Reviewerly/exHarmony huggingface dataset:
Sparse additive discriminant canonical correlation analysis for multiple features fusion

Canonical correlation analysis (CCA) is an unsupervised representation learning technique to correlate multi-view data by learning a set of projection matrices. Being complementary with CCA, many discriminant methods are proposed to extract discriminative features of multi-view data by introducing the supervised class information. However, the learned projection matrices in these methods are mathematically constrained to be equal rank to the class number, and thus cannot represent the original data comprehensively. In this paper, we propose a general multi-view information fusion technique, named sparse additive discriminative canonical correlation analysis (SaDCCA). On one hand, SaDCCA is equipped with a strong degree of discrimination by defining a new affinity matrix that reflects the high-order characteristics of intra-class and the separability of inter-class. On the other hand, SaDCCA can exploit the correlation among multi-view data by maintaining the spirit of CCA. The discrimination among classes and the correlation among views are integrated in an additive manner. To obtain the sparse solutions, we first establish the relationship between the objective function and the underdetermined linear system equations, and then obtain the ℓ1-norm solution by accelerated Bregman iteration with matrix form. SaDCCA has no rank constraint on the projection matrices and is capable to provide accurate recognition performance. Experiments conducted on some publicly available datasets demonstrate the effectiveness of the proposed approach.
Distributed Machine Learning based Mitigating Straggler in Big Data Environment

In big data era, utilizing the parameter server paradigm has been regarded as an efficient and practical way to improve performance in processing deep learning (DL) applications. One of the main problems is that straggler greatly hinders DL training progress, but the previous methods cannot fully consider the resource utilization of the cluster when dealing with straggler. To mitigate straggler problem in parameter server, we propose a Deep Reinforcement Learning (DRL)-based framework called Distributed Actor-critic Reinforcement Learning (DARL) that can automatically adapt each worker's training load to the dynamic cluster without parameter settings. DARL employs state-of-the-art techniques to stabilize training and improve convergence, including distributed framework, multiple actors and prioritized experience replay. Meanwhile, we also apply our customized experience sampling method to fully exploit potentially good samples. Experiments using real DL workloads show that DARL outperforms the representative Bulk Synchronous Parallel (BSP) scheme by 57.8% and Stale Synchronous Parallel (SSP) by 50.3% in terms of per-iteration time in heterogeneous environment.
Criminal Combat: Crime Analysis and Prediction Using Machine Learning

Crime is one of the most critical issues that the entire world is facing nowadays. The rate of crime should be minimized by using different techniques of machine learning in order to safeguard the global community from getting trapped into the activities of the criminals or anti-social elements. The paper identifies the crime patterns by utilizing the different mathematical and statistical models to forecast the probability of the crime. Crime datasets of the State of North Carolina have been used for this purpose. The paper aims to check different statistical parameters and Figure out the most common factors that affect crime. The univariate and bivariate exploratory analysis is used for extracting the most dominant features. The Akaike Information Criteria (AIC) method is used to drop out unimportant attributes followed by testing of model through Mean Absolute Error (MAE), Median Squared Error (MSE) and Root Mean Squared Error (RMSE) techniques. The work done in the paper concludes that crime predictability and criminology can be very useful in eliminating menace of crime from our society. These mathematical and statistical models can aid us in the process of making our society a safer place to live in.
Review of Blockchain Application in Education Data Management

The development and growth of blockchain technology today have gained widespread attention from researchers and practitioners. This popularity is due to the various positive benefits of this technology in multiple sectors such as finance, public administration, health care, education, science, culture, and the arts. Various studies have been conducted to examine the application of blockchain technology in the education sector. However, there are still many hidden things and research areas that have not been discussed. This literature review will discuss blockchain applications in the process of education management. This literature study focuses on answering three main questions: what type of application blockchain technology is used for, what kind of data is stored using blockchain, and unresolved constraints or challenges in adopting and implementing blockchain technology in education. The results of our study show that there is great potential that this technology can bring to education. We also found and discuss solutions to various challenges in certain data types and problems in implementing this technology in education management. The results of this literature study are aimed at researchers interested in carrying out research using blockchain technology in education by providing an overview of what needs to be done for further research.
What is this Cluster about? Explaining textual clusters by extracting relevant keywords

