Reviewerly/exHarmony huggingface dataset:
Online Learning in Variable Feature Spaces with Mixed Data

This paper explores a new online learning problem where the data streams are generated from an over-time varying feature space, in which the random variables are of mixed data types including Boolean, ordinal, and continuous. The crux of this setting lies in how to establish the relationship among features, such that the learner can enjoy 1) reconstructed information of the missed-out old features and 2) a jump-start of learning new features with educated weight initialization. Unfortunately, existing methods mainly assume a linear mapping relationship among features or that the multivariate joint distribution could be modeled as Gaussians, limiting their applicability to the mixed data streams. To fill the gap, we in this paper propose to model the complex joint distribution underlying mixed data with Gaussian copula, where the observed features with arbitrary marginals are mapped onto a latent normal space. The feature correlation is approximated in the latent space through an online EM process. Two base learners trained on the observed and latent features are ensembled to expedite convergence, thereby minimizing prediction risk in an online setting. Theoretical and empirical studies substantiate the effectiveness of our proposed approach. Code is released in https://github.com/xiexvying/OVFM.
Building small specialised corpora

The chapter focuses on computer-based language corpora – collections of texts that can be searched using special software – that are relatively small (compared to much larger such collections) and are specialised in some way, for example, in terms of the setting, text type, genre or topic. It begins by defining “a small corpus” and reviewing the different ways in which a corpus can be specialised with reference to a variety of existing corpora. Next, important considerations in designing and compiling a small, specialised corpus are discussed, in particular, how representativity and balance can be achieved for such smaller corpora. The final section looks at what can be learnt from small specialised corpora, illustrating this with examples from a range of corpus studies. The chapter argues that small, specialised corpora can have advantages over larger corpora due to the availability and retrievability of contextual features, which can be linked to specific language patterns.
QuadNet: Quadruplet loss for multi-view learning in baggage re-identification

Recently, baggage re-identification (ReID) has become an attractive topic in computer vision because it plays an important role in intelligent surveillance. However, the wide variations in different views of baggage items degrade baggage ReID performance. In this paper, a novel QuadNet is proposed to solve the multi-view problem in baggage ReID at three levels. At the sample level, we propose a multi-view sampling strategy which samples hard examples from multiple identities in multiple views. The sampled baggage items are used to construct quadruplets. At the feature level, view-aware attentional local features are extracted from discriminative regions in each view. These local features are fused with global features to obtain better representations of the quadruplets. At the loss level, a multi-view quadruplet loss operating on the representations of quadruplets is proposed to reduce the intra-class distances caused by view variations and increase the inter-class distances of baggage images captured in the same view. A random local blur data augmentation is proposed to handle the motion blur which is often found in baggage images. The multi-task learning of materials is introduced to obtain discriminative features based on the materials of baggage surfaces. Extensive experiments on three ReID datasets, MVB, Market-1501 and VeRi-776, indicate the remarkable effectiveness and good generalization of the QuadNet model. It has achieved the state-of-the-art performance on the three datasets.
Unmoderated Remote Usability Testing: An Approach during Covid-19 Pandemic

Online Nurse Test for Indonesian Nurse Competency (ONT UKNI) is a mobile application that was developed to help increase the success rate of nurse competency test participants.By using this application, users can learn more about the materials tested and conduct try out as a competency test simulation.However, ONT UKNI has not yet passed adequate testing stages, especially in terms of User Interface/User Experience (UI/UX).The Covid-19 pandemic situation presents challenges in the UI/UX testing process.Testing process which is ideally carried out face-to-face with respondents to get further insight, have to be carried out using another approach following the new normal protocol.This study aims to test the usability of UI/UX with an unmoderated remote testing approach on ONT UKNI application using a USE questionnaire.The test was performed using 26 respondents and all were nursing profession students of Universitas Muhammadiyah Yogyakarta.Respondents performed 8 tasks on ONT UKNI and answered set of questionnaire that will be tabulated and analyzed.The results indicate that usefulness, ease of learning, and satisfaction variables get the Very Good category while the ease of use variable gets the Good category.Overall, usability testing using an unmoderated remote testing approach can be carried out and able to provide information about areas where users are satisfied with ONT UKNI application.However, some areas still have room for improvement such as better UI design and implementation of gamification.
Privacy-preserving deep learning for pervasive health monitoring: a study of environment requirements and existing solutions adequacy

