Reviewerly/exHarmony huggingface dataset:
A Mobile Health Application for Healthy Living: HWOW (Healthier Work for Office Workers)

In this paper, the author shares the knowledge and experiences about a mobile health application named HWOW (Healthier Work for Office Workers) which is designed as a means to support a healthier work for office workers (gold or white-collar employees working relatively inactive at the computer or the desk all day). In this scope, initially, some important definitions and concepts related to electronic health (e-health), mobile health (m-health), and relevant applications are provided. What's more, all-inclusive specifics are provided on the subjects of the answers for the questions of what, how, and why regarding HWOW, the information about HWOW's development process, the results of the polls to decide on the minimum viable product, the differences of HWOW from related applications, and the features of HWOW. Lastly, the current status and plans for HWOW are presented. In the contexts of software engineering for health and mobile health, the author thinks this paper is going to be beneficial for both product developers and relevant researchers.
Network-Mediated Knowledge Spillovers in ICT/Information Security

Abstract A large literature has used patent data to measure knowledge spillovers across inventions but few papers have explicitly measured the impact of the collaboration networks formed by inventors on the quality of invention. This paper develops a method to measure the impact of collaboration networks of inventors on invention quality. We apply this methodology to the information and communication technology (ICT) and information security sectors in Israel and find that the quality of Israeli inventions are systematically linked to the structure of the collaborative network in these sectors. We are very grateful to the editor Lukasz Grzybowski and an anonymous referee for very helpful comments and suggestions that significantly improved the paper. We thank the Maurice Falk Institute for Economic Research in Israel, Start-Up Nation Central, the U. S. National Science Foundation (SciSIP grants 1360165 and 1360170), and Portugal’s Foundation for Science and Technology for financial support of this research. Lee Branstetter’s work on this project was supported by the National Science Foundation and we thank Britta Glennon for excellent research assistance. We are also grateful to Tim Bresnahan, Eugene Kandel, Imke Reimers, and seminar/conference participants at the 19th CEPR IO conference, the 10th Paris conference on Digital Economics, Collegio Carlo Alberto, Hebrew University, Stanford University, Tel Aviv University, ad UC-Berkeley and for helpful comments and suggestions. © 2020 by Neil Gandal, Nadav Kunievsky, and Lee Branstetter. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including the © notice, is given to the source.
Toward the Fundamental Limits of Imitation Learning

Imitation learning (IL) aims to mimic the behavior of an expert policy in a sequential decision-making problem given only demonstrations. In this paper, we focus on understanding the minimax statistical limits of IL in episodic Markov Decision Processes (MDPs). We first consider the setting where the learner is provided a dataset of $N$ expert trajectories ahead of time, and cannot interact with the MDP. Here, we show that the policy which mimics the expert whenever possible is in expectation $\lesssim \frac{|\mathcal{S}| H^2 \log (N)}{N}$ suboptimal compared to the value of the expert, even when the expert follows an arbitrary stochastic policy. Here $\mathcal{S}$ is the state space, and $H$ is the length of the episode. Furthermore, we establish a suboptimality lower bound of $\gtrsim |\mathcal{S}| H^2 / N$ which applies even if the expert is constrained to be deterministic, or if the learner is allowed to actively query the expert at visited states while interacting with the MDP for $N$ episodes. To our knowledge, this is the first algorithm with suboptimality having no dependence on the number of actions, under no additional assumptions. We then propose a novel algorithm based on minimum-distance functionals in the setting where the transition model is given and the expert is deterministic. The algorithm is suboptimal by $\lesssim \min \{ H \sqrt{|\mathcal{S}| / N} , |\mathcal{S}| H^{3/2} / N \}$, showing that knowledge of transition improves the minimax rate by at least a $\sqrt{H}$ factor.
Tracking and Nudging through Smartphone Apps: Public Health and Decisional Privacy in a European Health Union

