Reviewerly/exHarmony huggingface dataset:
Extended artificial immune system–based optimized access control for big data on a cloud environment

Summary Integrating parallel computing and distributed computing together can be obtained by cloud computing (CC). One of the major problems faced by public CC is security regarding data access control. CC permits people to share data, documents, videos, and other types of data. Generally, the cloud data are considered as big data, because the volume of the data is huge and it has a greater number of varieties. In recent days, attribute‐based data sharing applied only for selected data is a crucial problem. One of the existing approaches encrypts data using various kinds of keys based on several types of cryptosystems. However, those kinds of methods have some weaknesses such as inability to handle the attributes effectively, storage of more unwanted copies of the same data, and policy changes. It needs a high amount of computational cost and reduces the efficiency of memory utilization and the computational speed. This paper motivated to design and implement an efficient approach for optimized access control (OAC) for data stored in the cloud to overcome these kinds of issues. The efficiency of the proposed method is proved through a simulation‐based experiment in Cloud Simulator.
A curriculum model for cybersecurity master’s program: A survey of AACSB-accredited business schools in the United States

There is an increasing recognition of cybersecurity’s importance to organizations, and business schools are in a unique position to meet industry’s demand for cybersecurity professionals. Using MSIS 2016 and National Centers of Academic Excellence Cyber Defense Education standards, the author investigates cybersecurity master’s programs in U.S. business schools. The author surveyed 529 Association to Advance Collegiate Schools of Business–accredited schools and identified 37 cybersecurity master’s programs; the five most frequently required courses are information technology security; network security; cybersecurity planning and management; information technology infrastructure; and policy, legal, ethics and compliance. The author also found that the master’s programs currently pivot their curriculums toward courses that are cybersecurity and technical in nature. Using survey data, the author develops a new, graduate-level cybersecurity curriculum model for business schools.
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA

Memory-augmented neural networks (MANNs) are designed for question-answering tasks. It is difficult to run a MANN effectively on accelerators designed for other neural networks (NNs), in particular on mobile devices, because MANNs require recurrent data paths and various types of operations related to external memory access. We implement an accelerator for MANNs on a field-programmable gate array (FPGA) based on a data flow architecture. Inference times are also reduced by inference thresholding, which is a data-based maximum inner-product search specialized for natural language tasks. Measurements on the bAbI data show that the energy efficiency of the accelerator (FLOPS/kJ) was higher than that of an NVIDIA TITAN V GPU by a factor of about 125, increasing to 140 with inference thresholding.
An error analysis for image-based multi-modal neural machine translation

In this article, we conduct an extensive quantitative error analysis of different multi-modal neural machine translation (MNMT) models which integrate visual features into different parts of both the encoder and the decoder. We investigate the scenario where models are trained on an in-domain training data set of parallel sentence pairs with images. We analyse two different types of MNMT models, that use global and local image features: the latter encode an image globally, i.e. there is one feature vector representing an entire image, whereas the former encode spatial information, i.e. there are multiple feature vectors, each encoding different portions of the image. We conduct an error analysis of translations generated by different MNMT models as well as text-only baselines, where we study how multi-modal models compare when translating both visual and non-visual terms. In general, we find that the additional multi-modal signals consistently improve translations, even more so when using simpler MNMT models that use global visual features. We also find that not only translations of terms with a strong visual connotation are improved, but almost all kinds of errors decreased when using multi-modal models.
Joint Structured Graph Learning and Clustering Based on Concept Factorization

As one of the matrix factorization models, concept factorization (CF) achieved promising performance in learning data representation in both original feature space and reproducible kernel Hilbert space (RKHS). Based on the consensuses that 1) learning performance of models can be enhanced by exploiting the geometrical structure of data and 2) jointly performing structured graph learning and clustering can avoid the suboptimal solutions caused by the two-stage strategy in graph-based learning, we developed a new CF model with self-expression. Our model has a combined coefficient matrix which is able to learn more efficiently. In other words, we propose a CF-based joint structured graph learning and clustering model (JSGCF). A new efficient iterative method is developed to optimize the JSGCF objective function. Experimental results on representative data sets demonstrate the effectiveness of our new JSGCF algorithm.
Do We Trust the Internet?

