Reviewerly/exHarmony huggingface dataset:
Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster

In the past decade, we have witnessed a dramatically increasing volume of data collected from various sources. To maximize utilization, various machine and deep learning models have been developed to study data. While data-driven applications improve countless products, hyperparameter tuning for the models is still a time-consuming and resource-intensive process. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers create an isolated virtual environment for clients who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this article, we propose SpeCon, a novel container scheduler optimized for short-lived deep learning applications. Based on virtualized containers, such as Kubernetes and Docker, SpeCon analyzes the typical characteristics of training processes. We design a suite of algorithms to monitor the training’s progress and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves an individual job’s completion time by up to 41.5%, 14.8% system-wide, and 24.7% in terms of makespan.
Reliable experimental certification of one-way Einstein-Podolsky-Rosen steering

Quantum steering is a recently defined form of quantum correlation which lies at the heart of quantum mechanics. In contrast to other types of quantum correlations, quantum steering is inherently asymmetric, which implies that it could manifest in one direction but not in the opposite direction. This rather peculiar phenomenon, known as one-way steering, has been demonstrated in several experiments, but subtlety remains. In fact, all experiments were shown to be ambiguous until a very recent conclusive one, which, however, made crucial use of a high-dimensional embedding to get around assumptions. This leaves the question open of whether the one-way steering phenomenon can be reliably demonstrated in the genuine two-qubit system. Here, we report such an experimental demonstration of one-way steering for a family of two-qubit states. Our experimental setup and results thus resolve the subtlety caused by fidelity assumption in previous experiments without the need for higher dimensional embedding. Moreover, our work provides a universal method to characterize one-way steering phenomenon for generic two-qubit states.
An Adaptive Double-Layered Embedding Scheme for MP3 Steganography

In this letter, we devise an adaptive double-layered embedding scheme that is suitable for MP3 steganography. According to the encoding characteristics of MP3, the linbits are used as the embedding domain. The proposed scheme is divided into two layers and the messages are embedded into each layer with binary STCs. The cost function used in the first layer is designed by employing the masking effect to achieve optimal imperceptibility. In order to reduce the modification of coefficients, the cost function for the second layer is revised according to the embedding results of the first layer. Experiments demonstrate that our scheme is able to achieve better acoustic concealment and higher embedding modifying efficiency indeed. In addition, our scheme can well resist the attack from the statistical handcrafted analysis methods.
Analysis of Digital Forensic Tools

This paper aims to analyze different tools for Forensic Data Analysis comes under the branch of Digital Forensics. Forensic data analysis is done with digital techniques. Digital forensics becomes more important in law enforcement, due to the large use of computers and mobile devices. The pattern recognition system most appropriately fits into the Analysis Phase of the Digital Forensics. Pattern Recognition involves two processes. One Process is an analysis and the second process is recognition. The result of the analysis is taken out of the attributes from the patterns to be recognized i.e., a pattern of different faces and fingerprints. These attributes are then utilized for the further process in the analysis phase which provides attention on various techniques of pattern recognition that are applied to digital forensic examinations and is proposed to develop different forensic tools to collect evidence that would be helpful to solve specific types of crimes. This evidence further helps the examiner in the analysis phase of the digital forensic process by identifying the applicable data.
One to Many QKD Network System Using Polarization-Wavelength Division Multiplexing

The quantum key distribution (QKD) research, which is drawing attention as the next secure communication, is actively expanding from point to point system to network architecture.In QKD network system, it is important to increase the number of users who can securely communicate.Up to date, a wavelength division multiplexing (WDM) architecture has successfully expanded the number of channels without significant system loss, but there is a limitation of increasing channels considering the range of telecommunication wavelength and crosstalk noise, etc.In this article, we propose a polarizing division method that increases user channels independently of wavelengths.The proposed architecture can increase the number of wavelength multiplexed channels by a multiple of the polarization number.We identify the issues in the QKD network system that may occur when using polarization and wavelengths simultaneously, then provide solutions and optimize the system operation accordingly.Finally, we describe a field test result of a one to many QKD network system that shows successful key exchange with 3% QBER.
Natural Language Word Prediction Model Based on Multi-Window Convolution and Residual Network