In response to the SARS-CoV-2 pandemic, European Union (EU) Member States adopted technological solutions aimed at mitigating the effects of the virus, as well as enforcing newly adopted public health measures. Examples include apps for disseminating information, performing self-diagnosis, enforcing home quarantine orders and aiding contact tracing. This extensive use of technology for tracking and promoting public health raises important questions regarding EU citizens’ privacy. Thus far, the discourse in this regard has predominantly revolved around data protection, the risk of surveillance and the right to control access over one’s personal information (informational privacy). In light of the push towards a more unified approach to mitigating the current pandemic and future health crises through a European Health Union (EHU), we consider a different dimension of privacy that may be at risk when employing technology for public health, namely the right to non-interference with one’s decisions (decisional privacy). In particular, this article focuses on whether the advances in health-related persuasive technology, together with a more general movement towards “nudging” as an individual and public health tool, will require EU legislation to further protect decisional privacy by regulating “hypernudging” technologies and to guide the EHU in coordinating public health measures that utilise these technologies in a privacy-preserving way.
The AIDA Dashboard: Analysing Conferences with Semantic Technologies.

Scientific conferences play a crucial role in the field of Computer Science by promoting the cross-pollination of ideas and technologies, fostering new collaborations, shaping scientific communities, and connecting research efforts from academia and industry. However, current systems for analysing research data do not provide a good representation of conferences. Specifically, these solutions do not allow to track research trends, to compare conferences in similar fields, and to analyse the involvement of industrial sectors. In order to address these limitations, we developed the AIDA Dashboard, a tool for exploring and making sense of scientific conferences which integrates statistical analysis, semantic technologies, and visual analytics.
High-Quality Knowledge Graphs Generation: R2RML and RML Comparison, Rules Validation and Inconsistency Resolution

In this chapter, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences and explain how knowledge graphs, in the form of RDF graphs, can be generated with each one of the two mapping languages. Then we assess if the vocabulary terms were properly applied to the data and no violations occurred on their use, either using R2RML or RML to generate the desired knowledge graph.
RealSmileNet: A Deep End-to-End Network for Spontaneous and Posed Smile Recognition

Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways. Several methods have been proposed to recognize spontaneous and posed smiles. All follow a feature-engineering based pipeline requiring costly pre-processing steps such as manual annotation of face landmarks, tracking, segmentation of smile phases, and hand-crafted features. The resulting computation is expensive, and strongly dependent on pre-processing steps. We investigate an end-to-end deep learning model to address these problems, the first end-to-end model for spontaneous and posed smile recognition. Our fully automated model is fast and learns the feature extraction processes by training a series of convolution and ConvLSTM layer from scratch. Our experiments on four datasets demonstrate the robustness and generalization of the proposed model by achieving state-of-the-art performances.
Syntactically Restricting Bounded Polymorphism for Decidable Subtyping

Subtyping of Bounded Polymorphism has long been known to be undecidable when coupled with contra-variance. While decidable forms of bounded polymorphism exist, they all sacrifice either useful properties such as contra-variance (Kernel F $$_{<:}$$ ), or useful metatheoretic properties (F $$_{<:}^\top $$ ). In this paper we show how, by syntactically separating contra-variance from the recursive aspects of subtyping in System F $$_{<:}$$ , decidable subtyping can be ensured while also allowing for both contra-variant subtyping of certain instances of bounded polymorphism, and many of System F $$_{<:}$$ ’s desirable metatheoretic properties. We then show that this approach can be applied to the related polymorphism present in D $$_{<:}$$ , a minimal calculus that models core features of the Scala type system.
Expander Random Walks: A Fourier-Analytic Approach.

