Reviewerly/exHarmony huggingface dataset:
DASHbed

Recent years have witnessed an explosion of multimedia traffic carried over the Internet. Video-on-demand and live streaming services are the most dominant services. To ensure growth, many streaming providers have invested considerable time and effort to keep pace with ever-increasing users' demand for better quality and stall abolition. HTTP adaptive streaming (HAS) algorithms are at the core of every major streaming provider service. Recent years have seen sustained development in HAS algorithms. Currently, to evaluate their proposed solutions, researchers need to create a framework and numerous state-of-the-art algorithms. Often, these frameworks lack flexibility and scalability, covering only a limited set of scenarios. To fill this gap, in this paper we propose DASHbed, a highly customizable real-time framework for testing HAS algorithms in a wireless environment. Due to its low memory requirement, DASHbed offers a means of running large-scale experiments with a hundred competing players. Finally, we supplement the proposed framework with a dataset consisting of results for five HAS algorithms tested in various evaluated scenarios. The dataset showcases the abilities of DASHbed and presents the adaptation metrics per segment in the generated content (such as switches, buffer-level, P. 1203.1 values, delivery rate, stall duration, etc.), which can be used as a baseline when researchers compare the output of their proposed algorithm against the state-of-the-art algorithms.
Shedding Light on Mobile App Store Censorship

This paper studies the availability of apps and app stores across countries. Our research finds that users in specific countries do not have access to popular app stores due to local laws, financial reasons, or because countries are on a sanctions list that prohibit foreign businesses to operate within its jurisdiction. Furthermore, this paper presents a novel methodology for querying the public search engines and APIs of major app stores (Google Play Store, Apple App Store, Tencent MyApp Store) that is cross-verified by network measurements. This allows us to investigate which apps are available in which country. We primarily focused on the availability of VPN apps in Russia and China. Our results show that despite both countries having restrictive VPN laws, there are still many VPN apps available in Russia and only a handful in China. In addition, we have included findings of a global search for the availability of privacy-enhancing and other apps that are known to be censored. Finally, we observe that it is difficult to find out which apps have been removed or are unavailable on the examined app stores. As a consequence, we urge all app store providers to introduce app store transparency reports, which would include when apps were removed and for what reasons.
A Comparison of Allocation Algorithms for Partially Replicated Databases

Increasing demand for analytical processing capabilities can be managed by replication approaches. However, to evenly balance the replicas' workload shares while at the same time minimizing the data replication factor is a highly challenging allocation problem. As optimal solutions are only applicable for small problem instances, effective heuristics are indispensable. In this paper, we test and compare state-of-the-art allocation algorithms for partial replication. By visualizing and exploring their (heuristic) solutions for different benchmark workloads, we are able to derive structural insights and to detect an algorithm's strengths as well as its potential for improvement. Further, our application enables end-to-end evaluations of different allocations to verify their theoretical performance.
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs

Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data. Long Range Dependence (LRD) --- referring to long-range correlations decaying as a power law rather than exponentially w.r.t. distance --- demands a different set of tools for modeling the underlying dynamics of the sequential data. While it has been widely conjectured that LRD is present in language modeling and sequential recommendation, the amount of LRD in the corresponding sequential datasets has not yet been quantified in a scalable and model-independent manner. We propose a principled estimation procedure of LRD in sequential datasets based on established LRD theory for real-valued time series and apply it to sequences of symbols with million-item-scale dictionaries. In our measurements, the procedure estimates reliably the LRD in the behavior of users as they write Wikipedia articles and as they interact with YouTube. We further show that measuring LRD better informs modeling decisions in particular for RNNs whose ability to capture LRD is still an active area of research. The quantitative measure informs new Evolutive Recurrent Neural Networks (EvolutiveRNNs) designs, leading to state-of-the-art results on language understanding and sequential recommendation tasks at a fraction of the computational cost.
Efficient Partitioning and Query Processing of Spatio-Temporal Graphs with Trillion Edges

Real-world graphs often contain spatio-temporal information and evolve over time. Compared with static graphs, spatio-temporal graphs present more significant challenges in data volume, data velocity, and query processing. In this paper, we define a formal spatio-temporal graph model based on real-world applications, and propose PAST, a framework for efficient PArtitioning and query processing of Spatio-Temporal graphs. Our experimental results show that PAST improves query performance by orders of magnitude compared to state-of-the-art solutions.
Cyber defense matrix

