Reviewerly/exHarmony huggingface dataset:
Designing Efficient Dyadic Operations for Cryptographic Applications

Abstract Cryptographic primitives from coding theory are some of the most promising candidates for NIST’s Post-Quantum Cryptography Standardization process. In this paper, we introduce a variety of techniques to improve operations on dyadic matrices, a particular type of symmetric matrices that appear in the automorphism group of certain linear codes. Besides the independent interest, these techniques find an immediate application in practice. In fact, one of the candidates for the Key Exchange functionality, called DAGS, makes use of quasi-dyadic matrices to provide compact keys for the scheme.
Roomba+Clips Cam

We present a novel prototype to explore themes of unpredictable autonomy in everyday smart products. We construct an interactive prototype that combines two autonomous everyday consumer products: an autonomous vacuum cleaner that cleans where it decides, and an autonomous camera that takes photos when it decides. Through this process we reflect upon the value and risks associated with everyday unpredictable autonomy, and the need to introduce guardrails and overrides. This demo will allow conference participants to interact with variations of the prototype in order to experience unpredictable autonomy with everyday smart systems.
Reward Machines for Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines (RM) -- Mealy machines used as structured representations of reward functions -- to encode the team's task. The proposed novel interpretation of RMs in the multi-agent setting explicitly encodes required teammate interdependencies, allowing the team-level task to be decomposed into sub-tasks for individual agents. We define such a notion of RM decomposition and present algorithmically verifiable conditions guaranteeing that distributed completion of the sub-tasks leads to team behavior accomplishing the original task. This framework for task decomposition provides a natural approach to decentralized learning: agents may learn to accomplish their sub-tasks while observing only their local state and abstracted representations of their teammates. We accordingly propose a decentralized q-learning algorithm. Furthermore, in the case of undiscounted rewards, we use local value functions to derive lower and upper bounds for the global value function corresponding to the team task. Experimental results in three discrete settings exemplify the effectiveness of the proposed RM decomposition approach, which converges to a successful team policy an order of magnitude faster than a centralized learner and significantly outperforms hierarchical and independent q-learning approaches.
An Efficient Method for Graph Repartitioning in Distributed Environments

Due to most of the existing graph repartitioning methods are known for poor efficiency in distributed environments. In this paper, we introduce a new graph repartitioning method with two phases in distributed environments. In the first phase, a local method is designed to identify all the potential candidate vertices that should be moved to the other partitions at once in each partition locally. In the second phase, a streaming graph processing model is adopted to reassign the candidate vertices to achieve lightweight graph repartitioning. During the reassignment of the vertex, we propose an objective function to balance both the load balance and the number of crossing edges among the distributed partitions. The experimental results with a large set of real word and synthetic graph datasets show that the communication cost can be reduced by nearly 1 to 2 orders of magnitude compared with the existing methods.
Readiness of Socionomics Specialists for Using Information Technologies in Future Professional Activity

The article considers the problem of the readiness of future specialists in the field of socionomics who will use information technologies in their professional activity.The analysis of references in the field of our research revealed that the effecient intoduction of information technologies in the sphere of socionomics depends on the readiness of specialists for innovation activity, as such activity is one of important components of their professional readiness.We determined the components of readiness of the specialists in the field of socionomics (psychologists, teachers) for applying information technologies in their future professional work.Such components are motivational, cognitive, reflective, communication and creative components.In order to investigate the level of formaton of each component by the future specialists in socionomics we conducted testing and suggested quiestionnaires for the students of psychology at Khmenytskyi National University and pedagogical students at Khmelnytskyi Humanitarian-Pedagogical Academy.The diagnostic results demonstrate that the motivational component of readiness of the students in the field of socionomics to use information technologies in their future professional activity has been formed at a sufficiently high level.However, the diagnostic results of all other components of readiness for using innovations by students of socionomics revealed only sufficient and low levels of formation.Such results necessitate the research effective psychological and pedagogical conditions for increasing the level of development of the motivational, cognitive, reflexive, communicative and creative components of readiness to use information technologies in professional activity by future specialists in the sphere of socionomics.
Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN

