Reviewerly/exHarmony huggingface dataset:
A 220-900mV 179Mcode/s 36pJ/code Canonical Huffman Encoder for DEFLATE Compression in 14nm CMOS

A 82,000μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> canonical Huffman encoder (CHE) for accelerating DEFLATE data compression is fabricated in 14nm CMOS. Concurrent 2-stage sorting and binary tree construction, opportunistic skipping of missing symbols during Huffman code generation, and in-line length-limitation without recursive weight pruning eliminates serial data dependency in encoder pipeline enabling 179Mcode/s throughout at 650MHz operation measured at 0.75V, 25°C with 1150cycle total CHE latency, 17× faster than previously reported hardware accelerators. Unified datapath for literal-length and distance symbol processing, optimal register-file partitioning, shared address generation and reconfigurable ALU circuits maximize logic sharing resulting in fully synthesizable 330Kgate design that improves area-efficiency by 25% over prior-art. Absence of custom memory macros enables robust ultra-low voltage operation down to 220mV with 36pJ/code peak energy-efficiency (4.9× higher than nominal) measured at near-threshold voltage operation of 290mV.
A Machine Learning Approach to Recommending Files in a Collaborative Work Environment

Recommendation of items to users is a problem faced by many companies in a wide spectrum of industries. This problem was traditionally approached in a one-shot manner, such as recommending movies to users based on all the movie ratings observed so far. The evolution of user activity over time was relatively unexplored. This paper presents a Machine Learning approach developed at Box Inc. for making repeated recommendations of files to users in a collaborative work environment. Our results on historical data show that this approach noticeably outperforms the approach currently implemented at Box and also the traditional Matrix Factorization approach.
Energy Efficient Dynamic Slot Allocation of Map Reduce Tasks for Big Data Applications

Big data applications are very large in size in terms of map and reduce tasks and it takes large time to execute those map reduce based big data applications. Map Reduce is a familiar bulk data processing concept for extensive data computing in cloud environment. The existing Energy Conscious Arrangement is implemented with static map and reduce slot allocation, where there is no room for effective resource utilization. In case of static slot allocation, the pre-determination of map and reduce slots does not employ efficient run time performance and the slots can be strictly under-utilized. To deal with it, the proposed work discovers and enhances the resource utilization and execution time. In the proposed work, dynamic slot allocation technique is accomplished with the existing Energy Conscious Arrangement concept. The technique called Energy Efficient Dynamic Slot Allocation (EEDSA) is accomplished by using dynamic slot allocation technique, which ensures efficient resource utilization. It provides way for slots to be reassigned to map tasks or reduce tasks relying on the requirements. Since slots of the node in the cluster do not deploy any specific functionality, there is no constraint on using map slots in place of reduce slots and vice versa. The proposed technique EEDSA is able to show 10% reduction in execution time for various workloads in comparison with existing Energy Conscious Arrangement technique.
A Generic Approach for Video Indexing

From past decades, imaging systems have started to transform the way we communicate the information to the desired users. Most of image processing algorithm works with video promptly. In this way, video processing emerges as an expansion of image processing. Moreover, there are also appears some restricted apparatuses to index, portray, sort out and deal with the image information. A perfectly arranged and viable administration of video archives relies upon the accessibility of indexes. Manual Indexing is not feasible for vast video accumulations. This results in a need for innovative systems and structure that can store, handle, Search, and recover the information from the gigantic media file.
Set Based Similarity Measure for User Based Collaborative Filtering Recommendation System

With the advancement of technologies in the modern era, the amount of data that emerges from various sources like online websites, Internet of things, E-commerce etc., keeps on increasing at a larger pace. The data available is too much for a common user to handle. So recommendation system makes efforts to provide right information to the right user at their doorstep and makes it easy for the users. Similarity measure is considered as an important step to determine the accuracy of the recommendation system. A classical collaborative filtering is implemented either by using Pearson correlation coefficient or Cosine similarity which has got its own merits and shortcomings. An enhanced similarity measure is proposed by applying the set based methodology on basic similarity measures and analyze the impact of those various enhanced similarity measures such as set based cosine, set based Pearson correlation coefficient, set based spearman, set based Kendall on the user based collaborative filtering recommendation systems. It is observed that the enhanced similarity measure obtained from set based methodology is more significant than the basic measures.
Resource Efficiency Optimization for Big Data Mining Algorithm with Multi-MapReduce Collaboration Scenario