Most enterprises aiming to deploy cost-effective security configuration follow information security standards and guidelines to adopt cybersecurity controls such as CIS Critical Security Controls (CIS CSC) [2]. With the increased dependency over cyber, the landscape of cyber attacks is escalating quickly, and as a consequence, hundreds of cybersecurity controls have to be delineated to implement NIST Cybersecurity Framework (i.e., identify, protect, detect, respond and recover) [6]. The security configuration comprised of the appropriate set of security controls requires not only to be optimized regarding Return on Investment (RoI) but also to be resilient in order to tackle the failures against diversified cyber attacks. However, the composition of such optimal and resilient cybersecurity portfolio (security configuration) is a highly complex and error-prone task as there are exponential numbers of ways to construct a portfolio due to a large number of security controls, threats, resource, and usability constraints.
Ontology-driven hierarchical sparse coding for large-scale image classification

An ontology-driven hierarchical sparse representation is developed in this paper, which aims to support hierarchical learning for large scale image classification. Firstly, a two-layer ontology (semantic ontology and visual ontology) is built to organize large number of image classes hierarchically, where WordNet is used to construct semantic ontology and deep features extracted by Inception V3 are used to construct visual ontology (visual tree). Secondly, a novel algorithm based on Split Bregman Iteration is developed to learn hierarchical sparse representation, i.e., learning a shared dictionary and a set of class-specified dictionaries depending on the two-layer ontology. For multi-class image classification, a tree classifier is trained according to the two-layer ontology by using the hierarchical sparse representation. Thirdly, for a given test image, multiple paths are simultaneously evaluated to achieve optimal prediction of its class label. Our proposed approach has been evaluated over three benchmark datasets: ILSVRC2010, SUN397, and Caltech256, and the experimental results have demonstrated that our approach is better than the original joint dictionary learning method and can achieve better accuracy compared with other approaches which use handcrafted features.
An Optimal Lightweight Cryptographic Hash Function for Secure Image Transmission in Wireless Sensor Networks

In the recent years, numerous security schemes have been proposed to secure the data and Digital Images (DI) over WSNs. Especially, encryption and decryption algorithms are structured and actualized to provide secrecy and security in WSN during the transmission of image-based information just as in storage. In this chapter, Lightweight Cryptography (LWC) based hash function is used for image security in WSN. The hash function keeps up different guidelines which contain a set of tenets with user details, IP address, public and private keys. The hash value of encryption was developed upon the optimal secret key and it was recognized by the Enhanced Cuckoo Search (ECS) optimization. In this ECS model, cuckoo birds choose the nests of various birds to leave its eggs i.e., optimal keys. Further impressive fitness function parameters such as Peak Signal to Noise Ratio (PSNR) were kept consistent in this research. The proposed system provided expanded security and adequately utilized the algorithm when compared with ordinary encryption and optimization strategies.
A Fine-Grained Parallel Snappy Decompressor for FPGAs Using a Relaxed Execution Model

Snappy is a widely used (de) compression algorithm in many big data applications. Such a data compression technique has been proven to be successful to save storage space and to reduce the amount of data transmission from/to storage devices. In this paper, we present a fine-grained parallel Snappy decompressor on FPGAs running under a relaxed execution model that addresses the following main challenges in existing solutions. First, existing designs either can only process one token per cycle or can process multiple tokens per cycle with low area efficiency and/or low clock frequency. Second, the high read-after-write data dependency during decompression introduces stalls which pull down the throughput.
User-oriented evaluation of the metrological characteristics of areal surface topography measuring instruments

Areal surface topography measurement is becoming more and more widespread. In this context, increased activities in the field of standardization can be observed in order to ensure comparable measurement results. A current hot topic in the field of standardization is areal calibration, which is going to be defined in the upcoming standard ISO 25178-700. Besides, the metrological characteristics of the ISO 25178-600, representing the properties to be calibrated, as well as the corresponding material measures of the ISO 25178-70, used for imaging, are required for a comprehensive standardization of areal calibration. It can be expected that the application of the calibration guidelines is challenging as no easy-to-implement guidelines for the evaluation routines will be included. In this paper, the standards required for areal calibration are briefly presented and their metrological properties are described. A user-friendly implementation of the algorithms required for the evaluation will be discussed in detail. The basic metrological characteristics to be evaluated are the flatness deviation, measurement noise, the topography fidelity, the amplification coefficients and linearity deviations of all axes, the x/y mapping deviation and the topographic spatial resolution. With Two-Photon laser lithography, all required material measures to calibrate a measurement device according to ISO 25178 can be printed on one single calibration artefact. Based on this universal artefact, user-oriented evaluation routines to determine the aforementioned metrological characteristics are introduced and a software package implementing the algorithms and supporting the user effectively during the holistic calibration of areal surface topography measuring instruments is described.
Predicting Multi-document Comprehension: Cohesion Network Analysis

