Reviewerly/exHarmony huggingface dataset:
Improving a Run Time Job Prediction Model for Distributed Computing Based on Two Level Predictions

Nowadays, distributed computing environment faces many difficulties because the number of submitted jobs is increasing dramatically. One of the most used method to serve the jobs is to find the accurate run time of the submitted jobs. This paper proposes a new job prediction method, to predict on jobs’ run time using two level prediction namely linear regression model and fitting model. The proposed model uses six variables including user ID, group ID, executable ID, number of CPUs, memory size and average CPU time, furthermore to solve the problem of the categorical variables (i.e. user ID, group ID and executable ID) a dummy code is used. To adjust and to find the best combination between linear regression model and fitting models, different fitting models are used by combining linear and nonlinear fitting models. By simulation the results show that the proposed model is better than previous models when smoothing spline fitting is used, also the results indicate that proposed model is efficient with low error and high prediction rate compared with previous models.
Generating Multimedia Storyline for Effective Disaster Information Awareness

Storyline generation has emerged to be an effective method to describe the evolution of disaster. However, due to the temporal-spatial, heterogeneous, and information overload, most of the existing storylines are only based on textual data and deliver limited information. In this paper, we introduce a novel framework for generating multimedia storylines to provide more concise and vivid information and deeper understanding of real-time events. We first adopt generative adversarial networks to implement an unsupervised bilingual document summarizing model. Then, we transform image and text incorporation problem into a multi-label learning problem and use convolutional neural networks to train a classification model. And finally, the bilingual documents and images are jointly summarized and embedded into a two-layer storyline generating framework. The experiments on real Hurricane data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.
Methodology of Teaching Graphic Methods for Solving Problems with Parameters as a Means to Achieve High Mathematics Learning Outcomes at School

The introduction of new standards of mathematical education requires to stop understanding of the learning process as the transfer of ready-made knowledge and experience. Educational activity built on the principle of self-construction of knowledge by schoolchildren is highly demanded in new environment. Tasks with parameters have high learning, development, research and diagnostic potential. It allows to identify and in the process of teaching mathematics to prepare students who possess subject knowledge at the highest level, corresponding to the trends of the time. The urgency of the problem under study is determined by the need for students to form the ability to solve problems with parameters in order to achieve high results in mathematical, intellectual and personal development. The aim of the research is to develop a methodology for teaching students how to solve problems with parameters as an effective means of high-quality mathematical studies. The authors have identified main methods for solving problems with parameters and approaches to their study, and proved the theoretical basis for the application of these methods in the learning process. Therefore, they have shown the role of the propaedeutic stage of teaching graphic methods for solving problems with parameters, its goals, objectives and content. The authors suggest a methodology for designing a system of tasks that contributes to achieving high learning outcomes, which has passed multi-stage approbation. Moreover, they prove the need to use the Live Mathematics software as an effective visualization tool for studying graphic methods for solving problems with parameters. The methodology described in the article can be used by teachers at school and extracurricular mathematics classes, by the authors of textbooks for students and teachers, and it can also be the basis for a special course for students of pedagogical universities.
Combining multi-resolution wavelets with principal component analysis for face recognition

The modern technological development in the field of communications and electronic systems has contributed to reducing the complexity of the application systems. This paper introduces an advanced face recognition technology, using multiresolution wavelet transform and principal component analysis (PCA). In this method, five levels of discrete wavelet transform (DWT) are used, where each level is subjected to one type of wavelet family. Then, all images are projected to the principal component domain to produce the training features array with further reduction in the processing data. The classifier of this method is the Euclidean distance, such that the minimum distance guides to know the index of anonymous person. The proposed method achieves 99.5% and 98.89% using the ORL and Yale datasets, respectively. Experimental results show that the proposed method outperforms other approaches that used the DWT-PCA technique.
Estimation of Solar Irradiance at Weather Stations in Korea Using Regionally Trained Artificial Neural Network Models