Because any MapReduce job requires a series of complex operations such as task scheduling and resource allocation independently, there are a lot of redundant disk I/O and resource duplicate application operations among multiple MapReduce jobs coordinated by the same algorithm, causing inefficient resource utilization in job computing process. Big data mining algorithms are usually divided into several MapReduce Jobs, taking ItemBased algorithm as an example, this paper has analyzed the resource efficiency of mining algorithm with multi-MapReduce job collaboration scenario. It proposed an ItemBased algorithm based on DistributedCache, which used DistributedCache to cache I/O data between multiple MapReduce Jobs, breaks the defect of independence between jobs, and reduced the waiting delay between Map and Reduce tasks. The experimental results show that, DistributedCache can improve the data reading speed of MapReduce jobs. The algorithm reconstructed by DistributedCache greatly reduces the waiting delay between Map and Reduce tasks, and improves the resource efficiency by more than three times.
Winning Strategies for Streaming Rewriting Games

Context-free games on strings are two-player rewriting games based on a set of production rules and a regular target language. In each round, the first player selects a position of the current string; then the second player replaces the symbol at that position according to one of the production rules. The first player wins as soon as the current string belongs to the target language. In this paper the one-pass setting for context-free games is studied, where the knowledge of the first player is incomplete: She selects positions in a left-to-right fashion and only sees the current symbol and the symbols from previous rounds. The paper studies conditions under which dominant and undominated strategies exist for the first player, and when they can be chosen from restricted types of strategies that can be computed efficiently.
Integrated information system development planning at tropical biopharmaca research center using enterprise architecture planning

Abstract Tropical Biopharmaca Research Center (Trop BRC) of Bogor Agricultural University (IPB) is a national research center that has the vision to become an international standard research center. To achieve this vision, Trop BRC IPB has to improve its quality of all aspects, such as by developing an integrated information system. Trop BRC IPB requires an integrated information system to manage its research activities and enterprise data more effectively. This study aims to define the architecture requirement that supports Trop BRC IPB’s business process and to guide the development of an information system using Enterprise Architecture Planning (EAP) method and Zachman framework. This study is conducted in the following stages, including preliminary planning, review of an existing enterprise, architectural design, and implementation planning. The results of this study are represented on tables, matrices, and diagrams that define the needs of data architecture, applications, and technology which can be used as a blueprint for applications developer team of Trop BRC IPB to develop and implement an integrated information system.
Implementation of Web Browser Extension for Mitigating Clickjacking Attack

Clickjacking is an attack that attracts the web surfer to click on invisible elements on a malicious web page to perform an unwanted action which is beneficial for the attacker. Many recent research studies have shown that clickjacking is the primary source of different exploitations such as cross site request forgery (CSRF) and phishing attacks. In this paper we propose ClickDetector, a chrome extension to defeat the attacker attempt to perform clickjacking attacks; it detects all advanced clickjacking attacks techniques reported by OWASP, Our ClickDetector tool is composed of the following components, based on the following three steps: With the adoption of user's feedback to make future interactions to be more informed for new users. Users also are able to choose google safe browsing service for more protection. The obtained experimental results demonstrate that our ClickDetector extension successfully detects all generated attacks with no false positive; this proves the effectiveness of our extension without any effect on browser performance.
Integrated photonic devices for measurement-device-independent quantum key distribution

We experimentally demonstrate integrated photonic devices for measurement-device-independent quantum key distribution with state of the art error and clock rates which will lead to more cost effective, practical, and secure communication.
“First Things First”: An Inquisitive Plausibility-Urgency Model

There is a fruitful line of work in incorporating questions into epistemic logic (van Benthem and Minică 2009; Baltag et al. 2016; among others); among others). Based on the viewpoint that communication is a process of raising and resolving issues, inquisitive semantics introduces a uniform notion of meaning for statements and questions, thus can serve as a suitable device for this purpose. For instance, Inquisitive Plausibility Model (Ciardelli and Roelofsen 2014) is able to combine questions with the Epistemic Plausibility Model (IPM) (Baltag and Smets 2006a, b) to capture not only the belief and knowledge of agents, but also the issues they entertain. Building on this, we develop an Inquisitive Plausibility-Urgency Model (IPUM), which not only allows us to model knowledge, belief and issues, but also the urgency of the issues, hence lead us to towards formalizations of more dynamics of questions.
Implementing Cloud Technologies to Optimize Customer Contact Centers Operation

The purpose of this research is finding novel ways to make customer contact centers work more efficiently with the help of cloud technologies. The goal of the research is reducing average call handle time and time-to-answer in the call centers by introducing the developed cloud-based system. This was achieved by surveying a cohort of contact centers and building a software system which analyzes, presents and improves knowledge access patterns for call center agents.
The Cognitive Aspect of the Image Building of the Organization in the Framework of Economic Media Text