The threat profile of spyware is increasing rapidly when we consider the pervasiveness of computing in everyday life. The surreptitious nature of spyware can lead to information mining, data theft and exploitation of stolen data. This article aims to explore the relationship between trust in the Internet and neglecting risks in downloading and installing free potentially spyware-infected software. This study found that trust can be viewed as an indirect function of ignorance and overconfidence through the mechanism of the calculated probability of risk. An enhanced model of trust, based on Li and Betts' trust model has been proposed with two additional vectors. The inference drawn from the study is that in most users' minds, the calculated or perceived risk is substantially less than the anticipated benefit of the software. The analysis shows that users trust the Internet when it comes to downloading and installing potentially spyware-infected software, although such nonchalant trust appears to be based on ignorance and over-confidence.
Speech Act Theory as an Evaluation Tool for Human–Agent Communication

Effective communication in task-oriented situations requires high-level interactions. For human–agent collaboration, tasks need to be coordinated in a way that ensures mutual understanding. Speech Act Theory (SAT) aims to understand how utterances can be used to achieve actions. SAT consists of three components: locutionary act, illocutionary act, and perlocutionary act. This paper evaluates the agent’s verbal communication while collaborating with humans. SAT was used to anatomize the structure of the agent’s speech acts (locutionary acts), the agent’s intention behind the speech acts (illocutionary acts), and the effects on the human’s mental state (perlocutionary acts). Moreover, this paper studies the impact of human perceptions of the agent’s speech acts on the perception of collaborative performance with the agent.
Generating summary sentences using Adversarially Regularized Autoencoders with conditional context

Abstractive summarization is challenging problem, especially abstractive summarization based on unsupervised learning because it must generate whole, unique sentences. In the real world, companies use abstractive summarization to understand customer feedbacks. In many cases, this work is done by humans and so is expensive in terms of time and money. Therefore, there is an increasing demand for machine learning-based abstractive summarization systems. However, most previous abstractive summarization studies were of supervised models. In this paper, we proposed novel abstractive summarization methods that can be trained unsupervisedly. One of the proposed methods is based on Adversarially Regularized Autoencoder(ARAE) model, but abstractive summary generation method for each cluster of similar customers’ reviews, is newly proposed. We further proposed Conditional Adversarially Regularized Autoencoder(CARAE) model which is similar to the ARAE model but with the addition of condition nodes so that additional information about the cluster can be used during summarization. We first performed summary experiments based on Korean and additionally performed experiments on English. In the experiments, we set up some comparison models and used ROUGE and BLEU to evaluate our proposed models’ performance. Overall, our proposed models outperformed the comparison models and CARAE model performed better than the ARAE model.
Tracking a Person’s Behaviour in a Smart House

This paper proposes to use machine learning techniques with ultrasonic sensors to predict the behavior and status of a person when they live solely inside their house. The proposed system is tested on a single room. A grid of ultrasonic sensors is placed in the ceiling of a room to monitor the position and the status of a person (standing, sitting, lying down). The sensors readings are wirelessly communicated through a microcontroller to a cloud. An intelligent system will read the sensors values from the cloud and analyses them using machine learning algorithms to predict the person behavior and status and decide whether it is a normal situation or abnormal. If an abnormal situation is concluded, then an alert with be risen on a dashboard, where a care giver can take an immediate action. The proposed system managed to give results with accuracy exceeding 90%. Results out of this project will help people with supported needed, for example elderly people, to live their life as independent as possible, without too much interference from the caregivers. This will also free the care givers and allows them to monitors more units at the same time.
A Survey

An early vision in Computer Science was focused on Artificial Intelligence (AI) to produce intelligent systems capable of reasoning on huge amounts of data. Since the huge amount of data on the web are still formed and stored in relational databases (RDBs), such data cannot be used directly via the Semantic Web. Therefore, semantic integration of relational data sources into the Semantic Web is not a trivial task and has been addressed by developing approaches and tools for transformation RDB to ontology. The aim of this survey is to present the existing approaches contributed for integrating RDBs into Semantic Web in order to bridge the gap between RDB model and ontology model and to boost the current Web, by discussing the benefits and drawbacks of these approaches as well as to compare between them in specific terms.
Rahul Gandhi on Twitter: An Analysis of Brand Building Through Twitter by the Leader of the Main Opposition Party in India

The article analyses the tweets of Rahul Gandhi, President of the Indian National Congress (INC), the largest opposition party in India. The analysis covers his tweets over a period of around 6 months from the date he took over as the President of the INC. The study determines the main themes underlying his tweets, the frequency of words and phrases appearing in the text of the tweets and the sentiments conveyed through his Twitter communication. Also, as a special subset, a comparison of Gandhi’s tweets campaigning for elections to the legislative assembly of Karnataka, which were held during the sample period, has been made with those of his political rival, Prime Minister Narendra Modi, who is the most followed Indian on Twitter, and the relative strengths and weaknesses of Gandhi’s Twitter campaign in comparison with those of Modi have been brought out. Overall, the study highlights the effective use of Twitter as a political communication tool and how its data can be harnessed using different ways.
Steganalysis on Internet images via domain adaptive classifier

