Reviewerly/exHarmony huggingface dataset:
Adaptive Sampling for Best Policy Identification in Markov Decision Processes

We investigate the problem of best-policy identification in discounted Markov Decision Processes (MDPs) when the learner has access to a generative model. The objective is to devise a learning algorithm returning the best policy as early as possible. We first derive a problem-specific lower bound of the sample complexity satisfied by any learning algorithm. This lower bound corresponds to an optimal sample allocation that solves a non-convex program, and hence, is hard to exploit in the design of efficient algorithms. We then provide a simple and tight upper bound of the sample complexity lower bound, whose corresponding nearly-optimal sample allocation becomes explicit. The upper bound depends on specific functionals of the MDP such as the sub-optimality gaps and the variance of the next-state value function, and thus really captures the hardness of the MDP. Finally, we devise KLB-TS (KL Ball Track-and-Stop), an algorithm tracking this nearly-optimal allocation, and provide asymptotic guarantees for its sample complexity (both almost surely and in expectation). The advantages of KLB-TS against state-of-the-art algorithms are discussed and illustrated numerically.
Multi-Style Unsupervised Image Synthesis Using Generative Adversarial Nets

Unsupervised cross-domain image-to-image translation is a very active topic in computer vision and graphics. This task has two challenges: 1) lack of paired training data and 2) numerous possible outputs from a single image. The existing methods rely on either paired data or perform one-to-one translation. A novel Multi-Style Unsupervised image synthesis model using Generative Adversarial Nets (MSU-GAN) is proposed in this paper to overcome these disadvantages. Firstly, the encoder-decoder structure is used to map the image to domain-shared content features space and domain-specific style features space. Secondly, to translate an image into another domain, the content code and the style code are combined to synthesize the resulting image. Finally, the bidirectional cycle-consistency loss is used for the unpaired training data; the inter-domain adversarial loss and the reconstruction loss are used to ensure the output image's realism. Simultaneously, MSU-GAN is able to synthesize multi-style images due to disentangled representation. A Multi-Style Unsupervised Feature-Wise image synthesis model using Generative Adversarial Nets (MSU-FW-GAN) based on the MSU-GAN is proposed for the shape variation tasks. There are two different testing strategies, which include random style transfer and style guide transfer. For objective comparison, the proposed model performs well on all evaluation metrics. The random style transfer experiment results show that compared with CycleGAN on the photo2portraits dataset, MSU-FW-GAN FID, IS scores dropped by 12.77% and 8.06%. For the summer2winter dataset, MSU-GAN FID and IS scores increased by 24.51% and 3.64%. Qualitative results show that without paired training data, MSU-GAN and MSU-FW-GAN can synthesize multi-style and better realistic images on various tasks.
Multi-label Prediction in Time Series Data using Deep Neural Networks

This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. While fault (event) detection problems have been thoroughly studied in literature, most of the state-of-the-art techniques can’t reliably predict faults (events) over a desired future horizon. In the most general setting of these types of problems, one or more samples of data across multiple time series can be assigned several concurrent fault labels from a finite, known set and the task is to predict the possibility of fault occurrence over a desired time horizon. This type of problem is usually accompanied by strong class imbalances where some classes are represented by only a few samples. Importantly, in many applications of the problem such as fault prediction and predictive maintenance, it is exactly these rare classes that are of most interest. To address the problem, this paper proposes a general approach that utilizes a multi-label recurrent neural network with a new cost function that accentuates learning in the imbalanced classes. The proposed algorithm is tested on two public benchmark datasets: an industrial plant datasetfrom the PHM Society Data Challenge, and a human activity recognition dataset. The results are compared with state-ofthe-art techniques for time-series classification and evaluation is performed using the F1-score, precision and recall.
The Concept and Future of Intelligent Personal Assistant Based on Artificial Autonomy for Gamers in Indonesia