In this paper, we propose MCNN-ReMGU model based on multi-window convolution and residual-connected minimal gated unit (MGU) network for the natural language word prediction. First, the convolution kernels with different sizes are used to extract the local feature information of different graininess between the word sequences. Then, the extracted features are fed to the residual-connected MGU network. Finally, the prediction results are output by the SoftMax layer. Through the residual-connection processing of MGU network in the model, not only the problems of vanishing gradient and network degradation are effectively solved, but also the long-term dependence between word sequences is effectively extracted to predict the next word accurately. Meanwhile, the introduction of the convolution kernel in a convolutional neural network (CNN) enables the feature information between word sequences to be extracted more fully. The experimental results on the Penn Treebank and WikiText-2 datasets show that the proposed method has certain advantages in the word prediction task.
Automatic Release Notes Generation

Release Notes (RNs) are one of the important artifacts in software development and maintenance. As, RNs are required when a new release of a software is planned to deploy. They contain all the changes made to the new release of project i.e. description of new features, improvements, bug fixes, deprecated features, etc. Generating these notes manually is a very complex, and time-consuming task. In this paper, we present an approach for generating RNs automatically. We implemented the approach in python and generate these notes for node.js projects. Our system extracts changes from Git repository, summarize changes, get deprecated features, get library changes, fetch issues from issue tracker, and link these issues to code, etc. Our system hierarchically set up these changes and produce an output in a document. We evaluated our results manually from 14 industry developers. The results obtained from our system shows that these RNs are very good and accurate than ones always produced manually.
State of the art document clustering algorithms based on semantic similarity

The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures.
Secure Prediction of Neural Network in the Cloud

Neural networks have attracted much attention due to their excellent performance in providing insightful predictions. The trained neural network models usually have millions of parameters requiring massive storage resources, which motivates the model owner to deploy their models to cloud servers for relieving the storage burden. The client can also directly enjoy the prediction service provided by the cloud server. Albeit convenient, untrusted cloud servers may violate the privacy of the model owners and the clients, which hinders the wide applications of such a prediction service. This survey reviews various privacy-preserving neural network prediction services, which protect the privacy of the model and the query. Many protocols are in the basic secure two-party computation (S2C) setting, which protects the secret of the querier and the model owner against the counterparty. Secure outsourcing further protects the privacy of the model against the cloud hosting it for the prediction service. We compare the existing approaches in terms of security, accuracy, and efficiency. We then propose an optimized neural network prediction scheme in the outsourcing setting, which simultaneously achieves high accuracy, model privacy, and low overheads, and conduct an experimental evaluation for computation time and communication costs. Finally, we highlight several future research directions and provide new insights into open problems in strengthening security and improving efficiency.
PERAN PEMERINTAH DAERAH DALAM PENANGGULANGAN BENCANA

Natural disaster keeps occurring in Indonesia: earthquake, landslide, flood, forest fire, even drought. The effects are not only material loss, but also death and ecosystem loss. This writing would discover government role in disaster handling and the obstacles. Analysis was based on several researches with similar topic. Therefore, the study applied desk research technique. Based on the result, it was found that government has shown their role in disaster handling through 5 (five) aspects: legislative, agency, planning, financial, and capacity development. In this case, government has taken strategic attempt to deal with natural disaster risk and effects by building BNPB in central level and BPBD in regional level. This non-department agency has become a leading sector in disaster handling. It was also known that almost every region face similar obstacle in disaster handling: limited budget, human resource, tools and equipment; and lack of inter-sector coordination&#x0D; Keywords: Government Role; Disaster Management
Novel Architectures for Unsupervised Information Bottleneck Based Speaker Diarization of Meetings

