Reviewerly/exHarmony huggingface dataset:
Learning Self-Game-Play Agents for Combinatorial Optimization Problems

Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games as well as video games (e.g., Atari games and Dota2). DeepMind researchers have already implemented model-free RL to play Go and Chess at a superhuman level using neural Monte-Carlo-Tree-Search (neural MCTS). Therefore, it is plausible to consider that RL, starting from zero knowledge, can be applied to other problems which can be converted into games. We try to leverage the computational power of neural MCTS to solve a class of combinatorial optimization problems. Following the idea of Hintikka's Game-Theoretical Semantics, we propose the Zermelo Gamification (ZG) to transform specific combinatorial optimization problems into Zermelo games whose winning strategies correspond to the solutions of the original optimization problem. The ZG also provides a specially designed neural MCTS. We use a combinatorial planning problem for which the ground-truth policy is efficiently computable to demonstrate that ZG is promising.
Design and Implementation of a Hybrid Face Recognition Technique

The problem of face recognition is a subject that has been researched for many years. Face recognition technology can be utilized in many different industries, it isn’t solely designated for computer science relate fields. The purpose of this paper is to continue this research and attempt to provide a powerful classifier that works on par with the algorithms currently on the market. The proposed classification algorithm in this paper will be known as the EFL Hybrid algorithm and it is based on combining three face recognition algorithms to provide a higher accuracy. This classifier is created by combining three major algorithms that will be explored in this paper: Eigenfaces, Fisherfaces, and Local Binary Patterns Histograms. The experimental results show that both the LBPH and Fisherfaces classifiers perform better than the Eigenfaces classifier. The experimental results on the testing datasets show that the hybrid face recognition technique can provide a higher accuracy compared to other face recognition algorithms.
A Dialectical View on Conduction: Reasons, Warrants, and Normal Suasory Inclinations

When Carl Wellman (1971) introduced the reasoning-type conduction, he endorsed a dialectical view on natural language argumentation. Contemporary scholarship, by contrast, treats conductive argument predominantly on a product view. Not only did Wellman’s reasons for a dialectical view thus fall into disregard; a product-treatment of conduction also flouts the standard semantics of ‘argument’. Attempting to resolve these difficulties, our paper traces Wellman’s preference for a dialectical view to the role of defeasible warrants. These act as stand-ins for (parts of) value hierarchies that arguers of normal suasory inclination find acceptable. We also improve on extant ways of diagramming conduction and distinguish two of its structural variants.
Low transmission overhead for polar coding physical-layer encryption

In this paper, for physical-layer security (PLS), a novel scheme of polar coding encryption is introduced in the wiretap channel (WTC) model. To decrease transmission overhead of the shared secret information and enhance the security performance against an attacker, we have employed the two following encryption technologies: Firstly, randomization of output bits is not dependent on the traditional randomized bit-channels but they are directly flipped through the random bit sequence. Secondly, for employing Advanced Encryption Standard (AES), we utilize the secret seed to extend an initial secret key of AES cryptosystem, which it appears a good avalanche performance. Result analyses demonstrate that the proposed scheme is strongly resistant against conventional attacks.
Exploratory learning with convolutional autoencoder for discrimination of architectural distortion in digital mammography

This work presents a deep learning approach based on autoencoder to improve the detection of architectural distortion (AD) in digital mammography. AD can be the earliest sign of breast cancer, appearing before the formation of any mass or calcification. However, it is very diffcult to be detected and almost 50% of the cases are missed by the radiologists. Thus, we designed an autoencoder, based on a convolutional neural network (CNN), to work as a feature descriptor in a computer-aided detection (CAD) pipeline with the objective of detecting AD in digital mammography. This model was trained with 140,000 regions-of-interest (ROI) extracted from clinical mammograms. These samples were divided in two groups, with and without AD, according to the radiologist's report. Validation was done comparing the classifier performance when using the proposed autoencoder and other well-known feature descriptors, commonly used for the task of detecting AD in digital mammograms. The results showed that the performance of the autoencoder is slightly higher than that of other descriptors. However, the complexity and the computational cost of the autoencoder is much higher when compared to the hand-crafted descriptors.
Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks

In the field of cryptoeconomics the Ethereum (Ethereum Foundation) project gave opportunity to create “own” cryptocurrency – new token based on its smart-contract platform to everyone without lowlevel programming skills. Then it became obvious that tokens could be used for crowdfunding as the Ethereum did in 2014. Unregulated and easy to access such scheme became popular among related to the blockchain tech startups. It was named Initial Coin Offering (ICO/or ITO). Despite its name, which is similar to IPO, this scheme is usually used for venture funding of a new project instead of expanding already well-established working business. The authors use machine-learning algorithms to classify ICOs and estimate ROI based on public digital data and web-sources. The goal of the research is to develop sustainable and efficient model, which will predict target profit ROI (profit trends) of ICO startup. Data collection and analysis period: Feb-Mar 2018. The prediction model and the application (service) of ICO startups’ selection are developed as the result of the study. Results. There were over 3000 samples of ICO-startups in the research dataset. After cleaning and elimination of outliers, it contained only 518. The number of samples with positive ROI (which means that these ICOs were profitable) was 234. Cross validation metric was confirmed to be accurate. The model achieved 79 % accuracy (average value). To prove this score separated prediction was executed the metrics: for test dataset AUC is 0.78; for profitable samples Precision: 0.76; Recall: 0.9 for profitable; F1-score: 0.82. Discussions. In order to achieve the objectives of this study, various IT components of the service architecture (applications) were developed to monitor, analyze and predict the risks of ICO startups. An artificial neural network was developed to solve the problem of ROI classification and prediction. The average ROI among profitable ICOS was 47 %. Taking into consideration that the crypto market is highly volatile and that there is a possibility that such investments will not bring any profit, this model of monitoring, analysis and prediction can be very valuable for the purposes of critical selection (exclusion) of a number of ICO projects from potential investment. Conclusion. The developed components can be used as a basis of monitoring service of ICO startups. The risk-forecasting model can be improved, foremost, by using the most complete (and wider) set of data. In this case, individual data collection and processing tasks can be performed manually, which will require additional resources. It should be noted that other types of neural networks can be developed for both text analysis and trading data analysis. This may lead to the logic of using a combination of models, which will potentially help to provide the most accurate predictions.
Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation

Availability of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, datasets with ag ...
Variational Information Planning for Sequential Decision Making

We consider the setting of sequential decision making where, at each stage, potential actions are evaluated based on expected reduction in posterior uncertainty, given by mutual information (MI). As MI typically lacks a closed form, we propose an approach which maintains variational approximations of, both, the posterior and MI utility. Our planning objective extends an established variational bound on MI to the setting of sequential planning. The result, variational information planning (VIP), is an efficient method for sequential decision making. We further establish convexity of the variational planning objective and, under conditional exponential family approximations, we show that the optimal MI bound arises from a relaxation of the well-known exponential family moment matching property. We demonstrate VIP for sensor selection, experiment design, and active learning, where it meets or exceeds methods requiring more computation, or those specialized to the task.
Influence of a neighborhood shape on the efficiency of continuous variable neighborhood search

The efficiency of a Variable neighborhood search metaheuristic for continuous global optimization problems greatly depends on geometric shape of neighborhood structures used by the algorithm. Among the neighborhoods defined by balls in ?p, 1 ?p ? ? metric, we tested the ?1, ?2, and ?? ball shape neighborhoods, for which there exist efficient algorithms for obtaining uniformly distributed points. On many challenging high-dimensional problems, our exhaustive testings showed that, popular and the easiest for implementation, ?? ball shape of neighborhoods performed the worst, and much better efficiency was obtained with ?1 and ?2.
The State of Modern Offensive Security

The benefits of implementing red teams or similar services in an organization by using ethical hackers has been belabored. It is important at this point to turn to a discussion of the current challenges and obstacles in regard to fielding a successful offensive security capability. The issues in this industry are innumerable; however, I have found that the most pervasive ones are related to a handful of specific areas. Red teams are essentially in an unwinnable race with the adversary they are attempting to emulate. Standards surrounding the offensive security industry are often misrepresentative or prohibitive of adequate assessment. The customer–client relationship is, by definition, adversarial as a result of the nature of services being provided—a fact that can manifest itself into real issues for the assessors and the customers. Assuming all these assessment-related constraints can be addressed, there is still the personnel issues related to staffing a successful red team. This chapter covers the state of modern offensive security offerings as it pertains to these challenges.
Are You Ready When It Counts? IT Consulting Firm’s Information Security Incident Management

Information security incidents are increasing both in number and in scope. In consequence, the General Data Protection Regulation and the Directive on security of network and information systems fo ...
PEMODELAN REGRESI BERGANDA UNTUK DATA DALAM STUDI KECERDASAN EMOSIONAL

