Reviewerly/exHarmony huggingface dataset:
A Variational Perspective on the Assignment Flow

The image labeling problem can be described as assigning to each pixel a single element from a finite set of predefined labels. Recently, a smooth geometric approach for inferring such label assignments was proposed by following the Riemannian gradient flow of a given objective function on the so-called assignment manifold. Due to the specific Riemannian structure, this results in a coupled replicator dynamic incorporating local spatial geometric averages of lifted data-dependent distances. However, in this framework an approximation of the flow is necessary in order to arrive at explicit formulas. We propose an alternative variational model, where lifting and averaging are decoupled in the objective function so as to stay closer to established approaches and at the same time preserve the main ingredients of the original approach: the overall smooth geometric setting and regularization through geometric local averages. As a consequence the resulting flow is explicitly given, without the need for any approximation. Furthermore, there exists an interesting connection to graphical models.
A Neutral Temporal Deontic STIT Logic

In this work we answer a long standing request for temporal embeddings of deontic STIT logics by introducing the multi-agent STIT logic $$\mathsf {TDS}$$ . The logic is based upon atemporal utilitarian STIT logic. Yet, the logic presented here will be neutral: instead of committing ourselves to utilitarian theories, we prove the logic $$\mathsf {TDS}$$ sound and complete with respect to relational frames not employing any utilitarian function. We demonstrate how these neutral frames can be transformed into utilitarian temporal frames, while preserving validity. Last, we discuss problems that arise from employing binary utility functions in a temporal setting.
PHYSICO-CHEMICAL CHARACTERISTICS OF GROUNDWATER AROUND SINGRAULI COALFIELD AREAS, SINGRAULI DISTRICT OF MADHYA PRADESH (INDIA)

Groundwater is contaminated around Singrauli coalfield area due to mining and industrial activities.The major cause of the contamination of ground water may be due to improper disposal of industrial waste.The aim of the present study was to assess the groundwater quality and also to have a statistical analysis of physicochemical parameters of groundwater around Singrauli coalfield region, Singrauli District of Madhya Pradesh (India).Eight groundwater samples were collected from the hand pump & Dug-wells located around Singrauli coalfields area during pre-monsoons and post monsoons seasons in the year 2018and analyzed for physicochemical parameters such aspH, TDS, EC, DO, Fluoride, Chlorides, Nitrate, Sulphate, Lithium, Sodium, Potassium, Magnesium and Calcium.A systematic calculation of the correlation coefficient has also been carried out between different analyzed parameters.
Interledger Demo: IoT Integration

With the spread of pragmatic approaches to blockchains and distributed ledger technologies (DLTs), the need to operate securely across multiple DLTs has become apparent. The class of operations that span two or more DLTs is generally referred to as interledger. Various interledger approaches have been proposed, researched and taken into use, including for example, atomic cross-chain transactions, sidechains, bridging approaches, ledger-of-ledger structures, and layered value transfer protocols, such as the W3C Interledger Protocol (ILP).In this demonstration, we exemplify ongoing work in our research group at Aalto University. We demonstrate how a resource-constrained IoT device may be made able to interact with an “IoT-friendly” ledger, with an interledger gateway service bridging to a public permissionless ledger. The demo use case reflects a scenario where an IoT device advertizes a service that is available for anyone on the Internet, pending a successful and validated payment has been made on the Ethereum ledger. This demonstration shows how this can be accomplished in an auditable and secure manner across ledgers using hashed timelock agreements (HTLAs) to provide payment escrow.
Effect of mindfulness meditation on attention and visual scanning in elderly people - A randomized control trial

Background: World population is aging. Elderly age is associated with decline in cognitive functions.Aims and Objectives: This study aims to study the effect of mindfulness meditation (MM) on elderly age-induced decline in attention and visual scanning. Materials and Methods: A total of 100 elderly participants were randomly divided into meditation group (MG; n = 50) and control group (CG; n = 50). MG participants followed 45 days of MM schedule. Six letter cancellation test (SLCT) was used for pre- and post-study evaluation of attention and visual scanning. Results: There was a significant improvement (P < 0.05) in scores of SLCT for MG participant. There was no significant change in SLCT scores of CG participants. Conclusion: MM practice counters the elderly age-induced decline in attention and visual scanning.
A New Semiparametric Approach to Finite Mixture of Regressions using Penalized Regression via Fusion

