Reviewerly/exHarmony huggingface dataset:
Comparison of Machine Learning Algorithms for Classification Problems

Machine learning algorithms become wide tools that are used for classification and clustering of data. Several algorithms were proposed and implemented for different applications in multi-disciplinary areas. However, diversity of these algorithms makes the selection of effective algorithm difficult for specific application. Thus, comparison of benchmark algorithms is required. This paper presents preliminary results of the comparison of three different types of machine learning algorithms; Backpropagation Neural Network, Radial Basis Function Neural Network and Support Vector Machine using several numerical datasets for classification problems. Comparison is performed by considering the performance of these algorithms using obtained accuracy rates. The results show that Radial Basis Function Neural Network is superior to other considered algorithms for classification of numerical data.
Evaluation of State-of-the-Art Paraphrase Identification and Its Application to Automatic Plagiarism Detection

Paraphrase identification is a natural language processing (NLP) problem that involves the determination of whether two text segments have the same meaning. Various NLP applications rely on a solution to this problem, including automatic plagiarism detection, text summarization, machine translation (MT), and question answering. The methods for identifying paraphrases found in the literature fall into two main classes: similarity-based methods and classification methods. This paper presents a critical study and an evaluation of existing methods for paraphrase identification and its application to automatic plagiarism detection. It presents the classes of paraphrase phenomena, the main methods, and the sets of features used by each particular method. All the methods and features used are discussed and enumerated in a table for easy comparison. Their performances on benchmark corpora are also discussed and compared via tables. Automatic plagiarism detection is presented as an application of paraphrase identification. The performances on benchmark corpora of existing plagiarism detection systems able to detect paraphrases are compared and discussed. The main outcome of this study is the identification of word overlap, structural representations, and MT measures as feature subsets that lead to the best performance results for support vector machines in both paraphrase identification and plagiarism detection on corpora. The performance results achieved by deep learning techniques highlight that these techniques are the most promising research direction in this field.
Learning to Predict Population-Level Label Distributions

Machine learning problems are often subjective or ambiguous. That is, humans solving the same problems might come to legitimate but completely different conclusions, based on their personal experiences and beliefs. In supervised learning, particularly when using crowdsourced training data, multiple annotations per data item are usually reduced to a single label representing ground truth. This hides a rich source of diversity and subjectivity of opinions about the labels. Label distribution learning associates for each data item a probability distribution over the labels for that item, thus can preserve the diversity that conventional learning hides or ignores. We introduce a strategy for learning label distributions with only five-to-ten labels per item by aggregating human-annotated labels over multiple, semantically related data items. Our results suggest that specific label aggregation methods can help provide reliable representative semantics at the population level.
A User Perspective Overview of The Top Infrastructure as a Service and High Performance Computing Cloud Service Providers

This paper introduces an extensive overview of the Infrastructure as a Service offerings that are presented today by the top ranked cloud providers. It surveys their solutions related to Infrastructure as a Service and High Performance Computing, presenting their benefits and drawbacks from a cloud user perspective. Four leading cloud providers were chosen for this paper, Amazon Elastic Compute Cloud, Microsoft Azure Cloud, Google Cloud and Oracle Cloud. The selected service providers are compared to each other in this paper from many perspectives. The comparison was done from the IaaS perspective. Reviews about these providers are presented in addition to pricing and billing details. Each one of these providers has its competitive advantages that already persuade its existing customer to invest more in their cloud and to attract new ones to migrate their resources to. In addition to the IaaS perspective, we present a comparison between the selected cloud providers from the High Performance Computing user perspective. These solutions are discussed; features of these solutions such as virtual machines options, their operating systems, and networking are compared.
Hand Gesture Recognition with Multiple Leap Motion Devices

Gesture recognition is often used in various fields like sign language recognition, entertainment, virtual and augmented reality applications and many others. Vision based approaches with such devices like Leap Motion controllers and Kinect are often used in such systems and allow to achieve high performance in terms of recognition accuracy. This paper shows how one can overcome the most common problem of such systems - self-occlusion. In this paper we propose the method for the hand gesture recognition which requires 3 Leap Motion controller devices. This work shows that increasing the number of cameras can leads to better recognition accuracy. In addition we provide the methods of creating camera setup.
ScriptProtect