Regression analysis is frequently used to study forecasting and relation between some variables. In studying forecasting and relation between some variables using regression, needed the best model. This research aim to multiple regression modelling for data in emotional intelligence studies and applied to the case for identified of factors influence the student emotional intelligence in Institut Agama Islam Negeri Bone. The research objective was knowledge the best regression linear model and factors influence to the emotional intelligence. The results showed the best regression linear model to knowledge influence factors of the student emotional intelligence in Institut Agama Islam Negeri Bone is, i = 77.6 + 0.468X1i + 0.295X2i + 0.35X3i. Based on this model, imagination, cognition, and personalityis significant influence to the emotional intelligence.
RETRACTED: Efficient object analysis by leveraging deeply-trained object proposals prediction model

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Journal of Visual Communication and Image Representation was made aware that the paper “Efficient object analysis by leveraging deeply-trained object proposals prediction model” was previously published in Multimedia Tools and Applications, 2018, https://doi.org/10.1007/s11042-017-5390-6. The content was submitted to Journal of Visual Communication and Image Representation without disclosure of its previous publication. As such this article represents a misuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process. The authors have not responded to correspondence about this retraction.
Improved Asset Design for Educational Asynchronous Games @KAR with Visual Concept of Malang City

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How biometrics (and blockchain) could save bricks-and-mortar retail

The explosion of e-commerce over the past decade is testament to how digital advances are slowly consuming the domains of both finance and retail. Shoppers around the globe need only open a smartphone app, tap on the goods they wish to purchase and have them shipped, often with next-day delivery for domestic merchants. Combined with SciFi-worthy talk of drone deliveries, you'd be forgiven for thinking that bricks-and-mortar retailers are on the brink of extinction.
A framework of reading timestamps for surveillance video

This paper presents a framework to automatically read timestamps for surveillance video. Reading timestamps from surveillance video is difficult due to the challenges such as color variety, font diversity, noise, and low resolution. The proposed algorithm overcomes these challenges by using the deep learning framework. The framework has included: training of both timestamp localization and recognition in a single end-to-end pass, the structure of the recognition CNN and the geometry of its input layer that preserves the aspect of the timestamps and adapts its resolution to the data. The proposed method achieves state-of-the-art accuracy in the end-to-end timestamps recognition on our datasets, whilst being an order of magnitude faster than competing methods. The framework can be improved the market competitiveness of panoramic video surveillance products.
PSO Evolution Based on a Entropy Metric

Bioinspired search algorithms are widely used for solving optimization problems. The evolution progress isusially measured by the fitness value of the population fittest element. The search stops when the algorithm reaches a predetermined number of iterations, or when no improvement is achieved after some iterations. Usually, no information, behind the best global objective value, is fed into the algorithm to influence its behavior. In this paper, a entropy metric is proposed to measure the algorithm convergence. Several experiments are carried out using a particle swarm optimization to analyze the metric relevance. Moreover, the proposed metric is used to implement a strategy to prevent premature convergence to suboptimal solutions. The results show that the index is useful for analyzing and improving the algorithm convergence during the evolution.
Kernel null-space-based abnormal event detection using hybrid motion information

Abnormal event detection in crowded scenes is a challenging task in the computer vision community. A hybrid motion descriptor named the multiscale histogram of first- and second-order motion is proposed for abnormal event detection. The second-order motion describes the change in motion and is extracted by optical flow-based instantaneous tracking, which avoids object tracking in crowded scenes. For the modeling of normal events, a kernel null Foley–Sammon transform (KNFST) is introduced. KNFST makes a projection in the null space, where normal samples of all types are treated jointly instead of considering each known class individually. Experiments conducted on two benchmark datasets and comparisons to state-of-the-art methods demonstrate the superiority of the proposed method.
A specific type of permutation and complete permutation polynomials over finite fields

In this paper, we study polynomials of the form [Formula: see text], where [Formula: see text] and list all permutation polynomials (PPs) and complete permutation polynomials (CPPs) of this form. This type of polynomials were studied by Bassalygo and Zinoviev for the cases [Formula: see text] and [Formula: see text], Wu, Li, Helleseth and Zhang for the case [Formula: see text], [Formula: see text], Bassalygo and Zinoviev answered the question for the case [Formula: see text], [Formula: see text] and finally by Bartoli et al. for the case [Formula: see text]. Here, we determine all PPs and CPPs for the case [Formula: see text].
Impact Damage Identification for Composite Material Based on Transmissibility Function and OS-ELM Algorithm