In recent years, deep learning in healthcare applications has attracted considerable attention from research community. They are deployed on powerful cloud infrastructures to process big health data. However, privacy issue arises when sensitive data are offloaded to the remote cloud. In this paper, we focus on pervasive health monitoring applications that allow anywhere and anytime monitoring of patients, such as heart diseases diagnosis, sleep apnea detection, and more recently, early detection of Covid-19. As pervasive health monitoring applications generally operate on constrained client-side environment, it is important to take into consideration these constraints when designing privacy-preserving solutions. This paper aims therefore to review the adequacy of existing privacy-preserving solutions for deep learning in pervasive health monitoring environment. To this end, we identify the privacy-preserving learning scenarios and their corresponding tasks and requirements. Furthermore, we define the evaluation criteria of the reviewed solutions, we discuss them, and highlight open issues for future research.
Deep Learning Based Pashto Characters Recognition

In artificial intelligence, text identification and analysis that are based on images play a vital role in the text retrieving process. Automatic text recognition system development is a difficult task in machine learning, but in the case of cursive languages, it poses a big challenge to the research community due to slight changes in character’s shapes and the unavailability of a standard dataset. While this recognition task becomes more challenging in the case of Pashto language due to a large number of characters in its dataset than other similar cursive languages (Persian, Urdu, Arabic) and a slight change in character’s shape. This paper aims to address accept these challenges by developing an optimal optical character recognition (OCR) system to recognise isolated handwritten Pashto characters. The proposed OCR system is developed using multiple long short-term memory (LSTM) based deep learning model. The applicability of the proposed model is validated by using the decision trees (DT) classification tool based on the zoning feature extraction technique and the invariant moment approaches. An overall accuracy rate of 89.03% is calculated for the multiple LSTM-based OCR system while DT-based recognition rate of 72.9% is achieved using zoning feature vector and 74.56% is achieved for invariant moments-based feature map. Applicability of the system is evaluated using different performance metrics of accuracy, f-score, specificity, and varying training and test sets.
Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem

Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). The Workers Assignment Problem (WAP) is considered as a sub-class of RAP which aims to find an optimal assignment of workers to a number of tasks in order to optimize certain objectives. WAP is an NP-hard combinatorial optimization problem. Due to its importance, several algorithms have been developed to solve it. In this paper, it is considered that a manager is required to provide a training course to his workers in order to improve their level of skill or experience to have a sustainable competitive advantage in the industry. The training cost of each worker to perform a particular job is different. The WAP is to find the best assignment of workers to training courses such that the total training cost is minimized. Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. MATLAB Software is used to perform the simulation of the two proposed methods into WAP. The computational results for a set of randomly generated problems of various sizes show that the FPA is able to find good quality solutions.
Establishing Market and Monopoly Power in Tech Platform Antitrust Cases

In June 2021, a federal judge dismissed the Federal Trade Commission’s first monopolization complaint against Facebook on the grounds that it did not plead sufficient facts to establish that Facebook possesses monopoly power in online social networking. The ruling highlights two contentious aspects of antitrust jurisprudence: the legal necessity of establishing a defendant’s monopoly power as part of Sherman Act liability for unilateral conduct, and of establishing market power as part of liability for some forms of multi-lateral conduct, as well as the few mechanisms available to plaintiffs in both public and private enforcement to accomplish that, especially following Ohio v. American Express. This article makes two related claims: that direct evidence of market power is plentiful and should be understood as such by courts, and that exactly the direct evidence of market power that courts should consider also establishes that relevant markets on each side of tech platforms are small when properly defined, whatever defendants may say.
Advanced Cognitive Skill Deserts in the United States: Their Likely Causes and Implications

I use mapping and age trajectories of advanced cognitive skills to better understand why these skills are more prevalent in some local areas than in others. The study begins by explaining what advanced cognitive skills are. It offers a nonspecialist’s review of recent brain science that indicates that adolescence is the key period for the development of advanced cognitive skills. The paper considers three main explanations for why the prevalence of advanced cognitive skills varies substantially across US counties. Is it early childhood factors which could generate endogenous responses that are important later when advanced cognitive skills are developing? Is it factors whose influence is greatest during adolescence—the period when brain science argues that experience would most directly affect advanced cognitive skills? If so, adolescence is indeed the age of opportunity but also risk. Is the variation among counties explained by migration of individuals toward areas where other people have advanced cognitive skills similar to their own? Evidence based on cognitive skill trajectories, maps at different ages, and longitudinal regressions suggests that all three of these explanations play a role in generating areas where advanced cognitive skills are prevalent and areas where they are not—advanced cognitive skill deserts.
Survey on Sentiment Analysis of User Reviews

