Reviewerly/exHarmony huggingface dataset:
Provable secure lightweight attribute‐based keyword search for cloud‐based Internet of Things networks

Abstract As more data from Internet of Things (IoT) devices is outsourced to cloud service providers, the need for secure and efficient fine‐grained access control and data retrieving mechanisms will be increasingly pressing. Attribute‐based keyword search (ABKS) is a promising tool to provide the required infrastructures. However, most of the existing ABKS schemes suffer from expensive computational overhead on the user side. This feature makes such schemes unsuitable for IoT applications as IoT devices usually have limited computational resources. To address this problem, we design a lightweight ABKS scheme called LW‐ABKS. In our scheme, by performing a constant number of efficient computational operations, a data owner can generate a searchable ciphertext. Indeed, in generating a searchable ciphertext, the number of operations performed by data owners modeling IoT devices does not depend on the number of attributes in the access control policy. Moreover, in LW‐ABKS, an authorized data user can retrieve the outsourced data files by performing very lightweight computational operations. We also provide security definitions for the new primitive and prove its security in the standard model and under the hardness assumption of the discrete logarithm and the decisional bilinear Diffie‐Hellman problems.
Algorithm for Underwater Cable Tracking Using CLAHE based Enhancement

This paper proposes an algorithm for tracking cables from underwater images. The proposed algorithm goes through a pre-processing stage which aims at enhancing the image for facilitating the line detection process. Then the image is segmented using a clustering algorithm and the edges are detected using the Sobel operator. The detected edges are then passed through Hough Transform algorithm for getting the lines. The algorithm has been tested on underwater images having cables with different orientations and is seen that the cable gets tracked, whatever its position be. Cables having different colours on them pose a problem while choosing the number of clusters in the segmentation process. The proposed algorithm can be thus modified by introducing provisions for handling different colours on the same cable.
Food Waste Ontology: A Formal Description of Knowledge from the Domain of Food Waste

Recently, as a part of an EU-funded project called REFRESH, a new web-based tool named FoodWasteEXplorer was developed. It provides an easy access to valuable data on unavoidable food waste that can be explored by researchers, industry, governmental agencies and the public to find ways of its valorization. The food waste data was manually collected and stored in a relational database. To enrich and make best use of it, we automatically transform the collected information into a new food waste ontology. The created Food Waste Ontology provides a formal description of knowledge from the food waste domain. Examples of its application are: (i) database querying based on natural language questions, and (ii) finding new or missing data from other datasets.
Interaction Recognition Through Body Parts Relation Reasoning

Person-person mutual action recognition (also referred to as interaction recognition) is an important research branch of human activity analysis. It begins with solutions based on carefully designed local-points and hand-crafted features, and then progresses to deep learning architectures, such as CNNs and LSTMS. These solutions often consist of complicated architectures and mechanisms to embed the relationships between the two persons on the architecture itself, to ensure the interaction patterns can be properly learned. Our contribution with this work is by proposing a more simple yet very powerful architecture, named Interaction Relational Network, which utilizes minimal prior knowledge about the structure of the data. We drive the network to learn to identify how to relate the body parts of the persons interacting, in order to better discriminate among the possible interactions. By breaking down the body parts through the frames as sets of independent joints, and with a few augmentations to our architecture to explicitly extract meaningful extra information from each pair of joints, our solution is able to achieve state-of-the-art performance on the traditional interaction recognition dataset SBU, and also on the mutual actions from the large-scale dataset NTU RGB+D.
RETRACTED: A novel twin minimax probability machine for classification and regression

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request ofthe authors. The authors declared that they realized the work of the published study has not been fully completed and they discovered a few new results after the publication of this paper. In order not to confuse or mislead the readers, they decided to retract this paper. The authors apologise to the readers that this was not noticed in an earlier stage.
Finding appropriate settings for fairness and engagement in a newly designed game through self-playing AI program: A case study using Japanese crossword game 'MyoGo Renju’

