Reviewerly/exHarmony huggingface dataset:
Fast Eye-in-Hand 3-D Scanner-Robot Calibration for Low Stitching Errors

This article proposes a new eye-in-hand 3-D scanner-robot calibration approach to realize low data stitching errors during long-term continuous measurement. Eye-in-hand 3-D scanner-robot systems are commonly used for the complete measurement of an object under test (OUT) from multiple fields of view (FOVs). To align the multiple FOVs into a single coordinate system, marker-free stitching assisted by robot's positioning is attractive since it bypasses the cumbersome traditional fiducial marker-based method. Based on periodically capturing calibration images from a 2-D calibration target, scanner's and robot kinematic model's parameters are optimized. The challenges overcome in this article include: how to compensate for the center-detection error in scanner calibration; how to avoid the dependence of hand-eye calibration on DH parameters; and how to calculate an accurate world-to-robot transformation. These challenges were tackled by several new techniques including accurate scanner calibration with iterative refinement of control points, virtual arm-based scanner-robot kinematic modeling, and trajectory-based world-to-robot transformation calculation. Experimental results demonstrated a low-initial stitching error similar to the fiducial marker-based method (0.0446 mm versus 0.0542 mm) was achieved. The mean stitching error was effectively maintained to be $<; $0.1 mm during the continuous measurement with an average intermittent downtime of 78 s for recalibration.
A Blockchain-based Smart Grid Model for Rural Electrification in India

There are rural areas across India and many other countries who receive unreliable and inadequate electricity supply, or no electricity at all. Electricity is essential for the overall development of an individual, and the society in turn benefits from it. Lack of access to grid electricity forces the rural inhabitants to utilize conventional sources of energy, causing not just inconvenience but also deterioration of the environment. In this paper, the reasons preventing reliable power supply to rural places in India have been discussed. Applicability of Blockchain technology to address these challenges and promote rural electrification is being researched. This paper focuses specifically on problems faced by rural communities due to which they avoid electricity connections, and how these problems can be solved by Blockchain technology. Blockchain has the potential to eliminate expensive third-party service costs, generate additional revenue through peer-to-peer trade, planning the power consumption as per budget through smart metering and maintain transparency and accuracy of energy transactions. However, the road to implementation consists of many challenges to overcome which have been discussed in this paper.
Energy-efficient activity recognition framework using wearable accelerometers

Acceleration data for activity recognition typically are collected on battery-powered devices, leading to a trade-off between high-accuracy recognition and energy-efficient operation. We investigate this trade-off from a feature selection perspective, and propose an energy-efficient activity recognition framework with two key components: a detailed energy consumption model and a number of feature selection algorithms. We evaluate the model and the algorithms using Random Forest classifiers to quantify the recognition accuracy, and find that the multi-objective Particle Swarm Optimization algorithm achieves the best results for the task. The results show that by selecting appropriate groups of features, energy consumption for computation and data transmission is reduced by an order of magnitude compared with the raw-data approach, and that the framework presents a flexible selection of feature groups that allow the designer to choose an appropriate accuracy-energy trade-off for a specific target application.
Using blockchain to log genome dataset access: efficient storage and query

Abstract Background Genomic variants are considered sensitive information, revealing potentially private facts about individuals. Therefore, it is important to control access to such data. A key aspect of controlled access is secure storage and efficient query of access logs, for potential misuse. However, there are challenges to securing logs, such as designing against the consequences of “single points of failure”. A potential approach to circumvent these challenges is blockchain technology, which is currently popular in cryptocurrency due to its properties of security, immutability, and decentralization. One of the tasks of the iDASH (Integrating Data for Analysis, Anonymization, and Sharing) Secure Genome Analysis Competition in 2018 was to develop time- and space-efficient blockchain-based ledgering solutions to log and query user activity accessing genomic datasets across multiple sites, using MultiChain. Methods MultiChain is a specific blockchain platform that offers “data streams” embedded in the chain for rapid and secure data storage. We devised a storage protocol taking advantage of the keys in the MultiChain data streams and created a data frame from the chain allowing efficient query. Our solution to the iDASH competition was selected as the winner at a workshop held in San Diego, CA in October 2018. Although our solution worked well in the challenge, it has the drawback that it requires downloading all the data from the chain and keeping it locally in memory for fast query. To address this, we provide an alternate “bigmem” solution that uses indices rather than local storage for rapid queries. Results We profiled the performance of both of our solutions using logs with 100,000 to 600,000 entries, both for querying the chain and inserting data into it. The challenge solution requires 12 seconds time and 120 Mb of memory for querying from 100,000 entries. The memory requirement increases linearly and reaches 470 MB for a chain with 600,000 entries. Although our alternate bigmem solution is slower and requires more memory (408 seconds and 250 MB, respectively, for 100,000 entries), the memory requirement increases at a slower rate and reaches only 360 MB for 600,000 entries. Conclusion Overall, we demonstrate that genomic access log files can be stored and queried efficiently with blockchain. Beyond this, our protocol potentially could be applied to other types of health data such as electronic health records.
Hacker-engineers and Their Economies: The Political Economy of Decentralised Networks and ‘Cryptoeconomics’

