Reviewerly/exHarmony huggingface dataset:
Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.
Estimating the class prior for positive and unlabelled data via logistic regression

Abstract In the paper, we revisit the problem of class prior probability estimation with positive and unlabelled data gathered in a single-sample scenario. The task is important as it is known that in positive unlabelled setting, a classifier can be successfully learned if the class prior is available. We show that without additional assumptions, class prior probability is not identifiable and thus the existing non-parametric estimators are necessarily biased in general if extra assumptions are not imposed. The magnitude of their bias is also investigated. The problem becomes identifiable when the probabilistic structure satisfies mild semi-parametric assumptions. Consequently, we propose a method based on a logistic fit and a concave minorization of its (non-concave) log-likelihood. The experiments conducted on artificial and benchmark datasets as well as on a large clinical database MIMIC indicate that the estimation errors for the proposed method are usually lower than for its competitors and that it is robust against departures from logistic settings.
Sequential Classification of ASL Signs in the Context of Daily Living Using RF Sensing

RF sensing based human activity and hand gesture recognition (HGR) methods have gained enormous popularity with the development of small package, high frequency radar systems and powerful machine learning tools. However, most HGR experiments in the literature have been conducted on individual gestures and in isolation from preceding and subsequent motions. This paper considers the problem of American sign language (ASL) recognition in the context of daily living, which involves sequential classification of a continuous stream of signing mixed with daily activities. In particular, this paper investigates the efficacy of different RF input representations and fusion techniques for ASL and trigger gesture recognition tasks in a daily living scenario, which can be potentially used for sign language sensitive human-computer interfaces (HCI). The proposed approach involves first detecting and segmenting periods of motion, followed by feature level fusion of the range-Doppler map, micro-Doppler spectrogram, and envelope for classification with a bi-directional long short-term memory (BiL-STM) recurrent neural network. Results show 93.3% accuracy in identification of 6 activities and 4 ASL signs, as well as a trigger sign detection rate of 0.93.
A Conceptual Model to Address the Communication and Coordination Challenges During Requirements Change Management in Global Software Development

Global Software Development (GSD) is a widely used development practice because of various advantages offered to the customers, vendors, and other stakeholders involved in a software project development. However, GSD is not a simple process as it faces multiple challenges that arise due to the mismanagement of the communication and coordination process. Meanwhile, Requirements Change Management (RCM) is a tedious and high resource-consuming process in GSD, which is further negatively affected by the poorly managed communication and coordination mechanisms. Multiple research studies have presented various theoretical and conceptual models to overcome the challenges during RCM in the GSD context. However, the current methodologies lack in handling the communication and coordination challenges during the RCM process in the GSD context. In the literature, the researchers have concluded that a conceptual model can effectively reduce the communication and coordination challenges during RCM in GSD. Inspired by this, the current work aims at proposing a conceptual model to overcome and mitigate the communication and coordination challenges, while ensuring the effective requirement changes at offshore software development sites. Moreover, it would help multiple stakeholders in understanding and managing the necessary resources before initiating the RCM process. To validate the proposed conceptual model, we have conducted a questionnaire-based survey to procure the results from the industrial experts working in the GSD domain. After analyzing the obtained results, we found that the proposed conceptual model is effective to handle the communication and coordination challenges up to 87%. In addition, almost 87% of the experts have agreed upon the correctness, identified challenges, and the mitigation practices in the proposed conceptual model necessary to improve the RCM process in the GSD context. Furthermore, it was observed that 75% of the experts also agreed upon the practical implementation of the proposed conceptual model in the software development industry to observe the heuristic performance of the proposed conceptual model.
Concurrency Theorems for Non-linear Rewriting Theories

Sesqui-pushout (SqPO) rewriting along non-linear rules and for monic matches is well-known to permit the modeling of fusing and cloning of vertices and edges, yet to date, no construction of a suitable concurrency theorem was available. The lack of such a theorem, in turn, rendered compositional reasoning for such rewriting systems largely infeasible. We develop in this paper a suitable concurrency theorem for non-linear SqPO-rewriting in categories that are quasi-topoi (subsuming the example of adhesive categories) and with matches required to be regular monomorphisms of the given category. Our construction reveals an interesting “backpropagation effect” in computing rule compositions. We derive in addition a concurrency theorem for non-linear double pushout (DPO) rewriting in rm-adhesive categories. Our results open non-linear SqPO and DPO semantics to the rich static analysis techniques available from concurrency, rule algebra and tracelet theory.
“MumCare”: An Artificial Intelligence Based Assistant