Conventional object detection models inevitably encounter a performance drop as the domain disparity exists. Unsupervised domain adaptive object detection is proposed recently to reduce the disparity between domains, where the source domain is label-rich while the target domain is label-agnostic. The existing models follow a parameter shared siamese structure for adversarial domain alignment, which, however, easily leads to the collapse and out-of-control risk of the source domain and brings negative impact to feature adaption. The main reason is that the labeling unfairness (asymmetry) between source and target makes the parameter sharing mechanism unable to adapt. Therefore, in order to avoid the source domain collapse risk caused by parameter sharing, we propose an asymmetric tri-way Faster-RCNN (ATF) for domain adaptive object detection. Our ATF model has two distinct merits: 1) A ancillary net supervised by source label is deployed to learn ancillary target features and simultaneously preserve the discrimination of source domain, which enhances the structural discrimination (object classification vs. bounding box regression) of domain alignment. 2) The asymmetric structure consisting of a chief net and an independent ancillary net essentially overcomes the parameter sharing aroused source risk collapse. The adaption safety of the proposed ATF detector is guaranteed. Extensive experiments on a number of datasets, including Cityscapes, Foggy-cityscapes, KITTI, Sim10k, Pascal VOC, Clipart and Watercolor, demonstrate the SOTA performance of our method.
Spatial cellular robot in orbital truss collision-free path planning

Abstract. Aiming at the problem of moving path planning of a cellular robot on trusses in space station, a triangular prism truss is taken as the research object, and an optimized ant colony algorithm incorporating a gravitational search algorithm is proposed. The innovative use of the hierarchical search strategy which limits the exploration area, the use of gravity search algorithm to get the optimal solution of truss nodes, and further transform it into the initial value of pheromone in ant colony algorithm, can effectively prevent the algorithm from falling into the local optimal solution in the early stage, and make the optimization algorithm have a faster convergence speed. This paper proposes a heuristic function including the angle between the targets, which can effectively avoid blind search in the early stage and improve the ability of path search. The simulation results show that the path and planning time of the cellular robot can be effectively reduced when choosing truss path.
Organizational and Pedagogical Conditions for the Development of Research Activities of University Students

The author outlines that in connection with the need to develop students' abilities and skills for organizing independent research activities, a psychological and pedagogical analysis of the organization of university students’ research activities is made, and the basic tasks of developing the students' ability to do research work are formulated. Methods of research. To diagnose the development of this quality, research methods such as questionnaires, analysis, and comparative evaluation were used. The study defined quality, based on which we can generate interest in students to do research activities – the ability to cognitive activity, awareness of actions, independent approach to creative problem solving, commitment to self-development and self-education. It is noted that for development of skills for doing research work it is recommended to engage students in the situations where it is necessary to find the solution to problems, help them to analyze qualitative changes and to predict the results of a particular process development. In the course of experimental work carried out by lecturers of the Lipetsk State Technical University, positive changes, indicating the development of the ability in students to do research activity in the process, organized to introduce students to research work at different stages of their training were identified.
Capital Market and Financial Development on Growth: A Panel ARDL Analysis

This study evaluates the influence of capital market development and financial development ongrowth in the three West African countries. Data used for the research is from the World Bank and Pen World Table (PWT). This research uses the Panel ARDL test to examine the long-term relationship, as well as the error correction model to analyze the existence of a short-term relationship. The results show that in both the long term and short term, there is a negative influence of capital market development on economic growth. On the same line, in the long term, financial development is also negatively associated with growth and has no significant impact on economic growth. The ECM results indicate that there is a long-run causality effect between capital market development, financial development, and economic growth.
FIRST LANGUAGE INTERFERENCES INTO ENGLISH WRITING SKILL OF THE XIITH GRADE STUDENTS OF SMA NEGERI 1 KUPANG IN ACADEMIC YEAR 2017/2018

This project entitled “FIRST LANGUAGE INTERFERENCES INTO ENGLISH WRITING SKILL OF THE XIIth GRADE STUDENTS OF SMA NEGERI 1 KUPANG IN ACADEMIC YEAR 2017/2018.” This research is aimed at; 1) figuring aspects of first language (L1) interferences; and 2) the affecting factors of L1 interference into English writing skill. By applying descriptive qualitative approaches, there were some strategies using in both compiling and analyzing data. In collecting data, triangulation was applied such as interviewing teacher and students, classroom observation, and students’ writing assessment. The data were obtained by interviewing students and teacher and assessing writing skill of students of SMA N 1 Kupang in academic year 2017/2018 in the third grade of language program. Typology was applied in analyzing data using contrastive analysis. The result of this investigation proved that there are four levels of L1 interference found in students’ English writings. They are phonology, morphology, syntax, and semantics. There are also four affecting factors that cause the occurrence of L1 interferences. They are mental process happening in students’ brain, students’ L1, Teaching and Learning Process (TLP) model in classroom, and under-frequency of English use. Mental process in Students’ brain and students’ L1 are novelty dimensions of this research.
Efficient Splitting of Measures and Necklaces

