Reviewerly/exHarmony huggingface dataset:
Influences of Global and Local Features on Eye-Movement Patterns in Visual-Similarity Perception of Synthesized Texture Images

Global and local features are essential for visual-similarity texture perception. Therefore, understanding how people allocate their visual attention when viewing textures with global or local similarity is important. In this work, we investigate the influences of global and local features of a texture on eye-movement patterns and analyze the relationship between eye-movement patterns and visual-similarity selection. First, we synthesized textures by separately controlling global and local textural features through the primitive, grain, and point configuration (PGPC) texture model, a mathematical morphology-based texture model. Second, we conducted an experiment to acquire eye-movement data where participants identified the texture that was highly similar to the standard texture. Experiment data were obtained through an eye-tracker from 60 participants. The collected eye-tracking data were analyzed in terms of three metrics, including total fixation duration in each region of interest (ROI), fixation-point variance in each ROI, and fixation-transfer counts between different ROIs. Analysis results indicated the following. (1) The global and local features of a texture influenced eye-movement patterns. In particular, the texture image that was globally similar to the standard texture contained dispersed fixation points. By contrast, the texture image that was locally similar to the standard texture contained concentrated fixation points. The domination of global and local features influenced the viewers’ similarity choice. (2) The final visual-similarity selection was related to the fixation-transfer count between different ROIs, but not to the fixation time in each ROI. This research also extends the applicability of the mathematical morphology-based texture model to human visual perception.
Scope of Visual-Based Similarity Approach Using Convolutional Neural Network on Phishing Website Detection

Phishing website is an illegitimate website that is designed by dishonest people to mimic a real website. Those who are entering such a website may expose their sensitive information to the attacker who might use this information for financial and criminal activities. In this technological world, phishing websites are created using new techniques allows them to escape from most anti-phishing tool. So, the white list and blacklist based techniques are less effective when compared with the recent phishing trends. Advanced to that, there exist some tools using machine learning and deep learning approaches by examining webpage content in order to detect phishing websites. Along with the rapid growth of phishing technologies, it is needed to improve the effectiveness and efficiency of phishing website detection. This work reviewed many papers that proposed different real-time as well as non-real-time techniques. As the result, this study suggests a Convolutional Neural Network (CNN) framework with 18 layers and scope of transfer learning in Alex Net for the classification of websites using screenshot images and URLs of phishing and legitimate websites. CNN is a class of deep, feed-forward artificial neural networks (where connections between nodes do not form a cycle) & use a variation of multilayer perceptions designed to require minimal preprocessing.
Spliced Image Detection in 3D Lighting Environments Using Neural Networks

Digital image forensics is a trending research domain that validates the authenticity of the digital images. The traditional methods invested time on detecting the device used for capturing and identifying the traces. Nowadays, it is interesting to note that the illumination deviations in the image provide an effective trace for detecting the forgeries in the image. Accordingly, a method is developed for detecting the spliced image based on the illumination features. Initially, the human faces in the composite image are detected, and the three-dimensional model of all the faces is derived using the landmark-based 3D morphable model (L3DMM). The light coefficients are determined using the 3D shape model for extracting the features. To identify the spliced/pristine images in the input composite image, neural network (NN) is used, which is trained using the standard back-propagation algorithm. The experiments were conducted on DSO-1 and DSI-1 datasets. Performance metrics such as accuracy, true positive rate (TPR), true negative rate (TNR) and ROC are used to prove the efficiency of the proposed method.
Cross-Model Retrieval Via Automatic Medical Image Diagnosis Generation

Recent works in deep learning using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models have yielded state of the art results on a variety of image processing tasks. Multimodal representation, especially Image captioning is gaining popularity due to their primordial role in constricting heterogeneity gap among different modalities which are very helpful in cross-modality analysis tasks. The uncountable amounts of medical images, as well as medical documents, need to be processed to discover hidden knowledge. The purpose of this research is to present biomedical information retrieval system in order to know more about their strengths and weakness. Then we will propose our approach that tries to resolve some gaps and gives some solution to the existing systems and engine retrieval by giving an insight into the images captioning benefit in cross-modality retrieval.
Prediction of Short-Term and Long-Term Hourly Global Horizontal Solar Irradiation Using Artificial Neural Networks Techniques in Fez City, Morocco