Online games in Indonesia have existed since the 1990s and the internet development in the world. These online games have emerged into eSports, a now-vast growing industry in Indonesia. The government acknowledged two annual eSport tournaments, the eSport President Cup Tournament and the ESport Minister of Youth and Sports Affairs Cup Tournament. Online game users may be affected by addiction, overwhelming playing time, habit, sitting position and vision range, game console interaction, age factor, and gender. Intelligent Personal Assistant (IPA) offers some ways for gamer management. Artificial Autonomy-based IPA provides some solutions to minimize or even to avoid the negative impact of those problems. This study suggests The Concept of Intelligent Personal Assistants based on Artificial Autonomy for Gamers in Indonesia, which is systematically designed to identify of IPA technology for gamers and eSport athletes. We did some systematic analysis of relevant articles to build a concept and requirements in further IPA development. We found that IPA taxonomy is needed to be elaborately explained in detail from technology needs, data training experts, platforms, and Artificial Autonomy implementation. The taxonomy and research result may answer general and specific questions on the minimum requirement for IPA development..
Multilingual Approach to Word Sense Disambiguation Using word2vec and Sense Embedding With Semantic Cloud

Word Sense Disambiguation(WSD) is an open problem in the area of Natural language processing. Many WSD systems uses knowledge based unsupervised approach as it bares us from using tagged corpus. But this approach depends extensively on knowledge. Enhancing the knowledge for ambiguity resolution is one of the important issues in WSD. Although Multilingual lexical resource is an asset for knowledge based systems(KBS), many KBS have not yield satisfactory results due to lack of sufficient information required for disambiguation. Multilingual information can help boost the performance of WSD in various languages. Combining lexical semantic information from different languages helps improve WSD task. We make use of Babel Net, a huge multilingual resource provider to attain the task and design a semantic word cloud to provide high performance disambiguation by imposing additional knowledge.
Top 10 Key Skills in Olympiad in Informatics

This article presents the methodological experience of 30 years of work on the preparation of International Olympiad in Informatics (IOI) medalists from Russia, based on the great contribution of the IOI community to the development of the syllabus IOI, criteria for the success of students and the collection of IOI tasks for 30 years of its implementation.Based on the experience of the authors of the article on the preparation in Russia of gold medalists IOI and the curriculum of school informatics are presented 10 IOI key skills as criteria for the medal success of students at IOI.
A Computational Model for Interactive Transcription

Transcribing low resource languages can be challenging in the absence of a comprehensive lexicon and proficient transcribers.Accordingly, we seek a way to enable interactive transcription, whereby the machine amplifies human efforts.This paper presents a computational model for interactive transcription, supporting multiple modes of interactivity and increasing the likelihood of finding tasks that stimulate local participation.The approach also supports other applications which are useful in low resource contexts, including spoken document retrieval and language learning.
Transfer-learning analysis for sign language classification models

Alphabet sign language recognition with high accuracy is a tedious task in computer vision due to several reasons like the lack of sufficient quantities of the annotated dataset, signer variety, continuous signing, etc. The relative scarcity of labelled data in the sign language recognition field has impeded the exploitation of the different models. For the above-mentioned problems, the researchers perform other augmentation techniques. In this paper, we evaluate four different transfer learning approach on three publically available datasets. The various transfer learning models explore the influence of different data augmentation techniques that should increase the performance of the classification model. All the four model makes a comparison with data augmentation and without data augmentation. This study suggested that the transfer learning can improve the classification accuracy.
Graying the cyborg revisited : How age matters when technologies move under the skin

Abstract : Feminist scholars were among the first to argue for the importance of including ageing and later life in STS research. Remarkably, most studies on technoscience and ageing only address technologies external to bodies. Although this scholarship has provided important insights into the many different ways in which technologies intersect with ageing I suggest that it is important to expand our analyses to technologies inside bodies. How can we understand ageing and agency in times when technologies become increasingly implanted in our bodies? In this article I will present three conceptual approaches that correspond to growing discussions at the cross-roads of STS, age and disability studies, and feminist scholarship, including cyborg theory, constructivist perspectives on vulnerability and resilience, and intersectional approaches. I will build on some of my previous theoretical and empirical work on pacemakers and implantable cardioverter-defibrillators to discuss how age matters when technologies move under the skin. Keywords : technology and ageing; graying the cyborg; technologies inside bodies; pacemakers and cardioverter-defibrillators; vulnerability and resilience.
Subcontinental lithospheric and asthenospheric metasomatism in the region of Jagersfontein, South Africa