Bringing a new life to the world is a wonder to every mother. Experience faced by pregnant mothers vary from one pregnancy to another pregnancy. There is a vast amount of information available on the Internet and printed materials. Yet, this knowledge is too complex or lengthy and very few applications provide customized information to pregnant mothers. In a time where smart phones have become a necessity in our life, a mobile app is one of the easiest ways to obtain prenatal information. Hence, we have developed a mobile application to help pregnant mothers. This application includes an artificial intelligence (AI) based chatbot. AI chat bot communicates and guides the mother in a way that creates the illusion as if they are talking to their unborn child. The Spiral Model was used as the development methodology and the application was developed in an environment of continuous integration and deployment with GIT personal repository. This application was implemented using React Native and the Node.js. Chat bot was created with Dialogflow agent and integrated with the firebase through the Google cloud functions. Some existing applications were studied to identify the features and limitations of current pregnancy care mobile applications. This solution is realistic and successful and it has an upgradable model of growth. The rapid development of the Internet and mobile devices in the world has changed people's lifestyles. This mobile app will be helpful for pregnant mothers living in rural as well as in metropolitan areas alike and can enhance education and health.
PAWLS: PDF Annotation With Labels and Structure

Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup.This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult.In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format.PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately.PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models.A PAWLS demo server is available at https://pawls.apps.allenai.org
He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation

Social biases with respect to demographics (e.g., gender, age, race) in datasets are often encoded in the large pre-trained language models trained on them.Prior works have largely focused on mitigating biases in context-free representations, with recent shift to contextual ones.While this is useful for several word and sentence-level classification tasks, mitigating biases in only the representations may not suffice to use these models for language generation tasks, such as auto-completion, summarization, or dialogue generation.In this paper, we propose an approach to mitigate social biases in BERT, a large pre-trained contextual language model, and show its effectiveness in fill-in-the-blank sentence completion and summarization tasks.In addition to mitigating biases in BERT, which in general acts as an encoder, we propose lexical co-occurrence-based bias penalization in the decoder units in generation frameworks, and show bias mitigation in summarization.Finally, our approach results in better debiasing of BERT-based representations compared to post training bias mitigation, thus illustrating the efficacy of our approach to not just mitigate biases in representations, but also generate text with reduced biases.
Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test

The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text.Most recent work uses direct supervision on the task; we show that by simply finetuning a RoBERTa model, we can achieve a near perfect accuracy of 97.8%, a state-of-the-art.We argue that this outstanding performance is unlikely to lead to a good model of text coherence, and suggest that the Shuffle Test should be approached in a Zero-Shot setting: models should be evaluated without being trained on the task itself.We evaluate common models in this setting, such as Generative and Bi-directional Transformers, and find that larger architectures achieve highperformance out-of-the-box.Finally, we suggest the k-Block Shuffle Test, a modification of the original by increasing the size of blocks shuffled.Even though human reader performance remains high (around 95% accuracy), model performance drops from 94% to 78% as block size increases, creating a conceptually simple challenge to benchmark NLP models.
Knowledge Refinery: Learning from Decoupled Label

Recently, a variety of regularization techniques have been widely applied in deep neural networks, which mainly focus on the regularization of weight parameters to encourage generalization effectively. Label regularization techniques are also proposed with the motivation of softening the labels while neglecting the relation of classes. Among them, the technique of knowledge distillation proposes to distill the soft label, which contains the knowledge of class relations. However, this technique needs to pre-train an extra cumbersome teacher model. In this paper, we propose a method called Knowledge Refinery (KR), which enables the neural network to learn the relation of classes on-the-fly without the teacher-student training strategy. We propose the definition of decoupled labels, which consist of the original hard label and the residual label. To exhibit the generalization of KR, we evaluate our method in both fields of computer vision and natural language processing. Our empirical results show consistent performance gains under all experimental settings.
Fair Representations by Compression

Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data. We propose a novel unsupervised approach to map data into a compressed binary representation independent of sensitive attributes. We show that in an information bottleneck framework, a parsimonious representation should filter out information related to sensitive attributes if they are provided directly to the decoder. Empirical results show that the method achieves state-of-the-art accuracy-fairness trade-off and that explicit control of the entropy of the representation bit stream allows the user to move smoothly and simultaneously along both rate-distortion and rate-fairness curves.
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase

