Reviewerly/exHarmony huggingface dataset:
Deleted file fragment dating by analysis of allocated neighbors

Timestamps play a substantial role during digital forensic investigations and address two main objectives. First, they serve as a primary culling criterion to reduce the amount of digital evidence subject to analysis. Second, timestamps are the sole feature that allows reliable reconstruction of time-lines and they assist in locating temporal anomalies. File fragments, typically from previously deleted or relocated content, are often useful, especially when intact files are unavailable. Such fragments rarely contain embedded timestamps or have file-system timestamp information, which renders them less useful. In this work, we investigate and propose a framework for determining a time-window for deleted file fragments that are typically found in un-allocated space and file slack. We hypothesize that using the known temporal state of neighboring clusters allows us to derive a date-and-time range for when the file fragment was first written to media until it was subsequently deleted.
Bias-Corrected Q-Learning With Multistate Extension

Q-learning is a sample-based model-free algorithm that solves Markov decision problems asymptotically, but in finite time, it can perform poorly when random rewards and transitions result in large variance of value estimates. We pinpoint its cause to be the estimation bias due to the maximum operator in Q-learning algorithm, and present the evidence of max-operator bias in its Q value estimates. We then present an asymptotically optimal bias-correction strategy and construct an extension to bias-corrected Q-learning algorithm to multistate Markov decision processes, with asymptotic convergence properties as strong as those from Q-learning. We report the empirical performance of the bias-corrected Q-learning algorithm with multistate extension in two model problems: A multiarmed bandit version of Roulette and an electricity storage control simulation. The bias-corrected Q-learning algorithm with multistate extension is shown to control max-operator bias effectively, where the bias-resistance can be tuned predictably by adjusting a correction parameter.
A Scaler Design for the RNS Three-Moduli Set {2n+1−1,2n,2n−1} Based on Mixed-Radix Conversion

Adopting the moduli set [Formula: see text] for different DSP applications instead of the traditional moduli set [Formula: see text] has the advantage of excluding modulus [Formula: see text]. A multiply-and-accumulate modulo [Formula: see text] unit is more demanding than a modulo [Formula: see text] unit, which signifies the importance of this adoption. This paper introduces a new design for a scaling unit “Scaler”, that deals with the arithmetic-friendly residue number system (RNS) moduli set [Formula: see text]. The scaling factor is the power-of-two moduli [Formula: see text]. The scaling algorithm is based on the mixed-radix conversion (MRC) technique, which converts RNS-based representation into a weighted representation. The proposed approach is compared with other functionally-identical or functionally-similar scalers that perform scaling for the same moduli set under consideration or for the moduli set [Formula: see text]. The comparison is carried using theoretical unit-gate approach and experimental VLSI layout approach. The proposed scaler is shown to be more area and power-efficient than recently published competitive works.
Occlusion expression recognition based on non-convex low-rank double dictionaries and occlusion error model

Aiming at effectively correcting recognition error caused by partial facial occlusion, and simultaneously reducing the influence of facial individual′s difference on expression recognition, a novel method based on double dictionaries got by non-convex low-rank decomposition and occlusion error model is proposed in this paper. There are two steps in the training stage: 1° we use logarithmic determinant function for rank minimization problem of facial feature matrix, whose singular value fluctuates rather largely among different dimensions and causes that normal nuclear norm function cannot effectively estimate the minimal rank of the matrix. 2° non-convex low-rank decomposition algorithm is used to learn double dictionaries for expression features and individual identity features, respectively, and then an intra-class related dictionary and an inter-class structured dictionary are obtained. Three steps are taken in test stage: 1° we establish an occlusion error model to represent bias information of an occluded test sample, which is represented in a single matrix and added into iteration reconstructing process of the occluded test sample, based on double dictionaries obtained in the training stage. 2° The occluded test sample is decomposed into three sub-images, including expression feature, identity feature and occlusion error. 3° The occluded test sample is represented by the decomposed expression feature sub-image and then is classified into corresponding expression category. The following experiments in CK+ and KDEF expression databases show that, the proposed method is robust to the recognition of random occlusion facial expression images.
Enhanced Twofold-LDA Model for Aspect Discovery and Sentiment Classification

