Reviewerly/exHarmony huggingface dataset:
Adversarial Machine Learning - Industry Perspectives

Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine learning systems when viewed in the context of traditional software security development. We write this paper from the perspective of two personas: developers/ML engineers and security incident responders who are tasked with securing ML systems as they are designed, developed and deployed ML systems. The goal of this paper is to engage researchers to revise and amend the Security Development Lifecycle for industrial-grade software in the adversarial ML era.
Research Performance Evaluation of University Based on Super DEA Model

Based on the input-output relationship, this paper puts forward a five-dimensional evaluation index system of scientific research performance of colleges and universities with human resources input, material input, financial input, social benefit and economic benefit. Based on the super-efficiency DEA model with variable radial scale benefit, this paper conducts an empirical evaluation study on the scientific research level of colleges and universities in 31 regions of China in 2017. The results show that in 2017, the overall level of scientific research performance of universities in all regions of China is good, but there is a big difference among provinces. In China, 58.1% of the provinces were valid, while 41.9% were not. Beijing, Jiangsu province and Henan province ranked the top three in terms of university research performance.
Bliss in and Out of the Body: The (Extra)Corporeal Space Is Impervious to Social Pleasant Touch

Slow, gentle stimulation of hairy skin is generally accompanied by hedonic sensations. This phenomenon, also known as (positive) affective touch, is likely to be the basis of affiliative interactions with conspecifics by promoting inter-individual bindings. Previous studies on healthy humans have demonstrated that affective touch can remarkably impact behavior. For instance, by administering the Rubber Hand Illusion (RHI) paradigm, the embodiment of a fake hand enhances after a slow, affective touch compared to a fast, neutral touch. However, results coming from this area are not univocal. In addition, there are no clues in the existing literature on the relationship between affective touch and the space around our body. To overcome these lacks, we carried out two separate experiments where participants underwent a RHI paradigm (Experiment 1) and a Visuo-Tactile Interaction task (Experiment 2), designed to tap into body representation and peripersonal space processing, respectively. In both experiments, an affective touch (CT-optimal, 3 cm/s) and neutral touch (CT-suboptimal, 18 cm/s) were delivered by the experimenter on the dorsal side of participants’ hand through a “skin to skin” contact. In Experiment 1, we did not find any modulation of body representation—not at behavioral nor at a physiological level—by affective touch. In Experiment 2, no visuo-tactile spatial modulation emerged depending upon the pleasantness of the touch received. These null findings are interpreted in the light of the current scientific context where the real nature of affective touch is often misguided, and they offer the possibility to pave the way for understanding the real effects of affective touch on body/space representation.
Blockchain Technology based Supply Chain State of the art and Future Prospects

Initially developed for Bitcoin cryptocurrency, Blockchain Technology (BCT) is a decentralized transaction managing technology that provides security, anonymity and data integrity in transactions without the need of any trusted third party. The interest in BCT has been grown, as research topic for many fields, such as finance, health, government, agriculture and many particular Supply Chain (SC) industries. However, there is a lack of systematic literature reviews (SLR) on existing research concerning how BCT is well relevant in SC. This paper conducts a systematic review in order to examine all pertinent research on SC based on BCT. The main inspiration for this work was to synthesize existing evidence, classify research tendencies available in the literature, and identify open themes and gaps for development in this discipline. However, 45 primary papers have been extracted from scientific databases. This systematic review provides direction for future research regarding the applications of BCT for SC
Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

Detecting an anomaly in multichannel signal data is a challenging task in various domains. It should take into account the cross-channel relationship and temporal relationship within each channel. Moreover, the signal data is high-dimensional and making it difficult to gather sufficient abnormal labels. Consequently, unsupervised reconstruction-based anomaly detection methods have been applied successfully in many studies. However, they lose valuable channel information inherent in the reconstruction errors by merely averaging the errors for both the channel and time, then consider the average value as an anomaly score. In this study, we propose a method to explicitly employ channelwise reconstruction errors as a feature to detect abnormal signals. After a convolutional autoencoder produces the channelwise reconstruction errors, a machine learning anomaly detection model aggregates the errors as an anomaly score. To demonstrate the effectiveness and applicability of the proposed model, we conduct experiments using simulated data and real-world automobile data. The results show that the proposed method remarkably enhances the detectability compared to the simple average of the reconstruction errors. The reconstruction errors of abnormal and normal channels are shown to be different; therefore, it can be considered as an appropriate feature for anomaly detection. The best performance is obtained by using local outlier factors in the following anomaly detection model.
Wavelets on intervals derived from arbitrary compactly supported biorthogonal multiwavelets