The integration of renewable energy plants into the energy mix leads to serious problems in maintaining the balance of electricity grids. Indeed, renewable energy plants can produce electricity when there is not much need. Therefore, predicting renewable energy potentials and then the output of power plants can allow grid operators to prepare decision scenarios in advance. In this work, we are interested in predicting hourly short-term (h + 1) and long term (h + 48) global horizontal solar irradiation (GHI) by applying two types of Artificial Neural Networks (ANN): Multilayer Perceptron (MLP) and a Nonlinear AutoRegressive neural network with eXogenous inputs (NARX).
Requirements Change Requests Classification: An Ontology-Based Approach

Requirements for software system projects are becoming increasingly exposed to a large number of change requests. Change requests captured in natural language are difficult to analyze and evaluate. This may lead to major problems, such as requirements creep and ambiguity. To provide an appropriate understanding of a change request in a systematic way, this paper aims to develop an ontology for classifying change requests as either functional changes (FC) or technical changes (TC). Technical changes are further classified into nine categories including the ISO 25010 quality characteristics and Project requirement and constraints. To establish a comprehensive representation of change requests, we collected users’ reviews from PROMISE repository and classified them using the protégé editor. The feasibility of the proposed approach is illustrated through examples taken from PROMISE repository.
IEEE Access Special Section Editorial: Artificial Intelligence and Cognitive Computing for Communication and Network

With the rapid development of communication and network technologies, novel information services and applications are rapidly growing worldwide. Advanced communications and networks greatly enhance the user experience, and have a major impact on all aspects of people’s lifestyles in terms of work, society, and the economy. Although advanced techniques have extensively improved users’ quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and other scenarios. Therefore, it is a great challenge to develop smart communications and networks that support optimized management, dynamic configuration, and feasible services.
TRIPLE project: building a discovery platform to enhance collaboration

Social Sciences and Humanities research is divided across a wide array of disciplines, sub- disciplines and languages. While this specialisation makes it possible to investigate the extensive variety of SSH topics, it also leads to a fragmentation that prevents SSH research from reaching its full potential. Use and reuse of research is suboptimal, interdisciplinary collaboration possibilities are often missed partially because of missing standards and referential keys between disciplines. Often, the reuse of data may paradoxically complicate a relevant sorting of data and a trust relationship between researchers. As a result, societal, economic and academic impacts are limited. Conceptually, there is a wealth of transdisciplinary collaborations, but in practice there is a need to help researchers and research institutions to connect them and support them, to prepare the research data for these overarching approaches and to make them findable and usable. The T R IPL E (Targeting Researchers through Innovative Practices and Linked Exploration) project is a practical answer to the above issues, as it aims at designing and developing the European discovery platform dedicated to Social Sciences and Humanities resources. Funded under the European Commission program INFRAEOSC-02-2019 “Prototyping new innovative services”, thanks to a consortium of 19 partners, TRIPLE will develop a full multilingual and multicultural solution for the discovery and the reuse of Social Sciences and Humanities resources. The project started in October 2019 for a duration of 42 months thanks to European funding of 5.6 million €.
A Proposal Based on Instance Typicality for Dealing with Nominal Attribute Values in Instance-Based Learning Environments

Instance-Based Learning (IBL) is a research area with focus on supervised algorithms that use the training instances as the expression of the learned concept. Usually training instances are described by vectors of attribute values and an associated class. Attributes can be of different types, depending on the values they represent and, usually, can be of discrete or continuous type. A subtype of the discrete type is known as nominal. Attributes of nominal type usually represent categories and there is no order among its values. This paper proposes and investigates an alternative strategy for dealing with nominal attributes during the classification phase of the well-known instance-based algorithm NN (Nearest Neighbor). The proposed strategy is based on the concept of typicality of an instance, which can be taken into account as a possible tiebreaker, in situations where the instance to be classified has more than one nearest neighbor. Experiments using the proposed strategy and the default random strategy used by the conventional NN show that the typicality-based strategy can be a convenient choice to improve accuracy, when data instances have nominal attributes among the attributes that describe them.
Robust Speaker Recognition Based on Stacked Auto-encoders

Speaker recognition is a biometric modality which utilize speaker’s speech segments to recognize identity, determining whether the test speaker belongs to one of the enrolled speakers. In order to improve the robustness of i-vector framework on cross-channel conditions and explore the nova method for applying deep learning to speaker recognition, the Stacked Auto-encoders is applied to get the abstract extraction of the i-vector instead of applying PLDA. After pre-processing and feature extraction, the speaker and channel independent speeches are employed for UBM training. The UBM is then used to extract the i-vector of the enrollment and test speech. Unlike the traditional i-vector framework, which uses linear discriminant analysis (LDA) to reduce dimension and increase the discrimination between speaker subspaces, this research use stacked auto-encoders to reconstruct the i-vector with lower dimension and different classifiers can be chosen to achieve final classification. The experimental results show that the proposed method achieves better performance than the-state-of-the-art method.
Analysis and Definition of Data Flows Generated by Bio Stimuli in the Design of Interactive Immersive Environments

