Reviewerly/exHarmony huggingface dataset:
Blockchain- and IPFS-Based Data Distribution for the Internet of Things

Distributing data in a tamper-proof and traceable way is a necessity in many Internet of Things (IoT) scenarios. Blockchain technologies are frequently named as an approach to provide such functionality. Despite this, there is a lack of concrete solutions which integrate the IoT with the blockchain for data distribution purposes. Within this paper, we present a middleware which connects to IoT devices, and uses a blockchain to distribute IoT data with guaranteed integrity. Furthermore, the middleware also offers that data is distributed in real-time via a second channel. We implement our solution using the Ethereum blockchain and the InterPlanetary File System (IPFS).
Two sides of the same coin? Decentralized versus proprietary blockchains and the performance of digital currencies.

There is a shared view among practitioners that the blockchain is a revolutionary, decentralized technology that will have a larger impact than the Internet. Firms are increasingly using blockchains for various applications; the most prominent of which to date are digital currencies. In this article, we aim to increase our theoretical understanding of the driving forces behind the success and volatility of digital currencies. We use a detailed dataset of 345 digital currencies for our explorative analysis and identify some of the key factors that can explain their performance. We find that the success and volatility of digital currencies depend on their business type (i.e., whether they relate to a platform business or not) and on their technology type (i.e., whether they are based on their own specialized blockchain technology or on a third-party standardized platform blockchain). Our findings suggest that, paradoxically, to obtain the promised benefits of this decentralized technology, firms need to centralize part of it to retain control over critical strategic dimensions (data and rules for transaction). We discuss the implications of our discovery for other contexts undergoing digital transformation.
SEIMI: Efficient and Secure SMAP-Enabled Intra-process Memory Isolation

Memory-corruption attacks such as code-reuse attacks and data-only attacks have been a key threat to systems security. To counter these threats, researchers have proposed a variety of defenses, including control-flow integrity (CFI), code-pointer integrity (CPI), and code (re-)randomization. All of them, to be effective, require a security primitive—intra-process protection of confidentiality and/or integrity for sensitive data (such as CFI’s shadow stack and CPI’s safe region).In this paper, we propose SEIMI, a highly efficient intra-process memory isolation technique for memory-corruption defenses to protect their sensitive data. The core of SEIMI is to use the efficient Supervisor-mode Access Prevention (SMAP), a hardware feature that is originally used for preventing the kernel from accessing the user space, to achieve intra-process memory isolation. To leverage SMAP, SEIMI creatively executes the user code in the privileged mode. In addition to enabling the new design of the SMAP-based memory isolation, we further develop multiple new techniques to ensure secure escalation of user code, e.g., using the descriptor caches to capture the potential segment operations and configuring the Virtual Machine Control Structure (VMCS) to invalidate the execution result of the control registers related operations. Extensive experimental results show that SEIMI outperforms existing isolation mechanisms, including both the Memory Protection Keys (MPK) based scheme and the Memory Protection Extensions (MPX) based scheme, while providing secure memory isolation.
Rank-constrained nonnegative matrix factorization for data representation

Graph-based regularized nonnegative matrix factorization (NMF) methods performed well in many real-world applications. However, it is still an open problem to construct an optimal graph to effectively discover the intrinsic geometric structure of data. In this paper, we propose a new data representation framework, called rank-constrained nonnegative matrix factorization (RCNMF). We impose the rank constraint on the Laplacian matrix of the learned graph, so it can ensure that the number of connected components is consistent with the number of sample categories. Instead of a fixed graph-based regularization, the proposed framework can adaptively adjust the weight of the affinity matrix in each iteration. We develop two versions of RCNMF based on the l1 and l2 norms, and introduce their optimization schemes. In addition, their convergence and the complexity analyses are also provided. Experimental results on four benchmark datasets show that our methods outperform state-of-the-art methods in clustering.
Misconceptions in Privacy Protection and Regulation