Theories of discourse comprehension assume that understanding is a process of making connections between new information (e.g., in a text) and prior knowledge, and that the quality of comprehension is a function of the coherence of the mental representation. When readers are exposed to multiple sources of information, they must make connections both within and between the texts. One challenge is how to represent this coherence and in turn how to predict readers’ levels of comprehension. In this study, we represent coherence using Cohesion Network Analysis (CNA) in which we model a global cohesion graph that semantically links reference texts to different student verbal productions. Our aim is to create an automated model of comprehension prediction based on features extracted from the CNA graph. We examine the cohesion links between the four texts read by 146 students and their (a) self-explanations generated on target sentences and (b) responses to open-ended questions. We analyze the degree to which features derived from the cohesive links from the extended CNA graph are predictive of students’ comprehension scores (on a [0 to 12] scale) using either (a) students’ self-explanations, (b) responses to comprehension questions, or (c) both. We compared the use of Linear Regression, Extra Trees Regressor, Support Vector Regression, and Multi-Layer Perceptron. Our best model used Linear Regression, obtaining a 1.29 mean absolute error when predicting comprehension scores using both sources of verbal responses (i.e., self-explanations and question answers).
Modelling of coherence scanning interferometry for complex surfaces based on a boundary element method

Coherence scanning interferometry (CSI) is a well-established technique for measuring surface topography based on the coherence envelope and phase of interference fringes. The most commonly used surface reconstruction methods, i.e. frequency domain analysis, the envelope detection method, and the correlogram correlation method, obtain the phase of the measured field for each pixel and, from this obtain the surface height, by assuming the two are directly proportional. For surfaces with minor deviations from a plane, it is straightforward to show that the scattered field’s phase is a linear function of surface height. An alternative approach known as the “foil model” gives more generally the scattered field as the result of a linear filtering process operating on a “foil” representation of the surface. This model assumes that the surface slowly varies on the optical scale and that there is no multiple scattering. However, for surfaces that are rough at the optical scale or have coherent features (e.g. vee-grooves), the effect of multiple scattering cannot be neglected and remains a problem for reconstruction methods. Linear reconstruction methods cannot provide accurate surface topographies for complex surfaces, since for such surfaces, the measurement process of CSI is fundamentally non-linear. To develop an advanced reconstruction method for CSI, an accurate model of the imaging process is required. In this paper, a boundary elements method is used as a rigorous scattering model to calculate the scattered field at a distant boundary. Then, the CSI signal is calculated by considering the image formation as back-propagation of the scattered field, combined with the reflected reference field. Through this approach, the optical response of a CSI system can be predicted rigorously for almost any arbitrary surface geometry. Future work will include a comprehensive experimental verification of this model, and development of the non-linear surface reconstruction algorithm.
Empirical Evaluation of Map Reduce Based Hybrid Approach for Problem of Imbalanced Classification in Big Data

Imbalanced datasets are the ones with uneven distribution of classes that deteriorates classifier's performance. In this paper, SVM classifier is combined with K-Means clustering approach and a hybrid approach, Hy_SVM_KM is introduced. The performance of proposed method is also empirically evaluated using Accuracy and FN Rate measure and compared with existing methods like SMOTE. The results have shown that the proposed hybrid technique has outperformed traditional machine learning classifier SVM in mostly datasets and have performed better than known pre-processing technique SMOTE for all datasets. The goal of this article is to extend capabilities of popular machine learning algorithms and adapt it to meet the challenges of imbalanced big data classification. This article can provide a baseline study for future research on imbalanced big datasets classification and provides an efficient mechanism to deal with imbalanced nature big dataset with modified SVM classifier and improves the overall performance of the model.
An Interactive Data Visualization Design Based On WebGIS

In this paper, the web application of geographic information system (GIS) integrated with file uploading module was designed to achieve a better performance for interactive map applications. The B/S archtechture was used for the design of WebGIS system based on Spring Framework, providing features for adding and updating geospatial data as well as attribute information through the web. The novel design of map data visualization offers powerful web map applications, including features of spatial information storage and files uploading based on distributed archtechture. The successful result indicates that the enhancement of spatial analysis and data visualization based on WebGIS will support a feasible and flexible approach for interactive map applications and bring considerable economic benefits.
The Design methodology of Bauman Moscow State Technical University