User’s sentiments or opinions stated on any website or app have enormous impact on the product sellers, customers, and readers. The formless data from the sites or apps is required to be explored and analyzed. To do this, sentiment analysis has familiar major interest. Sentiment Analysis (SA) is sometimes described as archeology or opinion mining as a research field to analyze people’s feelings or ideas about organizations such as topics, events, individuals, issues, services, products, organizations, and their characteristics. As the number of reviews rapidly increases, online reviews’ sentiment analysis has become a trending topic in research. In this paper, survey is done which highlights the latest research about the implementation of machine learning and deep learning models like support vector machine (SVM), linear regression (LR), Naïve Bayes (NB), convolutional neural networks (CNN), Long short-term memory (LSTM), deep neural networks (DNN), and many others proposed for resolving various challenges in sentiment analysis such as product and app(s) review analysis, textual and visual analysis, classification of sentiments, and cross lingual problems.
A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid.
Bullying, Cyberbullying, and Hate Speech

The Internet's design and raison d'être are complete freedom, but complete freedom might lead to anarchy and to harmful and anti-social activities. In this paper I address the concepts of moral and social responsibility, applying them to the Internet realm in considering the most troubling phenomenon of cyberbullying that results in loss of life. Specifically, I probe the moral and social responsibilities of Internet users (agents), of parents and of the education system in fighting cyberbullying. Balance needs to be struck between two most important principles: freedom of expression and social responsibility. Illustrative examples in which this disturbing and harmful phenomenon of cyberbullying had cost young life are mentioned. It is argued that all relevant stakeholders need to think of the consequences of their conduct, that Internet abusers should be accountable for their wrongdoing and be penalized, and that people who have the ability to stop or at least reduce the risk of cyberbullying should take proactive steps, exhibiting zero tolerance to cyberbullying.
Utilizing Blockchain for Electronic Wellbeing Records

Blockchain has been an interesting area of ​​exploration for some time and the advantages it provides have been exploited by a variety of different businesses. Basically, the medical service area has benefited greatly from blockchain innovations due to safety, security, confidentiality and decentralization. Overall, the Electronic Wellbeing Record (EHR) system addresses issues related to information security, honesty and fairness. In this paper, we talk about how blockchain innovation can be used to change the EHR framework and can be the answer to this problem. We present a system that can be used for the implementation of blockchain innovations in the field of medical services for EHR. The essence of our proposed system is first and foremost to implement blockchain innovations for EHR and in addition to provide secure electronic records capacity by characterizing granular access rules for clients of the proposed structure. Moreover, this structure also speaks to the flexibility issue that blockchain innovation sees as a whole through the use of the off-chain capacity of the record. This structure gives the EHR framework the advantage of having a blockchain-based setup that is versatile, secure, and basic. The information on the blockchain is apparent to everybody that is available on the chain this makes the information weak which is anything but an ideal result for a decentralized stage.
Authigenic Green Mica in Interflow Horizons within Late Cretaceous Deccan Volcanic Province, India and Its Genetic Implications

Green authigenic mica, i.e., celadonite, is commonly associated with submarine alteration of basic igneous rock. However, very few studies have reported the formation of celadonite under nonmarine conditions. An integrated study involving field investigation, petrography, mineralogy, and mineral chemistry highlighted the origin of celadonite in two clay-rich horizons (green boles) of the Late Cretaceous Deccan volcanic province. Within the Salher green bole, the celadonite occurred as the dissolution and alteration of plagioclase, volcanic glass, and pore-filling cement. In the case of the Pune green bole, the celadonite was formed by the alteration of plagioclase, pyroxene, and precipitation as film within intergranular pores, along with zeolite. The celadonite in the Salher green bole exhibited slightly lower K2O and Fe2O3 and higher Al2O3 than in the Pune. The mineral chemistry of the former showed a composition closer to ferro-aluminoceladonite. Although the mineral chemistry of celadonite overlaps with glauconite, the distinct 10 Å and 15 Å reflections in XRD, euhedral lath and honeycomb morphology under SEM, and characteristic absorption bands in VNIR spectroscopy (0.4–2.5 µm) and FTIR spectroscopy (400–4000 cm−1) identified celadonite and Fe-smectite within green boles. The green boles were formed either by the alteration of a volcaniclastic deposit in local pools of water or by the in situ alteration of the fragmentary flow top. The present study is significant due to the occurrence of celadonite in a nonmarine environment, as it otherwise forms under submarine conditions.
LLP-GAN: A GAN-Based Algorithm for Learning From Label Proportions

