Reviewerly/exHarmony huggingface dataset:
Classification of Illegal Activities on the Dark Web

The strong anonymity and hard-to-track mechanisms of the dark web provide shelter for illegal activities. The illegal content on the dark web is diverse and frequently updated. Traditional dark web classification uses large-scale web pages for supervised training. However, the difficulty of collecting enough illegal dark web content and the time consumption of manually labeling web pages have become challenges of current research. In this paper, we propose a method that can effectively classify illegal activities on the dark web. Instead of relying on the massive dark web training set, we creatively select laws and regulations related to each type of illegal activities to train the machine learning classifiers and achieve a good classification performance. In the areas of pornography, drugs, weapons, hackers, and counterfeit credit cards, we select relevant legal documents from the United States Code for supervised training and conduct a classification experiment on the illegal content of the real dark web we collected. The results show that combined with TF-IDF feature extraction and Naive Bayes classifier, we achieved an accuracy of 0.935 in the experimental environment. Our approach allows researchers and the network law enforcement to check whether their dark web corpus contains such illegal activities based on the relevant laws of the illegal categories they care about in order to detect and monitor potential illegal websites in a timely manner. And because neither a large training set nor the seed keywords provided by experts are needed, this classification method provides another idea for the definition of illegal activities on the dark web. Moreover, it makes sense to help explore and discover new types of illegal activities on the dark web.
Experimental Analysis of Streaming Algorithms for Graph Partitioning

We report a systematic performance study of streaming graph partitioning algorithms. Graph partitioning plays a crucial role in overall system performance as it has a significant impact on both load balancing and inter-machine communication. The streaming model for graph partitioning has recently gained attention due to its ability to scale to very large graphs with limited resources. The main objective of this study is to understand how the choice of graph partitioning algorithm affects system performance, resource usage and scalability. We focus on both offline graph analytics and online graph query workloads. The study considers both edge-cut and vertex-cut approaches. Our results show that the no partitioning algorithms performs best in all cases, and the choice of graph partitioning algorithm depends on: (i) type and degree distribution of the graph, (ii) characteristics of the workloads, and (iii) specific application requirements.
Gravitational Laws of Focus of Attention

The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision. In this paper, we describe a model of scanpath as a dynamic process which can be interpreted as a variational law somehow related to mechanics, where the focus of attention is subject to a gravitational field. The distributed virtual mass that drives eye movements is associated with the presence of details and motion in the video. Unlike most current models, the proposed approach does not estimate directly the saliency map, but the prediction of eye movements allows us to integrate over time the positions of interest. The process of inhibition-of-return is also supported in the same dynamic model with the purpose of simulating fixations and saccades. The differential equations of motion of the proposed model are numerically integrated to simulate scanpaths on both images and videos. Experimental results for the tasks of saliency and scanpath prediction on a wide collection of datasets are presented to support the theory. Top level performances are achieved especially in the prediction of scanpaths, which is the primary purpose of the proposed model.
Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks

With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly quantified. We used DNN-based transfer learning to analyze histologic images of periodic acid-Schiff-stained renal sections from a cohort of mice with different genotypes. We demonstrate that DNN-based machine learning has strong generalization performance on multiple histologic image processing tasks. The neural network extracted quantitative image features and used them as classifiers to look for differences between mice of different genotypes. Excellent performance was observed at segmenting glomeruli from non-glomerular structure and subsequently predicting the genotype of the animal on the basis of glomerular quantitative image features. The DNN-based genotype classifications highly correlate with mesangial matrix expansion scored by a pathologist (R.E.C.), which differed in these animals. In addition, by analyzing non-glomeruli images, the neural network identified novel histologic features that differed by genotype, including the presence of vacuoles, nuclear count, and proximal tubule brush border integrity, which was validated with immunohistologic staining. These features were not identified in systematic pathologic examination. Our study demonstrates the power of DNNs to extract biologically relevant phenotypes and serve as a platform for discovering novel phenotypes. These results highlight the synergistic possibilities for pathologists and DNNs to radically scale up our ability to generate novel mechanistic hypotheses in disease.
Survey on Face Expression Recognition using CNN