This work focuses on interactivity as one of the essential factors for creating immersive environments, particularly interactivity that generates involuntary responses over which the user does not have conscious control. A dynamic and adaptive model was designed to analyze and define the data flow generated by bio stimuli for the design of interactive immersive environments.
Peter Bailis Speaks Out on building tools users want to use

Welcome to this installment of ACM Sigmod Records series of interviews with distinguished members of the database community. I'm Marianne Winslett and today we're at the 2017 SIGMOD and PODS Conference in Chicago. I have here with me Peter Bailis who's a professor at Stanford University. Peter won the 2017 ACM SIGMOD Jim Gray Dissertation award for his thesis entitled "Coordination Avoidance in Distributed Databases." Peter's advisors were Joseph Hellerstein, Ion Stoica, and Ali Ghodsi at Berkeley.
COMMUNITY COLLEGE CORNER: Information technology two-year transfer programs

column Share on COMMUNITY COLLEGE CORNER: Information technology two-year transfer programs Authors: Cara Tang Portland Community College Portland Community CollegeView Profile , Markus Geissler Cosumnes River College Cosumnes River CollegeView Profile Authors Info & Claims ACM InroadsVolume 11Issue 3September 2020 pp 11–14https://doi.org/10.1145/3410476Published:18 August 2020Publication History 1citation42DownloadsMetricsTotal Citations1Total Downloads42Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Trend of Mobile-assisted Language Learning from 2014 to 2018

In order to trace the development of mobile-assisted language learning (MALL) in recent years, this paper analyzes their characteristics and research trends for research papers published from 2014 to 2018. The studies were published in international journals listed in the Social Science Citation Index (SSCI). The goal of the study is to understand the trends, research focus, methodologies and current hot issues of publications. The results show that the publication of periodicals is on the rise. Students' perceptions of mobile learning techniques and language skills are the most common subjects of study. The most commonly used mobile technology devices are smartphones, mobile phones and personal digital assistants, while the most common target language is English as a foreign language.
Drive into Future World Using Artificial Intelligence with Its Application in Sensor-Based Car Without Driver

In the cutting edge period, artificial intelligence (AI) revolutionizes the world. Intelligent machines will alter the human capabilities in several areas. Artificial knowledge is the insight shown by different machines and software. It is considered as the subordinate of software engineering. Computerized analysis is turning into a renowned field in as it has upgraded the life of humans in many areas. Artificial insight over the last two decades has significantly improved the execution of the manufacturing and management systems. Study in the sector of artificial intelligence has offered ascend to the master framework, and its application in the vehicles is engaged to be computerized to give human driver loosened up driving. In the field of car, different viewpoints have been viewed as which makes a vehicle computerized. This paper discusses the artificial intelligence, types, space of artificial intelligence and its applications in field of the car without the driver using sensor-based technology and also talks about difficulty in the pathway of sensor-based cars and its future scope.
Formation of Ethical and Axiological Competencies when Using ICT in Education

At present, the concept of a competency-based approach to education is gaining ground, which is the basis of substantial changes to ensure that education meets the needs and capabilities of the public in the period of informatization and global mass communication. From the standpoint of the competency-based approach, the meaning of education is the development of the ethical and axiological abilities of students to independently solve problems in various fields and activities based on the use of social experience, an element of which is also the students' own experience. It is the competency-based approach that is determined by one of the foundations of the Strategy for the Modernization of Education - the main state document in the field of Russian education today and in the near future.
Multi-level Thresholding Selection Based on Multi-Verse optimization With Levy Flight for Image Segmentation