Knowledge Graphs have emerged as a compelling abstraction for capturing key relationship among the entities of interest to enterprises and for integrating data from heterogeneous sources. JPMorgan Chase (JPMC) is leading this trend by leveraging knowledge graphs across the organization for multiple mission critical applications such as risk assessment, fraud detection, investment advice, etc. A core problem in leveraging a knowledge graph is to link mentions (e.g., company names) that are encountered in textual sources to entities in the knowledge graph. Although several techniques exist for entity linking, they are tuned for entities that exist in Wikipedia, and fail to generalize for the entities that are of interest to an enterprise. In this paper, we propose a novel end-to-end neural entity linking model (JEL) that uses minimal context information and a margin loss to generate entity embeddings, and a Wide &amp; Deep Learning model to match character and semantic information respectively. We show that JEL achieves the state-of-the-art performance to link mentions of company names in financial news with entities in our knowledge graph. We report on our efforts to deploy this model in the company-wide system to generate alerts in response to financial news. The methodology used for JEL is directly applicable and usable by other enterprises who need entity linking solutions for data that are unique to their respective situations.
How to Survive Identity Management in the Industry 4.0 Era

Industry 4.0 heavily builds on massive deployment of Industrial Internet of Things (IIoT) devices to monitor every aspect of the manufacturing processes. Since the data gathered by these devices impact the output of critical processes, identity management and communications security are critical aspects, which commonly rely on the deployment of X.509 certificates. Nevertheless, the provisioning and management of individual certificates for a high number of IIoT devices involves important challenges. In this paper, we present a solution to improve the management of digital certificates in IIoT environments, which relies on partially delegating the certificate enrolment process to an edge server. However, in order to preserve end-to-end security, private keys are never delegated. Additionally, for the protection of the communications between the edge server and the IIoT devices, an approach based on Identity Based Cryptography is deployed. The proposed solution considers also the issuance of very short-lived certificates, which reduces the risk of using expired or compromised certificates, and avoids the necessity of implementing performance expensive protocols such as Online Certificate Status Protocol (OCSP). The proposed solution has been successfully tested as an efficient identity management solution for IIoT environments in a real industrial environment.
Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-Based Method

Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have been widely deployed in the real world. However, DNNs are found to be vulnerable to adversarial examples, which raises people's concerns about intelligent IoT systems' reliability and security. Testing and evaluating the robustness of IoT systems becomes necessary and essential. Recently various attacks and strategies have been proposed, but the efficiency problem remains unsolved properly. Existing methods are either computationally extensive or time-consuming, which is not applicable in practice. In this paper, we propose a novel framework called Attack-Inspired GAN (AI-GAN) to generate adversarial examples conditionally. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. We apply AI-GAN on different datasets in white-box settings, black-box settings and targeted models protected by state-of-the-art defenses. Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly. Moreover, for the first time, AI-GAN successfully scales to complex datasets e.g. CIFAR-100 and ImageNet, with about $90\%$ success rates among all classes.
The Impact of Specific Social Factors on Changes in Education in Serbia

The political and economic changes that followed the adoption of the Strategy for the Development of Education in Serbia 2020 essentially betrayed the basic ideas and intentions of the strategy, creating a systematic threat to education and its role in the development of Serbia. This created an almost experimental situation for analysing the impact of political and social factors on changes in education. In the sphere of politics, new trends have emerged (centralisation of power; marginalisation of democratic institutions; encouraging foreign investment in companies with a low technological level, etc.) that strongly influenced changes (‘reforms’) in education (great centralisation in education, the strong influence of politics on education, imposing of some lower-level forms of education, reducing professional autonomy, etc.). The basic mechanism of transferring the general policy to education is changing the role of the most important national institutions in defining and implementing education policy: the National Education Council, the National Council for Vocational and Adult Education, the National Council for Higher Education, and the National Accreditation Body. The adoption of new education laws (2017) radically changed their status and competencies, resulting in a reduction of their independence and professionalism and strengthening the role of the ministry, through which the influence of the ruling political regime is transferred. Also, the role of the Chamber of Commerce in education has been strengthened. Such a system endangers the autonomy of educational institutions and teachers, as well as the quality of education. Consequently, these changes have a clear impact on the country’s development and its international position.
I-voting – opportunities and threats. Conditions for the effective implementation of Internet voting on the example of Switzerland and Estonia

The aim of the article is to present the opportunities and threats resulting from the implementation of voting via the Internet (i-voting) and to discuss the conditions for effective implementation of this alternative voting procedure on the example of Estonia and Switzerland. Estonia is the only country in the world where i-voting is widely used. In Switzerland, on the other hand, this voting method has been used most often, although its use has been suspended for several years due to legal, infrastructural and political problems. What are the conditions for successfully implementing Internet voting? The attempt to answer this research question was possible thanks to the use of the following research methods: comparative, formal-dogmatic, behavioral and modified historical method. The key conclusion is that the implementation of i-voting must be preceded by many years of political, legal, infrastructural and social activities, and that the created system must be as transparent as possible.
Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.