There is a need to automatically classify information from online reviews. Customers want to know useful information about different aspects of a product or service and also the sentiment expressed towards each aspect. This article proposes an Enhanced Twofold-LDA model (Latent Dirichlet Allocation), in which one LDA is used for aspect assignment and another is used for sentiment classification, aiming to automatically determine aspect and sentiment. The enhanced model incorporates domain knowledge (i.e., seed words) to produce more focused topics and has the ability to handle two aspects in at the sentence level simultaneously. The experiment results show that the Enhanced Twofold-LDA model is able to produce topics more related to aspects in comparison to the state of arts method ASUM (Aspect and Sentiment Unification Model), whereas comparable with ASUM on sentiment classification performance.
An Embodied Cognitive Assistant for Visualizing and Analyzing Exoplanet Data

In this paper, we describe a cognitive assistant that helps astrophysicists visualize and analyze exoplanet data via natural interactions. When users speak about and point to objects of interest, the assistant finds relevant data or performs requested computations, shows the results on a large display wall, and explains them with synthesized speech.
Didactic trends in LIS education and their reflection in curricula design

The information disciplines are rapidly developing and that development brings twofold challenge for library and information science (LIS) education; in terms of delivering the content, and in terms of the content itself. The former means that, as with any other discipline, it needs to implement ne w technologies and didactic approaches to provide its students with better learning experiences, greater retention of knowledge, and skills for rapidly changing and challenging labour market. The latter means that LIS education needs to train its students to understand how technical and social changes affect diverse information ecologies and successfully work in such environments. To address these ideas this paper aims to investigate how trends in LIS education are identified, implemented and evaluated in the LIS study programmes, both in terms of technology used for teaching, and in terms of teaching methods. The methodology used to address the research questions and objectives consisted of a literature review of the topic, a content analysis of LIS curricula from a sample of eight European universities that offer education in the LIS field, and an interview study with programme directors from the respective universities. The results have shown that there is a strong and positive culture of innovation in the LIS field that derives from the identity of the discipline and its natural inclination towards responding promptly to social and technical changes. Diverse didactic methods are applied in the analysed study programmes with an aim to offer a more flexible, authentic and individually tailored learning experience. There are obstacles in implementing innovations, and they usually arise from a mismatch between efforts to innovate and how university policies and procedure support those efforts. Another significant obstacle is the general learning culture of a country.
Critter

Checklists and guidelines have played an increasingly important role in complex tasks ranging from the cockpit to the operating theater. Their role in creative tasks like design is less explored. In a needfinding study with expert web designers, we identified designers' challenges in adhering to a checklist of design guidelines. We built Critter, which addressed these challenges with three components: Dynamic Checklists that progressively disclose guideline complexity with a self-pruning hierarchical view, AutoQA to automate common quality assurance checks, and guideline-specific feedback provided by a reviewer to highlight mistakes as they appear. In an observational study, we found that the more engaged a designer was with Critter, the fewer mistakes they made in following design guidelines. Designers rated the AutoQA and contextual feedback experience highly, and provided feedback on the tradeoffs of the hierarchical Dynamic Checklists. We additionally found that a majority of designers rated the AutoQA experience as excellent and felt that it increased the quality of their work. Finally, we discuss broader implications for supporting complex creative tasks.
Instant Secure Mobile Payment Scheme

