Reviewerly/exHarmony huggingface dataset:
An Exploratory Inspection of the Detection Quality of Pose and Object Detection Systems by Synthetic Data

Many modern systems use image understanding components to inspect, observe and react. Often, the training is realized and limited to manually annotated real world data, but dangerous or resource expensive scenarios are rare. We create a solution to overcome these limitations and reduce the manual annotation process by producing synthetic scenarios of arbitrary content and composition.
Estimating Software Cost with a Weighted Feature Selection and Support Vector Regression with Mixture of Kernels Ensemble Learning Method

In traditional feature selection methods, there are only two possible outcomes: the feature is selected or the feature is not selected, which will lead to the loss of feature information. In this paper, considering the deficiencies of traditional methods and the requirement of software cost estimation, a weighted feature selection (WFS) method with the supervised wrapper mode is used in software cost estimation, which can effectively distinguish the influence of different features on the cost. In view of the good application effect of support vector regression (SVR), as well as a good performance of the mixture of kernels, the relationship model among the features and the software cost is established based on SVR with the mixture of kernels. In addition, considering the consistency of feature selection and the establishment of cost estimation model, a joint optimization method based on hybrid particle swarm optimization (HPSO) is adopted, which can achieve the influence analysis of features and the optimization of cost estimation model. Experiments show that the proposed ensemble learning method is effective.
Application Research of Evolutionary Algorithm in Synthesis of Reversible Logic Circuits

Abstract In order to avoid the algorithm falling into premature convergence, a big mutation adaptive evolutionary algorithm for reversible logic circuit synthesis with tabu list is proposed. Big Mutation Variable Length Evolutionary Algorithm Reversible Logic Circuit (BM_VLEA_RLC) is based on Variable-Length Evolutionary Algorithm Reversible Logic Circuit (VLEA_RLC) algorithm that designs the corresponding fitness function, adaptive evolution probability, etc., and tests using benchmark functions. The experimental results show that the proposed algorithm can obtain several feasible solutions, and some solutions are better than the existing algorithms, which verifies the feasibility of the algorithm.
How E-Learning Can Facilitate Information Security Awareness

Users of information systems are increasingly being attacked and exploited by cyber criminals. Information Security Awareness addresses how users can be convinced to behave compliantly to a company’s information security policies. This paper explores the potential of e-Learning as a tool to increase the information security awareness of users. The factors that ultimately lead to information security-compliant behavior are the factors knowledge, habit, salience, and behavioral intent. By looking at the peculiarities of e-Learning, the chances and limitations of influencing these factors are examined exploratory. The basis for this is Bloom’s Taxonomy from learning theory. The paper shows that e-Learning can help influencing knowledge and habit of a person. The salience and intention of a person, however, can only be influenced in combination with other factors. Especially with affective emotions and beliefs, e-Learning can also have negative effects. The paper also gives an outlook on how further quantitative research could help to ultimately shape effective e-Learning courses.
Learning About Autism Using VR

This paper describes a project that was carried out at the University of Malta, merging the digital arts and information technologies. The project, 'Living Autism', uses virtual reality (VR) technologies to describe daily classroom events as seen from the eyes of a child diagnosed with autism. The immersive experience is proposed as part of the professional development program for teachers and learning support assistants in the primary classrooms, to aid in the development of empathic skills with the autism disorder. The VR experience for mobile technologies has also been designed in line with user experience (UX) guidelines, to help the user assimilate and associate the projected experiences into newly formed memories of an unfamiliar living experience. Living Autism, is framed within a 4-min audio-visual interactive project, and has been piloted across a number of schools in Malta with 300 participants. The qualitative results collected gave an indication that the project had a positive impact on the participants with 85% of them reporting that they felt they became more aware of the autistic children's needs in the primary classroom.
Coarse to Fine 3D Face Reconstruction from Single Image

In this demo we propose a coarse to fine reconstruction pipeline, which takes a single RGB image as input and outputs a detailed 3D model of the face. The pipeline is composed by two main blocks, the coarse reconstruction block, which is based on a 3D Morphable Model, and the refinement block, which instead grounds on a Generative Adversarial Network (GAN).
Collecting Visual Effect Linked Data Using GWAP