Document clustering is a powerful method with numerous applications, where the core idea is to group text into smaller and more manageable pieces of semantically related information. While there has been progress in the research community on improving the quality of clusters, the extraction of information that explains the semantic content of the clusters is still mostly a manual activity, as it requires the inspection of sample documents. In this work, we propose three main cluster explanation approaches that extend the current state of the art, which is mostly based on predicting a label for each cluster (i.e. cluster labelling). The first approach is based on scores extracted from the word distributions; the second is based on augmenting the score-based explanations using external knowledge-bases, and the third one combines the first two methods to exploit the knowledge coming from a labelled dataset. We also discuss the limitations of the current metric presented in the literature and extend it to evaluate our approaches using ground truth. We also provided an extensive set of evaluations to verify the effectiveness of the proposed algorithms, by running multiple experiments to compare our approaches with different baselines. Results indicate that improved explanations are possible and that linking external knowledge can provide more general cluster descriptions. Semi-supervised approaches also demonstrated meaningful insight. User studies were also conducted highlighting that users prefer specialized explanations.
Information Retrieval for Malay Text: A Decade Review of Research (2008–2019)

In this paper, we survey and classify most of the information retrieval (IR) approaches to Malay text in order to assess their benefits and limitations. We also summarized the information retrieval tools and related methods, in which ontology is a widely used tool for all countries' researchers. This research selects Malay language as the primary test collection because there are more issues in Malay languages, particularly those related to deep semantics, including the use of ontology. The traditional Malay retrieval system mostly focused on syntax extraction and keywords only. Mostly this technique will ignore the semantic element and the real meaning of query text and corpus which not fulfil the requirement of the user. Most of the previous study in information retrieval was using English and Arabic language as a test collection. Therefore, advance research is needed and it will be experimented in the future work. The finding of the paper will help other researchers discover the information and research gap regarding the Malay text.
An Unsupervised Model for Identifying and Characterizing Dark Web Forums

Dark Web forums are significantly exploited to trade confidential information and illicit products by criminals.This paper addresses the problem of how to identify the cluster of discussion forums and their characteristics on the Dark Web.Exiting methods are mostly dependent on the continuous labeled contents, which are expensive and not feasible due to the nature of Dark Web data.Therefore, an approach that does not need a continuous availability of labeled forum and related knowledge is required.To this end, we propose an unsupervised model to identify and characterize Dark Web forums by combining clustering algorithm and decision tree algorithm.The proposed method presents the characteristics in an explainable form that can be used by the cyber threat intelligence system and law enforcement as scientific evidence to analyze any data breach or illicit activities in the Dark Web forums.To evaluate the performance of our model comprehensive experiments were conducted using real Dark Web forum data.The proposed approach achieves 98% accuracy and F1 score of 98% validating the efficacy of our proposed model to successfully characterize Dark Web forums.The experimental results suggest that the proposed model could be useful to the cyber threat intelligence and law enforcement community for building an intelligent source of knowledge that can be used for detecting data breach and illicit activities happening in the Dark Web forums.
GaitWear: a smartwatch application for in-the-wild gait normalisation based on a virtual field study assessing the effects of visual and haptic cueing

We explore the use of Virtual Reality as a way to simulate field studies via what is known as Virtual Field Studies. This is particularly relevant when inviting participants to the lab is not feasible, or in the case of our work to simulate locomotion in crowded streets from the safety of the lab. We rely on this to assess the effects of four different cues in normalising gait performance in a simulated environment: two baselines from literature (visual and haptic) that have been traditionally explored in the context of a controlled lab environment, and two novel haptic cues that combine temporal and spatial feedback. We compare these in a holistic manner for the first time, capturing not only gait and gaze performance, but usability, perceived workload, and participant preference. Our haptic baseline performed according to the results described in the literature, and together with participants' gaze behaviour and sense of embodiment we start to validate Virtual Field Studies in this domain. We further report that the haptic baseline was the preferred cue by participants, and led to an overall better performance. We conclude with our implementation of GaitWear, a smart watch application that produces this haptic baseline on the fly.
Deep Dichromatic Model Estimation Under AC Light Sources