The Cretaceous Jagersfontein kimberlite is located close to the edge of the Kaapvaal craton, displaced from the Kimberley cluster in the interior of the craton.Historically, the diatreme is of interest because it was at Jagersfontein that the association between diamonds and host rock was first established.Mineralogically, it is one of four localities at which high-pressure alkali titanates of the crichtonite series (lindsleyite-mathiasite) have been recognized along with Nb-Cr-rutile and armalcolite (Haggerty et al.,1983).In addition to the metasomatic oxides, metasomatic silicates, most notably amphiboles and phlogopites, have been identified in peridotite nodules recovered from the diatreme (Johnston, 1983^ Harte and Gurney, 1980).The chemistries and textures of these silicates, major targets of this study, can be used to model metasomatic events in asthenospheric and lithospheric peridotites in the upper mantle beneath Jagersfontein.The position of this kimberlite with respect to the Kimberley cluster permits a comparison of metasomatism at the cratonic ec^ with interior-craton metasomatism.Jagersfontein is, therefore, a potentially important region for modelling the geometry of the subcontinental lithosphere.
Kinerja Tenaga Administrasi Berbasis Teknologi Informasi dan Komunikasi Dalam Meningkatkan Mutu Pelayanan di SMA Negeri 11 Kota Jambi

Information and Communication Technology (ICT) has directly contributed greatly in realizing the quality of service performance for school administrative personnel. The contribution of ICT in improving service quality is; (1) with ICT work is done on time, so that services can be better, (2) more efficient use of time and minimizes errors, (3) ICT-based school administration is very helpful in compiling dapodic data and school archives, (4) assists in filling in the report card (e-report card) and so on. This research was conducted at SMA Negeri 11 Jambi City, Kel. Bagan Pete, Kec. Alam Barajo, Jambi City. This study focuses more on the performance of the school administration in the student sector. The data collection method uses observation, interview and documentation techniques. Data analysis techniques using qualitative analysis. The steps in data analysis techniques include; data reduction, data display and verification.
Monitoring Data in <i>R</i> with the <b>lumberjack</b> Package

Monitoring data while it is processed and transformed can yield detailed insight into the dynamics of a (running) production system. The lumberjack package is a lightweight package allowing users to follow how an R object is transformed as it is manipulated by R code. The package abstracts all logging code from the user, who only needs to specify which objects are logged and what information should be logged. A few default loggers are included with the package but the package is extensible through user-defined logger objects.
Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm

In this paper, we provide finite-sample convergence guarantees for an off-policy variant of the natural actor-critic (NAC) algorithm based on Importance Sampling. In particular, we show that the algorithm converges to a global optimal policy with a sample complexity of $\mathcal{O}(\epsilon^{-3}\log^2(1/\epsilon))$ under an appropriate choice of stepsizes. In order to overcome the issue of large variance due to Importance Sampling, we propose the $Q$-trace algorithm for the critic, which is inspired by the V-trace algorithm \cite{espeholt2018impala}. This enables us to explicitly control the bias and variance, and characterize the trade-off between them. As an advantage of off-policy sampling, a major feature of our result is that we do not need any additional assumptions, beyond the ergodicity of the Markov chain induced by the behavior policy.
Software Defects Rules Discovery

Developing highly qualitative software systems is important but at the same time difficult to be achieved due to a constant increase in size and complexity of the systems. Early detection of software defects becomes a must, various methods being researched. The Software Defects Rules Discovery (SDRD) approach aims to address the generation of rules for software defects. The model is based on code metric values and uses the ant colony system algorithm to discover the best solution. Performed experiments considered one theoretical example and two open-source projects with various setups, in total 22 different configurations were investigated. The obtained results indicated that from the set of considered metrics the ones that provide the best rules are: CBO (Coupling Between Objects), LOC (Lines Of Code), and NPM (Number of Private Methods).
Black-box Fuzzing Approaches to Secure Web Applications: Survey

