Reviewerly/exHarmony huggingface dataset:
Biclustering and boolean matrix factorization in data streams

We study clustering of bipartite graphs and Boolean matrix factorization in data streams. We consider a streaming setting in which the vertices from the left side of the graph arrive one by one together with all of their incident edges. We provide an algorithm which after one pass over the stream recovers the set of clusters on the right side of the graph using sublinear space; to the best of our knowledge this is the first algorithm with this property. We also show that after a second pass over the stream the left clusters of the bipartite graph can be recovered and we show how to extend our algorithm to solve the Boolean matrix factorization problem (by exploiting the correspondence of Boolean matrices and bipartite graphs). We evaluate an implementation of the algorithm on synthetic data and on real-world data. On real-world datasets the algorithm is orders of magnitudes faster than a static baseline algorithm while providing quality results within a factor 2 of the baseline algorithm. Our algorithm scales linearly in the number of edges in the graph. Finally, we analyze the algorithm theoretically and provide sufficient conditions under which the algorithm recovers a set of planted clusters under a standard random graph model.
Emotional Tendency Analysis of Twitter Texts Based on CNNs Model

With the development of mobile communication technology, increasing online social media provides opportunities for people to express their views, which plays an important role in public opinion analysis and monitoring. The main topic of this study is the emotional analysis of Twitter English users. Reptile technology is applied to obtain and clean tweets related to China, after tagging data referred the Dictionary of strong words which is composed of statistical word frequencies, GloVe is used to generate word vectors and convolutional neural networks (CNN) is used to automatically classify tweets. Thus an accurate CNN model is established to analyze the popular tweets related to China from 2014 to 2018. The results show that China occupies an important position in world politics in 2018 with more negative Twitter comments. The number of supporters is the largest in 2014, but it has a sudden decline in 2015 and remains around 45% from 2016 to 2018.
Automatic RNN Cell Design for Knowledge Tracing using Reinforcement Learning

Empirical results have shown that deep neural networks achieve superior performance in the application of Knowledge Tracing. However, the design of recurrent cells like long short term memory (LSTM) cells or gated recurrent units (GRU) is influenced largely by applications in natural language processing. They were proposed and evaluated in the context of sequence to sequence modeling, like machine translation. Even though the LSTM cell works well for knowledge tracing, it is unknown if its architecture is ideally suited for knowledge tracing. Despite the fact that there are several recurrent neural network based architectures proposed for knowledge tracing, the methodologies rely on empirical observations and trial and error, which may not be efficient or scalable. In this study, we investigate using reinforcement learning for the automatic design of recurrent neural network cells for knowledge tracing, showing improved performance compared to the LSTM cell. We also discuss a potential method for model regularization using neural architecture search.
An empirical study to investigate the impact of data resampling techniques on the performance of class maintainability prediction models

With the increasing complexity of the software systems nowadays, the trend has been shifted to object-oriented (OO) development. The classes are the central construct in an OO software that are expected to be of utmost quality and high maintainability. The maintainability of a class is the probability that a class can be effortlessly modifiable in the maintenance phase. Unfortunately, it is very tough to determine the maintainability of a class with confidence before the release of the software. However, maintainability can be predicted with the help of internal quality attributes (viz. complexity, cohesion coupling, inheritance, etc.). The researchers in the literature have studied the relation amongst the internal quality attributes and class maintainability. Many class maintainability prediction models have been developed in the past with the help of internal quality attributes. Effective prediction models are vital to forecast class maintainability accurately. However, various datasets used to build prediction models for class maintainability suffer from imbalanced data problem. In that scenario, a model trained with imbalanced data gives erroneous predictions of class maintainability, which results in the inaccurate allocation of testing and maintenance resources to the misclassified classes. Therefore towards this direction, this study assesses the applicability of techniques to take care of imbalanced data. In this study the imbalanced data is treated with nine oversampling and three undersampling methods. A comprehensive comparison of fourteen machine learning (ML) techniques and fourteen search based (SB) techniques is conducted for class maintainability prediction. The results of the study support the applicability Safe-Level Synthetic Minority Oversampling Technique (Safe-SMOTE) to handle the imbalanced data for class maintainability prediction.
Deluceva: Delta-Based Neural Network Inference for Fast Video Analytics