The study deals with topical issues affecting the sphere of influence of media texts on mass consciousness within the information economy during the development of post-industrial society. It analyzes the features of PR-media texts aimed at creating the image of an organization which has a multi-component structure. The paper uses a cognitive approach based on the theoretical and methodological basis of research conducted by foreign and domestic scientists, to identify the most typical conceptual metaphors of different types in the structure of media texts of image character. The arguments confirming the value of cognitive approach in the process of the company’s image formation to determine the factors that have a key impact on the formation of stable concepts of a particular company and the creation of a certain assessment and attitude to its activities. As a result of the study of image PR-texts, it is concluded that the cognitive structure of the company’s image is a blended space, including frames and slots with significant and typical information about the particular features of the organization, having stable relationships that arise between them that allows to consider it as a dynamic process, on the one hand, and to choose the most effective cognitive strategies for creating texts of this genre on the other.
Single‐camera special stereo‐digital image correlation applied for accurate virtual fields method identification

Abstract A single‐camera special stereo‐DIC (SS‐DIC) is proposed for accurate virtual fields method (VFM) identification. The single‐camera SS‐DIC allows accurate surface 3D deformation measurements using a single colour camera and a specially designed colour separation device. It not only effectively eliminates the unavoidable out‐of‐plane movement/rotation due to unideal in‐plane loading but also delivers uniformly distributed measurement points that brings great simplicity and convenience for internal virtual work calculation in VFM. In addition, since only a single camera is used for stereovision, the proposed SS‐DIC system requires no complicated synchronisation devices. The effectiveness and practicality of the proposed method are evaluated by heterogeneous deformation experiments of a holed aluminium alloy and 304 stainless steel plate. Combined with a high‐speed colour camera, the proposed method is expected to be a simple and practical method for the calibration of material constitutive model under intermediate and high strain rate conditions using VFM.
Mobile Application for Practical Skills Testing Based on Augmented Reality

Advanced technologies of virtualization and digital realization of the real world find their application in a wide variety of practical fields, including entertainment; medicine; education; military; repair services; aviation; vehicle operations; security services; transport companies; construction, etc. Recently, technologies of virtual and augmented reality and their application in education are rapidly developing. Application of technologies of virtual and augmented reality in education gives many additional advantages over traditional media, in particular the development of spatial imagery, which enhances the deep understanding of processes, properties, and so on. Particularly relevant in this context is the issue of improving the skills of workers in industries that require substantial expenditure on training systems, in particular aircraft construction. Therefore, the task of creating a new software system for testing the skills in aircraft engineering based on the means of augmented reality and mobile devices described in this paper.
Mis.Config: Finding Misreferred Configuration Bugs in Web Application Using Thin Slicing

There are many web-based applications for multi-agent systems (MAS). Yoda,Minami However, developing MAS is not easy, because agents have many different variables, and it is difficult to validate them. Sei,Yuichi PHP is one of the most popular web programming languages. PHP is dynamically typed, which means that the type of variable is assigned when the variable is accessed. Tahara,Yasuyuki This flexibility makes it easier to develop MAS. Ohsuga,Akihiko On the contrary, when a programmer refers variables to other PHP files, misreferences among the PHP files may occur because it is difficult to find references across files. To solve these misreferences, we propose a bug-finding tool called Mis.Config using static analysis. We used control flow graphs (CFGs) and thin slicing to realize this purpose. In our experiment, we applied our tool to real-world software to investigate whether Mis.Config can find misreferenced configurations.
How to Solve Multiple Short-Exponent Discrete Logarithm Problem

Let $$\mathbb {G}$$ be a group of prime order p with a generator g. It is known that one can find $$x_1, \ldots , x_L$$ from $$g^{x_1}, \ldots , g^{x_L}$$ in time $$O(\sqrt{Lp})$$ . On the other hand, suppose that $$0 \le x < w$$ . Then Pollard’s kangaroo algorithm (or Pollard’s lambda algorithm) can find x from $$g^x$$ in time $$O(\sqrt{w})$$ . It is used in the decryption algorithm of the homomorphic encryption scheme of Boneh, Goh and Nissim. Now suppose that $$0 \le x_i < w$$ for $$i=1, \ldots , L$$ . This paper shows that we can find $$x_1, \ldots , x_L$$ from $$g^{x_1}, \ldots , g^{x_L}$$ in time $$O(\sqrt{Lw})$$ . We further show an application of our algorithm to the model of preprocessing.
Scale Adaptive Kernel Correlation Filter Tracker with Multi-Feature Fusion