We provide approximation algorithms for two problems, known as NECKLACE SPLITTING and $ε$-CONSENSUS SPLITTING. In the problem $ε$-CONSENSUS SPLITTING, there are $n$ non-atomic probability measures on the interval $[0, 1]$ and $k$ agents. The goal is to divide the interval, via at most $n (k-1)$ cuts, into pieces and distribute them to the $k$ agents in an approximately equitable way, so that the discrepancy between the shares of any two agents, according to each measure, is at most $2 ε/ k$. It is known that this is possible even for $ε= 0$. NECKLACE SPLITTING is a discrete version of $ε$-CONSENSUS SPLITTING. For $k = 2$ and some absolute positive constant $ε$, both of these problems are PPAD-hard. We consider two types of approximation. The first provides every agent a positive amount of measure of each type under the constraint of making at most $n (k - 1)$ cuts. The second obtains an approximately equitable split with as few cuts as possible. Apart from the offline model, we consider the online model as well, where the interval (or necklace) is presented as a stream, and decisions about cutting and distributing must be made on the spot. For the first type of approximation, we describe an efficient algorithm that gives every agent at least $\frac{1}{nk}$ of each measure and works even online. For the second type of approximation, we provide an efficient online algorithm that makes $\text{poly}(n, k, ε)$ cuts and an offline algorithm making $O(nk \log \frac{k}ε)$ cuts. We also establish lower bounds for the number of cuts required in the online model for both problems even for $k=2$ agents, showing that the number of cuts in our online algorithm is optimal up to a logarithmic factor.
MODEL FOR ASSESSING DIGITAL LEADERSHIP OF ORGANIZATION

A mathematical model of the level of development of digital leadership in leading scientific and technological sectors has been proposed. The assessment is proposed to be carried out in five main areas, each of which includes both quantitative (for example, the share of modern (digital) training areas in the total number) and qualitative criteria (for example, the speed and effectiveness of interaction between the organization’s departments).Using the lower level of the hierarchy, the criteria of one direction are compared in pairs by the strength of their influence on the key direction as a whole. As a result, a matrix of pairwise comparisons is created. The level of development of digital leadership is characterized by an aggregated indicator calculated on the basis of the normalized matrix vector. The assessment methodology can be used both by organizations in the scientific and technical sectors (universities, institutes, research centers, etc.), and by government bodies responsible for the implementation of activities related to digitalization.
Comparative Performance Analysis of Color Image watermarking Scheme Using Hessenberg Decomposition and Schur Decomposition

This blind color image watermarking technique based on Hessenberg Decomposition and Schur Decomposition examines the performance in terms of invisibility and robustness. For the security purpose, the Fibonacci-Lucas Transform is used to scramble the watermark image. This technique is tested with several color images from the image database CVG-UGR as the host image and QR code image is used as a binary watermark image. In this method the Y component of the YCbCr color space of the color host image is used to embed the binary QR code as the watermark image. In term of average PSNR value i.e. 64.1177 dB of all the watermarked images, better Perceptual quality achieved using Hessenberg Decomposition as compared to the average PSNR value 55.5823 dB using Schur Decomposition. Moreover, all the recovered watermark image i.e. QR codes have been detected too. The analysis shows good robustness against several attacks. Hessenberg Decomposition satisfies the good robustness against several noise like salt and pepper noise, resize, cropping, gaussian noise, resize and JPEG compression as compared to Schur Decomposition.
High-dimensional Linear Discriminant Analysis Classifier for Spiked Covariance Model

This work was funded by the office of sponsored research at KAUST. Part of the results in this paper has been presented at the IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2018.
Gravity: a blockchain-agnostic cross-chain communication and data oracles protocol.