The direct client-side inclusion of cross-origin JavaScript resources in Web applications is a pervasive practice to consume third-party services and to utilize externally provided libraries. The downside of this practice is that such external code runs in the same context and with the same privileges as the first-party code. Thus, all potential security problems in the code directly affect the including site. To explore this problem, we present an empirical study which shows that more than 25% of all sites affected by Client-Side Cross-Site Scripting are only vulnerable due to a flaw in the included third-party code. Motivated by this finding, we propose ScriptProtect, a non-intrusive transparent protective measure to address security issues introduced by external script resources. ScriptProtect automatically strips third-party code from the ability to conduct unsafe string-to-code conversions. Thus, it effectively removes the root-cause of Client-Side XSS without affecting first-party code in this respective. As ScriptProtect is realized through a light-weight JavaScript instrumentation, it does not require changes to the browser and only incurs a low runtime overhead of about 6%. We tested its compatibility on the Alexa Top 5,000 and found that 30% of these sites could benefit from ScriptProtect's protection today without changes to their application code.
Efficient Interaction-based Neural Ranking with Locality Sensitive Hashing

Interaction-based neural ranking has been shown to be effective for document search using distributed word representations. However the time or space required is very expensive for online query processing with neural ranking. This paper investigates fast approximation of three interaction-based neural ranking algorithms using Locality Sensitive Hashing (LSH). It accelerates query-document interaction computation by using a runtime cache with precomputed term vectors, and speeds up kernel calculation by taking advantages of limited integer similarity values. This paper presents the design choices with cost analysis, and an evaluation that assesses efficiency benefits and relevance tradeoffs for the tested datasets.
Holistic Strategy-Based Threat Model for Organizations

Data breaches, privacy violations and cyber-attacks are growing problems for companies and governmental organizations. Threat modelling serves as a heuristic procedure of methodological validation of organizations, system designs, software architectures to identify threats. The earlier this happens in the design process, the more cost-effective it is to identify and fix security vulnerabilities and therefore it reduces the possibility of risk happening. Classical literature sources and Internet sources offer different representations of attacker strategies and threat classifications. It is often difficult to apply these schemes to one’s own organization and often the size of them is comprehensible only for experts. In order to improve the understanding of security threats, particularly in the management levels, we provide a structured overview of the most common threat classification schemes and propose a classification model focusing on threats that first considers the specific organization and in a further step presents the courses of action of an attacker in this organization.
Hybrid Attention for Chinese Character-Level Neural Machine Translation

This paper proposes a novel character-level neural machine translation model which can effectively improve the Neural Machine Translation (NMT) by fusing word and character attention information. In our work, the bidirectional Gated Recurrent Unit (GRU) network is utilized to compose word-level information from the input sequence of characters automatically. Contrary to traditional NMT models, two kinds of different attentions are incorporated into our proposed model: One is the character-level attention which pays attention to the original input characters; The other is the word-level attention which pays attention to the automatically composed words. With the two attentions, the model is able to encode the information from the character level and word level simultaneously. We find that the composed word-level information is compatible and complementary to the original input character-level information. Experimental results on Chinese-English translation tasks show that the proposed model can offer a boost of up to +1.92 BLEU points over the traditional word based NMT models. Furthermore, our translation performance is also comparable to the latest outstanding models, including the state-of-the-art.
BlocKit

Blockchain is a disruptive technology which has significantly challenged assumptions that underpin financial institutions, and has provoked innovation strategies that have the potential to change many aspects of the digital economy. However, because of its novelty and complexity, mental models of blockchain technology are difficult to acquire. Building on embodied cognition theories and material centered-design, we report an innovative approach for the design of BlocKit, a physical three-dimensional kit for materializing blockchain infrastructure and its key entities. Through an engagement with different materials such as clay, paper, or transparent containers we identified important properties of these entities and materialized them through physical artifacts. BlocKit was evaluated by 15 experienced bitcoin users with findings indicating its value for their high level of engagement in communicating about, and designing for blockchain infrastructure. Our study advances an innovative approach for the design of such kits, an initial vocabulary to talk about them, and design implications intended to inspire HCI researchers to engage in designing for infrastructures.
Lifelong representation learning in dynamic attributed networks