In order to solve the problems of illumination variation, scale variety and target occlusion during the process of tracking, the traditional kernel correlation filter tracking algorithm uses a single feature to track, which is easily affected by noise, so the performance is greatly limited. To this end, we propose a new multi-feature fusion visual tracking model, which combines the Color-naming (CN), Histogram of Oriented Gradient (HOG) and Lab spatial features after dimensionality reduction, and trains better classifier model to improve the tracking performance of targets in complex environments. In addition, we have proposed a simple and effective scale estimation method that can better adapt to scale change. In the face of occlusion, we use the depth difference between the two frames and the maximum response value of the classifier to judge the occlusion. Then we use the simplified random ferns detection algorithm to recover tracking. The experimental results on Princeton dataset show that the tracking accuracy and success rate of the proposed algorithm compared to other three tracking algorithms are greatly improved.
SVC ANALYTICS a Software for Plotting and Extracting Features from Online Handwriting Signals

In this paper we present a software tool for displaying and analyzing handwriting signals recorded by digitizing devices that capture both in-air and on-surface trajectories. This tool has two salient features: it can visualize the invisible trajectories described by the writing device while it is not exerting any pressure on the writing surface and it can calculate entropies and fractal dimensions, two measures that are relevant when analyzing handwriting signals for health-related purposes, mainly in the diagnose of some neurodegenerative conditions like Parkinson's disease and Alzheimer's disease.
Iterative Adversarial Inference with Re-Inference Chain for Deep Graphical Models

Deep Graphical Model (DGM) based on Generative Adversarial Nets (GANs) has shown promise in image generation and latent variable inference. One of the typical models is the Iterative Adversarial Inference model (GibbsNet), which learns the joint distribution between the data and its latent variable. We present RGNet (Re-inference GibbsNet) which introduces a re-inference chain in GibbsNet to improve the quality of generated samples and inferred latent variables. RGNet consists of the generative, inference, and discriminative networks. An adversarial game is cast between the generative and inference networks and the discriminative network. The discriminative network is trained to distinguish between (i) the joint inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space pairs. We show empirically that RGNet surpasses GibbsNet in the quality of inferred latent variables and achieves comparable performance on image generation and inpainting tasks.
Community Algorithm Tool for Educational Domain (CATED)

This research paper is based on CATED, which is the Community Algorithm Tool in Educational Domain. This tool is designed for implementation of the Community Algorithm (CA), which is written by Dr. Muhammad Shahab Siddiqui. CA is actually a population based algorithm, which is used to analyze the interaction between the users in the community or interaction between different communities. This is a research based Final Year Project and the targeted community (Faculty of Science, Jinnah University for Women) will be analyzed on the basis of the data that we will collect from the community. In this paper we will discuss all the modules of CATED and also elaborate the extensions made in the original Community Algorithm.
Social Art – “Echoes of Machim”

Taking the project “Echoes of Machim” by RIGO 23 as a starting point, I will attempt here to reflect on the meaning of the emergence of practices which aim to do social good, in both art and design, and on their potential as agents for the remodelling of society and for social emancipation. “Echoes of Machim” is a social interaction project directed by RIGO 23 under the orientation of curator Michelle Kasprzak, in the neighbourhood of Palmeira, also known as Malvinas, a much-degraded district of the municipality of Câmara de Lobos, on the island of Madeira. The project attempts to improve the self-esteem of members of the community through a number of initiatives with a group of 12 young people with learning difficulties. RIGO 23’s creative process has in common with social design the fact that it attempts to tackle social injustice by activating social creativity, thereby feeding the public sphere and fostering the necessary debate to create new visions of the future.
Ethics Education of Information and Big Data

With the rapid development of information technology, the extensive application of information network technology and smart devices in the era of big data has not only brought great convenience to people, but also triggered a series of new ethical issues.College students, as the main group of information activities in the era of big data, are unable to integrate information ethics knowledge with behaviors due to their lack of information screening ability and output ability, resulting in various information anomies. Based on the actual curriculum research, this paper points out the current situation and reasons of information ethics faced by college students, and puts forward the corresponding countermeasures, which has a positive and farreaching significance for coordinating the ethical order of network society and promoting the construction of a harmonious society.
Analysing and Solving the Reduced-Ability and Excessive-Use Dilemmas in Technology Use

Abstract Technology use provides great benefits by extending human ability, but the negative effects cannot always be ignored. The author examined the dilemmas of technology use based on Shibata's analysis of McLuhan's extension theory and indicated two types of dilemmas in continuous technology use. First is the decreased human ability as the innate functionality is substituted by the technology. Second is excessive utilisation of the technology, which may instil a false sense of naturally extended ability. Subsequently, the author considered and suggested approaches for mitigating both types of dilemmas. The decreased-ability dilemma might be resolved by continuously utilising the technology and designing technology relevant to the degree of human ability. Meanwhile, the excessive-utilisation dilemma might be resolved by regulating the technology use and designing technology that achieves the desired disposition change in users. Finally, the possibility of advancing the existing design approaches to further resolve the dilemmas was discussed.