Privacy protection legislation and policy is heavily dependent on the notion of de-identification. Repeated examples of its failure in real-world use have had little impact on the popularity of its usage in policy and legislation. In this paper we will examine some of the misconceptions that have occurred to attempt to explain why, in spite of all the evidence, we continue to rely on a technique that has been shown not to work, and further, which is purported to protect privacy when it clearly does not. With a particular focus on Australia, we shall look at how misconceptions regarding de-identification are perpetuated. We highlight that continuing to discuss the fiction of de-identified data as a form of privacy actively undermines privacy and privacy norms. Further, we note that ‘de-identification of data’ should not be presented as a form of privacy protection by policy makers, and that greater legislative protections of privacy are urgently needed given the volumes of data being collected, connected and mined.
Sarcasm, the smiling poop, and E-discourse aggressiveness: getting far too emotional with emojis

Emojis are a form of electronic communication found in text messages that fundamentally alter the exchange of emotion. Images and signs that depict feelings have replaced the nuanced selection of the right words and phrases. However, where feelings subside once expressed, emoticons transcend the spatio-temporality of emotion in ways that can become recurrent acts of aggression and bullying that are impossible not to see and even harder to erase. Across the globe, the expressive range of human emotion through the static ideogram of the emoji, or emoticon, presents an increasing challenge for the visually immediate, non-verbal exchange of capricious emotive communication.
Recent Development Trend of Blockchain Technologies: A Patent Analysis

Blockchain is essential for cryptocurrencies, but its use is not limited to cryptocurrency. Blockchain can be used in a wide range of applications. Recently, many blockchain inventions and applications have been proposed, which have received considerable attention from academics, industry practitioners, and the government. However, few studies have analyzed the development of blockchain technology. In this study, a patent analysis of the U.S. patents of blockchain was conducted. With an analysis of 335 patents awarded, a profile of recent blockchain technology development was obtained. The results indicated that three-quarters of the U.S. blockchain patents were originated in the United States. Half of the awarded U.S. patents are from foreign origin, and the proportion of the blockchain patent originated in the United States is more extensive, which revealed that the United States is a leader in the blockchain technology. Furthermore, 14% of blockchain patents were awarded to IBM, indicating that IBM as a technology giant in the blockchain technology. Additionally, word cloud and N-gram were used to reveal the relevant terms and phrases used in the abstracts of blockchain patents.
Tag embedding based personalized point of interest recommendation system

E-tourism websites such as Foursquare, Tripadvisor, Yelp etc. allow users to rate the preferences for the places they have visited. Along with ratings, the services allow users to provide reviews on social media platforms. As the use of hashtags has been popular in social media, the users may also provide hashtag-like tags to express their opinion regarding some places. In this article, we propose an embedding based venue recommendation framework that represents Point Of Interest (POI) based on tag embedding and models the users (user profile) based on the POIs rated by them. We rank a set of candidate POIs to be recommended to the user based on the cosine similarity between respective user profile and the embedded representation of POIs. Experiments on TREC Contextual Suggestion data empirically confirm the effectiveness of the proposed model. We achieve significant improvement over PK-Boosting and CS-L2Rank, two state-of-the-art baseline methods. The proposed methods improve [email protected] by 12.8%, [email protected] by 4.4%, and MRR by 7.8% over CS-L2Rank. The proposed methods also minimize the risk of privacy leakage. To verify the overall robustness of the models, we tune the model parameters by discrete optimization over different measures (such as AP, NDCG, MRR, recall, etc.). The experiments have shown that the proposed methods are overall superior than the baseline models.
Football Ontology Construction using Oriented Programming

According to the W3C, the semantic web is the future of the www. The data that is based on the semantic web can be understood by machines and devices. The main component of the semantic web is the ontology, which is known as the backbone of the semantic web. There are many tools used to edit and create an ontology, however, few kinds of research construct an ontology using oriented programming. SPARQL and API OWL are used to access and edit ontologies, though they are not using oriented programming. The main objective of this paper is to build an ontology using oriented programming and allowable to access OWL entities. Owlready module is effectively used in sport ontology for football in 11 European Leagues.
Modified multi-key fully homomorphic encryption based on NTRU cryptosystem without key-switching

