Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Large-Scale Cluster Workload Traces via Multiview Analysis

Understanding the characteristics of workloads of the large-scale Clusters is the key to diagnose the system bottlenecks, making optimal configuration decisions, improving the system throughput and resource usage. Due to the diversity and multiview of the workload traces, featuring the good designs and bottlenecks by analyzing the workloads under realworld scenarios becomes increasingly challenging. This paper introduces a multiview based trace comparative analysis method by comparatively characterizing how the architecture, jobs, tasks, machines, and resources usage were managed among different platforms. A case study is performed which verified the effectiveness of our method by comparatively analyzing two most representative big traces: Google trace and Alibaba 2018 trace. Quantitative findings, together with the performance bottleneck inferences and suggestions are also presented. To the best of our knowledge, we are the first to perform such comparative empirical study on these two traces using a multiview based approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, IT practitioners and users, but also can promote more researches on big trace data comparative analysis in large-scale clusters.
Crowdsourcing evaluation of the quality of automatically generated questions for supporting computer-assisted language teaching

Abstract How can state-of-the-art computational linguistic technology reduce the workload and increase the efficiency of language teachers? To address this question, we combine insights from research in second language acquisition and computational linguistics to automatically generate text-based questions to a given text. The questions are designed to draw the learner’s attention to target linguistic forms – phrasal verbs, in this particular case – by requiring them to use the forms or their paraphrases in the answer. Such questions help learners create form-meaning connections and are well suited for both practice and testing. We discuss the generation of a novel type of question combining a wh - question with a gapped sentence, and report the results of two crowdsourcing evaluation studies investigating how well automatically generated questions compare to those written by a language teacher. The first study compares our system output to gold standard human-written questions via crowdsourcing rating. An equivalence test shows that automatically generated questions are comparable to human-written ones. The second crowdsourcing study investigates two types of questions ( wh- questions with and without a gapped sentence), their perceived quality, and the responses they elicit. Finally, we discuss the challenges and limitations of creating and evaluating question-generation systems for language learners.
A Chaotic Boltzmann Machine Working as a Reservoir and Its Analog VLSI Implementation

Reservoir computing is attracting great interest because of its high computing ability especially for time-series prediction, despite its simple structure and learning scheme. This paper proposes a reservoir computing hardware model using a chaotic Boltzmann machine (CBM) as the reservoir, which can achieve complex motion in a dynamical system on a high-dimensional hypercube. The CBM uses analog nonlinear dynamics, unlike the stochastic operation of the original Boltzmann machine model. To utilize CBMs as a reservoir, chaotic operation must be suppressed, and the echo state property should be satisfied. We modify the CBM model for simpler analog complementary metal-oxide-semiconductor very-large-scale integration (CMOS VLSI) implementation, and propose its use as a reservoir by adding an external reference clock signal. We then verify its proper operation by numerical simulation. We also refine the CMOS VLSI circuit design based on the proposed modified CBM model to improve power consumption and calculation precision.
Analysis of Software Patches Using Numerical Abstract Interpretation

We present a static analysis for software patches. Given two syntactically close versions of a program, our analysis can infer a semantic difference, and prove that both programs compute the same outputs when run on the same inputs. Our method is based on abstract interpretation, and parametric in the choice of an abstract domain. We focus on numeric properties only. Our method is able to deal with unbounded executions of infinite-state programs, reading from infinite input streams. Yet, it is limited to comparing terminating executions, ignoring non terminating ones. We first present a novel concrete collecting semantics, expressing the behaviors of both programs at the same time. Then, we propose an abstraction of infinite input streams able to prove that programs that read from the same stream compute equal output values. We then show how to leverage classic numeric abstract domains, such as polyhedra or octagons, to build an effective static analysis. We also introduce a novel numeric domain to bound differences between the values of the variables in the two programs, which has linear cost, and the right amount of relationality to express useful properties of software patches. We implemented a prototype and experimented on a few small examples from the literature. Our prototype operates on a toy language, and assumes a joint syntactic representation of two versions of a program given, which distinguishes between common and distinctive parts.
A Shortcut for Caret Positioning on Touch-Screen Phones

