Reviewerly/exHarmony huggingface dataset:
Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert

This article proposes a novel methodology to detect malicious uniform resource locators (URLs) using simulated expert (SE) and knowledge-base system (KBS). The proposed study not only efficiently detects known malicious URLs but also adapts countermeasure against the newly generated malicious URLs. Moreover, this article also explored which lexical features are contributing more in final decision using a factor analysis method, and thus help in avoiding the involvement of human experts. Furthermore, we apply the following state-of-the-art machine learning (ML) algorithms, i.e., naïve Bayes (NB), decision tree (DT), gradient boosted trees (GBT), generalized linear model (GLM), logistic regression (LR), deep learning (DL), and random rest (RF), and evaluate the performance of these algorithms on a large-scale real data set of data-driven Web applications. The experimental results clearly demonstrate the efficiency of NB in the proposed model as NB outperforms when compared to the rest of the aforementioned algorithms in terms of average minimum execution time (i.e., 3 s) and is able to accurately classify the 107 586 URLs with 0.2% error rate and 99.8% accuracy rate.
A Computationally Efficient Approach for Distributed Sensor Localization and Multitarget Tracking

In the context of distributed target tracking based on a mobile peer-to-peer sensor network, the relative locations between the sensors are critical for their internode information exchange and fusion. For accurate coordinate calibration between the neighboring sensors, namely sensor localization, we propose a computationally efficient approach that minimizes the mismatch error between position estimates of the common targets yielded at neighbor sensors. This mismatch error is given by a Wasserstein-like distance that is a mean square error between two sets of position estimates which are associated efficiently via Hungarian assignment. Simulations have demonstrated that our approach, on the testbed of an arithmetic average fusion based probability hypothesis density filter, performs similar to the cutting-edge approach based on loopy belief propagation, but computes much faster and has much lower communication cost.
Gold enrichment in hydrothermal sulfides from the Okinawa Trough: An in situ LA-ICP-MS study

We assess the distribution of gold and investigate the physicochemical controls on gold enrichment in the Okinawa Trough, an immature back-arc basin. We conducted a systematic study of gold and other trace-element concentrations using in situ laser ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS) for various minerals in hydrothermal sulfide samples from the Iheya North Knoll and the Yonaguni Knoll IV hydrothermal fields. Orpiment, colloform pyrite, and colloform sphalerite are relatively enriched in Au (mean contents of 30.60, 17.48, and 8.68 ppm, respectively) compared with gold-poor massive chalcopyrite, sphalerite, and pyrite. Based on gold budget estimation, colloform pyrite is the most important Au host, followed by colloform sphalerite and orpiment, with these three minerals containing the majority of gold in hydrothermal sulfides. Gold occurs as "invisible" in host minerals: as lattice-bound and submicron inclusions in colloform pyrite, as homogeneously distributed lattice-bound or nano-inclusions in colloform sphalerite, and generally as lattice-bound in orpiment and tetrahedrite. Three sets of gold host minerals are identified with distinct ore-formation temperatures. Colloform sulfides and orpiment incorporate the majority of gold at low temperatures, whereas tetrahedrite and massive sulfides incorporate only minor or trace amounts of gold at medium and high temperatures, respectively. Our results show that gold enrichment in Si–Ba–Fe-rich hydrothermal sulfides at low temperatures is a significant contributor to gold enrichment in hydrothermal sulfide deposits in the Okinawa Trough, along with the more common Zn- and Cu-rich hydrothermal sulfides. Thermodynamic modeling calculations suggest that the near-neutral pH of hydrothermal fluids and the dominant transport species, namely Au–HS complexes rather than Au–Cl complexes, are likely responsible for gold enrichment in low-temperature mineral assemblages in hydrothermal precipitates from the Okinawa Trough.
Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and Evaluation