The Multi-Key Fully Homomorphic Encryption (MKFHE) based on the NTRU cryptosystem is an important alternative to the post-quantum cryptography due to its simple scheme form, high efficiency, and fewer ciphertexts and keys. In 2012, Lopez-Alt et al. proposed the first NTRU-type MKFHE scheme, the LTV12 scheme, using the key-switching and modulus-reduction techniques, whose security relies on two assumptions: the Ring Learning With Error (RLWE) assumption and the Decisional Small Polynomial Ratio (DSPR) assumption. However, the LTV12 and subsequent NTRU-type schemes are restricted to the family of power-of-2 cyclotomic rings, which may affect the security in the case of subfield attacks. Moreover, the key-switching technique of the LTV12 scheme requires a circular application of evaluation keys, which causes rapid growth of the error and thus affects the circuit depth. In this paper, an NTRU-type MKFHE scheme over prime cyclotomic rings without key-switching is proposed, which has the potential to resist the subfield attack and decrease the error exponentially during the homomorphic evaluating process. First, based on the RLWE and DSPR assumptions over the prime cyclotomic rings, a detailed analysis of the factors affecting the error during the homomorphic evaluations in the LTV12 scheme is provided. Next, a Low Bit Discarded & Dimension Expansion of Ciphertexts (LBD&DEC) technique is proposed, and the inherent homomorphic multiplication decryption structure of the NTRU is proposed, which can eliminate the key-switching operation in the LTV12 scheme. Finally, a leveled NTRU-type MKFHE scheme is developed using the LBD&DEC and modulus-reduction techniques. The analysis shows that the proposed scheme compared to the LTV12 scheme can decrease the magnitude of the error exponentially and minimize the dimension of ciphertexts.
Lightweight Authentication Protocol for NFC Based Anti-Counterfeiting System in IoT Infrastructure

Counterfeit medications are known as the medications that were manufactured for the purpose of deceptively representing as authentic, effective and original in the market. Such medications cause severe health issues for patients. Counterfeited drugs have an inimical effect on the human health. The legal manufacturing companies also face threats to their revenue loss due to these counterfeited medicines. In this paper, we introduce a novel authentication protocol for anti-counterfeited drugs systems based on Internet of Things (IoT) to help checking the validity of drugs “unit dosage”. Our protocol uses the near-field communication (NFC) as it is convenient for mobile environment. The protocol also offers reliable update phase for NFC. Furthermore, our scheme is complemented with performance evaluation along with the use of random oracle model for formal security analysis. We also evaluate our protocol broadly using Py-Charm tool. Results show that proposed protocol resists most of common related flaws almost in equal computing cost with more added security features.
RETRACTED: Path planning for unmanned wheeled robot based on improved ant colony optimization

This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.
A Fuzzy Mining Approach for Energy Efficiency in a Big Data Framework

The discovery and exploitation of hidden information in collected data have gained attention in many areas, particularly in the energy field due to their economic and environmental impact. Data mining techniques have then emerged as a suitable toolbox for analyzing the data collected in modern network management systems in order to obtain a meaningful insight into consumption patterns and equipment operation. However, the enormous amount of data generated by sensors, occupational, and meteorological data involve the use of new management systems and data processing. Big Data presents great opportunities for implementing new solutions to manage these massive data sets. In addition, these data present values whose nature complicates and hides the understanding and interpretation of the data and results. Therefore, the use of fuzzy methods to adequately transform the data can improve their interpretability. This article presents an automatic fuzzification method implemented using the Big Data paradigm, which enables, in a later step, the detection of interrelations and patterns among different sensors and weather data recovered from an office building.
DLT Based Authentication Framework for Industrial IoT Devices