Moving a caret to the desired position in a text field is challenging and inefficient, especially when the user has only one hand available to hold and interact with a mobile phone. We propose a shortcut on keyboards that enables precise and efficient caret positioning in a text field on mobile devices. The proposed method uses a long press on a key to determine the desired position for the caret in the text field. A lab experiment was conducted to compare the shortcut with the traditional handle method and an existing method provided by the Google Keyboard. Even though participants were using the proposed shortcut for the first time, the technique achieved a higher task completion speed comparing to the Google method and was as quick as the traditional handle method. It also showed advantages in accuracy and user perceptions than the other two methods for one-handed interaction.
A Study on Global Investors’ Criteria for Investment in the Local Currency Bond Markets Using AHP Methods: The Case of the Republic of Korea

Global investors’ investment in local currency bonds, especially Korea Treasury Bonds, has increased significantly since the mid-2000s, and their influence on bonds and financial markets has grown consistently. In this paper, we investigate global investor’s priority of decision factors in investing in Korea Treasury Bonds by distributing a pairwise comparative survey to experts and analyzing the results using the analytical hierarchy process technique. For analysis, we created model frames with experts in the field of investment based on literature analysis, selected survey participants by considering their institution of their employment, work experience and region, and obtained responses. We find that investors with short-term investment propensities are more sensitive to international and domestic factors and less to risk factors, and more heavily influenced by U.S. dollar funding conditions. On the other hand, investors with long-term investment tendencies are found to be more sensitive to international and risk factors as opposed to domestic factors, and influenced by: global policy rate decisions and fiscal soundness, sovereign credit rating, possible global economic recession, and geographical risks. Our findings not only contribute to enhancing investors’ understanding of the Korean bond market by discussing consensus among investors, but also provide policy implications for Korean government policymakers who need stable and sustained funding.
Non-contact low-frequency vibration rapid measurement based on hue-height mapping

A non-contact vibration measurement system based on camera-projector system and hue-height mapping is proposed. The CCD camera acquires the color deformed fringes with red (R) green (G) blue (B) channels, which is modulated by a vibrating loudspeaker, with the aim to demodulate the information of vibrations. In order to avoid the crosstalk between RGB channels, the sampling frames from RGB space are converted to hue (H) saturation (S) intensity (I) space. By using hue information, height value of each pixel point at different sampling frames can be restored, so as to obtain the vibration curve of the loudspeaker. The experiments compare the vibrations of different frequencies at several Hertz, and different amplitudes lower than millimeter degree. The results show that this optical image system can detect low-frequency vibrations with high accurate amplitudes, and meet the basic industrial measurement requirements.
Nash Equilibrium of Multiple, Non-Uniform Bitcoin Block Withholding Attackers

This research analyzes a seemingly malicious behavior known as a block withholding (BWH) attack between pools of cryptocurrency miners in Bitcoin-like systems featuring blockchain distributed databases. This work updates and builds on a seminal paper, The Miner's Dilemma, which studied a simplified scenario and showed that a BWH attack can be rational behavior that is profitable for the attacker. The new research presented here provides an in-depth profit analysis of a more complex and realistic BWH attack scenario, which includes mutual attacks between multiple, non-uniform Bitcoin mining pools. As a result of mathematical analysis and MATLAB modeling, this paper illustrates the Nash equilibrium conditions of a system of independent mining pools with varied mining rates and computes the equilibrium rates of mutual BWH attack. The analysis method quantifies the additional profit the largest pools extract from the system at the expense of the smaller pools. The results indicate that while the presence of BWH is a net negative for smaller pools, they must participate in BWH to maximize their remaining profits, and the results quantify the attack rates the smaller pools must maintain. Also, the smallest pools maximize profit by not attacking at all-that is, retaliation is not a rational move for them.
Natural Test for Random Numbers Generator Based on Exponential Distribution

We will prove that when uniformly distributed random numbers are sorted by value, their successive differences are a exponentially distributed random variable Ex(λ). For a set of n random numbers, the parameters of mathematical expectation and standard deviation is λ =n−1. The theorem was verified on four series of 200 sets of 101 random numbers each. The first series was obtained on the basis of decimals of the constant e=2.718281…, the second on the decimals of the constant π =3.141592…, the third on a Pseudo Random Number generated from Excel function RAND, and the fourth series of True Random Number generated from atmospheric noise. The obtained results confirm the application of the derived theorem in practice.
From hindrance to challenge