The dichromatic reflection model has been popularly exploited for computer vison tasks, such as color constancy and highlight removal. However, dichromatic model estimation is an severely ill-posed problem. Thus, several assumptions have been commonly made to estimate the dichromatic model, such as white-light (highlight removal) and the existence of highlight regions (color constancy). In this paper, we propose a spatio-temporal deep network to estimate the dichromatic parameters under AC light sources. The minute illumination variations can be captured with high-speed camera. The proposed network is composed of two sub-network branches. From high-speed video frames, each branch generates chromaticity and coefficient matrices, which correspond to the dichromatic image model. These two separate branches are jointly learned by spatio-temporal regularization. As far as we know, this is the first work that aims to estimate all dichromatic parameters in computer vision. To validate the model estimation accuracy, it is applied to color constancy and highlight removal. Both experimental results show that the dichromatic model can be estimated accurately via the proposed deep network.
How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users' behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent "improved" prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl's causal do(.) operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users' behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers' intentions, and harmful to manipulated users.
TransQuake: A transformer-based deep learning approach for seismic P-wave detection

Recent years, we have witnessed the increasing research interest in developing machine learning, especially deep learning which provides approaches for enhancing the performance of microearthquake detection. While considerable research efforts have been made in this direction, most of the state-of-the-art solutions are based on Convolutional Neural Network (CNN) structure, due to its remarkable capability of modeling local and static features. Indeed, the globally dynamic characteristics contained within time series data (i.e., seismic waves), which cannot be fully captured by CNN-based models, have been largely ignored in previous studies. In this paper, we propose a novel deep learning approach, TransQuake, for seismic P-wave detection. The approach is based on the most advanced sequential model, namely Transformer. To be specific, TransQuake can exploit the STA/LTA algorithm for adapting the three-component structure of seismic waves as input, and take advantage of the multi-head attention mechanism for conducting explainable model learning. Extensive evaluations of the aftershocks following the 2008 Wenchuan MW 7.9 earthquake clearly demonstrates that TransQuake is able to achieve the best detection performance which excels the results obtained using other baselines. Meanwhile, experimental results also validate the interpretability of the results obtained by TransQuake, such as the attention distribution of seismic waves in different positions, and the analysis of the optimal relationship between coda wave and P-wave for noise identification.
Skills mismatch in the labor market: The future of work from the viewpoint of enterprises in case of Kosovo

The transition path from education to employment is a key determinant of sustainable economic growth and development. A poorly trained workforce penalizes companies when they try to grow (Cojocaru, 2017, p. 25). It is generally accepted that university graduates as workforce are the key driver of economic growth and development. The main aim of this study is to identify the difficulties faced by enterprises in the Republic of Kosovo to provide the necessary profiles with adequate skills. Employers say students don’t have the ability to think critically, innovate, solve complex problems and work well in a team (Alsop, 2015) In order to analyze the problems that companies face during the selection process, the necessary training, and also the projections for new employments for the coming years, this study uses the primary data provided by the face-to-face questionnaire. The program used for data analysis in SPSS. We came to the conclusion that enterprises find it difficult to ensure the right skills they require, the university graduates lack practice experience and soft skills. The right person with the right skills in the right workplace is the driving force behind the well-functioning of the labor market. The study comes up with further recommendations for the well-functioning of the triangle higher education institutions, policymakers, and enterprises.
Usability Textual Data Analysis: A Formulaic Coding Think-Aloud Protocol Method for Usability Evaluation