Cloud computing is a novel paradigm, where the limitations of ubiquitous connected devices in terms of computing, data access, networking and storage are solved through the use of cloud infrastructure. The pervasive adoption of cloud computing results in a rising carbon footprint due to the high energy consumption of computing servers. This negatively affects the environment and entails an associated increase in electricity costs and consequently operational costs. Many works proposed scheduling algorithms using software-centric power models in order to predict electric power consumption in underlying data centers and to schedule cloud tasks so as to reduce energy consumption. Linear models which are based on the lowest-and highest-power data points (referred to here as the “Power Endpoints Model” - PEM) and the simple linear regression (SLR) model are the most used in the literature. However, these models have traditionally been evaluated using different environments, experimental setups, workloads, and error calculation formulas. In this paper, a unified classification and evaluation for these linear power models is presented, under unified setup, benchmarking applications, and error formula with the main goal being to achieve an objective comparison. A new power model is proposed, named Locally Corrected Multiple Linear Regression (LC-MLR), in order to increase prediction accuracy. A simulation framework for a cloud energy-aware scheduler is introduced. The framework combines the Energy-Aware Task Scheduling on Cloud Virtual Machines (EATSVM) with the LC-MLR power model, and facilitates performance measurement for cloud data centers. The scheduler with the new power model increases energy efficiency without degrading the qualities of service of the system. The workloads used for performance evaluation and comparisons in this work are generated using a diverse set of applications. The results show that LC-MLR outperforms the most-used models for simulation of power consumption of cloud data centers. The detailed performance analysis is elaborated in the paper.
Adaptive feature guidance: Modelling visual search with graphical layouts

We present a computational model of visual search on graphical layouts. It assumes that the visual system is maximising expected utility when choosing where to fixate next. Three utility estimates are available for each visual search target: one by unguided perception only, and two, where perception is guided by long-term memory (location or visual feature). The system is adaptive, starting to rely more upon long-term memory when its estimates improve with experience. However, it needs to relapse back to perception-guided search if the layout changes. The model provides a tool for practitioners to evaluate how easy it is to find an item for a novice or an expert, and what happens if a layout is changed. The model suggests, for example, that (1) layouts that are visually homogeneous are harder to learn and more vulnerable to changes, (2) elements that are visually salient are easier to search and more robust to changes, and (3) moving a non-salient element far away from original location is particularly damaging. The model provided a good match with human data in a study with realistic graphical layouts.
Social and Psychological Predictors of Youths’ Attitudes to Cryptocurrency

The objectives of the study were to verify the "Attitudes Toward Cryptocurrencies Questionnaire" and to identify predictors of attitudes toward Bitcoin. Sample: 262 participants aged 17 to 30, of which 45% were male. Methods: Associations, "Value Scale", "The Moral Foundations Questionnaire", "Money Beliefs and Behaviors Scale" by A. Furnham, and "The Baseline Confidence Scale" by L. Haff. Confirmatory factor analysis proved the three-factor structure of the questionnaire. A linear regression analysis showed that beliefs in the potential of cryptocurrency as a payment instrument are directly related to people-centered care and value of freedom, and are inversely related to sanctity; they are associated positively with confidence in the financial system and negatively with confidence in the government. Age and gender also matter. Worries about the introduction of cryptocurrency are directly related to a negative attitude to money, the value of self-confidence, age, and confidence in the financial system and government, and are inversely related to trust in people and money anxiety. Willingness to use cryptocurrency in perspective directly depends on trust in the power of money, orientation towards independence in actions, age, and gender, and is inversely dependent on confidence in the government. The data state that the intention to use cryptocurrency is directly related to the desire for financial autonomy and distrust of social institutions.
Aspect-Based Sentiment Analysis for Arabic Government Reviews

Government services are available online and can be provided through multiple digital channels, clients' feedback on these services can be submitted and obtained online. Enormous budgets are invested annually by governments to understand their clients and adapt services to meet their needs. In this paper, a unique dataset that consists of government smart apps Arabic reviews, domain aspects and opinion words is produced. It illustrates the approach that was carried out to manually annotate the reviews, measure the sentiment scores to opinion words and build the desired lexicons. Furthermore, this paper presents an Arabic Aspect-Based Sentiment Analysis (ABSA) that combines lexicon with rule-based models. The proposed model aims to extract aspects of smart government applications Arabic reviews, and classify all corresponding sentiments. This model examines mobile government app reviews from various perspectives to provide an insight into the needs and expectations of clients. In addition, it aims to develop techniques, rules and lexicons for language processing to address variety of SA challenge. The performance of the proposed approach confirmed that applying rules settings that can handle some challenges in ABSA improves the performance significantly. The results reported in the study have shown an increase in the accuracy and f-measure by 6%, and 17% respectively when compared with the baseline.
The importance of domains to security management

Domains play a key role in establishing and maintaining brands online. Whether they are used to support the launch of new products or services, support e-commerce, or connect with customers, it is critical that they be properly secured and managed. Every business should maximise the chance for its domain names to be memorable, to reflect the company ethos and ultimately stand out on an overcrowded Internet. This ensures that the domain will resonate with customers, leading to more searches and ultimately increased business.
Secure and robust host-adapted color image watermarking using inter-layered wavelet-packets