We developed a game with a purpose (GWAP), which collects structured data corresponding to adjectives to build a visual effect dictionary. In this system, new semantic links can be acquired. Under the guise of a fighting game, the system encourages the user to vote on the commonsense knowledge associated with an object, because our previous research indicated that the rules of showing appropriate visual effect according to the adjective is related to commonsense knowledge of the target object. This system displays visual effects on the target object and the data structure is updated based on user’s vote. This structured data underlies a new type of communication support system that continuously improves visual effects that modify adjectives and objects. In this paper, we discuss the structure of the visual effect dictionary through an experiment. Findings show that GWAP effectively improves the relationship between commonsense knowledge and objects, while creating new linkages via deduction.
Beware of the Fakes – Overview of Fake Detection Methods for Online Product Reviews

Online reviews about products and services, such as reviews in stores, are a valuable source of information for customers. Unfortunately, reviews are contaminated by fake reviews, which may lead to wrong conclusions when including them in the analyses of user feedback. As these fake reviews are not marked as advertisement, they might lead to wrong conclusions for customers. If customers are trusting fake reviews their user experience is significantly lowered as soon as they find out that they were betrayed. Therefore, online stores and social media platforms have to take countermeasures against fake reviews. Thus, we performed a systematic literature review to create an overview of the available methods to detect fake reviews and relate the methods to their necessarily required data. This will enable us to identify fake reviews within different data sources easier in order to improve the reliability of the used customer feedback. We have analyzed 141 methods for fake detection. As the reporting quality of a substantial part lacked understandability in terms of method description and evaluation details, we have provided recommendations for method and evaluation descriptions for future method proposals. In addition, we have performed an assessment in terms of detection effectiveness and quality of those methods.
Utility-Aware and Privacy-Preserving Mobile Query Services

Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users’ locations to untrusted LBS providers may lead to privacy concerns. This article proposes <small>StarCloak</small> , a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. <small>StarCloak</small> combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, <small>StarCloak</small> uses the concept of <i>star</i> s and proposes <i>cloaking graphs</i> for effective location cloaking on road networks. Second, <small>StarCloak</small> supports user-specified <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -user anonymity and <inline-formula><tex-math notation="LaTeX">$l$</tex-math></inline-formula> -segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, <small>StarCloak</small> achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, <small>StarCloak</small> makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate <small>StarCloak</small> on two datasets using real-world road networks, under various privacy and utility constraints. Results show that <small>StarCloak</small> achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to <small>XStar</small> , its most relevant competitor.
Development of Inter-primitive Grammar for Construction of Kannada Language Vowels and Consonants Based on Their Hierarchical Structures

Kannada, 2500 years old, the administrative and official language of Karnataka state, India. The language script comprises of 16 vowels and 34 consonants, which are formed with primitives, connection points and relative positions of other primitives. The writing skill of the language needs teaching, the teachers and parents put efforts on children to make them learn the good writing skills. Here lies the scope for automation. Robots assist children in constructing the characters of the language and improve their handwriting skills. Till date, formalism has been applied to languages to check their syntax and semantics to frame words, sentences and paragraphs. But not for the character construction, which needs a formal approach. This paper presents the development of Inter-Primitive Grammar for Construction of Kannada Language Vowels and Consonants based on their Hierarchical Structures. The unambiguous Context Free Grammar (CFG), consisting of a combination of a set of primitives written in specific sequence for construction of Kannada vowels and consonants is devised. Since two primitives are connected at a time, productions are written in Chomsky Normal Form (CNF). To corroborate the grammar, a given string of primitives as input to the tools, the corresponding transliteration code for the given character is generated. Lex and Yacc tools are used, to verify the completeness and soundness of the grammar.
A Hardware Realization of Superresolution Combining Random Coding and Blurring