Currently, Bauman Moscow State Technical University (BMSTU) has more than 25000 students in 95 specialties and areas of training. For almost two centuries of development, scientific schools were formed in it, serious scientific and technical tasks were solved. Engineering education developed in close connection with the formation and modernization of leading industries. Today, students and graduates of the university need to acquire innovative technical and technological and skills, not only in classical engineering areas such as robotics, biomedical engineering, special engineering, but also knowledge on the organization of modern production, intellectual property protection, design methodology and industrial products.
Blockchain Federation for Complex Distributed Applications

Blockchains are immutable distributed ledger systems usually without a central authority. Blockchains enables people to establish trusted application among untrusted parties. But, the performance of blockchain is a challenge for massive applications. There are many researches to improve the performance of blockchain including side blockchain, interconnection of blockchains. In fact, a distributed application usually needs resource of computing, storage and transportation, even with possible permissioned access. It means that any current blockchain cannot satisfy all the demands simultaneously. This paper proposes a new Blockchain Federation that consolidates several blockchains to support complex distributed applications. Two typical application scenarios are implemented following the proposed concept of Blockchain Federation. The new emerging blockchain technology are combined together to meet the demands of complex peer-to-peer applications. And, the conclusions are drawn and future direction of Blockchain Federation evolution, Federation Blockchain, is discussed as well.
Human action recognition using a corners and blob detector with different classification methods

Abstract Human Action Recognition trying to recognize the motion and movement of the person, the recognition of human movement is a very important field in many applications of security such as for smart surveillance and monitoring systems, also in another application such as computer interaction and entertainment. This paper tries to detect and identify the action of human but there is a big challenge in recognize similar actions for example walking and running. The dataset has been used is KTH dataset and the methods are harrier detector and blob detector to extract the corners from each frame. the second topic has been discuses in this paper is to examine between two different classification methods: supervised (SVM) and unsupervised (KNN) Machine Learning to find the most suitable way classification for this paper, we were find that the KNN classification is overcome the SVM Classification in this work. After the result has been extracted we find the using of KNN method is the most suitable for this topic. In future work we hope using more features to extract the corner to increase the efficiency of classification process.
Self-dual cyclic and quantum codes over ℤ2 × (ℤ2 + uℤ2)

In this paper, we introduce self-dual cyclic codes over the ring [Formula: see text]. We determine the conditions for any [Formula: see text]-cyclic code to be self-dual, that is, [Formula: see text]. Since the binary image of a self-orthogonal [Formula: see text]-linear code is also a self-orthogonal binary linear code, we introduce quantum codes over [Formula: see text]. Finally, we present some examples of self-dual cyclic and quantum codes that have good parameters.
Organizational Quality: A Systemic Functioning Theory†

A conceptual framework for evaluating the quality of organizations is developed, based on the view that an organization constitutes a behavioral action system. Applying the general Faceted Action System Theory (FAST) to the case of organizations, 16 organizational functioning modes, as well as their interrelationships, are identified. Organizational quality is defined as the effective functioning in these 16 modes. Principles and procedures of Facet Theory are employed (1) for explicating the axiomatic foundation of “organizations as systems” in terms of elementary content facets (classifications); (2) for sampling observations needed for organizational quality evaluation, by means of a mapping sentence; (3) for testing hypotheses empirically validating the conceptual constructs of organizational functioning modes and their interrelationships, using faceted Multidimensional Scaling (MDS) (faceted Smallest Space Analysis (SSA)); and (4) for suggesting a rationalized procedure (Multiple Scaling by Partial Order Scalogram Analysis by base Coordinates (POSAC)) for compiling the 16 modal scores into an optimal organizational quality profile consisting of the smallest number of scales commensurate with organizational functioning complexity.
Discovery of Web Services Using Mobile Agents in Cloud Environment

Recently, there is an increase in the use of web services and also increase the interest in the use of cloud computing technologies. A huge set of services is published on the internet every day. To discover and utilize these services based on human approach is quite time consuming and it also requires continuous user interaction. In modern network, there is rapid growth in web service application. So, the main task is how to discover and use proper web services. In this paper, the web service discovery and selection model based on the mobile agent is studied. The mobile agents are self-adaptable, intelligent, and collaborative, and so on. The system is composed of two clouds. The first cloud deals with keyword-based research and second cloud deals with the selection and filtering of web services found by search mobile agents. The selection algorithm is implemented using OpenMP.
Mining Social Networks from Linked Open Data

The richness and openness of Linked Open Data (LOD) make them invaluable resources of information, and create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks of entities. This enables the application of techniques from Social Network Analysis to study social interactions among entities, providing deep insights into their latent social structure.