This paper intends to propose the architecture of a blockchain-agnostic protocol designed for communication of blockchains amongst each other (i.e. cross-chain), and for blockchains with the outside world (i.e. data oracles). The expansive growth of cutting-edge technology in the blockchain industry outlines the need and opportunity for addressing oracle consensus in a manner both technologically and economically efficient as well as futureproof. Blockchain-agnosticism is inherently limited if proposing a technological solution involves adding one more architectural layer. As such, Gravity protocol is designed to be a truly blockchain-agnostic protocol. By ensuring parity through direct integration and by leveraging the stability and security of the respective interconnected ecosystems, Gravity circumvents the need for a dedicated, public blockchain and a native token. Ultimately, Gravity protocol intends to address scalability challenges by providing a solid infrastructure for the creation of gateways, cross-chain applications, and sidechains. This paper introduces and defines the concept of Oracle Consensus and its implementation in the Gravity protocol named the Pulse Consensus algorithm. The proposed consensus architecture allows Gravity to be considered a singular decentralized blockchain-agnostic oracle.
Modeling individual differences in risk feeling of autonomous driving behavior with a prediction error

Studies on autonomous driving systems are being conducted to realize a safe and secure mobile society. According to these studies, the safety of the system and the driver’s feeling of safety often do not match. To enable automatic driving control according to the driver's comfort, previous researchers introduced a risk feeling index to quantify the driver’s feeling of safety. These studies, however, did not consider the effects of the drivers’ individual differences for the risk feeling index. We hypothesized that the risk perception was formed by the difference between the actual autonomous driving behavior and the driver's prediction for it. The driver's risk feeling is expected to vary depending on his/her prior prediction of autonomous driving behavior. We considered that this difference, which we term as the prediction error, explains an individual difference in the risk perception. The purpose of this study is to reveal the effect of the prediction error on the risk perception. We conducted an experiment to investigate the effect of these factors on the risk feeling in the overtaking scene using a driving simulator. We obtained two types of subjective evaluations to examine the effects of the prediction errors on risk perception. One is perceived risk based on the driver’s original prediction, and the other is perceived risk based on the predictions that we manipulated by conducting a learning session during the experiment. The results suggest that the risk is perceived based on both the experimentally manipulated prediction and the individual driving characteristic of anxiety while driving. These findings will enable the development of a secure automatic driving system that suits each driver by controlling the system appropriately based on the driving characteristic of the driver.
FORENSIC ACCOUNTING: A WAY TO FIGHT, DETER AND DETECT FRAUD

Forensic Accounting is one of the emerging fields for accountancy professionals. Forensic Accounting is the function of bookkeeping, auditing and analytical expertise to combat the financial and white-collar crimes. The scope of forensic accounting has expanded due to cases of corporate scams and financial frauds. Forensic Accounting is the dynamic and strategic tool which helps in combating the corruption, financial crimes and frauds through the application of forensic auditing techniques. There is a huge demand for forensic auditors in the field of insurance, banks, police armed forces and government departments. This paper shows the application of information technology and cyber forensics to curb the malfunctioning done by corporate. In this paper role of the forensic auditor has been explained and the various techniques employed by forensic auditors have also been discussed. The latest cases in the field of forensic accounting have also been included in this paper. Forensic auditors can help the economy to grow and protect the interest of various stakeholders.
Handwritten Arabic Characters Recognition using a Hybrid Two-Stage Classifier

Handwritten Arabic character recognition presents a big challenge to researchers in the field of pattern recognition.Arabic characters are characterized by their highly-cursive nature and many of them have a similar appearance.For example, the only difference between some of the alphabet characters is the existence of a number dots above or below the main character shape.This paper proposes a system for isolated off-line handwritten Arabic character recognition using the Discrete Cosine Transform (DCT) as the feature extraction method and a two-stage hybrid classifier.The two stages are a Support Vector Machine (SVM) and a neural network (NN).The first stage is a two-class SVM classifier which classifies a character either a character with dot(s) or without dot(s).The output of this stage is used to extend the feature vector of the character by the class value to give it an extra unique feature.The extend feature vector is fed to a multi-class neural network model to classify the character.The proposed approach is tested on a database of Arabic handwritten characters called AlexU Isolated Alphabet (AIA9K) containing 8,737 character images.The experimental results of the first stage classifier showed a high recognition accuracy rate of 99.14%.The proposed twostage hybrid classifier obtained an average recognition accuracy rate of 91.84% over all Arabic Alphabet characters.