Modern video analytics requires efficient machine learning model serving and evaluation. We present Deluceva, a system that optimizes video applications by applying incremental and approximate computation techniques. Experiments on three real models and six videos show that our prototype system can achieve significant performance gains up to 79% with F1 errors below 0.1.
Modeling Data Curation to Scientific Inquiry: A Case Study for Multimodal Data Integration

Scientific data publications may include interactive data applications designed by scientists to explore a scientific problem. Defined as knowledge systems, their development is complex when data are aggregated from multiple sources over time. Multimodal data are created, encoded, and maintained differently, and even when reporting about identical phenomena, fields and their values may be inconsistent across datasets. To assure the validity and accuracy of the application, the data has to abide by curation requirements similar to those ruling digital libraries. We present a novel, inquiry-driven curation approach aimed to optimize multimodal datasets curation and maximize data reuse by domain researchers. We demonstrate the method through the ASTRIAGraph project, in which multiple data sources about near earth space objects are aggregated into a central knowledge system. The process involves multidisciplinary collaboration, resulting in the design of a data model as the backbone for both data curation and scientific inquiry. We demonstrate a) how data provenance information is needed to assess the uncertainty of the results of scientific inquiries involving multiple data sources, and b) that continuous curation of integrated datasets is facilitated when undertaken as integral to the research project. The approach provides flexibility to support expansion of scientific inquiries and data in the knowledge system, and allows for transparent and explainable results.
Teaching Cloud Computing: Motivations, Challenges and Tools

Teaching Cloud Computing is becoming crucial since this recent computing paradigm is used in many fields and it is changing the way we use the applications and the technology. As a matter of the fact, most of the applications that we use everyday through the web are based on cloud services. Unfortunately, the difficulty to set up a real testbed for students and, at the same time, the lack of an easy, open and collaborative educational material freely available make teaching Cloud Computing a hard task. In this paper we discuss the state of the art concerning teaching Cloud Computing and we propose education materials and tools that make Cloud Computing easy to use even for students/educators without any computer science skills.
Classification of Vehicle Images through Deep Neural Networks for Camera View Position Selection

This paper shows the experimentation for the classification of vehicle images by artificial vision creating a deep neural network model using Keras and Tensorflow libraries with Python language, a machine learning algorithm. The results are used to select the best camera position to record the vehicular traffic to identify the highway occupancy.
Hardware-Algorithm Co-Design of a Compressed Fuzzy Active Learning Method

Active learning method (ALM) is a powerful fuzzy-based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real-time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(2N) for a multi-input single-output (MISO) system. Also, the computational costs in both training and inference modes are decreased to only a few additions and multiplications. Furthermore, we develop a memory-efficient digital architecture for the proposed compressed ALM algorithm that can be leveraged for various computing systems through configuring a few registers. Finally, we assess the performance of the proposed approach using various function modeling and classification applications and provide a comparison with conventional ALM and some other well-know approaches. Simulation and hardware implementation results demonstrate that the proposed approach achieves reduced noise sensitivity with 128× reduction in the average memory usage while realizing comparable accuracy compared to the other approaches studied herein.
Attacker-centric thinking in security

In response to diverging perspectives on the usefulness of attacker-centric approaches in security, this paper examines the current role of such thinking in security, incorporating 12 in-depth interviews with senior financial services practitioners working in the areas of security, fraud and risk. The presentation of results is supported by a condensed systematic literature review on the topic and followed by the provision of a list of suggested guidelines on practical implementation strategies, enabling further theoretical reframing and extension.
An experimental study for software quality prediction with machine learning methods

Software quality estimation is an activity needed at various stages of software development. It may be used for planning the project's quality assurance practices and for benchmarking. In earlier previous studies, two methods (Multiple Criteria Linear Programming and Multiple Criteria Quadratic Programming) for estimating the quality of software had been used Also, C5.0, SVM and Neutral network were experimented with for quality estimation. These studies have relatively low accuracies. In this study, we aimed to improve estimation accuracy by using relevant features of a large dataset. We used a feature selection method and correlation matrix for reaching higher accuracies. In addition, we have experimented with recent methods shown to be successful for other prediction tasks. Machine learning algorithms such as Xgboost, Random Forest and Decision Tree are applied to the data to predict the software quality and reveal the relation between the quality and development attributes. The experimental results show that the quality level of software can be well estimated by machine learning algorithms.
Krill Herd Algorithm – Modifications and Applications

