Reviewerly/exHarmony huggingface dataset:
APPLICATION OF FUZZY MULTIPLE CRITERIA DECISION MAKING (MCDM) IN SELECTION OF PROSPECTIVE EMPLOYEES

The recruitment process is the initial process that determines the sustainability and success of a company. In the process, effective and efficient selection tests are the key. The level of professionalism and academic ability of prospective employees are two things that are very much needed as a reference and criteria that are used as selection factors in the recruitment process. This study uses the Fuzzy Multiple Criteria Decision Making (MCDM) method by solving problems using the Simple Addictive Weighting Method (SAW). The use of this method is expected to produce an electronic selection test application that can help the recruitment team in carrying out the selection process at PT. X. The results of the research are in the form of prospective employee selection test applications to simplify the process of selecting prospective employees according to their needs. &#x0D; Keywords: Selection Test, Application, FMCDM, SAW
Continuous Leakage-Resilient Identity-Based Encryption Scheme with CCA Security

Leakage of private information including private key has become a threat to the security of computing systems. It has become a common security requirement that a cryptographic scheme should withstand various leakage attacks, including continuous leakage attacks. In order to obtain an Identity-based encryption (IBE) scheme which can keep its original security in the continuous leakage setting, we propose a new construction method of IBE scheme with Chosen-ciphertext attacks (CCA) security, which can tolerate continuous leakage attacks on many private keys of each identity, and whose security is proved based on the hardness of the classical Decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model. The leakage parameter is independent of the plaintext space and has the constant size.
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior

Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior. Examples include natural language processing, natural images or networks. There is also growing empirical evidence that some datasets exhibit a two-regime power-law behavior: one regime for small frequencies, and a second regime, with a different exponent, for high frequencies. In this paper, we introduce a class of completely random measures which are doubly regularly-varying. Contrary to the Pitman-Yor process, we show that when completely random measures in this class are normalized to obtain random probability measures and associated random partitions, such partitions exhibit a double power-law behavior. We discuss in particular three models within this class: the beta prime process (Broderick et al. (2015, 2018), a novel process called generalized BFRY process, and a mixture construction. We derive efficient Markov chain Monte Carlo algorithms to estimate the parameters of these models. Finally, we show that the proposed models provide a better fit than the Pitman-Yor process on various datasets.
DIGITAL TECHNOLOGIES IN MATHEMATICS TEACHING AT PRIMARY SCHOOLS

1 Palacky University, Olomouc (CZECH REPUBLIC)2 Elementary school Wolkerova, Havlickuv Brod (CZECH REPUBLIC)
MULTIMEDIA TECHNOLOGIES AS A MEANS OF INTRODUCING GAME ELEMENTS IN THE PROCESS OF TEACHING MATHEMATICS

MULTIMEDIA TECHNOLOGIES AS A MEANS OF INTRODUCING GAME ELEMENTS IN THE PROCESS OF TEACHING MATHEMATICS
The information-theoretic value of unlabeled data in semi-supervised learning

We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by $\Theta(\log n)$ multiplicative factor for the class of projections over the Boolean hypercube of dimension $n$. We prove that there is no separation for the class of all functions on domain of any size. Learning with the knowledge of the distribution (a.k.a. fixed-distribution learning) can be viewed as an idealized scenario of semi-supervised learning where the number of unlabeled data points is so great that the unlabeled distribution is known exactly. For this reason, we call the separation the value of unlabeled data.
Few-shot Learning for Domain-specific Fine-grained Image Classification

Learning to recognize novel visual categories from a few examples is a challenging task for machines in real-world industrial applications. In contrast, humans have the ability to discriminate even similar objects with little supervision. This paper attempts to address the few shot fine-grained image classification problem. We propose a feature fusion model to explore discriminative features by focusing on key regions. The model utilizes the focus area location mechanism to discover the perceptually similar regions among objects. High-order integration is employed to capture the interaction information among intra-parts. We also design a Center Neighbor Loss to form robust embedding space distributions. Furthermore, we build a typical fine-grained and few-shot learning dataset miniPPlankton from the real-world application in the area of marine ecological environments. Extensive experiments are carried out to validate the performance of our method. The results demonstrate that our model achieves competitive performance compared with state-of-the-art models. Our work is a valuable complement to the model domain-specific industrial applications.
RRNet: Repetition-Reduction Network for Energy Efficient Decoder of Depth Estimation