Recognition of facial expressions plays a major role in many automated system applications like robotics, education, artificial intelligence, and security. Recognizing facial expressions accurately is challenging. Approaches for solving FER (Facial Expression Recognition) problem can be categorized into 1) Static single images and 2) Image sequences. Traditionally, different techniques like Multi-layer Perceptron Model, k-Nearest Neighbours, Support Vector Machines were used by researchers for solving FER. These methods extracted features like Local Binary Patterns, Eigenfaces, Face-landmark features, and Texture features. Among all these methods, Neural Networks have gained very much popularity and they are extensively used for FER. Recently, CNNs (Convolutional Neural Networks) have gained popularity in field of deep learning because of their casual architecture and ability to provide good results without requirement of manual feature extraction from raw image data. This paper focuses on survey of various face expression recognition techniques based on CNN. It includes state-of-the-art methods suggested by different researchers. The paper also shows steps needed for usage of CNN for FER. This paper also includes analysis of CNN based approaches and issues requiring attention while choosing CNN for solving FER.
A quick semantic artificial bee colony programming (qsABCP) for symbolic regression

Artificial bee colony programming (ABCP) is a novel evolutionary computation based automatic programming method, which uses the basic structure of artificial bee colony (ABC) algorithm. In this paper, some studies were conducted to improve the performance of ABCP and three new versions of ABCP are introduced. One of these improvements is related to the convergence performance of ABCP. In order to increase the local search ability and achieve higher quality solutions in early cycles, quick ABCP algorithm was developed. Experimental studies validate the enhancement of the convergence performance when the quick ABC approach is used in ABCP. The second improvement introduced in this paper is about providing high locality. Using semantic similarity based operators in the information sharing mechanism of ABCP, semantic ABCP was developed and experiment results show that semantic based information sharing improves solution quality. Finally, combining these two methods, quick semantic ABCP is introduced. Performance of these novel methods was compared with some well known automatic programming algorithms on literature test problems. Additionally, ABCP based methods were used to find approximations of the Colebrook equation for flow friction. Simulation results show that, the proposed methods can be used to solve symbolic regression problems effectively.
FMaxCloHUSM: An efficient algorithm for mining frequent closed and maximal high utility sequences

Mining all frequent high utility sequences (FHUS) in quantitative sequential databases (QSDBs) is a generalization of the problem of mining all frequent sequences in non-quantitative sequence databases. In the last decade, the former problem has attracted the attention of many researchers because utility-based sequences are more informative and actionable for decision-making than frequent sequences. Although utility-based sequences have many real-life applications, their number is often very large, especially for low minimum utility thresholds and long sequences. It can thus be difficult for users to analyze them and mining utility-based sequences often requires much time and memory. To solve this problem, this paper proposes two concise representations of FHUS, having a small cardinality that provides a concise summary of all FHUS. Those representations are defined as two sets, FCHUS and FMaxHUS, of all frequent maximal and closed high utility sequences. To efficiently mine these concise representations, two width and depth pruning strategies are proposed for eliminating low utility sequences early and a novel local pruning strategy is proposed named LPCHUS using a new extended measure on projected databases for eliminating non-closed and non-maximal high utility sequences early as well as their extensions. Based on these strategies and a novel data structure named SIDUL in vertical format, an algorithm named FMaxCloHUSM is designed for efficiently mining the sets of FCHUS and FMaxHUS, separately or simultaneously. To our best knowledge, this is the first algorithm for discovering these two concise representations. An experimental study conducted using both real-life and synthetic QSDBs shows that the proposed algorithm is efficient in terms of time and memory consumption, and that the developed strategies greatly reduce the search space.
Non-linearities, cyber attacks and cryptocurrencies

This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015–2/28/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets.
Cloud Health Resource Sharing Based on Consensus-Oriented Blockchain Technology: Case Study on a Breast Tumor Diagnosis Service