For some modeling problems a population may be better assessed as an aggregate of unknown subpopulations, each with a distinct relationship between a response and associated variables. The finite mixture of regressions (FMR) model, in which an outcome is derived from one of a finite number of linear regression models, is a natural tool in this setting. In this article, we first propose a new penalized regression approach. Then, we demonstrate how the proposed approach better identifies subpopulations and their corresponding models than a semiparametric FMR method does. Our new method fits models for each person via grouping pursuit, utilizing a new group-truncated L1 penalty that shrinks the differences between estimated parameter vectors. The methodology causes the individuals' models to cluster into a few common models, in turn revealing previously unknown subpopulations. In fact, by varying the penalty strength, the new method can reveal a hierarchical structure among the subpopulations that can be useful in exploratory analyses. Simulations using FMR models and a real-data analysis show that the method performs promisingly well.
Sociology of Higher Education in Structure of Branch Sociological Knowledge: a Problem of Constitution

The article deals with the problem of the constitution of sociology of higher education as a branch of sociological knowledge. Describing the features of this branch, the authors give an interpretation of the object and subject of sociology of higher education, show their relationship with the object-subject field of sociology. The constitution of sociology of higher education is shown within the process of branch differentiation of sociological knowledge as one of the main trends of its development. Considerable attention is paid to the analysis of special sociological theories in the structure of branch sociology, their correlation with branch knowledge is emphasized. Among the special sociological theories of higher education, the authors separate and describe in detail the theory of non-linear higher education.
Identifying the Challenges of the Blockchain Community from StackExchange Topics and Trends

Software developers around the globe have shown tremendous interests in blockchain with more than seven thousand active blockchain software (BCS) projects on Github. Yet, little research has focused on understanding the challenges encountered by the developers of those projects as well as its' users. Therefore, the objective of this study is to better understand the primary areas of challenges encountered by the BCS community. Using a Latent Dirichlet Allocation based topic modeling, we identify discussion topics from the two Blockchain related StackExchange sites. We manually investigated the posts belonging to each topic to understand challenges encountered by the developers. The results of our study revealed that while the ratios of posts on BCS development are increasing, the ratios of posts on mining cryptocurrencies are decreasing. Due to the scarcity of expert blockchain developers, posts on BCS development are more likely to go either answered or encounter more delays than posts on other topics. Based on our findings, we recommend project maintainers to spend efforts to improve documentations on BCS development as the community lacks supporting materials on that area the most.
Efficient Method in Association Rule Hiding for Privacy Preserving with Data Mining Approach

Security protecting and information mining is an inspection zone worried about the protection driven from identifiable data when measured for information mining.This paper tends to the security issue by allowing for the protection and algorithmic necessities at the same time.The target of this paper is to execute an Association hiding calculation for safeguarding information mining which would be proficient in giving secrecy and enhance the execution when the database stores and recovers immense measure of information.One of the procedures of information mining is association lead mining.Association rules Hiding.(ARH) is one of the real issues in the information mining space.The association rules hold numerous mysteries.So before distributing, these tenets must be hidden.Sensitive data must be covered up, since uncovering the mystery or critical association data may cause issues.In our paper, Privacy protection is finished by Association rule hiding.Amid hiding of delicate association rules, false guidelines are not created and least alteration degree is accomplished and data isn't lost.
Benchmarking GNN-CMA-ES on the BBOB noiseless testbed

We evaluate in this paper the GNN-CMA-ES algorithm on the BBOB noiseless testbed. The GNN-CMA-ES algorithm was recently proposed as a plug-in extension to CMA-ES, introducing the possibility to train flexible search distributions, in contrast to standard search distributions (such as the multivariate Gaussian). By comparing GNN-CMA-ES and CMA-ES, we show the benefits of this extension on some unimodal functions as well as on a variety of multimodal functions. We also identify a family of unimodal functions where GNN-CMA-ES can degrade the performances of CMA-ES and discuss the possible reasons behind this behavior.
Security Analysis of Camera File Transfer Over Wi-Fi

The proliferation of smartphone and tablet empowers ubiquitous Device-to-device (D2D) networks. Using a locally handled wireless link between devices provides significant convenience to all use cases of Internet of Things, such as home monitors, garage door, and Google home. Nowadays, digital cameras have also adapted Wi-Fi technology to ease the process of transferring pictures and videos to smartphones and laptops. This paper introduces the D2D file transfer mechanisms between cameras and mobile platforms and presents an in-depth empirical security analysis on the D2D network created by those devices. Cameras and applications are close-sourced black boxes, which makes security investigation considerably challenging. In this paper, the analysis concentrates on the most popular camera brands in the market and our team reveals some critical vulnerabilities. We exploit the discovered flaws to construct a proof-of-concept attack to demonstrate how to steal an image from a camera in a busy Wi-Fi environment. We conclude the paper with improvement suggestions and possible solutions. The experimental setup we have developed could be used for future related research.
The Use of Commercial Advertisement to Teach University Students in English for Specific Purpose Context