Purpose This paper is to investigate how employees respond to information security policies (ISPs) when they view the policies as a challenge rather than a hindrance to work. Specifically, the authors examine the roles of challenge security demands (i.e. continuity and mandatory) and psychological resources (i.e. personal and job resources) in influencing employees’ ISP non-compliance. Design/methodology/approach Applying a hypothetical scenario-based survey method, the authors tested our proposed model in six typical ISPs violation scenarios. In sum, 347 responses were collected from a global company. The data were analyzed using partial least square-based structural equation model. Findings Findings indicated that continuity and mandatory demands increased employees’ level of perseverance of effort, which, in turn, decreased their ISPs non-compliance intention. In addition, job resources, such as the trust enhancement gained from co-workers and the opportunities for professional development, enhanced the perseverance of effort. Practical implications The findings offer implications to practice by suggesting that organizations should design training programs to persuade employees to understand the ISPs in a positive way. Meanwhile, organizations should encourage employees to invest more personal resources by creating a trusting atmosphere and providing them opportunities to learn security knowledge and skills. Originality/value This study is among the few to empirically explore how employees respond and behave when they view the security policies as challenge stressors. The paper also provides a novel understanding of how psychological resources contribute to buffering ISP non-compliance.
An Image Acquisition Method for Face Recognition and Implementation of an Automatic Attendance System for Events

Facial image acquisition systems produce low quality face images. This happens because the imaging conditions like illumination, occlusion or noise might change among images. To achieve optimal images, we proposed an image acquisition method for face recognition. Then, with this method, it was created the Smart Event Faces Database that contains video frames from videos taken by smartphones and Raspberry Pi. Also, it was measured the accuracy for face recognition and execution time for the Smart Event Faces Database using ResNet 34 for feature extraction and the next classifiers: K-Nearest Neighbors, Naive Bayes, Random Forest, Multi-Layer Perceptron, Decision Tree, Adaboost and Support Vector Machine. Additionally, we compared these classifiers to show which was effective for the dataset in terms of accuracy and execution time. Then, we used the Smart Event Faces Database to create an automatic attendance system for events using Raspberry Pi, ResNet-34 and K-Nearest Neighbors classifier. The results achieved in the Smart Event Faces Database showed that K-Nearest Neighbors and Support Vector Machine had the best results with more than 0.96 of accuracy for face recognition and less than 1.5 seconds respectively of execution time. The automatic attendance system had an accuracy for face recognition of 0.94 and 0.5 seconds approximately per frame in execution time for 19 persons in 2 events.
Gate-Layer Autoencoders with Application to Incomplete EEG Signal Recovery

Autoencoders (AE) have been used successfully as unsupervised learners for inferring latent information, learning hidden features and reducing the dimensionality of the data. In this paper, we propose a new AE architecture: Gate-Layer AE (GLAE). The novelty of GLAE lies in its ability to encourage learning of the relationships among different input variables, which affords it with an inherent ability to recover missing variables from the available ones and to act as a concurrent multi-function approximator.GLAE uses a network architecture that associates each input with a binary gate acting as a switch that turns on or off the flow to each input unit, while synchronising its action with data flow to the network. We test GLAE with different coding sizes and compare its performance against the Classic AE, Denoising AE and Variational AE. The evaluation uses Electroencephalograph (EEG) data with an aim to reconstruct the EEG signal when some data are missing. The results demonstrate GLAE's superior performance in reconstructing EEG signals with up to 25% missing data in an input stream.
Playing Chess at a Human Desired Level and Style

Human chess players prefer training with human opponents over chess agents as the latter are distinctively different in level and style than humans. Chess agents designed for human-agent play are capable of adjusting their level, however their style is not aligned with that of human players. In this paper, we propose a novel approach for designing such agents by integrating the theory of chess players' decision-making with a state-of-the-art Monte Carlo Tree Search (MCTS) algorithm. We demonstrate the benefits of our approach using two sets of analyses. Quantitatively, we establish that the agents attain their desired Elo ratings. Qualitatively, through a Turing-inspired test with a human chess expert, we show that our agents are indistinguishable from human players.
On the importance of relative humidity for prediction of diffuse solar radiation in warm and humid climate of Iran

In numerous applications of the solar energy, diffuse solar radiation plays crucial role while their availability in many countries is scarce. Many models have been developed to estimate the diffuse solar radiation that each of them is a function of different metrological parameters. The objective of this study is obtaining a model to estimate the diffuse solar radiation in warm and humid climate in Iran and specify the importance of metrological parameters especially relative humidity. Many empirical models in three categories are selected. By using the long-term data for horizontal global solar radiation and sunshine duration and by employing the regression analysis, nine new models have been created. Then, by entering two parameters, the monthly mean daily ambient temperature and relative humidity in the empirical models, nine other new models have been developed. Performances of all the models are evaluated by means of different statistical indicators. Obtained results showed adding of the ambient temperature and especially relative humidity parameters cause increasing a few accuracy even though in warm and humid climate of Iran. The result shown in the two-degree model, in terms of the clearness index and relative sunshine duration, is the best model.
Image steganography using texture features and GANs