Speaker diarization is an important problem that is topical, and is especially useful as a preprocessor for conversational speech related applications. The objective of this article is two-fold: (i) segment initialization by uniformly distributing speaker information across the initial segments, and (ii) incorporating speaker discriminative features within the unsupervised diarization framework. In the first part of the work, a varying length segment initialization technique for Information Bottleneck (IB) based speaker diarization system using phoneme rate as the side information is proposed. This initialization distributes speaker information uniformly across the segments and provides a better starting point for IB based clustering. In the second part of the work, we present a Two-Pass Information Bottleneck (TPIB) based speaker diarization system that incorporates speaker discriminative features during the process of diarization. The TPIB based speaker diarization system has shown improvement over the baseline IB based system. During the first pass of the TPIB system, a coarse segmentation is performed using IB based clustering. The alignments obtained are used to generate speaker discriminative features using a shallow feed-forward neural network and linear discriminant analysis. The discriminative features obtained are used in the second pass to obtain the final speaker boundaries. In the final part of the paper, variable segment initialization is combined with the TPIB framework. This leverages the advantages of better segment initialization and speaker discriminative features that results in an additional improvement in performance. An evaluation on standard meeting datasets shows that a significant absolute improvement of 3.9% and 4.7% is obtained on the NIST and AMI datasets, respectively.
Empirical Evaluation of the Effect of Optimization and Regularization Techniques on the Generalization Performance of Deep Convolutional Neural Network

The main goal of any classification or regression task is to obtain a model that will generalize well on new, previously unseen data. Due to the recent rise of deep learning and many state-of-the-art results obtained with deep models, deep learning architectures have become one of the most used model architectures nowadays. To generalize well, a deep model needs to learn the training data well without overfitting. The latter implies a correlation of deep model optimization and regularization with generalization performance. In this work, we explore the effect of the used optimization algorithm and regularization techniques on the final generalization performance of the model with convolutional neural network (CNN) architecture widely used in the field of computer vision. We give a detailed overview of optimization and regularization techniques with a comparative analysis of their performance with three CNNs on the CIFAR-10 and Fashion-MNIST image datasets.
Characteristics of modern adolescents’ digital reading: Results of theoretical and empirical research

Introduction. This article investigates the relationship between digital and paper reading within modern adolescents’ reading activities. The purpose of the study is to reveal the characteristics of adolescents’ digital reading compared to paper reading. Materials and Methods. The study involved theoretical analysis and consolidation of research findings on digital reading. Empirical data were collected via questionnaires, the focus group method, expert evaluation and structured interviews. Quantitative and qualitative analyses of the obtained data were carried out. Participants were 1835 schoolchildren in grades 5-8 (aged between 11 and 15 years) from 14 regions of the Russian Federation. Results. The authors summarized the findings of the theoretical and empirical study of adolescents’ digital reading compared to paper reading. In order to identify the specifics of modern adolescents’ digital reading, ‘Paper vs screen (reading from paper and reading from screens)’ questionnaire has been developed and empirically verified. The authors analyzed the content of students’ online reading, its range and intensity, reading preferences regarding the environment (analog/digital) and the medium (the type of device). Conclusions. The article concludes that at present adolescents’ reading activities are characterized by the cohesion of traditional and new reading practices. Nevertheless, paper reading remains the dominant format of book reading. Characteristics of modern adolescents’ digital reading include spontaneity, pragmatism, and situatedness. The findings suggest that digital reading preferences include informative and entertaining texts related to the field of everyday communication. Purposeful teaching of digital reading at school is highlighted as an important factor enhancing the effectiveness of reading performance.
Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

The amount of data that is traveling across the internet today, including very large and complex set of raw facts that are not only large, but also, complex, noisy, heterogeneous, and longitudinal data as well.Companies, institutions, healthcare system, mobile application capturing devices and sensors, traffic management, banking, retail, education etc., use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer.Recently, Big data is one of the most important topics in IT industry.Managing Big data needs new techniques because traditional security and privacy mechanisms are inadequate and unable to manage complex distributed computing for different types of data.New types of data have different and new challenges also.A lot of researches treat with big data challenges starting from Doug Laney's landmark paper, during the previous two decades; the big challenge is how to operate a huge volume of data that has to be securely delivered through the internet and reach its destination intact.The present paper highlights important concepts of Fifty-six Big Data V's characteristics.This paper also highlights the security and privacy Challenges that Big Data faces and solving this problem by proposed technological solutions that help us avoiding these challenging problems.
Decoherence of up to 8-qubit entangled states in a 16-qubit superconducting quantum processor