This paper explores an innovative way to find the comfortable settings of a newly designed game under development using a computer program. A Japanese crossword game ‘MyoGo Renju’ has been chosen as a benchmark for this research, whereas some important aspects such as fairness and engagement are evaluated to find the most optimum settings for the player. The game ‘MyoGo Renju’ can be played by Japanese language learners with competitive purposes as well as educational purposes. An artificial intelligence program of the ‘MyoGo Renju’ has been developed and various parameters had been evaluated such as board size, word length, bonus point rule, block system, weighted score system, and round mode. The experiment is performed using self-playing Myogo AI where some interesting results have been demonstrated where the 5 × 5 board with the minimum word length of 3-g, 3 number of blocks, and 15 Hiragana characters chosen in a single round is expected to provide the best-expected fairness and engagement to the overall game experiences. The limitations and future works of the research are also discussed.
Secure automatic bounds checking: prevention is simpler than cure

Recent Spectre attacks exploit hardware speculative execution to read forbidden data. The attacks speculatively load forbidden data in misspeculated paths creating a side channel via the microarchitectural state which is not cleaned up after a misspeculation. The side channel then leaks the data. We focus on the most-challenging Spectre variant (Spectre-v1) which exploits sandboxing through bounds checking. Because the forbidden data can be accessed in only three ways only one of which remains challenging (Spectre-v1), whereas the data can be leaked through numerous side channels all of which must be plugged, preventing the access in the first place is more practical. Recent hardware schemes plug some side channels but incur significant complexity and performance loss and remain susceptible to other side channels. Most current software mitigations are architecture-dependent, have performance or semantic uncertainty problems, or both. We propose a compiler-based mitigation, called Secure Automatic Bounds Checking (SABC), which uses a simple sequence of three instructions to prevent forbidden access. The instructions have straightforward semantics and are found in all 32- and 64-bit architectures. An alternative, architecture-independent technique that leverages process boundaries– site isolation – incurs 1.8x memory overhead and 30% performance overhead over the baseline with no isolation. SABC is architecture-independent, has assured semantics, incurs little performance overhead, and renders current and future side channels useless for Spectre-v1.
Application of Wavelet Neural Network Prediction Model Based on Particle Swarm Optimization

Talent is an important resource in the 21st century. For enterprises, cities and countries, the number of them is undoubtedly the key to increasing competitiveness. Cross-analysis was carried out on China’s first-line and new-tier cities, and the artificial intelligence industry was developed horizontally and vertically. Taking Xi’an, the bridgehead in the “Belt and Road” as an example, select Xi’an “GDP, social fixed investment, undergraduate and above talent demand ratio, undergraduate and above talent supply and demand ratio and the proportion of talent demand in various industries” indicators, using improved PSO algorithm to optimize wavelet neural network. The demand for talent in the seven major industries in Xi’an is predicted, and the employment situation of the IT industry is analyzed. Finally, based on the forecast results, we put forward decision-making suggestions for the future development of Xi’an.
Development of Educational Programs for System Creators and Business Producers in Future Strategy Design in Action Project Group Activities Through Industry-University Cooperation

Our Japanese industry-university project group from Aoyama Gakuin University investigated and studied new educational programs to cultivate “future strategy design” human resources over several years. The purpose of this research paper is to describe two types of educational programs. The first program proposes the system creator, which is capable of new product design and innovative management that uses advanced technologies such as IoT, AI, and augmented and virtual reality. The second program proposes the business producer that is capable of platform service business modeling, product development and strategy, and customer service. The characteristics of these programs involve to apply new educational methods to combine project based learning (PBL) and active learning.
A Framework for GDPR Compliance in Big Data Systems

The verification and implementation of the GDPR regulation that aims at protecting European citizens' privacy, is still a real challenge. In particular, in Big Data systems where data is of huge volume and heterogeneous, it is hard to track data evolution through its complex life cycle ranging from collection, ingestion, storage and analytics. In this context, from 2016 to 2019 research has been conducted and security tools designed. However, they are either specific to special applications or address only partially the regulation articles. In order to identify the covered parts, the missed ones and the necessary metrics for comparing different works, we propose a framework for GDPR compliance that identifies the main components for the regulation implementation. Based on this framework, we compare the main GDPR solutions in Big Data domain and we propose a guideline for GDPR verification and implementation in Big Data systems.
Hardware Acceleration of SVM Training for Real-Time Embedded Systems: Overview