Research by political economists typically highlights policymakers, regulators, economists and consultants as the makers of economies. This paper foregrounds a different actor entirely, what I call the 'hacker-engineer' as an important protagonist in the making of decentralised digital network economies that are forged through the emerging field of 'cryptoeconomics' and blockchain and other distributed ledger technologies. Responding to critical literature stating that blockchain and 'cryptoeconomics' merely extend neoliberal processes of economisation, the paper recovers the neglected hacker culture of cypherpunk and histories of peer-to-peer decentralised networks in order to foreground concerns that depart from the continuation of economics and economies as usual. Hacker-engineers are dedicated to decentralisation as a 'disruptive' response to network control and surveillance, and share a pragmatist sensibility that seeks to make decentralised networks 'work' in order to provide informational security and privacy. While further broadening the range of agents that provide the focus for political economy research into the production of economies, the paper also draws attention to the technical decisions of hacker-engineers that attempt to reconfigure the material infrastructures of digital economies.
Understanding Phishing Email Processing and Perceived Trustworthiness Through Eye Tracking

Social engineering attacks in the form of phishing emails represent one of the biggest risks to cybersecurity. There is a lack of research on how the common elements of phishing emails, such as the presence of misspellings and the use of urgency and threatening language, influences how the email is processed and judged by individuals. Eye tracking technology may provide insight into this. In this exploratory study a sample of 22 participants viewed a series of emails with or without indicators associated with phishing emails, whilst their eye movements were recorded using a SMI RED 500 eye-tracker. Participants were also asked to give a numerical rating of how trustworthy they deemed each email to be. Overall, it was found that participants looked more frequently at the indicators associated with phishing than would be expected by chance but spent less overall time viewing these elements than would be expected by chance. The emails that included indicators associated with phishing were rated as less trustworthy on average, with the presence of misspellings or threatening language being associated with the lowest trustworthiness ratings. In addition, it was noted that phishing indicators relating to threatening language or urgency were viewed before misspellings. However, there was no significant interaction between the trustworthiness ratings of the emails and the amount of scanning time for phishing indicators within the emails. These results suggest that there is a complex relationship between the presence of indicators associated with phishing within an email and how trustworthy that email is judged to be. This study also demonstrates that eye tracking technology is a feasible method with which to identify and record how phishing emails are processed visually by individuals, which may contribute toward the design of future mitigation approaches.
Chinese Micro-Blog Sentiment Classification Based on Emotion Dictionary and Semantic Rules

Emotion dictionary is an important resource of micro-blog sentiment analysis. Based on the existing emotion dictionary, the paper improves the calculation method of emotion intensity of different emotion categories of emotion words in emotion dictionary. Firstly, we use word2vec to train the vectors of emotion words from the large-scale micro-blog text, select the benchmark words of each emotion category, and calculate the similarity between the emotion words vector and the benchmark words vector to obtain the emotion intensity of different emotion categories of emotion words. Then we analyze the influence of negative words and adverbs of degree on emotion tendency of different categories of emotion words, and define emotion semantic calculation rules. Finally, we combine with the emotion dictionary and the semantic rules to calculate the emotion intensity of different emotional categories of micro-blog, and realize the emotion classification. The experimental results show that our method is effective.
Blockchain Based Zero-Knowledge Proof of Location in IoT

With the development of precise positioning technology, a growing number of location-based services (LBSs) facilitate people's life. Most LBSs require proof of location (PoL) to prove that the user satisfies the service requirement, which exposes the user's privacy. In this paper, we propose a zero-knowledge proof of location (zk-PoL) protocol to better protect the user's privacy. With the zk-PoL protocol, the user can choose necessary information to expose to the server, so that hierarchical privacy protection can be achieved. The evaluation shows that the zk-PoL has excellent security to resist main attacks, moreover the computational efficiency is independent of input parameters and the zk-PoL is appropriate to delay-tolerant LBSs.
Digital image correlation assisted characterization of Mode I fatigue delamination in composites