The latest technological progress in the industrial sector has led to a paradigm shift in manufacturing efficiency and operational cost reduction. More often than not, this cost reduction comes at the price of dismissing information security, especially when multiple stakeholders are involved and the complexity increases. As a further matter, most of the legacy systems and smart factoring processes lack a security by design approach, making them highly vulnerable to cyber-attacks. Taking into consideration the aforementioned issues, we propose an architectural framework for Industrial Internet of Things (IIoT) that provides authentication and guaranteed integrity. Our proposal properly addresses the security by design principle while combining some of the emerging technologies like Secure Multi-Party Computation (SMPC) for grounded policy rules and Distributed Ledger Technology (DLT) for an immutable and transparent registry.
Comparison of CNNs and SVM for voice control wheelchair

In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+ to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.
An Information-Theoretic Approach to Personalized Explainable Machine Learning

Automated decision making is used routinely throughout our every-day life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. Many of these decision making systems use machine learning methods that fit complex models to massive datasets. The successful deployment of machine learning (ML) methods to many (critical) application domains crucially depends on its explainability. Indeed, humans have a strong desire to get explanations that resolve the uncertainty about experienced phenomena like the predictions and decisions obtained from ML methods. Explainable ML is challenging since explanations must be tailored (personalized) to individual users with varying backgrounds. Some users might have received university-level education in ML, while other users might have no formal training in linear algebra. Linear regression with few features might be perfectly interpretable for the first group but might be considered a black-box by the latter. We propose a simple probabilistic model for the predictions and user knowledge. This model allows to study explainable ML using information theory. Explaining is here considered as the task of reducing the “surprise” incurred by a prediction. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction, given the user background.
Social Media Usage for Enhancing English Language Skill

One of the obstacles of learning English Language as a Foreign Language is the limitation of practicing media. Social media has many contents and facilities which can be used for practicing English. The aim of this study is to find out students’ intensity of using English-language social media content. The study was conducted by quantitative approach and survey method among the Senior High School students in Jakarta, Indonesia. Data analysis shows that as students’ intensity who used English-language social media content is low. Students’ perception of English skill in reading and writing is good, while students’ perception of listening and speaking is still low. The intensity of using English-language social media content is positively and significantly related to students' perceptions of their ability to speak English including reading, writing, listening and speaking aspects. Hence, social media is needed to be learning media for learning English language as a Foreign Language in Indonesia.
Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM

In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated. The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making s upport for evaluation of clustering algorithms (DMSECA), to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process. The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18,310 instances and 313 attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets. Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment.
Image Watermarking in Curvelet Domain Using Edge Surface Blocks

Digital image watermarking aims to protect the information in an image without significantly affecting visual quality. In this paper, a new image watermarking technique has been proposed that uses Gaussian filters and first-order partial differential matrix to extort the edge surface of a host image. This paper influence on the edge surface curvelet coefficients as human eyes are not equally sensitive to a smooth and an edged surface. To preserve the quality of the artwork and to increase the resistance against attacks, the author utilizes the edge surface area of an image, coarse levels of curvelet transform, and strength parameters. The selection of host coefficients are conforming to the human visual system (HVS) is the uniqueness of the research. The exploitation of the Gaussian filters and first-order partial differential coarse curvelet coefficients and the watermark strength parameter offers robustness against image processing attacks. The standard visual quality perception of HVS evaluation metrics are used to measure the superiority of the presented work.
Reliability of the head tracking measured by Microsoft Hololens during different walking conditions

(2019). Reliability of the head tracking measured by Microsoft Hololens during different walking conditions. Computer Methods in Biomechanics and Biomedical Engineering: Vol. 22, 44th Congress of the Société de Biomécanique, pp. S169-S171.
Class-Center Involved Triplet Loss for Skin Disease Classification on Imbalanced Data

It is ideal to develop intelligent systems to accurately diagnose diseases as human specialists do. However, due to the highly imbalanced data issue between common and rare diseases, it is still an open problem for the systems to effectively learn to recognize both common and rare diseases. In this paper, we novelly applied triplet modelling to overcome the data imbalance issue particularly for diagnosis of rare diseases. Moreover, we further applied a class-center based triplet loss in order to make the triplet-based learning more stable. Extensive evaluation on two skin image classification tasks shows that the triplet-based approach is very effective and outperforms the widely used methods for solving the imbalance problem.