In recent years, researchers have made significant efforts in advancing blockchain technology. This technology, with distinct features of decentralization and security, can be applied to many fields. In areas of health data and resource sharing, applications of blockchain technology are also emerging.In this study, we propose a cloud health resource-sharing model based on consensus-oriented blockchain technology and have developed a simulation study on breast tumor diagnosis.The proposed platform is built on a consortium or federated blockchain that possesses features of both centralization and decentralization. The consensus mechanisms generate operating standards for the proposed model. Open source Ethereum code is employed to provide the blockchain environment. Proof of Authority is selected as the consensus algorithm of block generation.Based on the proposed model, a simulation case study for breast tumor classification is constructed. The simulation includes 9893 service requests from 100 users; 22 service providers are equipped with 22 different classification methods. Each request is fulfilled by a service provider recommended by the weighted k-nearest neighbors (KNN) algorithm. The majority of service requests are handled by 9 providers, and provider service evaluation scores tend to stabilize. Also, user priority on KNN weights significantly affects the system operation outcome.The proposed model is feasible based on the simulation case study for the cloud service of breast tumor diagnosis and has the potential to be applied to other applications.
Involving Syrian Refugees in Design Research

With the Syrian crisis entering its 8th year, refugees have become the focus of research across multiple disciplines, including design and HCI research. While some researchers have reflected upon designing with refugees, these accounts have been limited to conducting design workshops in formal spaces. Through reflecting on our experiences of conducting design research in informal refugee settlements in Lebanon we unpack lessons learnt, design practices and research approaches that facilitate design engagements with refugees. We highlight the value in participants configuring the design space, using a dialogical approach as well as creating a safe space for both participants and the researcher. We also reflect on the roles that researchers may take on when conducting similar research. By doing so we contribute specific design practices that may be transferrable to other similar contexts.
Chatbot Acceptance

According to industry reports, the lack of trust in non-human interaction prevents widespread Chatbot acceptance. Since the willingness and the ability to trust varies between individuals, this study examines to what extent the trust in Chatbots varies accordingly to different personality profiles. Drawing on the HEXACO dimensions of personality, we apply a latent profile analysis and identify three distinct personality profiles, which significantly vary in their trust in Chatbots. A high level of trust in Chatbots, e.g. Alexa, is mainly affected by the two personality dimensions Extraversion and Agreeableness and only slightly by Honesty-Humility. To prevent commercial underperformance and the shutdown of their Chatbot, providers should make sure that users trust in their Chatbot. This can be accomplished, if the Chatbot treats each user based on his or her membership in one of the three profiles identified in this study.
Designing Co-Creative AI for Public Spaces

Artificial intelligence (AI) is becoming increasingly pervasive in our everyday lives. There are consequently many common misconceptions about what AI is, what it is capable of, and how it works. Compounding the issue, opportunities to learn about AI are often limited to audiences who already have access to and knowledge about technology. Increasing access to AI in public spaces has the potential to broaden public AI literacy, and experiences involving co-creative (i.e. collaboratively creative) AI are particularly well-suited for engaging a broad range of participants. This paper explores how to design co-creative AI for public interaction spaces, drawing both on existing literature and our own experiences designing co-creative AI for public venues. It presents a set of design principles that can aid others in the development of co-creative AI for public spaces as well as guide future research agendas.
Mobile Augmented Reality to Influence Design and Constructability Review Sessions