In this chapter a short description of krill herd optimization algorithm (KH) is presented. Next, some modifications of KH such as chaotic KH, levy-flight KH, multi-stage KH, stud KH, fuzzy KH, and binary-based KH are discussed. Finally, an application of KH to one of the real-world problems is addressed. To this end, the application of KH to the optimum design of shallow foundations as one of the most complicated geotechnical engineering problems is demonstrated.
MODERNIZING THE VOCATIONAL EDUCATIONAL TRAINING IN THE FIELD SOCIAL SCIENCES AND HUMANITIES TOWARDS THE KEY ENABLING TECHNOLOGIES IN THE DIGITAL TRANSFORMATION

MODERNIZING THE VOCATIONAL EDUCATIONAL TRAINING IN THE FIELD SOCIAL SCIENCES AND HUMANITIES TOWARDS THE KEY ENABLING TECHNOLOGIES IN THE DIGITAL TRANSFORMATION
JSKernel: Fortifying JavaScript against Web Concurrency Attacks via a Kernel-Like Structure

As portals to the Internet, web browsers constitute prominent targets for attacks. Existing defenses that redefine web APIs typically capture information related to a single JavaScript function. Thus, they fail to defend against the so-called web concurrency attacks that use multiple interleaved functions to trigger a browser vulnerability. In this paper, we propose JSKernel, the first generic framework that introduces a kernel concept into JavaScript to defend against web concurrency attacks. The JavaScript kernel, inspired from operating system concepts, enforces the execution order of JavaScript events and threads to fortify security. We implement a prototype of JSKernel deployable as add-on extensions to three widely used web browsers, namely Google Chrome, Mozilla Firefox, and Microsoft Edge. These open-source extensions are available at (https://github.com/jskernel2019/jskernel) along with a usability demo at (https://jskernel2019.github.io/). Our evaluation shows the prototype to be robust to web concurrency attacks, fast, and backward compatible with legacy websites.
Denoising of Sparse Three- and Four-dimensional Hyperspectral Electron Microscopy Data Using a Total Variational Method

Journal Article Denoising of Sparse Three- and Four-dimensional Hyperspectral Electron Microscopy Data Using a Total Variational Method Get access Steven Zeltmann, Steven Zeltmann University of California-Berkeley, Berkeley, California, United States Search for other works by this author on: Oxford Academic Google Scholar Andrew Minor, Andrew Minor University of California-Berkeley, Berkeley, California, United StatesLawrence Berkeley National Laboratory, Berkeley, California, United States Search for other works by this author on: Oxford Academic Google Scholar Colin Ophus Colin Ophus Lawrence Berkeley National Laboratory, Berkeley, California, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 26, Issue S2, 1 August 2020, Pages 1724–1726, https://doi.org/10.1017/S1431927620019121 Published: 01 August 2020
Improved Side-Channel Resistance by Dynamic Fault-Injection Countermeasures

Side-channel analysis and fault-injection attacks are known as serious threats to cryptographic hardware implementations and the combined protection against both is currently an open line of research. A promising countermeasure with considerable implementation overhead appears to be a mix of first-order secure Threshold Implementations and linear Error-Correcting Codes.In this paper we employ for the first time the inherent structure of non-systematic codes as fault countermeasure which dynamically mutates the applied generator matrices to achieve a higher-order side-channel and fault-protected design. As a case study, we apply our scheme to the PRESENT block cipher that do not show any higher-order side-channel leakage after measuring 150 million power traces.
Diversity in swarm robotics with task-independent behavior characterization

Evolutionary computation provides methods to automatically generate controllers for swarm robotics. Many approaches rely on optimization and the targeted behavior is quantified in form of a fitness function. Other methods, like novelty search, increase exploration by putting selective pressure on unexplored behavior space using a domain-specific behavioral distance function. In contrast, minimize surprise leads to the emergence of diverse behaviors by using an intrinsic motivation as fitness, that is, high prediction accuracy. We compare a standard genetic algorithm, novelty search and minimize surprise in a swarm robotics setting to evolve diverse behaviors and show that minimize surprise is competitive to novelty search.
Blockchain-Based Boothless E-Voting System