(Bi)orthogonal (multi)wavelets on the real line have been extensively studied and employed in applications with success. A lot of problems in applications are defined on bounded intervals or domains. Therefore, it is important in both theory and application to construct all possible wavelets on intervals with some desired properties from (bi)orthogonal (multi)wavelets on the real line. Vanishing moments of compactly supported wavelets are the key property for sparse wavelet representations and are closely linked to polynomial reproduction of their underlying refinable (vector) functions. Boundary wavelets with low order vanishing moments often lead to undesired boundary artifacts as well as reduced sparsity and approximation orders near boundaries in applications. From any arbitrarily given compactly supported (bi)orthogonal multiwavelet on the real line, in this paper we propose two different approaches to construct/derive all possible locally supported (bi)orthogonal (multi)wavelets on $[0,\infty)$ or $[0,1]$ with or without prescribed vanishing moments, polynomial reproduction, and/or homogeneous boundary conditions. The first approach generalizes the classical approach from scalar wavelets to multiwavelets, while the second approach is direct without explicitly involving any dual refinable functions and dual multiwavelets. (Multi)wavelets on intervals satisfying homogeneous boundary conditions will also be addressed. Though constructing orthogonal (multi)wavelets on intervals is much easier than their biorthogonal counterparts, we show that some boundary orthogonal wavelets cannot have any vanishing moments if these orthogonal (multi)wavelets on intervals satisfy the homogeneous Dirichlet boundary condition. Several examples of orthogonal and biorthogonal multiwavelets on the interval $[0,1]$ will be provided to illustrate our construction approaches and proposed algorithms.
Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process

In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures. Dropout is a recent solution to mitigate overfitting that has witnessed significant success in various classification applications. Recently, many efforts have been made to improve the Standard dropout using an unsupervised merit-based semantic selection of neurons in the latent space. However, these studies do not consider the task-relevant information quality and quantity and the diversity of the latent kernels. To solve the challenge of dropping less informative neurons in deep learning, we propose an efficient end-to-end dropout algorithm that selects the most informative neurons with the highest correlation with the target output considering the sparsity in its selection procedure. First, to promote activation diversity, we devise an approach to select the most diverse set of neurons by making use of determinantal point process (DPP) sampling. Furthermore, to incorporate task specificity into deep latent features, a mutual information (MI)-based merit function is developed. Leveraging the proposed MI with DPP sampling, we introduce the novel DPPMI dropout that adaptively adjusts the retention rate of neurons based on their contribution to the neural network task. Empirical studies on real-world classification benchmarks including, MNIST, SVHN, CIFAR10, CIFAR100, demonstrate the superiority of our proposed method over recent state-of-the-art dropout algorithms in the literature.
Real-Time Credit Card Fraud Detection Using Spark Framework

As corporate and finance businesses are increasing day by day, digitalization becomes a rampant and easy mode of payment. Customers showing interest in e-shopping with online payments which saves time and compute costs (Popat and Chaudhary in 2018 2nd international conference on trends in electronics and informatics (ICOEI), Tamilnadu, Tirunelveli, pp 1120–1125, [1]). Both companies and customers focusing more on credit cards became rapid in the past decade as they are easy, convenient, and fast mode of payment. The surge in credit card transactions results in the rise of credit card frauds from year-to-year exponentially (Kumar and Iqbal in 2019 1st international conference on innovation in info and communication technology (ICIICT), TN, Chennai, India, pp 1–4, [2]) Companies are also spending vast amounts every year on detecting fraud ways and trying to reduce them at possible best. In this provision, we designed a method that helps in handling real-time credit card fraud transactions that shows good results (Kuruwitaarachchi et al. in Real-time credit-card fraud detect using machine learning, [3]). We used the Apache Spark library (MLlib) to handle fraud detection using a random forest ensemble model and real-time processing using Kafka and Spark streaming jobs delivered the optimal results (Rajeshwari and Babu in 2016 2nd international conference on applied and theoretical computing and communication technology (iCATCCT), Bangalore, pp 439–444, [4]).
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions

Abstract In this article we study the stochastic gradient descent (SGD) optimization method in the training of fully connected feedforward artificial neural networks with ReLU activation. The main result of this work proves that the risk of the SGD process converges to zero if the target function under consideration is constant. In the established convergence result the considered artificial neural networks consist of one input layer, one hidden layer, and one output layer (with $$d \in {\mathbb {N}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> neurons on the input layer, $$H\in {\mathbb {N}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>H</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> neurons on the hidden layer, and one neuron on the output layer). The learning rates of the SGD process are assumed to be sufficiently small, and the input data used in the SGD process to train the artificial neural networks is assumed to be independent and identically distributed.
Neural Feature Selection for Learning to Rank

Abstract LEarning TO Rank (LETOR) is a research area in the field of Information Retrieval (IR) where machine learning models are employed to rank a set of items. In the past few years, neural LETOR approaches have become a competitive alternative to traditional ones like LambdaMART. However, neural architectures performance grew proportionally to their complexity and size. This can be an obstacle for their adoption in large-scale search systems where a model size impacts latency and update time. For this reason, we propose an architecture-agnostic approach based on a neural LETOR model to reduce the size of its input by up to 60% without affecting the system performance. This approach also allows to reduce a LETOR model complexity and, therefore, its training and inference time up to 50%.
What is the future for immersive virtual reality in memory rehabilitation? A systematic review

A growing interest in non-pharmacological approaches aimed at cognitive rehabilitation and cognitive enhancement pointed towards the application of new technologies. The complex virtual reality (VR) presented using immersive devices has been considered a promising approach.The article provides a systematic review of studies aimed at the efficacy of VR-based rehabilitation. First, we shortly summarize literature relevant to the role of immersion in memory assessment and rehabilitation.We searched Web of Science, ScienceDirect, and PubMed with the search terms "memory rehabilitation", "virtual reality", "memory deficit". Only original studies investigating the efficacy of complex three-dimensional VR in rehabilitation and reporting specific memory output measures were included.We identified 412 citations, of which 21 met our inclusion criteria. We calculated appropriate effect sizes for 10 studies including control groups and providing descriptive data. The effect sizes range from large to small, or no effect of memory rehabilitation was present, depending on the control condition applied. Summarized studies with missing control groups point out to potential positive effects of VR but do not allow any generalization.Even though there are some theoretical advantages of immersive VE over non-immersive technology, there is not enough evidence yet to draw any conclusions.
IIITT@DravidianLangTech-EACL2021: Transfer Learning for Offensive Language Detection in Dravidian Languages

This paper demonstrates our work for the shared task on Offensive Language Identification in Dravidian Languages-EACL 2021. Offensive language detection in the various social media platforms was identified previously. But with the increase in diversity of users, there is a need to identify the offensive language in multilingual posts that are largely code-mixed or written in a non-native script. We approach this challenge with various transfer learning-based models to classify a given post or comment in Dravidian languages (Malayalam, Tamil, and Kannada) into 6 categories. The source codes for our systems are published.
Small Flying Object Classifications Based on Trajectories and Support Vector Machines

This paper presents an alternative approach to identify and classify the group of small flying objects especially drones from others, notably birds and kites (inclusive of kiteflying), in near field, by examining the pattern of their flight paths and trajectories. The trajectories of the drones and other flying objects were extracted from multiple clips of videos including various natural and synthetic database. Four trajectories characteristics are observed and extracted from the object’s flight paths, i.e., heading or turning angle, curvature, pace velocity, and pace acceleration. Subsequently, principal component analyses were applied to reduce the number of these trajectory features from 4 to 2 parameters. Multi-class classification by support vector machine (SVM) with non-linear transformation kernel was used. Multiple classification models were developed by several algorithms with various transformation kernels. The hyperparameters were optimized using Bayesian optimization. The performances of the different models are compared through the prediction accuracy of the test data.
Multi-Agent Systems for the Management of Resources and Activities in a Smart Classroom