The ability to estimate solar irradiance is necessary for efficient installation and management of solar photovoltaic systems. Weather observation data and Communication, Ocean and Meteorological Satellite (COMS) images were used to estimate solar irradiance at 94 Korea Meteorological Administration (KMA) weather stations. First, Korea was divided into 13 regions by applying K-means clustering to cloud indices derived from visible channel images of COMS, and artificial neural network (ANN) models were trained in 10 regions. Ten variables were selected as inputs for ANN by mutual information feature selection to build multi-layer perceptron networks in MATLAB. The cross validation resulted in a 0.96 correlation coefficient and 80.87 W/m2 (22.62%) root mean square error (RMSE). The regionally trained ANN models could reduce the relative RMSE by 2%p compared to non-regionally trained models. The solar irradiances at the 94 KMA weather stations were estimated by applying these models. The solar irradiance of points that have no measured data from a pyranometer can be estimated using these regional ANN models.
High-capacity reversible data hiding in encrypted images based on two-phase histogram shifting

With the extensive use of cloud services in different applications, it's a problem for the cloud service provider to manage or process the privacy data that are encrypted by the content owner. Therefore, signal processing technology in the encrypted domain has attracted the attention of researchers. In this paper, we propose a new reversible data hiding method for encrypted images based on two-phase histogram shifting. In the proposed method, the original image is encrypted by using special image division and additive homomorphic encryption. After image encryption, the encrypted image can partially maintain spatial correlation for data embedding while the content security of the encrypted image is ensured. Due to the spatial correlation, the data hider can generate two difference histograms from the encrypted image, which provide high embedding capacity. A two-phase histogram shift scheme is used to embed the secret data into the two difference histograms. At the receiver side, the secret data can be extracted from the encrypted image or the decrypted image, and the image can be recovered to its original version without any error. The experimental results demonstrated that the proposed method can efficiently improve the capacity of data embedding and outperform other related methods, while the visual quality of the marked image can be maintained.
Risk Mitigation Techniques in Agile Development Processes

The main purpose of agile development methods is to reduce risks leading to a more successful and effective information system. In fact, analysing priorities, finding and identifying risks are important activities in all development approaches, including Agile development. However, some studies seek to assess risk management based on agile global software development (GSD) and provide mitigation measures to address specific risks. The risk mitigation technique for good development for sustainable development is expected to be designed to achieve time efficiency improvements to obtain greater resources at lower cost and thereby gain and maintain a competitive advantage.
E<scp>st</scp>I<scp>m</scp>A<scp>gg</scp>: A Learning Framework for Groupwise Aggregated Data

Aggregation is a common technique in data-driven applications for handling issues like privacy, scalability and reliability in a vast range of domains including healthcare, sensor networks and web applications.However, despite the ubiquitousness, extending machine learning methods to the aggregation context is unfortunately not well-studied.In this work, we consider the problem of learning individual level predictive models when the target variables used for training are only available as aggregates.In particular, this problem is a critical bottleneck in designing effective bidding strategies in the context of online advertising where groundtruth cost-per-click (CPC) data is aggregated before being released to advertisers.We introduce a novel learning framework that can use aggregates computed at varying levels of granularity for building individuallevel predictive models.We generalise our modelling and algorithmic framework to handle data from diverse domains, and extend our techniques to cover arbitrary aggregation paradigms like sliding windows and overlapping/non-uniform aggregation.We show empirical evidence for the efficacy of our techniques with experiments on both synthetic data and real data from the online advertising domain as well as healthcare to demonstrate the wider applicability of our framework.
Learning to Convolve: A Generalized Weight-Tying Approach

Recent work (Cohen & Welling, 2016a) has shown that generalizations of convolutions, based on group theory, provide powerful inductive biases for learning. In these generalizations, filters are not only translated but can also be rotated, flipped, etc. However, coming up with exact models of how to rotate a 3 x 3 filter on a square pixel-grid is difficult. In this paper, we learn how to transform filters for use in the group convolution, focussing on roto-translation. For this, we learn a filter basis and all rotated versions of that filter basis. Filters are then encoded by a set of rotation invariant coefficients. To rotate a filter, we switch the basis. We demonstrate we can produce feature maps with low sensitivity to input rotations, while achieving high performance on MNIST and CIFAR-10.
Lessons from Real-World Reinforcement Learning in a Customer Support Bot.