Indrason, Ngangbam Khongbuh, Wanbanker Saha, GoutamA vote is considered as one of the most important legal rights a citizen can practice in a democratic country. Presently, votes are cast in two manners: manual mode, i.e., paper voting and the semi-manual mode using e-voting machine (EVM). Both systems require the accumulation of the voters in centralized voting booths. Several discrepancies arise due to the lack of security management in a centralized voting system. These can be avoided if a decentralized voting system can be implemented thereby providing a high-security impact. Blockchain is a technology that acts as a backbone for the highly secured parallel economy like the Bitcoin system. If this technology can be implemented as a security measure in a decentralized e-voting system, this will ensure the total security and authenticity of the voting system. In this paper, an endeavor has been made to design a blockchain-based e-voting system. Initial experimentation displayed satisfactory prospects of the system.
Diversity-driven wide learning for training distributed classification models

In order to address scalability issues which are a challenge for Deep Learning methods, we propose Wide Learning --- a model that scales horizontally rather than vertically enabling distributed learning. This approach first trains a repertoire of architecturally diverse neural networks of low complexity in parallel. Each network in the repertoire extracts a set of features from the dataset; these are then aggregated in a second short training phase in a centralised model to solve the classification task. The repertoire is generated using a quality diversity evolutionary algorithm (MAP-Elites) which returns a set of neural networks which are diverse w.r.t. feature descriptors partially describing their architecture and optimised w.r.t. their ability to solve the task. The technique is shown to perform well on two benchmark classification problems which have been tackled in the literature with Deep Learning techniques. Additional experiments provide insight into the role that diversity plays in contributing to the performance of the repertoire. We propose that evolving neural networks by promoting architectural diversity could in future lead to better results in some domains where current approaches have fallen short.
Pinocchio: A Blockchain-Based Algorithm for Sensor Fault Tolerance in Low Trust Environment

As the advancement of the IoT market and technology, sensor-equipped devices belonging to different interest groups have more opportunities to cooperate, fulfilling an assignment. How to ensure an agreement on correct sensed data in such a low trust environment is a new challenge in nowadays market. To bridge the gap, we propose Pinocchio, a blockchain-based algorithm which can tolerate faults in WSN (Wireless Sensor Network) along with the ability to defend sensed data against malicious attacks of data tampering and masquerade. Compared to other distributed approaches of sensor fault tolerance, Pinocchio greatly reduced the message complexity of the entire network to O(N) and that of a single node to O(1). Considering the possible waste of resources brought about by vicious competition of hash power in the blockchain-based approach, we design the Geppetto algorithm to supervise and control hash power in a distributed manner, and its effectiveness is demonstrated by experiments.
Generic approaches for parallel rule matching in learning classifier systems

The XCS classifier system (XCS) constitutes the most deeply investigated evolutionary rule-based machine learning algorithm. Due to its online learning nature, it is not as computationally intense as deep learning approaches. However, in order to increase XCS's realtime capabilities, it is worth to improve its runtime for the inference step. A iteration through XCS's main loop involves various steps. Among them, matching usually constitutes the computationally most expensive part. Various ways for improvements have been proposed, e.g. using specialized hardware or vector commands. However, existing approaches require either a certain hardware coupled with a specific instruction set, or a dedicated multiprocessing programming language. In this context, parallel programs are often evaluated in terms of their speed up, but it turns out that qualitative criteria such as flexibility and independence of a specific hardware are also relevant. We therefore present a generic parallel approach for matching. We only rely on the minimum assumption of a multicore CPU with standard synchronization mechanisms, e.g., locks. Those assumptions are satisfied in almost all modern computing systems and high-level programming languages today. Thus we show that our approaches cannot only decrease the runtime, but are also reusable for most learning classifier systems and computing systems.
Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients

In this paper, we report a novel deep neural network framework for prediction of chemo-sensitivity in ovarian cancer patients. The proposed model is based on Multiple Instance Learning (MIL) and a novel variant of Learning using Privileged Information (LUPI). LUPI allows knowledge transfer from highly informative privileged features that are available only at training time to give improved generalization performance on input space features which are available in both training and inference. The proposed model is trained on image patches from Hematoxylin and Eosin (H&E) stained multi-gigapixel whole-slide images (WSIs, the input space) of ovarian cancer tissue sections and their associated gene expression profiles, the privileged feature space. Through cross-domain knowledge transfer with a novel combination of MIL and LUPI, we achieve improved generalization with a limited number of labeled examples in the input space. Informed by the privileged space model output based on relatively expensive and time-consuming gene expression profiles in its training, the proposed LUPI model can generate accurate predictions using routine WSI data alone at the time of inference. The proposed method paves the way for further applications of LUPI in computational pathology and medical image analysis by cross-domain learning especially in cases with a limited number of labeled examples in training.