We introduce Repetition-Reduction network (RRNet) for resource-constrained depth estimation, offering significantly improved efficiency in terms of computation, memory and energy consumption. The proposed method is based on repetition-reduction (RR) blocks. The RR blocks consist of the set of repeated convolutions and the residual connection layer that take place of the pointwise reduction layer with linear connection to the decoder. The RRNet help reduce memory usage and power consumption in the residual connections to the decoder layers. RRNet consumes approximately 3.84 times less energy and 3.06 times less meory and is approaximately 2.21 times faster, without increasing the demand on hardware resource relative to the baseline network (Godard et al, CVPR'17), outperforming current state-of-the-art lightweight architectures such as SqueezeNet, ShuffleNet, MobileNet and PyDNet.
NEW METHODS DEEPENING UNDERSTANDING OF STUDENTS' EXPERIENCES AND THEIR RELATION TO PHYSIOLOGICAL ALERTNESS VARIATIONS DURING LEARNING

NEW METHODS DEEPENING UNDERSTANDING OF STUDENTS' EXPERIENCES AND THEIR RELATION TO PHYSIOLOGICAL ALERTNESS VARIATIONS DURING LEARNING
Heuristics as Decision-making Habits of Autonomous Sensorimotor Agents

Human judgement is better described as a heuristic process rather than maximisation of a utility function. Heuristics are high cognitive processes of decision-making whose rapid and effortless impl...
Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking

Improvements in data-capture and face modeling techniques have enabled us to create high-fidelity realistic face models. However, driving these realistic face models requires special input data, e.g. 3D meshes and unwrapped textures. Also, these face models expect clean input data taken under controlled lab environments, which is very different from data collected in the wild. All these constraints make it challenging to use the high-fidelity models in tracking for commodity cameras. In this paper, we propose a self-supervised domain adaptation approach to enable the animation of high-fidelity face models from a commodity camera. Our approach first circumvents the requirement for special input data by training a new network that can directly drive a face model just from a single 2D image. Then, we overcome the domain mismatch between lab and uncontrolled environments by performing self-supervised domain adaptation based on frame texture consistency based on the assumption that the appearance of the face is consistent over consecutive frames, avoiding the necessity of modeling the new environment such as lighting or background. Experiments show that we are able to drive a high-fidelity face model to perform complex facial motion from a cellphone camera without requiring any labeled data from the new domain.
Subquadratic Time Encodable Codes Beating the Gilbert–Varshamov Bound

We construct explicit algebraic geometry codes built from the Garcia-Stichtenoth function-field tower beating the Gilbert-Varshamov bound for alphabet sizes at least 192. Messages are identified with functions in certain Riemann- Roch spaces associated with divisors supported on multiple places. Encoding amounts to evaluating these functions at degreeone places. By exploiting algebraic structures particular to the Garcia-Stichtenoth tower, we devise an intricate deterministic ω/2 <; 1.19 runtime exponent encoding and 1 + ω/2 <; 2.19 expected runtime exponent randomized (unique and list) decoding algorithms. Here ω <; 2.373 is the matrix multiplication exponent. If ω = 2, as widely believed, the encoding and decoding runtimes are respectively nearly linear and nearly quadratic. Prior to this work, encoding time of code families beating the Gilbert-Varshamov bound were quadratic or worse.
A Game Theoretic Framework for Quality of Experience Enhancement in Dense Stadia

In crowded venues, such as sports stadia, maintaining an acceptable network quality of experience (QoE) is hard to achieve. Installing small cells, distributed antenna systems or high-density WiFi in every stadium is too expensive for mobile network operators. Hence, we propose a novel distributed low-cost solution based on user coordination to improve the average QoE when the network capacity cannot be enhanced. Specifically, fans take turns in disabling their cellular connectivity, such that the connected users utilize the relaxed network to obtain then share common match data with the disconnected users via Peer-to-Peer (P2P) connectivity. To eliminate a free-riding behavior, a limited punishment strategy in a large repeated game is proposed and shown to yield an approximate subgame-perfect Nash equilibrium. In addition, we model human irrationality as game noise incorporated into the proposed equilibria. A proposed application-oriented QoE model is first obtained via SimuLTE, an extension of OMNET++, then used in MATLAB simulations to verify the proposed solution. The results show tangible gains realized by the proposed solution under realistic scenarios and parameters.
The Progressive Mapping System Architecture for Global Resources Management