Learning from label proportions (LLP) is a widespread and important learning paradigm: only the bag-level proportional information of the grouped training instances is available for the classification task, instead of the instance-level labels in the fully supervised scenario. As a result, LLP is a typical weakly supervised learning protocol and commonly exists in privacy protection circumstances due to the sensitivity in label information for real-world applications. In general, it is less laborious and more efficient to collect label proportions as the bag-level supervised information than the instance-level one. However, the hint for learning the discriminative feature representation is also limited as a less informative signal directly associated with the labels is provided, thus deteriorating the performance of the final instance-level classifier. In this article, delving into the label proportions, we bypass this weak supervision by leveraging generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN. Endowed with an end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism without imposing restricted assumptions on distribution. Accordingly, the final instance-level classifier can be directly induced upon the discriminator with minor modification. Under mild assumptions, we give the explicit generative representation and prove the global optimality for LLP-GAN. In addition, compared with existing methods, our work empowers LLP solvers with desirable scalability inheriting from deep models. Extensive experiments on benchmark datasets and a real-world application demonstrate the vivid advantages of the proposed approach.
Side Channel Attack On Stream Ciphers: A Three-Step Approach To State/Key Recovery

Side Channel Attack (SCA) exploits the physical information leakage (such as electromagnetic emanation) from a device that performs some cryptographic operation and poses a serious threat in the present IoT era. In the last couple of decades, there have been a large body of research works dedicated to streamlining/improving the attacks or suggesting novel countermeasures to thwart those attacks. However, a closer inspection reveals that a vast majority of published works in the context of symmetric key cryptography is dedicated to block ciphers (or similar designs). This leaves the problem for the stream ciphers wide open. There are few works here and there, but a generic and systematic framework appears to be missing from the literature. Motivating by this observation, we explore the problem of SCA on stream ciphers with extensive details. Loosely speaking, our work picks up from the recent TCHES’21 paper by Sim, Bhasin and Jap. We present a framework by extending the efficiency of their analysis, bringing it into more practical terms.In a nutshell, we develop an automated framework that works as a generic tool to perform SCA on any stream cipher or a similar structure. It combines multiple automated tools (such as, machine learning, mixed integer linear programming, satisfiability modulo theory) under one umbrella, and acts as an end-to-end solution (taking side channel traces and returning the secret key). Our framework efficiently handles noisy data and works even after the cipher reaches its pseudo-random state. We demonstrate its efficacy by taking electromagnetic traces from a 32-bit software platform and performing SCA on a high-profile stream cipher, TRIVIUM, which is also an ISO standard. We show pragmatic key recovery on TRIVIUM during its initialization and also after the cipher reaches its pseudo-random state (i.e., producing key-stream).
A Comparative study for sentiment analysis of raw and translated text

Sentiment analysis is a type of opinion mining study analyzing people&apos;s thoughts, feelings, and assessments evaluations of society things such as products, services, and institutions individuals, organizations, events and etc. This paper focuses on an effective machine learning model, Recurrent Neural Network (RNN), for sentiment analysis of both Bengali and English text. For sentiment analysis of Bengali text, we construct a novel dataset accumulated from different open source sites and social media. The dataset comprises over 17,000 texts, which are labelled as neutral, positive, and negative. Our study provides a rigorous comparative analysis on the performance of RNN in terms of sentiment analysis of both raw text and corresponding translated text (for both English and Bengali language). In summary, we achieve 82.9% and 84.5% accuracies for sentiment analysis of Bengali and English text respectively using RNN.