In this work we ask the following basic question: assume the vertices of an expander graph are labelled by 0,1. What “test” functions f : { 0,1}t → {0,1} cannot distinguish t independent samples from those obtained by a random walk? The expander hitting property due to Ajtai, Komlos and Szemeredi (STOC 1987) is captured by the AND test function, whereas the fundamental expander Chernoff bound due to Gillman (SICOMP 1998), Heally (Computational Complexity 2008) is about test functions indicating whether the weight is close to the mean. In fact, it is known that all threshold functions are fooled by a random walk (Kipnis and Varadhan, Communications in Mathematical Physics 1986). Recently, it was shown that even the highly sensitive PARITY function is fooled by a random walk Ta-Shma (STOC 2017). We focus on balanced labels. Our first main result is proving that all symmetric functions are fooled by a random walk. Put differently, we prove a central limit theorem (CLT) for expander random walks with respect to the total variation distance, significantly strengthening the classic CLT for Markov Chains that is established with respect to the Kolmogorov distance (Kipnis and Varadhan, Communications in Mathematical Physics 1986). Our approach significantly deviates from prior works. We first study how well a Fourier character χS is fooled by a random walk as a function of S. Then, given a test function f, we expand f in the Fourier basis and combine the above with known results on the Fourier spectrum of f. We also proceed further and consider general test functions - not necessarily symmetric. As our approach is Fourier analytic, it is general enough to analyze such versatile test functions. For our second result, we prove that random walks on sufficiently good expander graphs fool tests functions computed by AC0 circuits, read-once branching programs, and functions with bounded query complexity.
Interactive call graph generation for software projects

This paper presents a tool aiming to help software developers visualize the call graph of their code while editing it. The tool can generate both the import graph and the call graph. It allows the user to select the nodes and edges to be displayed and, when s/he saves the document, it automatically updates the graph to reflect the current state of the project. The process of building the call graph or the import graph from the source code is based on using a query based architecture, commonly used by language servers. Performance benchmarks show that the tool can react to changes in the order of hundreds of milliseconds. To evaluate the tool usability, this was sent to 20 random users of which 9 decided to assess it. All respondents had an overall positive impression (gave a grade of 4 or 5 on a scale from 1 to 5) and also found the tool fast and easy to use.
Survey on DDoS and EDoS Attack in Cloud Environment

Cloud computing is a heterogeneous distributed environment that provides resources as service through Internet. Cloud consists of various resources like network, memory, computer processing, and user applications provided to the customer on pay-per-use scale. Cloud services are broadly divided as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Therefore, data that is stored in cloud needs to be secured from the attackers as it is remotely kept. However, security is one of the major challenges in cloud. EDoS is the latest kind of DDoS attack on the cloud. The purpose is to consume cloud resources although the price of services is pay off by the valid customer. The key intention of DDoS attack brings down the specific service by draining the server’s resources whereas EDoS’s objective is to create economic unsustainability in the cloud resources for the object and causes financial consequences by exhausting resources and leading to a heavy bill. This paper reviews several DDoS and EDoS modification methods that have been made known in the past years and presents the mechanism which is effective for the mitigation of DDoS and EDoS attack.
Interpretable Foreground Object Search as Knowledge Distillation

This paper proposes a knowledge distillation method for foreground object search (FoS). Given a background and a rectangle specifying the foreground location and scale, FoS retrieves compatible foregrounds in a certain category for later image composition. Foregrounds within the same category can be grouped into a small number of patterns. Instances within each pattern are compatible with any query input interchangeably. These instances are referred to as interchangeable foregrounds. We first present a pipeline to build pattern-level FoS dataset containing labels of interchangeable foregrounds. We then establish a benchmark dataset for further training and testing following the pipeline. As for the proposed method, we first train a foreground encoder to learn representations of interchangeable foregrounds. We then train a query encoder to learn query-foreground compatibility following a knowledge distillation framework. It aims to transfer knowledge from interchangeable foregrounds to supervise representation learning of compatibility. The query feature representation is projected to the same latent space as interchangeable foregrounds, enabling very efficient and interpretable instance-level search. Furthermore, pattern-level search is feasible to retrieve more controllable, reasonable and diverse foregrounds. The proposed method outperforms the previous state-of-the-art by $$10.42\%$$ in absolute difference and $$24.06\%$$ in relative improvement evaluated by mean average precision (mAP). Extensive experimental results also demonstrate its efficacy from various aspects. The benchmark dataset and code will be release shortly.
A Practical NIZK Argument for Confidential Transactions over Account-Model Blockchain

We propose a novel non-interactive zero-knowledge (NIZK) argument for confidential transactions. Our NIZK argument provides a highly practical prover against other existing works, in which proof generation and verification times are at the same level. Our NIZK argument is perfect zero-knowledge in the common reference string model, with its soundness holds in the random oracle model. Based on the NIZK argument, we construct a confidential transaction smart contract (CTSC) scheme which enables transferring coins between users confidentially and automatically over the account-model blockchain. Furthermore, We provide a formal security definitions of such a primitive: confidentiality and transaction soundness, along with a security proof of the construction.
Time and phase synchronization in communication system with a digital pseudo-random modulation