Multi-level thresholding(MLT) is a threshold-based image segmentation method. MLT is perhaps one of the most mainstream method of image segmentation because of its effective. For purpose of enhancing applicability and practicability of the MLT, a Levy Flight based multi-vese optimization(LFMVO) algorithm was proposed in this paper. Levy fight(LF) strategy is adopted to boost the global exploration ability of the multidimensional optimization problem and help the original algorithm to rapidly converge. It also can increase the vitality of the population of solution to let the algorithm avoid immature results. So as to evaluate the segmentation threshold of gray image, Kapurs entropy is selected as the segmentation basis. The performance and robustness of the proposed LFMVO algorithm were tested by using some randomly selected pictures from Berkeley image segmentation database (BSDS-500). LFMVO is compared with three other advanced meta-heuristic algorithm on the multilevel thresholding problem. The simulation results show the superiority of LFMVO and the final results give the proof that the LFMVO can enhance the image quality after segmentation, and further it be more stable than other algorithms.
Correction to: CDSnake: Snakemake pipeline for retrieval of annotated OTUs from paired-end reads using CD-HIT utilities

An amendment to this paper has been published and can be accessed via the original article.
World-Models for Bitrate Streaming

Adaptive bitrate (ABR) algorithms optimize the quality of streaming experiences for users in client-side video players, especially in unreliable or slow mobile networks. Several rule-based heuristic algorithms can achieve stable performance, but they sometimes fail to properly adapt to changing network conditions. Fluctuating bandwidth may cause algorithms to default to behavior that creates a negative experience for the user. ABR algorithms can be generated with reinforcement learning, a decision-making paradigm in which an agent learns to make optimal choices through interactions with an environment. Training reinforcement learning algorithms for bitrate streaming requires building a simulator for an agent to experience interactions quickly; training an agent in the real environment is infeasible due to the long step times in real environments. This project explores using supervised learning to construct a world-model, or a learned simulator, from recorded interactions. A reinforcement learning agent that is trained inside of the learned model, rather than a simulator, can outperform rule-based heuristics. Furthermore, agents that are trained inside the learned world-model can outperform model-free agents in low sample regimes. This work highlights the potential for world-models to quickly learn simulators, and to be used for generating optimal policies.
Sulphide-sulphosalt reactions and sphalerite remobilisation in siderite-Pb-Zn-Sb veins of the Siegerland district, Germany: A laser microprobe sulphur isotope study

The sulphur isotope characteristics of complex siderite-Pb-Zn-Sb veins in the Siegerland district, Germany, have been investigated using a laser microprobe technique. Hydrothermal overprinting of siderite-Pb-Zn veins by late-stage fluids caused extensive reaction and remobilisation processes and led to the formation of sulphosalt-rich assemblages. The sulphur isotope composition of remobilised mineral phases is essentially identical to the precursor base metal sulphides, which demonstrates recycling of sulphur. The local inheritance is most significant for diffusion-controlled reaction assemblages, whereas sphalerite and sulphosalts precipitated in fissure veins display greater isotopic variation, related to fluid-mineral fractionation.
mlr3proba: Machine Learning Survival Analysis in R.

As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. However, no consistent interface for evaluation and modelling of survival analysis has emerged despite its vital importance in many fields, including medicine, economics, and engineering. \texttt{mlr3proba} is part of the \texttt{mlr3} ecosystem of machine learning packages for R and facilitates \texttt{mlr3}'s general model tuning and benchmarking by providing a multitude of performance measures and learners for survival analysis with a clean and systematic infrastructure for their evaluation. \texttt{mlr3proba} provides a comprehensive machine learning interface for survival analysis, which allows survival modelling to finally be up to the state-of-art.
Cybersecurity Overview of a Robot as a Service Platform

Containerization solutions have spread widely in the industry due to their ease of deployment, agility, and portability. However, its adoption is not without challenges and difficulties in the field of security. This paper presents an overview of the vulnerabilities present in the application containerization solutions, paying special attention to the security aspects related to them. Applying the conclusions of the above analysis, a containerization system focused on offering AI and robotics services in the cloud is also proposed.
Robust Generalization via $\alpha$-Mutual Information

The aim of this work is to provide bounds connecting two probability measures of the same event using R\'enyi $\alpha$-Divergences and Sibson's $\alpha$-Mutual Information, a generalization of respectively the Kullback-Leibler Divergence and Shannon's Mutual Information. A particular case of interest can be found when the two probability measures considered are a joint distribution and the corresponding product of marginals (representing the statistically independent scenario). In this case, a bound using Sibson's $\alpha-$Mutual Information is retrieved, extending a result involving Maximal Leakage to general alphabets. These results have broad applications, from bounding the generalization error of learning algorithms to the more general framework of adaptive data analysis, provided that the divergences and/or information measures used are amenable to such an analysis ({\it i.e.,} are robust to post-processing and compose adaptively). The generalization error bounds are derived with respect to high-probability events but a corresponding bound on expected generalization error is also retrieved.