Web applications are increasingly important tools in our modern daily lives, such as in education, business transactions, and social media.Because of their prevalence, they are becoming more susceptible to different types of attacks that exploit security vulnerabilities.Exploiting these vulnerabilities may cause damage to the web applications as well as the endusers.Thus, web apps' developers should identify vulnerabilities and fix them before an attacker exploits them.Using blackbox fuzzing techniques for vulnerability identification is very popular during the web apps' development life cycle.These techniques pledge to find vulnerabilities in web applications by constructing attacks without accessing their source codes.This survey explores the research that has been done in the black-box vulnerability finding and exploits construction in web applications and proposes future directions.
RRL: Resnet as representation for Reinforcement Learning

The ability to autonomously learn behaviors via direct interactions in uninstrumented environments can lead to generalist robots capable of enhancing productivity or providing care in unstructured settings like homes. Such uninstrumented settings warrant operations only using the robot's proprioceptive sensor such as onboard cameras, joint encoders, etc which can be challenging for policy learning owing to the high dimensionality and partial observability issues. We propose RRL: Resnet as representation for Reinforcement Learning -- a straightforward yet effective approach that can learn complex behaviors directly from proprioceptive inputs. RRL fuses features extracted from pre-trained Resnet into the standard reinforcement learning pipeline and delivers results comparable to learning directly from the state. In a simulated dexterous manipulation benchmark, where the state of the art methods fail to make significant progress, RRL delivers contact rich behaviors. The appeal of RRL lies in its simplicity in bringing together progress from the fields of Representation Learning, Imitation Learning, and Reinforcement Learning. Its effectiveness in learning behaviors directly from visual inputs with performance and sample efficiency matching learning directly from the state, even in complex high dimensional domains, is far from obvious.
Covariate Shift Adaptation for Adversarially Robust Classifier

We show that adaptive batch normalization (BN) technique that involves re-estimating the BN parameters during inference, can significantly improve the robustness of adversarially trained models for any random perturbations, including the Gaussian noise. This simple finding enables us to transform an adversarially trained model into a randomized smoothing classifier to provide certified robustness for l2 norm. Moreover, we achieve l2 certified robustness even for adversarially trained models, learned using l∞-bounded adversaries. Further, adaptive BN significantly improves robustness against common corruptions, without any detrimental effect on their performance against adversarial attacks. This enables us to achieve both adversarial and corruption robustness using the same classifier.
Classification of Fish Species with Image Data Using K-Nearest Neighbor

Abstract— Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.
Blockchain Smart Contract Technology in Urban Energy Management System

Blockchain technology is considered a disruptive technology with real poten-tial for change after steam engines, electricity, and the internet. The regionalelectricity sales model based on blockchain technology will become anessential breakthrough in innovative city energy management. The thrust-less ingenious contract mechanism, the weakly centralized intelligent powerenergy micro-grid internal competitive energy business model, is based onblockchain technology. Its physical structure includes multiple levels suchas the energy use layer, control layer, and functional layer. All transactionentities of the innovative power energy microgrid have an equal status in theblockchain system. The transaction energy includes cold, heat, electricity,gas, etc., and is distributed after bidding through energy routers and com-petitive trading platforms. This model uses multi-party bidding to optimizethe allocation of resources such as cold, heat, and electricity, restores thecommodity and financial attributes of energy and dramatically improves theenergy efficiency and economic benefits of the power energy microgrid.
Mediating Role of Employee Retention on the Relationship between Employee Engagement and Employee Performance: An Empirical Study

Managing for employee retention involves strategic actions to keep employees motivated and focused so they elect to remain employed and fully productive for the benefit of the organization. A comprehensive employee retention program can play a vital role in both attracting and retaining key employees, as well as in reducing turnover and its related costs. All of these contribute to an organization's productivity and overall business performance. The other objective is to analyze the critical factor which can affect the level of retention & engagement of employees with the help of literature review. For the current article the researchers reviewed 30 relevant research papers/ literature comprising employee retention& engagement concepts and practices amply. The findings of the study like good training & development, compensation structures, autonomy, quality of work life, work polices and arrangements will lead the managers and management to a new dimension with holistic approach in the field of employee retention & engagement,