Think-aloud protocols are among the most standard methods for usability evaluation, which help to discover usability problems and to examine improvements because they provide direct information on a user’s thinking and cognitive processes; however, it is often difficult to determine how to analyze the data to identify usability problems because there is no formulaic analysis procedure for textual data. Therefore, the analysis is time-consuming, and the quality of the results varies depending on an analyst’s skills. In the present study, the author proposes a formulaic analysis think-aloud protocol method that specifies the procedure for analyzing participants’ verbal responses during usability tests. The aim of the proposed think-aloud protocol method was to deliver an explicit procedure using step coding (SCAT) and 70 design items for textual data analysis, and then, the method was applied to a case study of usability evaluation to confirm that the method could extract the target system’s problems. By using step coding and 70 design items, the process of extracting usability problems from textual data was made explicit, and the problems were extracted analytically. In other words, the proposed method was less ambiguous. Once a formulaic analysis procedure was established, textual data analysis could be performed easily and efficiently. The analysis could be performed without hesitation after data acquisition, and there were fewer omissions. In addition, it is expected that the procedure would be easy to use, even for novice designers.
A Knowledge Transfer-Based Evolutionary Algorithm for Multimodal Optimization

There are some natural similarities among fitness landscapes of different modals involved in a multimodal optimization problem (MMOP). However, existing multimodal evolutionary algorithms (MMEAs) tend to handle these modals separately, which limits their performance to a great extent. As the first attempt, this study proposes a knowledge transfer-based MMEA by mining and exploiting modal similarities. To this end, a center-aligned normalization strategy (CANS) is first designed to map all the subpopulations corresponding to identified modals into a unified 0-1 space, and then a distribution similarity-based transfer strategy (DSTS) is proposed to guide knowledge transfer among subpopulations. DSTS explicitly quantifies the similarity between two subpopulations and probabilistically transfers elite individuals in the subpopulation of the largest similarity to the target subpopulation. By integrating CANS and DSTS into a well-known MMEA, i.e., NEA2, the final knowledge transfer-based NEA named KTNEA is developed. Experimental results on CEC'2013 niching benchmark suite indicate that KTNEA performs competitively in comparison with state-of-the-art MMEAs.
DeepFreeze: Cold Boot Attacks and High Fidelity Model Recovery on Commercial EdgeML Device

EdgeML accelerators like Intel Neural Compute Stick 2 (NCS) can enable efficient edge-based inference with complex pre-trained models. The models are loaded in the host (like Raspberry Pi) and then transferred to NCS for inference. In this paper, we demonstrate practical and low-cost cold boot based model recovery attacks on NCS to recover the model architecture and weights, loaded from the Raspberry Pi. The architecture is recovered with 100% success and weights with an error rate of 0.04%. The recovered model reports maximum accuracy loss of 0.5% as compared to original model and allows high fidelity transfer of adversarial examples. We further extend our study to other cold boot attack setups reported in the literature with higher error rates leading to accuracy loss as high as 70%. We then propose a methodology based on knowledge distillation to correct the erroneous weights in recovered model, even without access to original training data. The proposed attack remains unaffected by the model encryption features of the OpenVINO and NCS framework.
FRAB: A Flexible Relaxation Method for Fair, Stable, Efficient Multi-user DASH Video Streaming

Dynamic adaptive streaming over HTTP (DASH) has been widely adopted in modern video streaming services. In DASH, the core technique is adaptive bitrate (ABR) control which can adjust the requested video bitrate level according to the network conditions to tradeoff between video quality and rebuffering risk. It is a challenge for the ABR methods in the scenarios when multiple DASH streaming users compete over the network bottleneck. This paper proposes a client-side ABR control method, flexible relaxation assisted by buffer (FRAB), to achieve fair, stable and efficient video streaming among different users. The idea of FRAB is to "relax" the change of the video quality based on current buffer level, which can enhance the stability of video streaming. Meanwhile, by flexibly adjusting the relaxation, the efficiency and fairness among all users are improved. FRAB is evaluated in real experiments under three different network conditions and compared with conventional multi-user ABR algorithms. Results indicate FRAB has the best performance in fairness, which reduces the unfairness by a maximum of 69.5% under real-world measured network condition. It also improves the efficiency by 71.3% comparing with PANDA, and enhances the stability by 73.3% comparing with TFDASH. The experiment results demonstrated that the proposed method has superior performances in multi-user DASH video streaming.