Network embedding or network representation learning aims at learning a low-dimensional vector for each node in a network. The learned embeddings could advance various learning tasks in the network analysis area. Most existing embedding methods focus on plain and static networks while ignoring network dynamics. However, in real world networks, structure often evolves over time. In addition, many networks contain rich attributes and their attributes are changing over time. Naively applying existing embedding algorithms to each snapshot of dynamic network independently usually leads to unsatisfactory performance. In this paper, we present a Lifelong Dynamic Attributed Network Embedding Framework – LDANE. LDANE has a good mechanism to automatically expand the deep neural networks with the sizes of network growing and preserving what have learned from previous time steps. Furthermore, the proposed framework is carefully designed to flexibly incorporate the topology and attribute of nodes. We perform extensive experiments on both synthetic and real attributed networks to corroborate the significant advantages of the proposed framework compared with the existing state-of-the-art embedding techniques.
Optimal transport, mean partition, and uncertainty assessment in cluster analysis

Abstract In scientific data analysis, clusters identified computationally often substantiate existing hypotheses or motivate new ones. Yet the combinatorial nature of the clustering result, which is a partition rather than a set of parameters or a function, blurs notions of mean, and variance. This intrinsic difficulty hinders the development of methods to improve clustering by aggregation or to assess the uncertainty of clusters generated. We overcome that barrier by aligning clusters via optimal transport. Equipped with this technique, we propose a new algorithm to enhance clustering by any baseline method using bootstrap samples. Cluster alignment enables us to quantify variation in the clustering result at the levels of both overall partitions and individual clusters. Set relationships between clusters such as one‐to‐one match, split, and merge can be revealed. A covering point set for each cluster, a concept kin to the confidence interval, is proposed. The tools we have developed here will help address the crucial question of whether any cluster is an intrinsic or spurious pattern. Experimental results on both simulated and real data sets are provided. The corresponding R package OTclust is available on CRAN.
Protecting RISC-V Processors against Physical Attacks

RISC-V is an emerging instruction-set architecture suitable for a wide variety of applications, which ranges from simple microcontrollers to high-performance CPUs. As an increasing number of commercial vendors now plans to adopt the architecture in their products, its security aspects are becoming a significant concern. For microcontroller implementations of RISC-V, one of the main security risks are attackers with direct physical access to the microchip. These physical attackers can perform highly powerful attacks that span from memory probing to power analysis up to fault injection and analysis. In this paper, we give an overview of the capabilities of attackers with direct physical device access, common threat models and attack vectors, and possible countermeasures. Besides, we discuss in more detail current approaches to secure RISC-V processors against fault injection attacks on the microchip itself. First, we show how to protect the control-flow against fault attacks by using an encrypted instruction stream and decrypting it on-the-fly in a newly added pipeline stage between the processor's fetch and decode unit. Second, we show how to protect conditional branches against fault injection by adding redundancy to the comparison operation and entangling the comparison result with the encrypted instruction stream. Finally, we discuss an approach to protect all pointers and memory accesses from tampering.
Validation of Random Dataset Using an Efficient CNN Model Trained on MNIST Handwritten Dataset

Image processing and Deep learning are two zones of excessive awareness to researchers and scientists around the world. It is having multiple applications fields such as robotics, medicine, and security and surveillance. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. MNIST data set is having a huge number of handwritten text data set and it is frequently used for training, testing, and validation of CNN deep model. In this article, we have created an efficient model with multiple convolutions, relu and pooling layers. Which is tested on MNIST data set with 98.45% accuracy? Further, this model is tested on similar kind of random image data set which gives significant results in terms of accuracy.
Methodology for EM Fault Injection: Charge-based Fault Model

Recently electromagnetic fault injection (EMFI) techniques have been found to have significant implications on the security of embedded devices. Unfortunately there is still a lack of understanding of EM fault models and countermeasures for embedded processors. For the first time, this paper proposes an extended fault model based on the concept of critical charge and a new EMFI backside methodology based on over-clocking. Results show that exact timing of EM pulses can provide reliable repeatable instruction replacement faults for specific programs. An attack on AES is demonstrated showing that the EM fault injection requires on average less than 222 EM pulses and 5.3 plaintexts to retrieve the full AES key. This research is critical for ensuring embedded processors and their instruction set architectures are secure and resistant to fault injection attacks.
Towards Blockchain Tactics: Building Hybrid Decentralized Software Architectures