A method is proposed based on the transmissibility function and the Online Sequence Extreme Learning Machine (OS-ELM) algorithm, which is applied to the impact damage of composite materials. First of all, the transmissibility functions of the undamaged signals and the damage signals at different points are calculated. Secondly, the difference between them is taken as the damage index. Finally, principal component analysis (PCA) is used to reduce the noise feature. And then, input to the online sequence limit learning neural network classification to identify damage and confirm the damage location. Taking the amplitude of the transmissibility function instead of the acceleration response as the signal analysis for structural damage identification cannot be influenced by the excitation amplitude. The OS-ELM algorithm is based on the ELM (Extreme Learning Machine) algorithm, in-creased training speed also increases the recognition accuracy. Experiment in the epoxy board shows that the method can effectively identify the structural damage accurately.
A New Encrypted Data Switching Protocol: Bridging IBE and ABE Without Loss of Data Confidentiality

Encryption technologies have become one of the most prevalent solutions to safeguard data confidentiality in many real-world applications, e.g., cloud-based data storage systems. Encryption outputting a relatively “static” format of encrypted data, however, may hinder further data operations. For example, encrypted data may need to be “transformed” into other formats for computation or other purposes. To enable encryption to be used in another device equipped with a different encryption mechanism, the concept of encryption switching was first proposed in CRYPTO 2016 for conversion particularly between Paillier and ElGamal encryptions. This paper considers the conversion between conventional identity-based and attribute-based encryptions and further proposes a concrete construction via the technique of proxy re-encryption. The construction is proved to be CPA secure in the standard model under q-decisional parallel bilinear Diffie-Hellman exponent assumption. The performance comparisons highlight that our bridging mechanism reduces computation and communication cost on the client side, especially when the data of the client is encrypted and outsourced to a remote cloud. The computational costs with respect to re-encryption (on the server side) and decryption (on the client side) are acceptable in practice.
Renal Cancer Cell Nuclei Detection from Cytological Images Using Convolutional Neural Network for Estimating Proliferation Rate

The Cytological images play an essential role in monitoring the progress of cancer cell mutation. The proliferation rate of the cancer cell is the prerequisite for cancer treatment. It is hard to accurately identify the nucleus of the abnormal cell in a faster way as well as find the correct proliferation rate since it requires an in-depth manual examination, observation and cell counting, which are very tedious and time-consuming. The proposed method starts with segmentation to separate the background and object regions with K-means clustering. The small candidate regions, which contain cell region is detected based on the value of support vector machine automatically. The sets of cell regions are marked with selective search according to the local distance between the nucleus and cell boundary, whether they are overlapping or non-overlapping cell regions. After that, the selective segmented cell features are taken to learn the normal and abnormal cell nuclei separately from the regional convolutional neural network. Finally, the proliferation rate in the invasive cancer area is calculated based on the number of abnormal cells. A set of renal cancer cell cytological images is taken from the National Cancer Institute, USA and this data set is available for the research work. Quantitative evaluation of this method is performed by comparing its accuracy with the accuracy of the other state of the art cancer cell nuclei detection methods. Qualitative assessment is done based on human observation. The proposed method is able to detect renal cancer cell nuclei accurately and provide automatic proliferation rate.
A New Memristive Chaotic System and the Generated Random Sequence

This paper concerned the research on a memristive chaotic system and the generated random sequence; by constructing a piecewise-linear memristor model, a kind of chaotic system is constructed, and corresponding numerical simulation and dynamical analysis are carried out to show the dynamics of the new memristive chaotic system. Finally the proposed memristive chaotic system is used to generate random sequence for the possible application in encryption field.
Robotic Musicianship and Musical Human Augmentation

Robotic Musicianship research at Georgia Tech Center for Music Technology (GTCMT) focuses on the construction of autonomous and wearable robots that can analyze, reason, and generate music. The goal of our research is to facilitate meaningful and inspiring musical interactions between humans and artificially creative machines. In this talk I present the work conducted by the Robotic Musicianship Group at GTCMT over the last 15 years, highlighting the motivation, research questions, platforms, methods, and underlining guidelines for our work.
Analysis of Behavioral Factors that Cause Student Academic Fraud

This study aims to examine a number of behavioral factors from students committing academic fraud in university. The subject of this research is the factors that motivate students to commit academic fraud. The Fraud Pentagon Theory and Gone Theory are used. Behavioral factors include arrogance, pressure, opportunity, rationalization, competence, greed, need, and exposure. The population in this study was the undergraduate students in the Accounting Study Program in the Faculty of Economics and Business at Surakarta Muhammadiyah University who had taken courses in Accounting Information Systems and Auditing. This study uses the convenience sampling method as the sampling technique. There were 110 respondents involved. In this study, the hypothesis is subjected to the multiple linear regression analysis.The results of this study show that the factors related to student academic fraud are mainly arrogance and opportunity. Other behavioral factors namely pressure, rationalization, competence, greed, need, and exposure have no effect on student academic fraud.