We propose a new communication network that directly connects banks to their customers through mobile phones.Our scheme aims to fulfill the needs of all stakeholders, achieve a high level of security without sacrificing efficiency, and have a reliable infrastructure.It will eliminate new expenses, minimize the overheads on all stakeholders, and result in time savings in implementation and marketing.All bank services will become readily available anytime and anywhere; besides, it will enable individuals, point of sale, service provider, or any other entity to access their own bank accounts.It will also relieve users from the need to carry cash or credit cards since they will be able to pay bills, transfer money, receive payments, or make reservations by using their mobile phone in real time.
ImagIngDev: A New Approach for Developing Automatic Cross-Platform Mobile Applications Using Image Processing Techniques

Abstract The aim of this work is propose and describe ImagIngDev, a new approach for developing automatic cross-platform mobile applications using image processing techniques. As proof of concept, we presented ImagIng Tool and compared its performance with respect to similar cross-platform application development tools. Our main contribution to software development is ImagingDev, a new, intuitive and agile approach aimed at novice developers for automatically developing cross-platform mobile applications. Our proof of concept, ImagIng Tool, can generate mobile applications on four different software platforms: Android™, Windows Phone™, iOS™ and FirefoxOS™. Also, it relies on image processing techniques to recognize user interface design patterns (UIDPs) inside user interfaces, and generates the source code for cross-platform and multi-device applications. As proof of concept, we developed ImagIng Tool for automatic generation of cross-platform mobile applications by using image processing techniques. Results from the evaluation demonstrated that ImagIng Tool has attractive benefits if compared to similar cross-platform application development tools. Such benefits include higher learnability and usability. ImagIngDev and ImagIng Tool can effectively solve current problems in mobile applications development, such as automatic code generation for cross-platform mobile applications, since they allow users to generate cross-platform and multi-device mobile applications in an easy and intuitive way.
Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features

In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players' behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers. The experimental results demonstrate that the proposed deep learning behavior system achieves an accuracy of greater than 95% compared to the proposed dataset.
Investigation Into Cloud Computing Adoption Within the Hedge Fund Industry

Cloud computing is not associated with a specific technology, instead it as an alternative method to deliver technology as a service. This article investigates current cloud computing adoption in the United States (USA) and United Kingdom (UK) hedge fund industry. Hedge fund technologists, prime service consultants, technology service providers, industry application vendors, investors and an independent information security consultant participated were surveyed for this article. The article acknowledges the growth of cloud computing in the hedge fund sector. This research work also highlights that the private cloud definition is vague and requires further classification, elaborating on the variants of private cloud. This is important as the variants of private cloud computing offer varying benefits and risk which the hedge fund sector has proven to be sensitive. Equally, this article argues that some of the current security concerns are over-stated and perhaps reflect a conservative decision making framework rather than a realistic consideration of the options.
Scalable Deep Multi-Agent Reinforcement Learning via Observation Embedding and Parameter Noise

In this paper, we explore a scalable deep reinforcement learning (DRL) method for environments with multi-agents. Due to the explosive increase of the input dimensionality with the number of agents, most existing DRL methods are only able to cope with single-agent settings, or for only a small number of agents. To address this problem, we adopt a centralized training with decentralized execution framework, in which an observation embedding is used to reduce the curse of dimensionality. Perturbations are injected into the parameter space of the actor network for each agent to encourage exploration. The experiments demonstrate the effectiveness of the proposed approach on both cooperative and competitive environments.
Fuzzy knowledge based performance analysis on big data

Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM) algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM) called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM.
A varying-gain recurrent neural-network with super exponential convergence rate for solving nonlinear time-varying systems

In order to solve a nonlinear time-varying system, a novel varying-gain recurrent neural network (termed as VG-RNN) is proposed and analyzed. To achieve a fast convergent performance, a vector-based unbounded error function is first defined. Second, a varying-gain neural dynamic approach is employed to design the recurrent neural network formula. Being different from the traditional constant-gain recurrent neural networks with fixed design parameters such as the gradient-based neural network (termed as GNN) and the zeroing neural network (termed as ZNN), the gain coefficient of the proposed VG-RNN is time-varying, which can change with time evolves. Otherwise, compared to the previous numerical methods on solving nonlinear time-varying systems, the solution obtained by VG-RNN is more precise. Third, rigorous mathematics analysis proves the super exponential convergence and accuracy of the proposed VG-RNN. Numerical experiments demonstrate the high accuracy, effectiveness and superiority of the VG-RNN compared with the conventional neural networks for solving nonlinear time-varying systems. Furthermore, we hope to apply the theory proposed in this paper to practical nonlinear time-varying automatic control systems, such as robots with nonlinear time-varying systems.
A Scalable Image/Video Processing Platform with Open Source Design and Verification Environment