The aim of this research is to develop a prototype of a multi-agent systems (MAS), which manages the resources and activities in a smart classroom (SaCI). We developed three specific objectives. First, we designed the architecture of the MAS that manages the resources and activities in a virtual learning environment (VLE), for which we used the related works on MAS in a VLE. Second, we implement the architecture of the MAS, the prototype proposed has been called GREAC (Gestion de Recursos y Actividades), which considers the learning style (LS) of the student to manage the resources and activities in an adaptive way, and uses the methodology Prometheus, Moodle 3.6 (VLE), and the framework Jade, for the development of the prototype of the MAS. Third, we evaluated the prototype GREAC in a SaCI in an academic context. In the evaluation participated 54 students from the Universidad Nacional de Loja, of the Carrera de Ingenieria en Sistemas. As a result of the evaluation by the students, we obtained a level of satisfaction 92.6 %. Finally, this work concludes that the implementation of a MAS in a VLE allows the dynamic management of resources and activities, taking into account the students LS, allowing an adaptive and personalized management.
LUX: Smart Mirror with Sentiment Analysis for Mental Comfort

As COVID-19 solidifies its presence in everyday life, the interest in mental health is growing, resulting in the necessity of sentiment analysis. A smart mirror is suitable for encouraging mental comfort due to its approachability and scalability as an in-home AI device. From the aspect of natural language processing (NLP), sentiment analysis for Korean lacks an emotion dataset regarding everyday conversation. Its significant differences from English in terms of language structure make implementation challenging. The proposed smart mirror LUX provides Korean text sentiment analysis with the deep learning model, which examines GRU, LSTM, CNN, Bi-LSTM, and Bi-GRU networks. There are four emotional labels: anger, sadness, neutral, and happiness. For each emotion, there are three possible interactive responses: reciting wise sayings, playing music, and sympathizing. The implemented smart mirror also includes more-typical functions, such as a wake-up prompt, a weather reporting function, a calendar, a news reporting function, and a clock.
RADAR: Data Protection in Cloud-Based Computer Systems at Run Time

The protection of confidential data (e.g., personal data) is a concern of increasing importance. Data processing applications are often deployed in cloud or fog/edge computing systems. Such cloud-based systems may change dynamically during operations, for example because of changes in the users, in the deployed software services, or in the infrastructure. As a result, both the threats to data protection and the availability of data protection mechanisms may change at run time, making efficient data protection very challenging. This paper presents RADAR (Run-time Adaptations for DAta pRotection), an approach for ensuring data protection in dynamically changing cloud-based systems. RADAR analyzes the configuration of the cloud-based system automatically at run time, to detect changes in the threats to data protection or in the availability of data protection mechanisms. If needed, RADAR automatically adapts the cloud-based system to ensure the continued satisfaction of data protection requirements. From multiple possible adaptations that lead to the satisfaction of data protection requirements, RADAR chooses an adaptation that has the lowest negative implication on other goals, like costs and the availability of functionality. RADAR is a comprehensive approach that combines pattern-based detection of problematic system configurations with model-based automatic run-time adaptations and a search algorithm for finding the best adaptation. RADAR is validated using two case studies from the cloud and fog computing domains, and the scalability of the approach is evaluated using a set of controlled experiments.
InferNER: an attentive model leveraging the sentence-level information for Named Entity Recognition in Microblogs

We investigate the problem of named entity recognition in the user-generated text such as social media posts. This task is rendered particularly difficult by the restricted length and limited grammatical coherence of this data type. Current state-of-the-art approaches rely on external sources such as gazetteers to alleviate some of these restrictions. We present a neural model able to outperform state of the art on this task without recurring to gazetteers or similar external sources of information. Our approach relies on word-, character-, and sentence-level information for NER in short-text. Social media posts like tweets often have associated images that may provide auxiliary context relevant to understand these texts. Hence, we also incorporate visual information and introduce an attention component which computes attention weight probabilities over textual and text-relevant visual contexts separately. Our model outperforms the current state of the art on various NER datasets. On WNUT 2016 and 2017, our model achieved 53.48\% and 50.52\% F1 score, respectively. With Multimodal model, our system also outperforms the current SOTA with an F1 score of 74\% on the multimodal dataset. Our evaluation further suggests that our model also goes beyond the current state-of-the-art on newswire data, hence corroborating its suitability for various NER tasks.