As steganography is the main practice of hidden writing, many deep neural networks are proposed to conceal secret information into images, whose invisibility and security are unsatisfactory. In this paper, we present an encoder-decoder framework with an adversarial discriminator to conceal messages or images into natural images. The message is embedded into QR code first which significantly improves the fault-tolerance. Considering the mean squared error (MSE) is not conducive to perfectly learn the invisible perturbations of cover images, we introduce a texture-based loss that is helpful to hide information into the complex texture regions of an image, improving the invisibility of hidden information. In addition, we design a truncated layer to cope with stego image distortions caused by data type conversion and a moment layer to train our model with varisized images. Finally, our experiments demonstrate that the proposed model improves the security and visual quality of stego images.
Episodic memory: A hierarchy of spatiotemporal concepts

We propose a new model of episodic memory. It consists of a hierarchy of partial sequences of events, blended for consistency across space and time by feedforward/feedback links to concepts expressing their shared information. This blended concept hierarchy is acquired and represented incrementally through synaptic or connection-weighted adaptation in a neural network with episodic memory capability. We derive this model through argumentation, explain its derivation within a category-theoretic model of representation, and discuss the capabilities we propose for this model.
Matching Patterns with Variables

A pattern $$\alpha $$ (i. e., a string of variables and terminals) matches a word w, if w can be obtained by uniformly replacing the variables of $$\alpha $$ by terminal words. The respective matching problem, i. e., deciding whether or not a given pattern matches a given word, is generally -complete, but can be solved in polynomial-time for classes of patterns with restricted structure. In this paper we overview a series of recent results related to efficient matching for patterns with variables, as well as a series of extensions of this problem.
SSDIS-BEM: A New Signature Steganography Document Image System based on Beta Elliptic Modeling

In this paper, a New Signature Steganography Document Image system based on Beta Elliptic Modeling is proposed, in the purpose of hiding the Beta Elliptic signature as a secret data into the host document image. The system is sub-divided into two main blocks: steganography embedding and steganography extraction. The Binary Robust Invariant Scalable Keypoints (BRISK) detector is applied to identify the embedding positions in the host document image. The Beta Elliptic signature is converted into a sequence of secret bits through the signature prepossessing. The prepossessing is composed of: Binary Transformation and Huffman Compression. The obtained sequence is added in the first Least Significant Bit (LSB) of the embedding positions in the host document image. The achieved experimental results on three types of benchmarks, Tobacco800, L3iDocCopies, and Standard grayscale test images, indicated that we have outperformed related works solutions in terms of Structural Similarity Index Matrix (SSIM), Peak Signal to Noise Ratio (PSNR), and Human Visual System (HSV).
Optimal Bandwidth Allocation with Edge Computing for Wireless VR Delivery

In this paper, we propose a optimal bandwidth allocation scheme which takes advantage of the user computation capability in wireless VR delivery system. To avoid the redundant transmission, according to the similarity, the VR videos are divided into the shared part transmitted by multicast and the separate part transmitted by unicast. In particular, to minimize the maximum user transmission and computation delay, we formulate the bandwidth allocation problem into a convex problem which can be solved by KKT conditions. Moreover, we analyze the special case where the shared part equal to zero, it means that there is no similarity between the videos requested by each user. Compared with other bandwidth allocation schemes, numerical results show the advantage of the proposed scheme.
Seeded-BTM: Enabling Biterm Topic Model with Seeds for Product Aspect Mining

One of the most challenging problems in aspect level-analysis of product reviews is aspect mining (or aspect extraction), which aims to extract and categorize the terms that describe aspects of the product. In recent years, as topic models like LDA can perform extraction and clustering in one step, most unsupervised and semi-supervised statistical models are LDA-based. These models often treat each short sentence from the review as the training unit. However, LDA is proposed for normal documents without considering the sparse word frequency of short texts, which makes LDA perform poorly on short texts. Instead of using LDA, this paper proposes a Seeded Biterm Topic Model (Seeded-BTM) that models the co-occurred word pairs (i.e., biterms) rather than sentences or whole reviews. And by using seed sets, the model enables unsupervised BTM to discover aspect topics that under the user guidance. Experimental results on real-world product reviews from a number of domains show that Seeded-BTM can find more human conformable product aspects and outperforms the state-of-the-art models.