This research aimed to propose multimodal-based English teaching through video for university students majoring in marketing management through project-based learning. Teaching English for university students are belonging to the field of English for Specific Purposes (ESP). It needs special treatments due to the fact that ESP students have different needs for English use for their future jobs, since the industrial revolution 4.0 typically affected the economic area. For presenting the research, the qualitative research design was employed. This research used interpretive analysis in the context of discourse analysis. The commercial advertisement video was analyzed in terms of Systemic Functional Linguistics (SFL) and semiotic features of multimodal discourse analysis. The analysis results of the video show that semiotic features are more powerful than linguistics aspects in promoting goods well. The video is expectedly used to teach language for promotion to the university students, particularly in marketing management majors. The teaching-learning process would be guided by the use of project-based learning method so that the outcomes would be in the form of the promotional video which employs both linguistics and semiotic features.
A Showcase of Medical, Therapeutic and Pastime Uses of Virtual Reality (VR) and How (VR) Is Impacting the Dementia Sector

Dementia is on the rise as our population ages and it is still untreatable. Our global society is changing but so is our technological landscape. Virtual Reality has been around for a while but it has not been widely adopted within the general healthcare sector, never mind for niche use cases, that is until now….
Exponential inequality for chaos based on sampling without replacement

We are interested in the behavior of particular functionals, in a framework where the only source of randomness is a sampling without replacement. More precisely the aim of this short note is to prove an exponential concentration inequality for special U-statistics of order 2, that can be seen as chaos.
Optical Flow for Real-Time Human Detection and Action Recognition Based on CNN Classifiers

Mobile robots equipped with camera sensors are required to perceive surrounding humans and their actions for safe and autonomous navigation. In this work, moving humans are the target objects. For robot vision, real-time performance is an important requirement. Therefore, we propose a robot vision system in which the original images captured by a camera sensor are described by optical flow. These images are then used as inputs to a classifier. For classifying images into human and not-human classifications, and the actions, we use a convolutional neural network (CNN), rather than coding invariant features. Moreover, we present a local search window as a novel detector for clipping partial images around target objects in an original image. Through the experiments, we ultimately show that the robot vision system is able to detect moving humans and recognize action in real time.
Index-Based Notation for Random Variable and Probability Space

In a conventional notation used in many studies, a probability space and state space of random variables is identified by its symbol. However, such a notation makes a formula ambiguous in a large equation. This letter proposes to use an index set to identify the probability space and state space of random variables. It is shown that the proposed notation can increase the generality of formulas without ambiguity.
Quantum complementarity and operator structures

We establish operator structure identities for quantum channels and their error-correcting and private codes, emphasizing the complementarity relationship between the two perspectives. Relevant structures include correctable and private operator algebras, and operator spaces such as multiplicative domains and nullspaces of quantum channels and their complementary maps. For the case of privatizing to quantum states, we also derive dimension inequalities on the associated operator algebras that further quantify the trade-off between correction and privacy.
AI-Enabled Litigation Evaluation

Lawsuits are expensive, time consuming, and often confusing to non-lawyers who are faced with making important legal decisions. For making informed legal decisions, as it stands today, there is a dearth of tools to aid legal decision makers in the costly and complex landscape of litigation. To address this need, we present a framework for standardizing and structuring key information contained in case dockets. This data can be used for numerous analytics use cases to provide legal decision makers with data-driven insights to help them understand and navigate the events that occur in their lawsuits.
Contextualization of Morphological Inflection

Ekaterina Vylomova, Ryan Cotterell, Trevor Cohn, Timothy Baldwin, Jason Eisner. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Prediction of a Ball Trajectory for the Humanoid Robots: A Friction-Based Study

Recent advances in robotics have made it necessary for robots to be able to predict actions like humans. This problem is well presented in international RoboCup competition leagues, especially for humanoid robots in challenges such as Goal-Kick from Moving Ball. In this paper, we proposed double exponential smoothing (DES), autoregressive (AR) and quadratic prediction (QP) as online methods and self-perturbing recursive least squares (SPRLS) as an offline method for prediction of the ball trajectory on ground. These prediction methods are compared in two scenarios by applying LuGre friction model. We simulated our proposed methods by Simmechanics library of MATLAB’s Simulink. By comparing results using root-mean-square error and normalized root-mean-square error, we could deduce that methods that were based on predefined models such as QP performed poorly when the friction deviated from the presumed model. Whereas numerical methods such as AR could adapt themselves to variation much better, depending on the friction force variation with time. Also offline methods such as SPRLS are good replacements for online ones when pre-training is possible.
A Software Analysis Based Vulnerability Detection System For Smart Contracts

