of this model in current human activities, we examine how the associated scientific problematic can find an interest and a solution by the integration of machine learning tools. Thus, the application based on a Hidden Markov model approach, is presented and evaluated using two sets of experimental data from literature. Comparing with results obtained by other daily living activities recognition methods, we point out the very satisfactory recognition performance of the Hidden Markov Model and the likelihood of our development associated to a user-friendly graphical interface. This work opens the way to applications dedicated to the supervision of human daily living activities and / or to the management of the electrical consumption within a smart home equipped with non-intrusive sensors.
Making Sense of Subtitles: Sentence Boundary Detection and Speaker Change Detection in Unpunctuated Texts

The rise of deep learning methods has transformed the research area of natural language processing beyond recognition. New benchmark performances are reported on a daily basis ranging from machine translation to question-answering. Yet, some of the unsolved practical research questions are not in the spotlight and this includes, for example, issues arising at the interface between spoken and written language processing.
FEATURES ORGANIZATION OF DISTANCE LEARNING IN A PEDAGOGICAL UNIVERSITY IN THE MODE OF SELF ISOLATION

The educational situation that arose as a result of the declared regime of self-isolation, activated the forced decisions on the organization of the educational process in remote mode. The research urgency is caused by the provision of distance learning taking into account the specifics of remote access to learning based on the experience of the Department of computer science, pedagogical Institute, North-Eastern Federal University named after M. K. Ammosov (NEFU). The purpose of the study: features of the organization of the educational environment in the NEFU Pedagogical Institute, which allows to organize the processes of distance education in the conditions of self-isolation. The article analyzes and summarizes the positions of promising directions for implementing distance learning tools and platforms, as well as determines the factors and conditions for organizing rapid access to the Internet in the educational process. We have studied the most common digital services at the moment, their assistance in the development of educational materials, considering the pedagogical possibilities of distance learning to organize remote access to training at a University. The importance of implementing remote distance learning in the training of future teachers and school teachers in a pedagogical University in the context of a pandemic is determined. The results of the study confirmed the need to develop educational and methodological support in the implementation of the above-mentioned new form of training at the NEFU pedagogical Institute.
The Application of PAD Teaching Method in Computer Network Course

Abstract As a new teaching mode and method, PAD has been developed in recent years. This teaching mode has been applied in the teaching of universities, ordinary primary and secondary schools and vocational colleges. It has achieved good teaching results. In order to improve teaching effect, improve the students’ autonomy and enthusiasm and cultivate the independent innovative talents needed by the society, more and more computer teachers have also begun to apply PAD to the computer classroom. “Computer Network” is a basic and compulsory course for learning computers. “transmission media” in this course is selected as the specific content, and PAD is used for teaching design.
Visual Question Answering Combining Multi-modal Feature Fusion and Multi-Attention Mechanism

To enhance the semantic information and more accurately capture the image features in visual question answering (VQA) models, this paper presented a new VQA approach based on the multimodal features fusion and multiple level attention mechanism. Firstly, the ResNet network and Faster R-CNN were used to extract the local and global feature maps of the images; and then a two-layer LSTM was built to semantically encode text feature vectors. On this basis, Multi-modal Factorized Bilinear pooling approach was applied to fuse the image features and the text features. In addition, we combined the self-attention mechanism, the guided attention mechanism and the multi-head attention mechanism to form a multilayer attention network so as to more accurately obtain the required target features from images. The proposed models were tested and verified on the VQA-v2 data set. Experimental results show that the proposed methods are efficient and can obtain 67.18% of accuracy.
Sustainable Catastrophic Cyber-Risk Management in IoT Societies

IoT-driven smart cities are popular service-networked ecosystems, whose proper functioning is hugely based on digitally secure and reliable supply chain relationships. However, the naivety in the current security efforts by concerned parties to protect IoT devices, pose tough challenges to scalable and expanding cyber-risk management markets for IoT societies, post a systemic cyber-catastrophe. As firms increasingly turn to cyber-insurance for reliable risk management, and insurers turn to reinsurance for their own risk management, questions arise as to how modern-day cyber risks aggregate and accumulate, and whether reinsurance is a feasible model for reliable catastrophic risk management and transfer in smart cities. In this introductory effort, we analyze (a) whether traditional cyber-risk spreading is a sustainable risk management practice and (b) under what conditions, for the quite conservative scenario when proportions of i.i.d. catastrophic cyber-risks of a significant heavy-tailed nature are aggregated by a cyber-risk manager.
Conversion between semantic data models: the story so far, and the road ahead