In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language processing and information retrieval we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges.
Fast Algorithms for Poisson Image Denoising Using Fractional-Order Total Variation

In this paper,Zhang, Jun a new Poisson image denoising model based on fractional-order total variation regularization is proposed.Ma, Mingxi To obtain its global optimal solution,Deng, Chengzhi the augmented Lagrangian method, the Chambolle’s dual algorithm and the primal-dual algorithm are introduced.Wu, Zhaoming Experimental results are supplied to demonstrate the effectiveness and efficiency of the proposed algorithms for solving our proposed model, with comparison to the total variation Poisson image denoising model.
A unified construction for series representations and finite approximations of completely random measures

Infinite-activity completely random measures (CRMs) have become important building blocks of complex Bayesian nonparametric models. They have been successfully used in various applications such as clustering, density estimation, latent feature models, survival analysis or network science. Popular infinite-activity CRMs include the (generalised) gamma process and the (stable) beta process. However, except in some specific cases, exact simulation or scalable inference with these models is challenging and finite-dimensional approximations are often considered. In this work, we propose a general and unified framework to derive both series representations and finite-dimensional approximations of CRMs. Our framework can be seen as a generalisation of constructions based on size-biased sampling of Poisson point process (Probab. Theory Related Fields 92 (1992) 21–39). It includes as special cases several known series representations and finite approximations as well as novel ones. In particular, we show that one can get novel series representations for the generalised gamma process and the stable beta process. We show how these constructions can be used to derive novel algorithms for posterior inference, including a generalisation of the slice sampler for normalised CRMs mixture models introduced by (J. Comput. Graph. Statist. 20 (2011) 241–259). We also provide some analysis of the truncation error.
Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning

An amendment to this paper has been published and can be accessed via a link at the top of the paper
The Key Factors of Evaluating Agile Approaches: A Systematic Literature Review

Currently, Agile Methods (AMs) are extensively being used in projects of various sizes and in different environments, thus surpassing their primary intended scope. For instance, they have been executed in distributed and non-distributed projects. In addition, AMs have been implemented in different project fields, such as engineering, medicine, banking, and manufacturing. Consequently, different Agile approaches have been proposed and integrated with other approaches in order to support the increased demand for diverse project environments. In this direction, various authors have examined the process of developing those approaches; however, the focus on explaining evaluation phases is scarce and scattered. Therefore, this study aims to review pertinent literature to identify the key factors and methodologies used to evaluate the proposed approaches in the Agile domain. The systematic literature review (SLR) methodology was adopted to identify, evaluate, and interpret all existing studies relevant to the research objective. SLR provides in-depth and more thorough results than an ordinary literature review. Forty-eight studies were selected and analyzed. The results show that applicability, effectiveness, and efficiency are the three most frequently examined evaluation factors, whereas case studies and surveys are the most frequently used research methods in evaluation studies. Factors identified in this review provide the evidence and the opportunity to design instruments or assessment forms that meet the needs of those researchers who are planning to evaluate their proposed Agile approaches.
Evaluation of the Role of <i>Society-Based Library</i> in Empowering Surabaya City People

The purpose of this research is to evaluate the role of society-based library or Taman Bacaan Masyarakat (TBM) in empowering Surabaya City people. Society-based library will be a place of Surabayans’ live-long learning according to the Law on Even Distribution of and Rights to Education. This research adopts a quantitative approach with qualitative data analysis. The sample of the research is fourteen TBMs that have an empowerment program or is near to a small and medium enterprise. Findings of the research include: (1) that TBM role in community empowerment is beginning to be noticeable in their empowerment programs such as trainings for local community, (2) that empowerment programs at TBM are conducted informally and intermittently, such as trainings of producing aromatherapy candles, beads, flannelette, and waste recycling, and (3) the TBM are constrained by shortage of collection and funding for programs. The research recommends that: community empowerment programs should be increased through TBM programs; TBM personnels should be encouraged to innovate; and collection development should be tailored with local needs.
Dead-ends and Secure Exploration in Reinforcement Learning

Many interesting applications of reinforcement learning (RL) involve MDPs that include many “dead-end” states. Upon reaching a dead-end state, the agent continues to interact with the environment in a dead-end trajectory before reaching a terminal state, but cannot collect any positive reward, regardless of whatever actions are chosen by the agent. The situation is even worse when existence of many dead-end states is coupled with distant positive rewards from any initial state (it is called Bridge Effect). Hence, conventional exploration techniques often incur prohibitively large training steps before convergence. To deal with the bridge effect, we propose a condition for exploration, called security. We next establish formal results that translate the security condition into the learning problem of an auxiliary value function. This new value function is used to cap “any” given exploration policy and is guaranteed to make it secure. As a special case, we use this theory and introduce secure random-walk. We next extend our results to the deep RL settings by identifying and addressing two main challenges that arise. Finally, we empirically compare secure random-walk with standard benchmarks in two sets of experiments including the Atari game of Montezuma’s Revenge.
Cryptanalysis of Server-Aided RSA Protocols with Private-Key Splitting