In recent years, various steganalysis algorithms have been proposed and achieved satisfactory performance. However, these conventional methods are not effective for mismatched steganalysis. In real world, there are millions of images captured by different cameras and users transmitted on the Internet every day. The steganalysis on Internet images will encounter steganographic algorithm mismatch (SAM) and cover source mismatch (CSM). Therefore, the steganalysis on the Internet is essentially to solve the mismatch problem. This paper proposes a method to solve the mismatched steganalysis on the Internet images by domain adaptation classifier. It makes the distribution between training and testing sets more similar to obtain better detection performance. We integrate joint distribution adaptation and geometric structure as regularization terms to a standard supervised classifier. Specifically, joint distribution adaptation contains marginal and conditional distributions. And considering the characteristics of steganalysis on the Internet images, we add the conditional regularization in the geometric structure to the existing algorithms. Experimental results (include SAM and CSM) on Internet images show that our method has a better performance than state-of-the-art methods.
fAST: Flattening Abstract Syntax Trees for Efficiency

Frequently source code analysis tools need to exchange internal representations of abstract syntax trees (AST) with each other. Conveniently, and intuitively, the externalised representations are in the form of hierarchical trees. We argue, counter-intuitively, that hierarchical representation is not the most efficient way for source analysis tools to exchange parsed AST. In this work, we propose to speed up AST parsing whilst preserving the equivalence of hierarchies in binary forms: (1) AST could be saved as a flat one-dimensional array where pointers to tree nodes are converted into integer offsets, and (2) such flattened AST are more efficient to access by programming tools through the generated application programming interfaces (API). In programming language-agnostic evaluations, we show that parsing flattened AST becomes 100x faster than in textual form AST on a benchmark of open-source projects of 6 different programming languages.
Robust Visual Tracking via Adaptive Occlusion Detection

Occlusion is a special challenge in visual tracking, which may cause target template corrupted by background information. In this paper, we propose an adaptive occlusion detection framework for robust tracking against occlusion. The framework consists of a patch tracker, an occlusion detector, a template updater and a search window predictor. The patch tracker applies KCF-based method to track background patch individually, which may occlude target. The occlusion detector searches for background patches occluding target with an adaptive threshold. The template updater evaluates the occlusion state and applies appropriate target template update strategy. The search window predictor adaptively rescales the size of search window based on occlusion state. Experiments in OTB50 demonstrate that our tracker achieves comparable performance compared with other state-of-art trackers and outperforms them in cases of occlusion.
Finding autofocus region in low contrast surveillance images using CNN-based saliency algorithm

How to automatically locate the focus region in low contrast image is a key issue for camera-equipped surveillance devices. Due to low signal to noise ratio, the performance of autofocus will seriously decline in low contrast image, making it quite difficult to recognize the focus region. To tackle this problem, we perform autofocus by conducting a salient object detection approach. A covariance based deep learning framework is proposed to evaluate the saliency of low contrast surveillance image. Based on the mechanism of human visual system, the autofocus region can be identified by the visual salient object. In this paper, low-level features of the low contrast images are first studied and extracted. Then the mutual covariances of the segmented blocks are trained via a 7-layers convolutional neural network (CNN). Next, the initial saliency map of the testing image can be obtained by estimating the saliency score of each block via the pre-trained CNN model. Finally, the resulting saliency map is refined by introducing the local-global difference and internal similarity approaches. Experimental results demonstrate that the proposed method outperforms existing ten state-of-the-art saliency models on three public datasets and a nighttime image dataset.
A Data Dependency and Access Threshold Based Replication Strategy for Multi-cloud Workflow Applications

Data replication is one of the significant sub-areas of data management in cloud based workflows. Data-intensive workflow applications can gain great benefits from cloud environments and usually need data management strategies to manage large amounts of data. At the same time, multi-cloud environments become more and more popular. We propose a cost-effective and threshold-based data replication strategy with the consideration of both data dependency and data access times for data-intensive workflows in the multi-cloud environment. Finally, the simulation results show that our approach can greatly reduce total cost of data-intensive workflow applications by considering both of data dependency and data access times in multi-cloud environments.
The impact of using different keyboards on free-text keystroke dynamics authentication for Arabic language