At this time and age where use of internet and its popularity is at peak and the data present on the World Wide Web is increasing day by day, the interrelatedness among these data also increases. The need of the hour is to make this data machine understandable and not merely machine readable. Many publishers publish their data online on a similar domain and as likely to happen, the data from these different publishers (sources) add on a little something to each other in a complementing manner. Another problem arises when these datasets are described in different formats (e.g., XML, CSV, DB, and Excel) that is, different way of representation and different Universal Resource Identifiers for the same concept. To handle this diversity among the data with respect to their formats and process the large quantity of data into a consistent, commutable, compatible, and suitable format, we need a technology which is capable of being extended and reused. The Resource Description Framework (RDF) is such a semantic common data format brought as a unifying model by the semantic computing community. There are many existing tools which have been proposed over the time which made conversion of data formats into RDF possible. Presenting data in an RDF format on the World Wide Web provides reusability and interoperability, which helps make it machine understandable. Thus such a solution is needed which converts the data into target format (such as RDF) and augments the reusability, reproducibility, and accessibility of that data. The aim of this chapter is to review and put forward many of such techniques which have been presented by learned authors, the limitations they overcome, what future scope they have, and what other points they could have covered.
Linear Cross-Modal Hash Encoding (LCMHE) for Visual and Near-Infrared Face Recognition

Face recognition in practical situations often encounters the change of acquisition sensors, i.e., the gallery images are captured in near-infrared and probe images captured in the visual domain or vice versa. Thus, for robust face recognition under such circumstance, a modality invariant representation is required. This letter proposes a hash-encoding-based descriptor to address this modality gap for face recognition problem, namely linear cross-modal hash encoding (LCMHE). LCMHE encodes the pixels of the face images based on a pretrained hash dictionary. The encoding highlights the reflectance part of the face and enables accurate recognition. We further explored the possibility of LCMHE by combining the deep-learning model to get better recognition. We experimented with publicly available CASIA NIR-VIS 2.0 databases and verified the effectiveness of our proposed letter.
Recasting Service Quality for AI-Based Service

Artificial intelligence service agents (AISA), such as chatbots and virtual assistants, are becoming increasingly pervasive in service. Research to date has not adequately addressed how the unique nature of AISA shape consumers’ service quality expectations. A deeper understanding of AISA service quality is important for their successful deployment in the service sector. To address this gap, we reviewed marketing and information systems literatures and conducted qualitative in-depth interviews with 37 informants, inclusive of 28 AISA users and nine AISA experts. We developed a conceptual framework for how consumers use and evaluate AISA. Twelve service quality dimensions emerged from the qualitative evidence representing AISA service quality, two of which align with AISA’s unique characteristics. The study extends the service quality theory to a new context and offers fresh insights for theory and practice. It culminates with a research agenda to advance research on AISA service quality.
WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets

In order to address scalability issues, which can be a challenge for Deep Learning methods, we propose Wide Learning of Diverse Architectures—a model that scales horizontally rather than vertically, enabling distributed learning. We propose a distributed version of a quality-diversity evolutionary algorithm (MAP-Elites) to evolve an architecturally diverse ensemble of shallow networks, each of which extracts a feature vector from the data. These features then become the input to a single shallow network which is optimised using gradient descent to solve a classification task. The technique is shown to perform well on two benchmark classification problems (MNIST and CIFAR). Additional experiments provide insight into the role that diversity plays in contributing to the performance of the repertoire.
Nonlocal advantage of quantum coherence of coupled qubits in thermal and dephasing reservoirs

Abstract We investigate the nonlocal advantage of quantum coherence (NAQC) for two qubits coupled via the Heisenberg interaction and embedded in their respective thermal and dephasing reservoirs. Unlike the case of two uncoupled qubits, we showed that in the presence of the Heisenberg interaction, the decay rate of the NAQC can be reduced and the strength of the NAQC can be enhanced for certain initial states. Moreover, the Heisenberg interaction of two spins can induce generation of NAQC from the initial product states, and the generated NAQC approaches asymptotically to its maximum with the increasing anisotropy of the Heisenberg interaction.
Graph Attention Networks for New Product Sales Forecasting in E-Commerce

Aiming to discover competitive new products, sales forecasting has been playing an increasingly important role in real-world E-Commerce systems. Current methods either only utilize historical sales records with time series based models, or train powerful classifiers (e.g., DNN and GBDT) with subtle feature engineering. Despite effectiveness, they have limited abilities to make prediction for new products due to the sparsity of product-related features. With the observation on real-world data, we find that some additional time series features (e.g., brand and category) implying product characteristics also play vital roles in new product sales forecasting. Hence, we organize them as a new kind of dense feature called CPV (Category-Property-Value) and propose a Time Series aware Heterogeneous Graph (TSHG) to integrate CPVs and products based time series into a unified framework for fine-grained interaction. Furthermore, we propose a novel Graph Attention Networks based new product Sales Forecasting model (GASF) that jointly exploits high-order structure and time series features derived from THSG for new product sales forecasting with graph attention networks. Moreover, a multi trend attention (MTA) mechanism is also proposed to solve temporal shifting and spatial inconsistency between the time series of products and CPVs. Extensive experiments on an industrial dataset and online system demonstrate the effectiveness of our proposed approaches.