This work presents a secure and robust color image watermarking for copyright protection applications, that is based on exploiting the multi-spectral properties of the primary color components of the RGB image. The proposed scheme employs the interconnection between the subbands of the primary color components in the wavelet-packet domain. The scheme is constructed to be adaptive, in the sense that the watermark bits are embedded in safe locations, depending on the inter-layer energy of coefficients in the wavelet-packets. The scheme immunity to attacks is improved by applying a two-level security procedure. To validate the high performance of the proposed scheme, several experimental tests were conducted and a comparative analysis was provided. The obtained results have shown improved watermarking robustness against a wide range of attacks while preserving a high watermarking imperceptibility.
Real-time Obstacle Detection Over Rails Using Deep Convolutional Neural Network

In vision-based environment perceptive system of urban rail transit, cameras installed in the front of the train can assist people to identify obstacles on rails. Varying environment makes small targets like pedestrians and bags hard to identify, thus the real-time and accuracy performance of the detection need to be improved. Inspired by the achievements of Single Shot Multibox Detection (SSD) successfully applied on images recognition in recent years, we presented an obstacle detection algorithm which consists of two steps: main network and feature fusion. In the first part, the input image is converted into multi-scale feature maps based on the Residual Neural Network. Next, a series of convolution layers are added to extract features, and the network outputs a confidence score and bounding boxes for possible obstacles. Experiments showed that the proposed method can detect obstacles in various environment. Compared with traditional object detection algorithms and other deep learning algorithms, it runs at a faster detection speed (26 frames per second, FPS) and higher detection accuracy (91.61% mean average precision, mAP) on our self-made dataset.
Deep Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis

This paper proposes a deep learning-based Mandarin-Tibetan cross-lingual speech synthesis to realize both Mandarin speech synthesis and Tibetan speech synthesis under a unique framework. Because Tibetan training corpus is hard to record, we train the acoustic models with a large scale Mandarin multi-speaker corpus and a small scale Tibetan one-speaker corpus. The acoustic models are trained with deep neural network (DNN), hybrid long short-term memory (LSTM), and hybrid bi-directional long short-term memory (BLSTM). We also further extend our Chinese text analyzer by adding a Tibetan text analyzer for generating context-dependent labels from input Chinese or Tibetan sentences. The Tibetan text analyzer includes a text normalization, a novel Tibetan word segmentation that combines a BLSTM with conditional random field, a prosodic boundary prediction, and a grapheme-to-phoneme conversion. We select the initials and the finals of both Mandarin and Tibetan as the speech synthesis units to train a speaker-independent mixed language average voice model (AVM) with DNN, hybrid LSTM, and hybrid BLSTM from Mandarin and Tibetan mixed corpus. Then the speaker adaptation is applied to train speaker-dependent DNN, hybrid LSTM, or hybrid BLSTM models of Mandarin or Tibetan with a small target speaker corpus from an AVM. Finally, we synthesize the Mandarin speech, or Tibetan speech though the speaker-dependent Mandarin or Tibetan models. The experiments show that the hybrid BLSTM-based cross-lingual speech synthesis framework is better than the other two cross-lingual frameworks and the Tibetan monolingual framework. The mixed Tibetan training corpus does not influence the voice quality of synthesized Mandarin speech. Furthermore, the hybrid BLSTM-based cross-lingual speech synthesis framework only needs 60% of the training corpus to synthesize a similar voice as the Tibetan monolingual framework. Therefore, the proposed method can be used for speech synthesis of low resource languages by borrowing the same tremendous resource language's corpus.
Blackmarket-Driven Collusion Among Retweeters–Analysis, Detection, and Characterization