Resolution enhancements are often desired in imaging applications where high-resolution sensor arrays are difficult to obtain. Many computational imaging methods have been proposed to encode high-resolution scene information on low-resolution sensors by cleverly modulating light from the scene before it hits the sensor. These methods often employ movement of some portion of the imaging apparatus, only to acquire images up to the resolution of a modulating element, or require extensive postprocessing to recover the high-resolution image. In this paper, a technique is presented for resolving beyond the resolutions of both apointwise-modulating mask element and a sensor array through the introduction of a controlled blur into the optical pathway. This proposed “hardware” superresolution method can be applied without machine learning and is shown to outperform standard software-only methods. The analysis contains an intuitive and exact expression for the overall superresolvability of the system, and arguments are presented to explain how the combination of random coding and blurring makes the superresolution problem well-posed. Experimental results demonstrate that resolution enhancements of approximately 4× and higher are possible in practice using standard optical components, without mechanical motion of the imaging apparatus, and without any a priori assumptions on scene structure.
Information Exposure (IEX): A New Class in the Bugs Framework (BF)

Exposure of sensitive information can be harmful on its own. In addition, it could enable further attacks. A rigorous and unambiguous definition of information exposure faults can help researchers and practitioners identify them, thus avoiding security failures. This paper describes Information Exposure (IEX), a new class in the Bugs Framework (BF). The IEX class comprises a rigorous definition and (static) attributes of the class, along with their related dynamic properties, such as proximate and secondary causes, consequences and sites. We use the IEX class to analyze specific vulnerabilities and provide clear descriptions. We also discuss lessons we learned that will help create additional BF classes.
A new method of terrain self-adaptive matching algorithm for autonomous underwater vehicle

Abstract As different underwater terrain features will affect the accuracy of unscented Kalman filter terrain matching algorithm, a new terrain self-adaptive matching method is proposed which adjusts sigma point distribution by terrain features. The connection between sigma point distribution distance and three basic terrain features were analysed. Positioning error range were calculated using navigation positioning errors and underwater digital map. Terrain elevation standard deviation was used to characterize the information quantity of decision region. Scaling parameter was adjusted using linear mapping method and sigma point distribution distance was determined by terrain features. The simulation results proved better terrain adaptability and positioning precision of improved self-adaptive matching algorithm.
The Use of the Virtual Fashion Tester: A Usability Study

Internet shopping has been around for a number of years, changing the way companies deal with their customers and technologies have followed that process through new tools and experiences. The fashion industry has followed these changes and has modernized itself to the point where consumers can get clothes without leaving their homes through the use of virtual tasters. From the simplest models that use few anthropometric measures to the most advanced ones with Augmented Reality, avatars and body movements, virtual testers have become allies for both business profits and user experiences. This paper portrays the experience of using a virtual fashion taster by a group of Fashion Design students at a university in Brazil. After evaluating this tool and answering a questionnaire about usability, some questions about ergonomic factors were discussed and proposals were generated for future designs.
Characterizing Adversarial Subspaces by Mutual Information

Deep learning is well-known for its great performances on images classification, object detection, and natural language processing. However, the recent research has demonstrated that visually indistinguishable images called adversarial examples can successfully fool neural networks by carefully crafting. In this paper, we design a detector named MID, calculating mutual information to characterize adversarial subspaces. Meanwhile, we use the defense framework called MagNet and mount the detector MID on it. Experimental results show that projected gradient descent (PGD), basic iterative method (BIM), Carlini and Wanger's attack (C&W attack) and elastic-net attack to deep neural network (elastic-net and L1 rules) can be effectively defended by our method.
Learning Partial-Value Variable Associations

Existing machine learning and data mining techniques have difficulty in handling three characteristics of real-world data sets all together in a computationally efficient way: (1) different data types with both categorical data and numeric data, (2) different variable relations for different values of variables, and (3) unknown variable dependency. The Partial-Value Association Discovery (PVAD) algorithm handles these three characteristics of real-world data all together, and enables the discovery of partial-value and full-value variable associations as well as univariate and multivariate effects of variable values. This paper illustrates the PVAD algorithm using a small data set, and presents the new development of a computationally fast method to discover the longest associations with one conditional variable value or one associative variable value.
Emergency Analysis Based on Affective Computing in Universities Forum