Blockchain-based applications usually consist of centralized elements (e.g., web servers and back-end logic) connected to decentralized elements such as smart contracts. The engineering of such hybrid software architectures poses a challenge as it is unclear which elements should be centralized or decentralized. Furthermore the impact of this decision (or the balance between those two areas) on software quality attributes such as security, maintainability, performance or costs is currently unknown. The goal is to build a software architecture using the benefits and handling the challenges of blockchain technology while fulfilling the relevant quality attributes. While there are several approaches examining the relation between architectural decisions and quality attributes in centralized systems, research is at an early stage for decentralized elements in software architectures. This paper presents a first step towards architectural blockchain tactics. With a simplified experiment comparing two implementation variants of an Ethereum smart contract we show that software design patterns are not always beneficial and that the expected usage scenarios have a strong impact on the operational costs. We argue that further research and validation is necessary for gaining more qualitative and quantitative insights to make informed architectural design decisions when using blockchain technology and give a first outline on how to achieve this.
A competence framework for artificial intelligence research

While over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating "artificial minded intelligence" (AMI) that can help to unify existing approaches and provide new avenues for research. I first outline three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the "generative methodology," and Section 3 develops a version of this methodology, the "Competence Framework for AI research" (CFAI).
Virtual titration laboratory experiment with differentiated instruction

Abstract This paper studies users' learning perception in a virtual titration experiment with differentiated instruction. We develop a virtual reality chemistry lab and use leap motion to detect users' hand gestures for operations. Users wear a head‐mounted display and use their bare hands to interact with virtual objects to perform a titration experiment. Our system implements a complete titration process and provides assistance tools for learning and operating virtual items. We report the essential ideas for building the system. We applied differentiated instruction to study the learning effectiveness of users under different learning intensities. Two groups of students with and without chemistry background participated in a user study. Our results indicated that the virtual reality chemistry lab could enhance and promote users' learning confidence under suitable learning intensities.
Reconstruction of three-dimensional anisotropic media based on analysis of morphological completeness

The physical properties of porous media can be more effectively observed and understood when analyzing three-dimensional (3D) structures. The use of computer-based mathematical modeling methods has been proven effective for reconstructing 3D structures of porous media from two-dimensional (2D) images. However, reconstructing a heterogeneous 3D structure from a single 2D image is currently challenging for computer-based mathematical modeling methods. Focusing on the reconstruction of heterogeneous 3D structures from a single anisotropic training image (TI), this paper first introduces a morphological completeness analysis method, which presents a completely new approach to reconstruction in which only two sections are controlled. Then, to enhance the constraint on the simulation points in this new approach to reconstruction, a double-layer and multi-grid system (DLMGS) is proposed. The updated pattern density function simulation, based on the DLMGS, is then taken as the specific reconstruction algorithm in this work. Finally, our reconstruction method is tested using a core image, and the performance of the reconstruction result is assessed using multiple functions. Detailed experiments show that our method can successfully reconstruct a heterogeneous 3D structure from a single anisotropic image, which reduces the required dimensionality of input data for 3D reconstruction and extends the applicable scope.
Witness-based searchable encryption with optimal overhead for cloud-edge computing

The security and utilization of cloud storage are well-known open issues. Public key encryption with keyword search (PEKS) provides an important primitive to discuss applications in the field of cloud storage, where the receiver can use the trapdoor of the keyword to search. However, the internal attack derived by the cloud server is an inherent issue of PEKS. There are two breakthroughs, so-called authenticated PEKS (APKES) and witness-based searchable encryption (WBSE), to tackle this issue by modifying framework of PEKS. The existing schemes of APEKS and WBSE can offer security against the internal attacks, but they require the trapdoor size is proportional to cn where n is the total number of senders, and c is the constant size of the ciphertext or proportion to the security parameter. In this paper, we propose a new WBSE scheme with constant overhead as long as n is bounded. We analyze the security of our scheme based on homomorphic encryption and digital signature. Finally, we provide implementations to show the effectiveness on the cloud-edge model.