The growth of online social media has led to a huge increase in the number of users who want to share and publicize various kinds of information. Twitter, the most popular micro-blogging platform, has become a hotbed for users who are involved in different activities such as news publishing, job hunting, recruiting, advertising and publicity. Retweeting a tweet is a major action to broadcast a user's message out to millions of users. Retweet action has two major advantages: (i) gaining quick exposure to the content, and (ii) increasing likelihood of gaining new Twitter followers in return. The organic way of gaining a larger number of retweets is a time consuming process, which leads to the creation of unfair methods to gain retweets. Thus, Twitter users often approach various blackmarket services to gain retweets inorganically in a short duration. Blackmarkets spread their collusive ecosystem in such a way that Twitter is unable to detect them even after devoting significant effort to purge the platform off bots, trolls, and fake accounts. One major reason behind the evasion is that the collusive users involved in blackmarket services exhibit a mix of organic and inorganic behavior - they organically reweet some genuine tweets; at the same time, they inorganically retweet tweets submitted to blackmarket services. This paper is the first attempt to provide a thorough study of the collusive users involved in two types of blackmarket services - Premium and Freemium. We collect a novel dataset of collusive users comprising of users from both types of blackmarket services. We provide network-centric, profile-centric, timeline-centric and retweet-centric characteristics of these users and show how users involved in premium blackmarket services exhibit diverse behavior as compared to those involved in freemium services. We further employ human annotators to label collusive users into three types: bots, promotional customers, and normal customers. We then curate 63 novel features to run state-of-the-art classifiers in two settings - binary classification (collusive vs. genuine) and multi-class classification (bot, promotional customers, normal customers, and genuine users). Bagging achieves the best accuracy (macro F1-score of 0.892) in the former setting, whereas Random Forest outperforms others (macro F1-score of 0.791) in the latter setting. We also develop a chrome extension, SCoRe++ which can detect collusive retweeters in real time.
The Public Blockchain Ecosystem: An Empirical Analysis

This paper provides an empirical overview of the largely unexplored public blockchain ecosystem. Our overview highlights that only a few blockchains dominate the ecosystem although no single blockchain, not even Bitcoin, dominates uniformly. We explain our empirical findings with a simple theoretical framework that establishes three key economic attributes - adoption, scale, and security - as the determinants of blockchain user utility. We examine each blockchain along those dimensions empirically. Comparing across these attributes, we establish whether a blockchain could be optimal relative to all other blockchains for some user type. Applying that comparison yields that only a few blockchains could be optimal for some user type. Our results thus explain why only a few blockchains dominate the broader blockchain ecosystem and further provide an empirical framework from which to evaluate progress within the blockchain ecosystem.
Improving Sentiment Polarity Detection Through Target Identification

In an opinionated long review, there may be several targets described by different potential terms. Traditional review-level techniques for Persian sentiment analysis addressed the problem using a one-method-fits-all solution in which the overall polarity of a review is calculated using all its opinionated words without considering their target. In this article, a new method is proposed, which first decomposes a long review into its constituent sentences and then detects the main target of each sentence. In the next step, five policies, including most occurring first (MOF), most general first (MGF), most specific first (MSF), first occurring first (FOF), and last occurring first (LOF), are proposed to come up with the main target of the review. Finally, using the part-of-speech (POS) tags, potential terms in the sentences are specified and a comprehensive sentiment lexicon is employed to compute the polarity of the sentences. In order to evaluate the proposed method, three data sets of user reviews about different topics, including digital equipment, hotels, and movies, are created as no previous study addressed the problem of target identification in the Persian language. The results of comparing the proposed method with a state-of-the-art lexicon-based method show that specifying the main targets of reviews can improve the performance of the systems about 17% and 12% in terms of accuracy and F1-measure. Moreover, the proposed method using the MGF policy achieves the best performance in finding the main target of reviews, while for finding the ultimate polarity of reviews, the MOF outperforms other policies.
Write Quick, Run Fast: Sparse Deep Neural Network in 20 Minutes of Development Time via SuiteSparse:GraphBLAS

SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written in GraphBLAS achieve high performance with minimal development time. Using GraphBLAS, it took a mere 20 minutes to write a first-cut computational kernel that solves the Sparse Deep Neural Network Graph Challenge. Understanding the problem description and file format, writing code to read in the files that define the problem, and comparing our results with the reference solution took a full day. The kernel consists of a single for-loop around 4 lines of code, all of which are calls to GraphBLAS, and it worked perfectly the first time it was compiled. The sequential performance of the GraphBLAS solution is 3x to 5x faster than the MATLAB reference implementation. OpenMP parallelism gives an additional 10x to 15x speedup on a 20-core Intel processor, 17x on an IBM Power8 system, and 20x on a Power9 system, for the largest problems. Since SuiteSparse:GraphBLAS does not yet employ MPI, this was added at the application level, a development effort that took one week, primarily because of difficulties in resolving a load-balancing issue in the MPI-based parallel algorithm.