Purpose Nowadays, there is a high demand for online services and applications. However, there is a challenge to keep these applications secured by applying different methods rather than using the traditional approaches such as passwords and usernames. Keystroke dynamics is one of the alternative authentication methods that provide high level of security in which the used keyboard plays an important role in the recognition accuracy. To guarantee the robustness of a system in different practical situations, there is a need to examine how much the performance of the system is affected by changing the keyboard layout. This paper aims to investigate the impact of using different keyboards on the recognition accuracy for Arabic free-text typing. Design/methodology/approach To evaluate how much the performance of the system is affected by changing the keyboard layout, an experimental study is conducted by using two different keyboards which are a Mac’s keyboard and an HP’s keyboard. Findings By using the Mac’s keyboard, the results showed that the false rejection rate (FRR) was 0.20, whilst the false acceptance rate (FAR) was 0.44. However, these values have changed when using the HP’s keyboard where the FRR was equal to 0.08 and the FAR was equal to 0.60. Research limitations/implications The number of participants in the experiment, as the authors were targeting much more participants. Originality/value These results showed for the first time the impact of the keyboards on the system’s performance regarding the recognition accuracy when using Arabic free-text.
Model Checking Speculation-Dependent Security Properties: Abstracting and Reducing Processor Models for Sound and Complete Verification

Though modern microprocessors embed several hardware security mechanisms, aimed at guaranteeing confidentiality and integrity of sensible data, recently disclosed attacks such as Spectre and Meltdown witness weaknesses with potentially great impact on CPU security. Both vulnerabilities exploit speculative execution of modern high-performance micro-architectures, allowing the attacker to observe data leaked via a memory side channel, during speculated and mispredicted instructions. In this paper we present a methodology to formally verify, by means of a model checker, speculative vulnerabilities, such as the class of Spectre/Meltdown attacks, in microprocessors based on speculative execution. In detail, we discuss the problem of formally verifying confidentiality violations, since we deem it will help preventing new vulnerabilities of the same typology. We describe our methodology on a pipelined CPU inspired by the DLX RISC processor architecture. Due to scalability issues, and following related approaches in formal verification of correctness, our approach simplifies the model under verification by proper abstraction and reduction steps. The approach is based on flushing the pipeline, abstracting data and most of the speculative execution logic, keeping a subset of control data, plus speculated data state and tainting logic. Illegal propagation (data leakage) is encoded in terms of taint propagation, from a protected/invalid memory address to the address bus on a subsequent memory read, affecting the cache. We introduce the theoretical flow, relying on known theoretical results combined and exploited to prove soundness and completeness. Finally, using a state-of-the-art model checking tool, we present preliminary data on formal verification based on Bounded Model Checking, that to support our claims and highlight the feasibility of the approach.
Intelligent Control of Autonomous and Anthropocentric On-board Systems

Hybrid technology of intelligent control of autonomous and anthropocentric on-board systems is proposed. It is based on non-clausal prover for automatic reasoning and combines symbolic knowledge representation and processing with other control methods. If a control task is not provided with all necessary means of its solving, the prover is able to deduce conditions for the task solvability and to ask another intelligent agents for the missing means to continue the control task solving. Since a logical approach can lead to non-unique solutions, e.g., in the problems of target pursuit in the operative area, then also non-logical models and optimization procedures are used. Applications to motion control problems in a threat environment are considered.
Age-Related Decline in Storage and Processing Components of Working Memory: An Eye-Tracking Study

Objectives:The goal of this study was to explore the age-related decline in the storage and processing components of working memory in dual-tasks (storage trials and processing trials) using eye-tracking.Methods: A total of 39 participants participated in this study.Twenty-two participants were in the younger group (age, 21.45± 3.14 years) and 17 were in the elderly group (age, 66.94 ± 3.58 years).Storage items were based on a word span task.Processing items were based on a sentence comprehension task.These two trials were performed concurrently.Participants completed storage and processing trials while their eye movements were recorded using an eye-tracking device.Performance on this task was indexed (%) by scoring correct items, and proportion of fixation duration (PFD) by recording eye movement data.Results: The results showed that the elderly group demonstrated significantly less accuracy than the younger group on dual-tasks.Also, there was a significant group difference in PFD on processing trials.However, there was no significant group difference in PFD on storage trials.The results suggest that there is a greater age-related decline in processing components in comparison to the storage components of working memory.Conclusion: The results of this study suggest that elderly adults have difficulty in processing components; in particular, the eye movement working memory tasks, due to a reduction in resources such as working memory capacity.The sentence complexity of processing trials did not affect the storage trials, and this suggests that the storage and processing functions of working memory have individual resources.