We report on the coherence of Greenberger–Horne–Zeilinger (GHZ) states comprised of up to 8 qubits in the IBM ibmqx5 16-qubit quantum processor. In particular, we evaluate the coherence of GHZ states with N = 1, ..., 8 qubits4, as a function of a delay time between state creation and measurement. We find that the decay in coherence occurs at a rate that is linear in the number of qubits. This is consistent with a model in which the dominant noise affecting the system is uncorrelated across qubits.
Towards NNGP-guided Neural Architecture Search

The predictions of wide Bayesian neural networks are described by a Gaussian process, known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels are known for many models, but computing the exact kernel for convolutional architectures is prohibitively expensive. One can obtain effective approximations of these kernels through Monte-Carlo estimation using finite networks at initialization. Monte-Carlo NNGP inference is orders-of-magnitude cheaper in FLOPs compared to gradient descent training when the dataset size is small. Since NNGP inference provides a cheap measure of performance of a network architecture, we investigate its potential as a signal for neural architecture search (NAS). We compute the NNGP performance of approximately 423k networks in the NAS-bench 101 dataset on CIFAR-10 and compare its utility against conventional performance measures obtained by shortened gradient-based training. We carry out a similar analysis on 10k randomly sampled networks in the mobile neural architecture search (MNAS) space for ImageNet. We discover comparative advantages of NNGP-based metrics, and discuss potential applications. In particular, we propose that NNGP performance is an inexpensive signal independent of metrics obtained from training that can either be used for reducing big search spaces, or improving training-based performance measures.
Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

Topic models have been widely used to discover hidden topics in a collection of documents.In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships.While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.
Knowledge discovery through text-based similarity searches for astronomy literature

The increase in the number of researchers coupled with the ease of publishing and distribution of scientific papers (due to technological advancements) has resulted in a dramatic increase in astronomy literature. This has likely led to the predicament that the body of the literature is too large for traditional human consumption and that related and crucial knowledge is not discovered by researchers. In addition to the increased production of astronomical literature, recent decades have also brought several advancements in computational linguistics. Especially, the machine-aided processing of literature dissemination might make it possible to convert this stream of papers into a coherent knowledge set. In this paper, we present the application of computational linguistics techniques to astronomy literature. In particular, we developed a tool that will find similar articles purely based on text content f rom an input paper. We find that our technique performs robustly in comparison with other tools recommending articles given a reference paper (known as recommender system). Our novel tool shows great power in combining computational linguistics with astronomy literature and suggests that additional research in this endeavor will likely produce even better tools that will help researchers cope with vast amounts of knowledge being produced.
Optimal control theory for quantum electrodynamics: an initial state problem

In conventional quantum optimal control theory, the parameters that determine an external field are optimised to maximise some predefined function of the trajectory, or of the final state, of a matter system. The situation changes in the case of quantum electrodynamics, where the degrees of freedom of the radiation field are now part of the system. In consequence, instead of optimising an external field, the optimal control question turns into a optimisation problem for the many-body initial state of the combined matter-photon system. In the present work, we develop such a optimal control theory for quantum electrodynamics. We derive the equation that provides the gradient of the target function, which is often the occupation of some given state or subspace, with respect to the control variables that define the initial state. We choose the well-known Dicke model to study the possibilities of this technique. In the weak coupling regime, we find that Dicke states are the optimal matter states to reach Fock number states of the cavity mode with large fidelity, and vice versa, that Fock number states of the photon modes are the optimal states to reach the Dicke states. This picture does not prevail in the strong coupling regime. We have also considered the extended case with more than one mode. In this case, we find that increasing the number of two-level systems allows to reach a larger occupation of entangled photon targets.