We analyze the security and the efficiency of interactive protocols where a client wants to delegate the computation of an RSA signature given a public key, a public message and the secret signing exponent. We consider several protocols where the secret exponent is splitted using some algebraic decomposition. We first provide an exhaustive analysis of the delegation protocols in which the client outsources a single RSA exponentiation to the server. We then revisit the security of the protocols RSA-S1 and RSA-S2 that were proposed by Matsumoto, Kato and Imai in 1988. We present an improved lattice-based attack on RSA-S1 and we propose a simple variant of this protocol that provides better efficiency for the same security level. Eventually, we present the first attacks on the protocol RSA-S2 that employs the Chinese Remainder Theorem to speed up the client's computation. The efficiency of our (heuristic) attacks has been validated experimentally.
Learning to Route in Similarity Graphs

Recently similarity graphs became the leading paradigm for efficient nearest neighbor search, outperforming traditional tree-based and LSH-based methods. Similarity graphs perform the search via greedy routing: a query traverses the graph and in each vertex moves to the adjacent vertex that is the closest to this query. In practice, similarity graphs are often susceptible to local minima, when queries do not reach its nearest neighbors, getting stuck in suboptimal vertices. In this paper we propose to learn the routing function that overcomes local minima via incorporating information about the graph global structure. In particular, we augment the vertices of a given graph with additional representations that are learned to provide the optimal routing from the start vertex to the query nearest neighbor. By thorough experiments, we demonstrate that the proposed learnable routing successfully diminishes the local minima problem and significantly improves the overall search performance.
A New Neighborhood-Based Outlier Detection Technique

Outlier detection is one of the most vital and essential issues in data mining tasks. We propose a new method to detect and analyze outliers. We apply neighborhood-based outlier detection technique to detect and analyze the outliers. Using weights of the neighbors of each data and a unique parameter OBN is used to identify the outlier. Our proposed algorithm is tested on real datasets and compared with the existing technique and the results are presented.
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning

In importance sampling (IS)-based reinforcement learning algorithms such as Proximal Policy Optimization (PPO), IS weights are typically clipped to avoid large variance in learning. However, policy update from clipped statistics induces large bias in tasks with high action dimensions, and bias from clipping makes it difficult to reuse old samples with large IS weights. In this paper, we consider PPO, a representative on-policy algorithm, and propose its improvement by dimension-wise IS weight clipping which separately clips the IS weight of each action dimension to avoid large bias and adaptively controls the IS weight to bound policy update from the current policy. This new technique enables efficient learning for high action-dimensional tasks and reusing of old samples like in off-policy learning to increase the sample efficiency. Numerical results show that the proposed new algorithm outperforms PPO and other RL algorithms in various Open AI Gym tasks.
Development of a Virtual Reality-Based Game Approach for Supporting Sensory Processing Disorders Treatment

Serious games for health are those that are not aimed solely at entertainment, but rather at supporting health treatments. In this paper, we develop and assess the proposal of a Virtual Reality (VR) game aimed at supporting Sensory Processing Disorder (SPD) treatment. SPD is a condition which affects the integration and processing of the various stimuli coming from inside and outside of the body and its treatment involves providing patients with controlled sensory stimuli. Our goal is to investigate whether a Virtual Reality game that can stimulate different sensory systems could be useful in SPD treatment. In order to do so, we have designed and developed Imaginator, a VR Roller Coaster Game connected with a Head Mounted-Display (HMD) which can be customized by therapists to adjust its stimuli to different patients’ needs in therapy. Imaginator was assessed by five occupational therapists through its use in their treatment sessions. Each therapist had the game available in their therapy environment for around 2–3 weeks. They were instructed to use the game whenever they thought it could be interesting to a patient’s treatment. After the trial period, they were interviewed about their experience in using the game and their perception of its impact in therapy sessions. Our results show that therapists in general perceived Imaginator as having a positive effects in patients’ treatment. They noticed that it was able to stimulate different senses, and they reported that in some cases it was able to help patients relax, increase their concentration and even work as an encouragement to explore similar activities in the physical world. There were a few patients who experienced some adverse reactions, such as nausea, but nothing that they felt was worrying or that could prevent the game from being used.