Abstract The paper is concerned a model of a digital communication system where the samples of white Gaussian noise with zero mean and dispersion σ 2 are used as a modulated (carrier) low-frequency signal. Methods of the digital-phase modulation are used in a system for transmission of information message. The communication technology reminds CDMA (Code Division Multiple Access). However, a key of feature of this system is that digital samples of white Gaussian noise are used to increase the spectrum of the information message instead of traditional Walsh-Hadamard binary sequences. In this system the repeat period of the pseudo-random sequence values is specified by the parameter and can be equal to the time (duration) of several information symbols contrast to CDMA, in which the duration of repetition period equals to the duration of modulating symbol. On the one hand, the solution allows you to increase the confidentiality of the transmitted message and more evenly expand its frequency spectrum as compared with the existing of CDMA methods. On the other hand, scientists face two significant scientific tasks that make the work meaningful. First, it is necessary to ensure the synchronous operation of pseudo-random sequence generators in the receiving and transmitting devices. Secondly, it is necessary to define a phase shift amount of the frequency-change oscillator in receiving device. Both problems are the result of the asynchronous starting of the receiver. A control signal is used to solve these problems in the radio communication system which is transmitted simultaneously with an information message. So the result of modulator work is the sum of both information and control modulated signals. The proposed methods and algorithms provide the coherent signal processing with the digital pseudorandom modulation in the receive path of communication channel. The paper is a course of early research studies and it contains a detailed mathematical description of modulation and demodulation signals in the communication system with the digital pseudorandom modulation. The numerical model of the system is described in MatLab language. All numerical experiments were carryout in a mathematical computing system of MatLab.
A Machine Learning guided Rewriting Approach for ASP Logic Programs.

Answer Set Programming (ASP) is a declarative logic formalism that allows to encode computational problems via logic programs. Despite the declarative nature of the formalism, some advanced expertise is required, in general, for designing an ASP encoding that can be efficiently evaluated by an actual ASP system. A common way for trying to reduce the burden of manually tweaking an ASP program consists in automatically rewriting the input encoding according to suitable techniques, for producing alternative, yet semantically equivalent, ASP programs. However, rewriting does not always grant benefits in terms of performance; hence, proper means are needed for predicting their effects with this respect. In this paper we describe an approach based on Machine Learning (ML) to automatically decide whether to rewrite. In particular, given an ASP program and a set of input facts, our approach chooses whether and how to rewrite input rules based on a set of features measuring their structural properties and domain information. To this end, a Multilayer Perceptrons model has then been trained to guide the ASP grounder I-DLV on rewriting input rules. We report and discuss the results of an experimental evaluation over a prototypical implementation.
Preparing Students to use Subject-Oriented Technologies in Teaching Activities

Due to the fact that the requirements for a University graduate, a future teacher, are significantly increasing, new guidelines in teaching activities are defined. This is due to the processes of development and modernization of education that are taking place in Russian society, as well as the complexity of the professional activity of the teacher associated with the formation of the subject position of the student. The relevance of using modern technologies is justified, their characteristics are determined, and a General subject-oriented technology is proposed that allows us to successfully solve modern problems of education. An analysis of the experience of graduate students and teachers working in educational organizations in Yaroslavl and other cities is presented. A low level of readiness of teachers to use modern technologies, including subject-oriented technology, is revealed. In this regard, a model of preparing students for the use of subject-oriented technologies at a pedagogical University is proposed, which includes three components: conceptual-target, content-organizational, and analytical-effective. The article briefly describes the content of each component. Testing of the model was carried out in the course of experimental work, which was carried out in the master’s program at the faculty of social management of the Yaroslavl state pedagogical University. The conducted research is characterized by practical orientation, touches on modern aspects of professional education, the use of modern pedagogical technologies in the educational process.
Technological Aids for Deaf and Mute in Modern World

Background: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective: Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods: The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. Result: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion: It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.