This paper presents a scalable image/video processing platform on Field-Programmable Gate Array (FPGA), capable of capturing images via a low-cost OV7670 camera and in real time displaying the original, in-process, and final results of images on a VGA-interfaced monitor. To make the platform expandable and reusable, not only is the design with Verilog hardware description language (HDL) offered, but also the verification environment including six open verification components (OVCs) is provided. To the best of our knowledge, this proposed work costs the least FPGA resource (753 LUTs and 277 Register) compared to the existing open source implementations on the design of the Camera-FPGA-VGA data path. We make this platform publicly available to encourage future research projects on image/video processing, computer vision, machine learning, etc., and to serve educational studies on digital system design with Verilog HDL and FPGAs.
Small Memory Footprint Neural Network Accelerators

Deep Neural Network (DNN) accelerators provide high-accuracy data recognition that are commonly used in edge devices. However, resource constrained edge devices usually have small form factors and limited amounts of resources, whereas DNN accelerators require large amounts of computations and memory footprint. In particular, the memory requirements of DNNs become a major bottleneck for realization of accelerators on small footprint devices. To reduce the memory footprint, fused-layer convolutional neural network (CNN) accelerators have been proposed for image recognition networks. Fused-layer CNNs reduce off-chip memory accesses by executing a set of CNN layers on-chip and by replacing the off-chip memory accesses with on-chip memory accesses between the layers. Although fused-layer CNNs reduce the off-chip memory accesses, they still require large amounts of on-chip memories that occupy significant chip area, making impractical their realization on small footprint devices. We address this problem by proposing a design technique that generates fused-CNN accelerators with small memory footprints, demonstrate its potential via a case study.
Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization

Concept drift is a change of the underlying data distribution which occurs especially with streaming data. Besides other challenges in the field of streaming data classification, concept drift should be addressed to obtain reliable predictions. The Robust Soft Learning Vector Quantization has already shown good performance in traditional settings and is modified in this work to handle streaming data. Further, momentum-based stochastic gradient descent is applied to tackle concept drift passively due to increased learning capabilities. The proposed work is tested against common benchmark algorithms and streaming data in the field.
MSnet: Multi-Head Self-Attention Network for Distantly Supervised Relation Extraction

Distant supervision for relation extraction is a task of recognizing semantic relations between entities in a large amount of plain text weakly supervised by external knowledge bases, which can benefit many NLP applications, such as knowledge graph completion and question answering.While it significantly alleviates the expensive cost for data labeling, it severely suffers from noisy labels.In this paper, we propose a Multi-head Self-attention Network (MSNet)-based label denoising method for relation extraction.More specifically, we encode the words, entities and their positions information into contextual embeddings via a multi-head self-attention mechanism, then extract the discriminative sentence features with max pooling operation.MSNet can capture the inherent structure of a sentence and model the relatedness between two words without regard to their distance.Moreover, we adopt a novel label confidence learning method to correct the noisy labels.A latent label is predicted step by step during training as the ground-truth according to a curriculum function of label confidence.This label denoising mechanism gradually incorporates the obtained latent label of easy relation patterns into later latent label prediction of hard patterns, which makes latent label consistent learning more reliable.To verify the effectiveness of our proposed method, in addition to the widely used PCNN-based architecture, we also perform the experiment on BiLSTM model as a comparison.The results demonstrate that our approach can outperform the state-of-the-art systems on the popular evaluation dataset.