Smart contracts bring Ethereum transactionsYe, Jiaming great convenience, meanwhile, they can have potentially devastating financial consequences. Among the existing tools, few can handle bytecode detection tasks. To addressMa, Mingliang the lack of bytecode security guarantee, wePeng, Tianyong design a software-based detection system that can perform both source code and bytecode. Finally, we conduct preliminaryXue, Yinxing experiments towards building a reliable vulnerability database and concise analysis is provided.
Flexibility of Emulation Learning from Pioneers in Nonstationary Environments

This is an extension from a selected paper from JSAI2019. Social learning is crucial in acquisition of the intelligent behaviors of humans and many kinds of animals, as it makes behavior learning far more efficient than pure trial-and-error. In imitation learning, a representative form of social learning, the agent observes specific action-state pair sequences produced by another agent (expert) and reflect them into its own action. One of its implementations in reinforcement learning is the inverse reinforcement learning. We propose another form of social learning, emulation learning, which requires much less information from another agent (pioneer). In emulation learning, the agent is given only a certain level of achievement by another agent, or a record. In this study, we implement emulation learning in the reinforcement learning setting by applying a model of satisficing action policy. We show that the emulation learning algorithm works well both in stationary and non-stationary reinforcement learning tasks, breaking the often observed trade-off like relationship between efficiency and flexibility.
Research on Full Homomorphic Encryption Algorithm for Integer in Cloud Environment

With the continuous development of computer networks, cloud storage is now the mainstream way for people to store information, but some important information leaks in the process of storage, and information security issues have become the most concerned issue at present. Homomorphic encryption not only has the property of data encryption of traditional encryption algorithm, but also has the result of ciphertext operation equivalent to the corresponding plaintext operation. This paper proposes an improved scheme based on DGHV, it (MDGHV) is mainly implemented from two points: changing the encryption formula c = pq + 2r + m to c = pqr + m, the domain of clear text m is expanded to a custom value, which can be used as a parameter; without acquiring sensitive information, data owners can directly calculate multiple ciphertexts and ensure high efficiency to meet the security requirements of cloud environment. An improved scheme based on DGHV, which expands the plaintext domain and enable search encryption operation to encrypt larger values, thus improving the efficiency. The security of this algorithm still depends on the similar GCD problem, which can solve some practical security problems in the cloud environment.
ANALISIS DIGITAL FORENSIK REKAYASA IMAGE MENGGUNAKAN JPEGSNOOP DAN FORENSICALLY BETA

The development of information technology is not only beneficial for humans, but often also used for negative purposes. Crime by utilizing digital image technology is very easy to do using a computer, so it is not surprising if more and more cases of cybercrime occur. The spread of digital images on social media in the form of images or videos raises public anxiety that these images or videos cannot be considered reliable evidence, because both images and videos can be easily engineered. The purpose of this study is to analyze a digital image that has been manipulated or engineered using a particular software or program. Image manipulation techniques that will be used are: Image splicing, copy-move, and retouching images. While the analysis tool uses the ELA method introduced by Krawetz which is available online from the website (https://29a.ch/photo-forensics/#forensic-magnifier) ​​and Jpegsnoop software.
Model of Random Field with Piece-Constant Values and Sampling-Restoration Algorithm of Its Realizations

We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels can be arbitrary. Realizations of a random field model of the desired shape are created by appropriate selection of parameters for formative realization of Markov processes. For the proposed field model, we investigated the sampling and restoration algorithm of any selected realizations. As a result, we determined the optimal sampling and recovery algorithms. The resulting sampling is fundamentally non-periodic. Recovery errors are calculated. Two examples are considered.
Using Chatbot in Trading System for Small and Medium Enterprise (SMEs) by Convolution Neural Network Technique

In several businesses, chatbots are used for providing customer information, answering questions, helping customer reservations, virtual assistants; serve as call centers to serve ten million customers automatically. This research presents a method for developing chatbots to serve their users. A deep learning based conversational artificial intelligence technique was used as tools for learning conversation between machine and customer. The convolution neural network technique by using Tensorflow training was used to improve the accuracy of these chatbots. Moreover, in this research, we developed a system for online sales assistant application system via Facebook Page.