Support vector machines (SVMs) have proven to yield high accuracy and have been used widespread in recent years. However, the standard versions of the SVM algorithm are very time-consuming and computationally intensive, which places a challenge on engineers to explore other hardware architectures than CPU, capable of performing real-time training and classifications while maintaining low power consumption in embedded systems. This paper proposes an overview of works based on the two most popular parallel processing devices: GPU and FPGA, with a focus on multiclass training process. Since different techniques have been evaluated using different experimentation platforms and methodologies, we only focus on the improvements realized in each study.
Software Effort Estimation Using Particle Swarm Optimization: Advances and Challenges

The objective of software effort estimation is the analytical calculation for estimating the size, effort, time, and cost of the project, which are interrelated to the software development life cycle. Software effort estimation is a composite activity that requires information about various properties that influences the results of the project. The most basic issue is the part of the information required to keep away from unexpected outcomes, but it is difficult to get data in the required quantities for software estimation. At the initial stage of development, the lack of adequacy are certainty, off base, trickiness which are the major constraints of algorithmic and expert judgment estimation models. The non-algorithmic approaches depend on new methodologies like soft computing overcomes the above models. Evolutionary algorithms are theoretical pursuit strategies that copy the natural advancement and the social conduct of the species. Such calculations have been created to land close ideal solutions, for which conventional algorithmic methods may fizzle. The primary point of this study is to give an inside and out survey of particle swarm optimization technique and a general trend toward their use in diverse domains to progress the outcome of the particle swarm optimization algorithm.
An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.
Learning Data Transformations with Minimal User Effort

Data collected from heterogeneous sources often have inconsistencies in data format and thus require transformation before the data can be used. A major issue of existing approaches is their dependency on parallel input-output data to learn the transformations. However, parallel data are not always available, and annotation requires excessive human interaction because of format diversity. Therefore, these approaches have limitations when applied to large-scale real-world problems. To address this issue, we introduce UDATA, a novel unsupervised system for non-parallel data transformation. Because the transforming data usually share common syntactic patterns, UDATA discovers common syntactic patterns from input/output examples and synthesizes the transformations between the patterns. Moreover, in UDATA, transformation results are verified by an active learning model and ambiguous results are reported to users for labeling. UDATA achieves accuracy close to other state-of-the-art supervised systems without the need for any labeled data.
A Study on Characterizing the Ecosystem of Monetizing Video Spams on YouTube Platform

In this work, we specifically study YouTube videos promoting easy money gaining tricks. We observe a noticeable presence of such videos on YouTube that are being used to - 1) mislead users into sharing their personal identifiable information, i.e., phone numbers publically or 2) direct users away from YouTube on sites causing negative user experience. For our study, we call them - phone spam and link spam based videos respectively. We provide a detailed analysis of the behavioral characteristics of these two categories of videos based on metadata content and graph based features. For our study, a total of 80 phone spam videos and 2000 link spam videos were collected and analyzed. Our analysis shows that such videos are highly related to each other, and specific motifs discriminating such videos exist. To our knowledge this is the first attempt towards characterizing such types of videos on YouTube.
An Intelligent System for Phishing Attack Detection and Prevention

Cyber security is a most important trepidation in the widespread adoption of internet technologies in the everyday activities of human being. Even though more sophisticated technologies emerged on the Internet, but different kinds of attacks and threats are also increasing day by day. Cyber attacks causes loss of customer confidence in adopting internet based applications. Phishing attack is one of the common vulnerabilities in the cyber space. Most of the anti-phishing solutions proposed so are focused only on a single issue and needs improvement. For malicious web page detection and prevention, an intelligent multi agent solution is proposed in this paper with the help of machine learning methods. The proposed approach detects both phishing sites and websites with malicious content. This multi-agent system contains four autonomous intelligent agents, which communicate with each other using the Extensible Messaging and Presence Protocol (XMPP) for decision-making. The first is a monitoring agent, second and third is for decision-making (using the machine-learning classifiers) and the fourth is for action-performing. The first agent is responsible for extracting URLs. It passes the extracted URLs to the second agent for feature extraction and classification. If any phishing is detected, the second agent communicates with the fourth agent and the site is blocked. Otherwise, the second agent communicates with the third agent for malicious script detection. If any malicious script is detected then the fourth agent blocks the entire web page. We have tested the performance and accuracy of the proposed method and obtained results ensures its efficiency.
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning

With the rapid scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for improving deployment efficiency. This work aims to advance the compression beyond the weights to neuron activations. We propose the joint regularization technique which simultaneously regulates the distribution of weights and activations. By distinguishing and leveraging the significance difference among neuron responses and connections during learning, the jointly pruned network, namely JPnet, optimizes the sparsity of activations and weights for improving execution efficiency. The derived deep sparsification of JPnet reveals more optimization space for the existing DNN accelerators dedicated for sparse matrix operations. We thoroughly evaluate the effectiveness of joint regularization through various network models with different activation functions and on different datasets. With 0.4% degradation constraint on inference accuracy, a JPnet can save 72.3% ~ 98.8% of computation cost compared to the original dense models, with up to 5.2x and 12.3x reductions in activation and weight numbers, respectively.
A computationally efficient BiLSTM based approach for the binary sentiment classification

Sentiment analysis is a crucial task in natural language processing (NLP), aiming at the extraction of opinions expressed in the texts. This paper aims at a computationally efficient approach for the classification of positive and negative sentiments by using publicly available datasets about movie reviews, namely, Movie Review (MR) and Stanford Sentiment Tree (SST2) datasets. We exploited merely one bidirectional long short-term memory (BiLSTM) layer with a global maximum pooling layer and got F1 scores of 85.78 and 80.21 for SST2 and MR datasets, respectively. We concluded that our results are competitive with recently published methods with complex architectures. Also, our approach requires minimal computational cost and thus may help in real-time applications in general opinion categorization.
Exploring accelerator and parallel graph algorithmic choices for temporal graphs

Many real-world systems utilize graphs that are time-varying in nature, where edges appear and disappear with respect to time. Moreover, the weights of different edges are also a function of time. Various conventional graph algorithms, such as single source shortest path (SSSP) have been developed for time-varying graphs. However, these algorithms are sequential in nature and their parallel counterparts are largely overlooked. On the other hand, parallel algorithms for static graphs are implemented as ordered and unordered variants. Unordered implementations do not enforce local or global order for processing tasks in parallel, but incur redundant task processing to converge their solutions. These implementations expose parallelism at the cost of high redundant work. Relax-ordered implementations maintain local order through per-core priority queues to reduce the amount of redundant work, while exposing parallelism. Finally, strict-ordered implementations achieve the work efficiency of sequential version by enforcing a global order at the expense of high thread synchronizations. These parallel implementations are adopted for temporal graphs to explore the choices that provide optimal performance on different parallel accelerators. This work shows that selecting the optimal parallel implementation extracts geometric performance gain of 46.38% on Intel Xeon-40 core and 20.30% on NVidia GTX-1080 GPU. It is also shown that optimal implementation choices for temporal graphs are not always the same as their respective static graphs.
Facility Locations Utility for Uncovering Classifier Overconfidence

Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evaluate individual predictions; presenting the challenge to create query algorithms to guide the search for points that provide the most information about the classifier's predictive characteristics. Previous works have focused on developing utility models and query algorithms for discovering unknown unknowns - misclassifications with a predictive confidence above some arbitrary threshold. However, these methods tend to reward the discovery of misclassifications that occur at the rate indicated by their confidence values. These search methods may reveal nothing more than a correct assessment of predictive certainty, and as a result, we are unable to properly mitigate the risks associated with model deficiency when the model's confidence in prediction exceeds the actual model accuracy. We propose a novel problem formulation to instead search for overconfident unknown unknowns. Specifically, we propose a facility locations utility model and corresponding greedy query algorithm to search for overconfident unknown unknowns. Through robust empirical experiments we demonstrate that the greedy query algorithm with the facility locations utility model outperforms previous methods in discovering overconfident unknown unknowns.