There are a myriad of computing resources in the World, most of which are underused. Efficiently using some of these devices raises several interesting opportunities. For instance, devices are spread everywhere, so locality can be deeply exploited. Furthermore, combining the computational capacity of all, or some, of them may conform a virtual cluster more powerful than any current data center. And, finally, by using resources already there avoids the need for a specific investment on specific infrastructures, as well as significantly reduces the operational costs. In this paper we propose a research architecture for global resources management which aims at exploiting the availability of this vast amount of heterogeneous resources at the edge of the network. We have designed the Progressive Mapping System (PMS) as a system that keeps track of the available resources and, upon an application launching, finds the best devices configuration for optimal application execution, considering both, the application performance requirements and the resources availability and performance features, while guaranteeing the Quality of Service (QoS). The PMS implements a graph of devices where nodes describe their performance features and edges describe their geographic distance as well as communication capabilities. Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. One of the novelties of the PMS system is that it considers the geographic positioning of the devices and the application locality requirements to deeper exploiting locality issues. The PMS system is yet in a preliminary implementation stage. In this paper we describe the system overview, the architectural design, and discuss some research challenges which are critical for an effective PMS implementation.
Evaluation of supply chain configuration criteria using fuzzy cognitive map

This study proposes a comprehensive configuration for supply chain management process and it enables to understand relationships among supply chain integration, supply chain strategies, supply chain risk factors, and performance criteria. Supply chain configuration criteria are determined both reviewing the literature and using experts’ knowledge and fuzzy cognitive map methodology is employed to consider the interrelations between criteria. Fuzzy cognitive map methodology is a suitable tool due to the presence of causalities, positive as well as negative directions of relationships among criteria, and the difficulty of expressing the interrelations with crisp numbers. The application is conducted in an automobile manufacturer in Turkey, and the results are analyzed.
Abstract Solvers for Computing Cautious Consequences of ASP programs

Abstract Abstract solvers are a method to formally analyze algorithms that have been profitably used for describing, comparing and composing solving techniques in various fields such as Propositional Satisfiability (SAT), Quantified SAT, Satisfiability Modulo Theories, Answer Set Programming (ASP), and Constraint ASP. In this paper, we design, implement and test novel abstract solutions for cautious reasoning tasks in ASP. We show how to improve the current abstract solvers for cautious reasoning in ASP with new techniques borrowed from backbone computation in SAT, in order to design new solving algorithms. By doing so, we also formally show that the algorithms for solving cautious reasoning tasks in ASP are strongly related to those for computing backbones of Boolean formulas. We implement some of the new solutions in the ASP solver wasp and show that their performance are comparable to state-of-the-art solutions on the benchmark problems from the past ASP Competitions.
Methodological Issues of Social Pedagogy Under Conditions of Information Society

The article justifies the necessity of fundamental renewal of social pedagogy’s methodological foundations under the influence of information civilization establishment, because adequacy of social-pedagogical technologies to the requirements of the new age will ensure harmonic interdevelopment of the dual unity that is “individuality-sociality” and sociality of the “real-virtual” person and their societies: from traditional ones – family, ethnic, civic, to innovational ones – regional and global. It reviews theoretical-methodological differences of sociocivic, essentially, social pedagogy of the industrial society and socioglobal – the information society. It proves the dependence of methodology transformation of pedagogy’s social branch on renewal of its philosophical, interdisciplinary (cultural studies, sociology, psychology etc.) and general-pedagogical levels regarding cybersocialization of social subjects in the global information space. The issues of developing methodological basis of social-pedagogical accompaniment are made topical: social adaptation of the elderly in “the culture of new” to prevent their marginalization; development of youth’s sociality under conditions of the global “personality triumph”, unlimited value pluralism of virtual information space, dynamics of types and forms of family, possibility of person’s transfer not only by gender and civic qualities, but also by cultural-ethnic and cultural-racial qualities to avoid egocentrism of the new generation; social integration and individualization of the second age members in times of long-term changes of types, forms and contents of sociality (especially professional) to ensure social transformations in “the culture of human”. The prospects of developing a reliable methodological basis of information society social pedagogy to overcome “Brownian” qualities of global social education are justified in order to preserve real social and natural environment of humanity and improve it through virtual social space.
Fast Fourier Transform-Based Steganalysis of Covert Communications over Streaming Media

Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a new steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. Results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media.
Semantic Expressive Capacity with Bounded Memory

We investigate the capacity of mechanisms for compositional semantic parsing to describe relations between sentences and semantic representations.We prove that in order to represent certain relations, mechanisms which are syntactically projective must be able to remember an unbounded number of locations in the semantic representations, where nonprojective mechanisms need not.This is the first result of this kind, and has consequences both for grammar-based and for neural systems.
USING ORAI APPLICATION IN TEACHING PRONUNCIATION

Teaching in 4.0 era cannot be separated with Information and Communication Technology (ICT). There are many applications that can be used in learning and teaching process. ORAI application is chosen to be used as teaching tool in learning pronunciation. The main objective of this research is to discover the pro and cons from students as the main user in using ORAI application in learning pronunciation. This research implemented qualitative as research method and the participants of the research was 35 students of third semester of English students in IKIP Siliwangi. The data were collected by observation and interview. The result of the research showed that most of the participants agree that ORAI brings more benefits in learning pronunciation practice class. Their agreement can be classified into four categories: accessibility, comfortability, technicality, and integrated learning. The students were motivated and they could learn effectively. While, some of the students indicated that they did not want to use this application, they just delivered that this application has weaknesses in their process of practicing English pronunciation. Their cons on the use of ORAI are classified into two categories; topic difficulties and technicalities. Overall, the implementation of ORAI has positive effect for students in pronouncing correctly and they have good feedback to trigger them for practicing a lot. On the other hand, pronouncing English well will make a big contribution to their communication skills.��
Fractal representation of the power demand based on topological properties of julia sets

In a power system, the load demand considers two components such as the real power (P) because of resistive elements, and the reactive power (Q) because inductive or capacitive elements. This paper presents a graphical representation of the electric power demand based on the topological properties of the Julia Sets, with the purpose of observing the different graphic patterns and relationship with the hourly load consumptions. An algorithm that iterates complex numbers related to power is used to represent each fractal diagram of the load demand. The results show some representative patterns related to each value of the power consumption and similar behaviour in the fractal diagrams, which allows to understand consumption behaviours from the different hours of the day. This study allows to make a relation among the different consumptions of the day to create relationships that lead to the prediction of different behaviour patterns of the curves.
Open DNN Box by Power Side-Channel Attack

Deep neural networks are becoming popular and important assets of many AI companies. However, recent studies indicate that they are also vulnerable to adversarial attacks. Adversarial attacks can be either white-box or black-box. The white-box attacks assume full knowledge of the models while the black-box ones assume none. In general, revealing more internal information can enable much more powerful and efficient attacks. However, in most real-world applications, the internal information of embedded AI devices is unavailable, i.e., they are black-box. Therefore, in this work, we propose a side-channel information based technique to reveal the internal information of black-box models. Specifically, we have made the following contributions: (1) we are the first to use side-channel information to reveal internal network architecture in embedded devices; (2) we are the first to construct models for internal parameter estimation; and (3) we validate our methods on real-world devices and applications. The experimental results show that our method can achieve 96.50\% accuracy on average. Such results suggest that we should pay strong attention to the security problem of many AI applications, and further propose corresponding defensive strategies in the future.
Penerapan Konsep Non-Deterministic Finite Automata (NFA) pada Aplikasi Simulasi Mesin Kopi Vending

In this research, an application for the simulation of vending coffee machines will be discussed using the concept of Non-Deterministic Finite Automata (NFA). With the projected growth in coffee consumption in Indonesia, which increases every year, vending machines can be one of the innovations that can be applied to coffee machines. This coffee vending machine can make 6 coffee variants, with its temperature and desired glass size. There is also a system of cash payments that are applied to this vending coffee machine, where the change will be issued according to the excess balance that is available. The method used is the formal method, to link the design of formal specifications with the application flowchart. Based on the test results obtained, the concept of Non-Deterministic Finite Automata (NFA) can be used as an alternative in the design of vending coffee machines.
Improved Extensible Authentication Protocol Using Only a Password

Passwords are a popular form of credential for user authentication.EAP-pwd (RFC 5931) is a popular method of performing secure passwordauthenticaiton. EAP-pwd requires a secret element in a finite cyclicgroup, unfortunately the technique it uses to derive this secret isopen to timing and cache attacks. This improved version, EAP-pwd',uses a different technique to derive the secret element which isresistant to timing and cache attacks.