A digital image correlation (DIC) based method to characterize the Mode I fatigue delamination onset and propagation in composite laminates is proposed. The characterization is performed using the double cantilever beam test (DCB) under fatigue loading. The DIC is used to analyse the displacement field on the edge of a DCB specimen, from which the crack tip position is determined as a place where opening displacement converges to zero. The method was successfully applied to both the crack onset detection (fatigue onset life) and propagation monitoring under fatigue cycling. The DIC-based method, which relies on the actual crack tip position, gives conservative values for the fatigue onset life in comparison with the compliance based method recommended in the ASTM standard. The DIC assisted method reduces operator and structure dependent errors and shows promise for real-time delamination detection under fatigue loadings.
Modeling and optimization of sugarcane juice clarification process

Sugarcane juice clarification is an important operation in the production process of sugar industry. At present, the two major production indicators (clear juice color value and gravity purity of the mixed juice) in sugarcane juice clarification process still cannot be measured online, and can only be obtained by offline test. In addition, when production condition changes, the setting and adjustment of the key operational variables are made by experienced workers. The delay caused by the offline test and the randomness of the key operational variables caused by manual adjustment will affect the quality of final product. To address these two issues, we firstly constructed a data-driven model based on the deep kernel extreme learning machine (DK-ELM) to predict the key production indicators for clarification process. The model which combined the kernel method and the multi-layer extreme learning machine (ML-ELM) proved successful in prediction of clear juice color value and gravity purity. Then, we proposed a strategy for optimization of operational variables, avoiding the randomness caused by manual setting. A multi -objective function including the purity and color value was constructed. The niche multi-objective particle swarm optimization (PSO) algorithm was used to solve the multi -objective function under typical working conditions in the production process to get optimal set of key operational variables. The results showed that the niche multi-objective particle swarm optimization (PSO) algorithm can provide optimum production indexes for sugarcane juice clarification.
Deep-broad Learning System for Traffic Flow Prediction toward 5G Cellular Wireless Network

Nowadays, accurate traffic flow prediction toward 5G cellular wireless network has become an indispensable part for future artificial intelligence (AI)-assisted network. Meanwhile, low delay communication is also the essential part in the upcoming 5G era. However, traditional deep learning models applied in traffic flow prediction have many drawbacks, such as too much running time and computational resources. To tackle these issues, especially jointly considering effectiveness and efficiency, we design a deep-broad learning system (DBLS) for traffic flow prediction. Specifically, based on broad learning system (BLS), we firstly adopt deep representative learning to extract meaningful information from raw data in mapped feature nodes. Then, to further improve the performance of prediction, we add some other nodes i.e., enhancement nodes generated from mapped features as extra inputs to enhance the representative capability. Finally, taking mapped features nodes and enhancement nodes as inputs of the last-layer neural network, we apply ridge regression to compute the final weights quickly. Experimental results demonstrate that our proposed DBLS can make full use of advantages of both deep neural network and traditional BLS to increase the accuracy of traffic flow prediction, meanwhile, maintaining low complexity and running time.
Enhanced Video Analytics for Sentiment Analysis Based on Fusing Textual, Auditory and Visual Information

With the widespread of online videos and digital transformation, video informatics and analytics have recently gained substantially increasing importance with an impressive success in a variety of tasks such as digital marketing, video surveillance and security systems, healthcare systems, talk show analysis, analysis of influencing groups in social media, and target tracking.This paper evaluates the potential contribution of various video modalities and how they are correlated to video analytics for sentiment analysis in the morphologically-rich Arabic language.Moreover, an enhanced approach is presented for video analytics to predict the speaker's sentiment of multi-dialect Arabic through the integration of textual, auditory and visual modalities.Different features are extracted to represent each modality including prosodic and spectral acoustic features to represent audio, neural word embedding to represent audio text transcript, and dense optical-flow descriptors to represent visual modality.The extracted features are used individually to train two machine learning classifiers to provide a baseline.Then, the effectiveness of various combinations of modalities is verified using multi-level fusion (feature, score and decision).The experimental results demonstrate that the proposed approach of combining different modalities can lead to more accurate prediction of speaker's sentiment with above 94% accuracy.
High Capacity Lossless Data Hiding in JPEG Bitstream Based on General VLC Mapping

As a branch of reversible data hiding (RDH), lossless data hiding (LDH) technique is important especially. Because LDH can not only reconstruct the cover image losslessly but also keep the visual quality of the marked image no degradation. This article proposes a new LDH scheme for JPEG images by general variable length code (VLC) mapping. In this scheme, the run size values are first reordered. Then a feasible solution space is generated. A simulated embedding model is established to find the optimal solution from the feasible solution space. The optimal mapping relationship is constructed according to the optimal solution. According to the optimal GVM relationship, the Huffman table in the file header is modified and then the additional data can be embedded by replacing the used VLC with the VLCs in the same mapping set. Experimental results demonstrate that most of the JPEG images using the proposed scheme cause less file size increments than previous RDH schemes while keeping the marked JPEG image with no distortion. Furthermore, the proposed scheme can obtain high embedding capacity.
Knowledge-based preference learning model for recommender system using adaptive neuro-fuzzy inference system