In modern construction projects, architects, engineers, contractors, and owners who are actively involved in projects use different methods of visualization, such as building information modeling (BIM), physical mock-ups, virtual reality (VR), and augmented reality (AR), to support the conceptualizations, representations, and final appearances of their design ideas. These approaches can support design visualization for decision making before final construction. However, unlike BIM visualization where users can only interact with a virtual environment and physical mock-ups where the interaction is only with physical design components, AR can merge physical conditions with computer-generated visual information. This may aid professionals in the architecture, engineering, and construction (AEC) industries during design and constructability review sessions. This research studied the use of AR through a structured design review activity involving current industry practitioners in design and constructability review sessions and tested whether the combined virtual–physical nature of AR facilitates the same actions and outcomes suggested by prior work that used virtual reality–based mock-ups or physical mock-ups. In addition, this study identified and analyzed various actions such as decision making, problem solving, and design alternatives that occur as users interact with AR on different mobile computers. This analysis provides an understanding of how different mobile computers, such as wearables and handheld devices showing the same technical AR environment, can lead to different actions among users. This research found that AR can facilitate some of the actions of virtual reality and physical mock-ups in design and constructability review sessions, including decision making, design alternatives, and descriptive, explanative, and problem-solving actions. In addition, different mobile computers led to different observed actions during the design review sessions. For example, handheld devices between 15.24 cm (6 in.) and 25.5 cm (10 in.) enabled more decision-making actions than any other device tested. As additional testing is completed, future findings may be compared with those presented in this work to determine the actions that are consistently seen with AR in design and constructability review sessions. Eventually, this may provide a valuable tool to allow future researchers and practitioners to strategically plan for AR technology use based on what specific human actions are desirable for a given application.
Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-the-art methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.
Multi-target tracking method based on improved firefly algorithm optimized particle filter

Joint probabilistic data association-particle filter (JPDA-PF) algorithm is often used to solve the problem of data association in multi-target tracking, it has the advantages of particle filter in dealing with nonlinear and non-Gaussian systems, but at the same time, the resampling process of particle filter will lead to the problem of particle impoverishment, which will affect the tracking accuracy. In this article, a new multi-target tracking method based on particle filter optimized by improved firefly algorithm is proposed. To solve the problem of weight degradation and avoid the problem of particle impoverishment, the improved firefly algorithm replaces the traditional resampling method and optimizes the distribution of particles in particle filter. According to the simulation results, the improved algorithm has better tracking accuracy than JPDA-PF as the number of particles is equal.
Data-driven ontology generation and evolution towards intelligent service in manufacturing systems

To support intelligent manufacturing, providing a unified production data view by integrating distributed data collected by different enterprise information systems is critical. Because various information systems are often heterogeneous, ontology is widely adopted to present a global reference view for data integration. However, construction and maintenance of these ontologies is difficult because of the heterogeneity and dynamism of these large-scale data. In this paper, with the objective of intelligent manufacturing application implementation, we propose a comprehensive ontology generation and evolution method that automatically abstracts ontology from raw production data and dynamically adjusts the ontology in accordance with changes in the manufacturing data environment. The proposed method comprises four phases: data extraction, ontology construction, ontology connection, and ontology evolution. In the first phase, data from different sources are mapped to data entities to form a unified data structure. In the second phase, an initial ontology is generated via instance-driven ontology construction. In the third phase, to support intelligent manufacturing, the initial ontologies are organised in terms of the dimensions of the various business elements, such as stuff, machine, product, process, and scenarios. In the fourth phase, rules regarding ontology restrictions are formulated to realise ontology evolution that respond to changes in the manufacturing environment. To verify the efficacy of the proposed method, a prototype was implemented with real data from a manufacturing factory, in which the constructed ontology was used as the metadata of product data in intelligent manufacturing.
DeepRoof

Rooftop solar deployments are an excellent source for generating clean energy. As a result, their popularity among homeowners has grown significantly over the years. Unfortunately, estimating the solar potential of a roof requires homeowners to consult solar consultants, who manually evaluate the site. Recently there have been efforts to automatically estimate the solar potential for any roof within a city. However, current methods work only for places where LIDAR data is available, thereby limiting their reach to just a few places in the world. In this paper, we propose DeepRoof, a data-driven approach that uses widely available satellite images to assess the solar potential of a roof. Using satellite images, DeepRoof determines the roof's geometry and leverages publicly available real-estate and solar irradiance data to provide a pixel-level estimate of the solar potential for each planar roof segment. Such estimates can be used to identify ideal locations on the roof for installing solar panels. Further, we evaluate our approach on an annotated roof dataset, validate the results with solar experts and compare it to a LIDAR-based approach. Our results show that DeepRoof can accurately extract the roof geometry such as the planar roof segments and their orientation, achieving a true positive rate of 91.1% in identifying roofs and a low mean orientation error of 9.3 degree. We also show that DeepRoof's median estimate of the available solar installation area is within 11% of a LIDAR-based approach.