Emergency analysis for social networking sites have gained increased attention. In order to detect emergencies in University Forums, we present an emergency analysis method based on affective computing. The method detects the emergency and calculates the scores in real-time based on affective computing and the University Public Opinion Ontology (UPO_Ontology) established manually. The method is evaluated on two test datasets. The results show that the UPO_Ontology and affective computing greatly improved the accuracy, recall and F-measure. The method proposed in this paper meets the requirement of real-time detection. What’s more, It gain of 1.24% in F-measure over the “event-specific detection” proposed by Laylavi et al. and outperforms emergency detect using contextual semantics by nearly 7.1% in F-measure. The analysis of the emergencies in University Forums will help Universities deal with emergencies more effectively and provide students with better service.
A Target Detection Method Based on Statistical Mean Value Difference Model

Abstract In the field of oil pipeline monitoring applications, the guarantee of high accuracy target detection is required. Aiming at the problem of moving target detection under static camera, a method of mean background model based on probability and statistics is proposed. The background model is quickly constructed. The background threshold is adaptively used to eliminate the influence of illumination changes. The target is detected quickly and accurately. Finally, the proposed target is proposed. The background update method updates the background model in real time. The simulation results show that the better detection results can be obtained under the influence of light changes. The background model has strong stability and adaptability, and can be applied to the real-time monitoring system of the field oil pipeline.
The Inspection of Power Quality Disturbances by Using Improved Extreme-value Lifting Morphological Wavelet Method

Abstract For solving the difficulty of extracting the disturbing characteristics of the signals in power quality processing, and restraining the noise in the process of sampling, an improved extreme-value lifting morphological wavelet method is constructed. Firstly, morphological extreme-value operator is chosen to be the prediction operator and update operator, then the max-lifting and min-lifting morphological wavelets are constructed. Secondly, the improved extreme-value lifting morphological wavelet method is designed to further highlight local maxima and minima information of the disturbing signals based on the first step. Finally, the signals are dealt with this method, and the detail coefficients can be obtained, which have kept the disturbing characteristics of the signals. The result of simulation and analysis shows that this algorism owns the trait of simple, exactness, and de-noising.
Toward self-learning model-based EAs

Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostly used to repeatedly optimize different instances of a single problem class, a setting in which specialized algorithms generally perform better. In this paper, we introduce the concept of a new type of MBEA that can automatically specialize its behavior to a given problem class using tabula rasa self-learning. For this, reinforcement learning is a naturally fitting paradigm. A proof-of-principle framework, called SL-ENDA, based on estimation of normal distribution algorithms in combination with reinforcement learning is defined. SL-ENDA uses an RL-agent to decide upon the next population mean while approaching the rest of the algorithm as the environment. A comparison of SL-ENDA to AMaLGaM and CMA-ES on unimodal noiseless functions shows mostly comparable performance and scalability to the broadly used and carefully manually crafted algorithms. This result, in combination with the inherent potential of self-learning model-based evolutionary algorithms with regard to specialization, opens the door to a new research direction with great potential impact on the field of model-based evolutionary algorithms.
Multidimensional Data Security Exchange Modeling and its Optimization Method

In order to improve the security of multi-dimensional data exchange and reduce the risk of information leakage, this paper proposes a multi-dimensional data security exchange modeling machine optimization method. Firstly, the data security exchange model is analyzed. Then, the process behavior evaluation model and Boolean operation of the fusion hidden Markov model are used to evaluate the multi-dimensional data security exchange model, so as to obtain better performance evaluation. An optimal process behavior evaluation model selection method based on cost decision tree is proposed, which coordinates the contradiction between the accuracy rate of the evaluation model and the false alarm rate. Finally, a simulation experiment was carried out and the results were compared to verify the effectiveness and reliability of the proposed method.
Relation Extraction from Cross-Genre Unstructured Text

In this work, we present our approach to find relationships, both taxonomic and non-taxonomic, among the named entities extracted from texts of different genres. Instead of trying to assess the taxonomic relationships among named entities, we have adopted a context word-matching technique to assign separate scores to a different pair of entities, for different taxonomic relations. For non-taxonomic relations, we have mostly focused on verb-based relations and have also proposed a simple system to assign possible sentiment polarity labels among entity-pairs. This is an attempt to build a single unified system which can scan through texts of any genre and provide a fair idea of how the possible named entities are related among each other, assessed on three distinct space-intrinsic property, action, and sentiment.