A recommender system (RS) delivers personalized suggestions on products based on the interest of a particular user. Content-based filtering (CBF) and collaborative filtering (CF) schemes have been previously used for this task. However, the main challenge in RS is cold start problem (CSP). This originates once a new user joins the system which makes the recommendation task tedious due to the shortage of information (clickstream, dwell time, rating, etc.) regarding the user’s interest. Therefore, CBF and CF are combined together by developing a knowledge-based preference learning (KBPL) system. This system considers the demographic data that includes gender, occupation, and age for the recommendation task. Initially, the dataset is clustered using the self-organizing map (SOM) technique, then the high dimensional data is decomposed by higher-order singular value decomposition (HOSVD) and finally, Adaptive neuro-fuzzy inference system (ANFIS) predicts the output. For the big dataset, SOM is a robust clustering method and the similarities among the users can be easily observed by grid clustering. The HOSVD extracts the required information from the available data set to find the user similarity by decomposing the dataset in lower dimensions. ANFIS uses IF-THEN rules to recommend similar product to the new users. The proposed KBPL system is evaluated with the Black Friday dataset and the obtained error value is compared with the existing CF and CBF techniques. The proposed KBPL system has obtained root mean squared error (RMSE) of 0.71%, mean absolute error (MAE) of 0.54%, and mean absolute percentage error (MAPE) of 37%. Overall, the outcome of the comparative analysis shows minimum error and better performance in terms of precision, recall, and f-measure for the proposed KBPL system compared to the existing techniques and therefore more suitable for accurately recommending the products for the new users.
Blockchain security research: theorizing through bibliographic-coupling analysis

Purpose The continuous presence and intensity of the Internet of things (IoT) in our lives and the risk of security breaches in traditional transactional and financial platforms are the major cause of personal and organizational data losses. Blockchain emerges as a promised technology to ensure higher levels of data encryption and security. Thus, this study aims to develop a systematic literature review analyzing the previous literature and to purpose of a framework to better understand the process of blockchain security. Design/methodology/approach The 75 articles reviewed were obtained through the Scopus database and a bibliographic-coupling analysis was developed to identify the main themes of this research area, via VOSviewer software. Findings The results enable the categorization of the existing literature revealing four clusters: 1) feasibility, 2) fintech and cryptocurrency, 3) data trust and share and 4) applicability. Blockchain technology is still in its early stage of development and counting on researchers in security and cryptography to take it further to new highs, to allow its applicability to different areas and in long-term scenarios. Originality/value This systematic literature creates a base to reduce the blockchain security literature gap. In addition, it provides a framework that enables the scientific community to access the main subjects discussed and the articulation between concepts. Furthermore, it enhances the state-of-the-art literature on blockchain security and proposes a future research agenda.
Evolution of Defenses against Transient-Execution Attacks

Transient-execution attacks, such as Meltdown and Spectre, exploit performance optimizations in modern CPUs to enable unauthorized access to data across protection boundaries. Against these attacks, we have noticed a rapid growth of deployed and proposed countermeasures. In this paper, we show the evolution of countermeasures against transient-execution attacks by both industry and academia since the initial discoveries of the attacks. We show that despite the advances in the understanding and systematic view of the field, the proposed and deployed defenses are limited.
Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units

Power grid operators rely on solar irradiance forecasts to manage uncertainty and variability associated with solar power. Meteorological factors such as cloud cover, wind direction, and wind speed affect irradiance and are associated with a high degree of variability and uncertainty. Statistical models fail to accurately capture the dependence between these factors and irradiance. In this paper, we introduce the idea of applying multivariate Gated Recurrent Units (GRU) to forecast Direct Normal Irradiance (DNI) hourly. The proposed GRU-based forecasting method is evaluated against traditional Long Short-Term Memory (LSTM) using historical irradiance data (i.e., weather variables that include cloud cover, wind direction, and wind speed) to forecast irradiance forecasting over intra-hour and inter-hour intervals. Our evaluation on one of the sites from Measurement and Instrumentation Data Center indicate that both GRU and LSTM improved DNI forecasting performance when evaluated under different conditions. Moreover, including wind direction and wind speed can have substantial improvement in the accuracy of DNI forecasts. Besides, the forecasting model can accurately forecast irradiance values over multiple forecasting horizons.