Reviewerly/exHarmony huggingface dataset:
Hyperspectral Inverse Skinning

Abstract In example‐based inverse linear blend skinning (LBS), a collection of poses (e.g. animation frames) are given, and the goal is finding skinning weights and transformation matrices that closely reproduce the input. These poses may come from physical simulation, direct mesh editing, motion capture or another deformation rig. We provide a re‐formulation of inverse skinning as a problem in high‐dimensional Euclidean space. The transformation matrices applied to a vertex across all poses can be thought of as a point in high dimensions. We cast the inverse LBS problem as one of finding a tight‐fitting simplex around these points (a well‐studied problem in hyperspectral imaging). Although we do not observe transformation matrices directly, the 3D position of a vertex across all of its poses defines an affine subspace, or flat. We solve a ‘closest flat’ optimization problem to find points on these flats, and then compute a minimum‐volume enclosing simplex whose vertices are the transformation matrices and whose barycentric coordinates are the skinning weights. We are able to create LBS rigs with state‐of‐the‐art reconstruction error and state‐of‐the‐art compression ratios for mesh animation sequences. Our solution does not consider weight sparsity or the rigidity of recovered transformations. We include observations and insights into the closest flat problem. Its ideal solution and optimal LBS reconstruction error remain an open problem.
Emergency Assistive System for Tetraplegia Patient Using Eye Waver Computer Vision Technique

The development in the healthcarePatgar, Tanuja sector improves people life more digitized day by day. The adaptation of electronic devices makes patient’s normal activities become more easier without physical movement. Assistive technology is considered as one such excellent healthcare solution to disable person to support the growth of ecosystem. Tetraplegia is a paralysis condition where a patient cannot move parts below the neck and in some cases the patient may even become dumb. A new framework called robust integrated tetraplegia assistive (RITA) is proposed in this work for detectionPatel, Ripal and tracking the eye movement and makes easier communication between patient and caretaker. Further, the real value of eye movement and blink of the patient is recorded, processed and converted into corresponding voice output. The voice output can use the system for device automation of controlling fan, lights and basic needs. Test results show 100% detection accuracy for a distance of 13 in. Eye aspect ratio is almost in the range 0.32–0.36 when the eye is opened and value rapidly drops to a value in the range 0.09–0.13 and then raises again, indicating that blink has taken place. To improve the accuracy of detection and blinking for a distance equal to 13 in., artificial light is used as add-on service. Artificial light is used to improve the accuracy of detection as well as blinking for a distance equal to 13 in.
Investigating Cybercrime in Nigeria

Advances in ICT have brought both joy and sadness to individuals and organizations. Cybercrime, a byproduct of advances in ICT, is a relatively new area where crime flourishes in Nigeria, and many such cases have been reported by victims at different social forums. This study investigates cybercrime as it affects individuals and the society at large with a view to exposing the tricks often used by fraudsters and offering possible solutions. Data were collected from personal experiences (phone calls and text messages), WhatsApp messages and stories posted on Facebook and Nairaland forums by victims. They were analysed descriptively. The article suggests that tougher legislative sanctions and strict law enforcement by government can curb this menace. Also, through grammatical competence, improved morality as dictated by culture, logical reasoning, and avoidance of greed, individuals can detect fraudulent proposals and escape cyberattacks.
Implementation of Single Image De-hazing System on DSP TMS320C6748 Processor

Due to haze, the light reflecting from an outdoor object gets scattered before reaching the camera in almost all the outdoor scenes. They also decrease the photographic quality in the aerial atmosphere. It results in a massive delay in airlines and trains during the winter season. Hence, the hardware implementation for the removal of haze is an essential task in the field of computer vision. In this paper, we implemented a novel method for removing the fog from a single image using the dark channel prior method (DCM) with pixel by pixel operation. The DCM is adopted based on statistical data of the outdoor images which are haze-free. Based on the information from the DCM on hazy images, the thickness of haze is estimated. Thereby, a haze-free of a high-quality image is recovered. The implementation is done for single image on TMS320c6748 from Texas Instruments DSP kit and is simulated using Code Composer Studio v6.0. The validity of implemented techniques verified and compared with simulation during experimental approbation on the real hardware.
Research on Local Path Planning for the Mobile Robot Based on QL-anfis Algorithm

A local path planning method for the mobile robot is proposed based on QL-anfis algorithm to resolve the common problems in traditional method, such as the dimension disaster, the slow learning speed and deadlock avoidance of QL algorithm. Firstly, state-action pairs <; s, a> are learned by QL algorithm, and the obstacle avoidance table of the robot and the counter of the robot reaching the target are designed to solve the obstacle avoidance and deadlock problem in complex environment. Then anfis is used to learn <; s, a> to get the best <; s, a> instead of the iteration update of Q matrix, which can speed up the speed of path planning and avoid the excessive dimension of Q matrix. Simulation results show that efficiency of obtaining the optimal <; s, a> and planning speed of the optimal or sub-optimal path for the robot can be speeded up by using the learning of anfis system instead of Q matrix replacement.
Obstacle Detection Approach for Robotic Wheelchair Navigation

Over the past decade, many efforts have been taken to give mobility to disabled and disordered persons. Many people are totally depending on their caregiver and their hospitality but caregiver cannot remain 24 h with them. These disabled and disordered people also want mobility and connections with surrounding environment and society. There is still need for the development of such devices which could control, monitor, maintain, and assist these people, so that they could lead their work and with greater ease. Augmentative technology has many issues to deal in a broad area dealing with deep R&D on assistive devices. Robotic wheelchair is a subarea of augmentative technology, which ensures safer and easier maneuverability of the user of the wheelchair both in indoor and outdoor environments, even in the absence of the caregiver. For safer movement in an unknown environment of a robotic wheelchair, designing of a proper obstacle detection system is important. This work deals with proposition of new approach for obstacle detection through image processing. The proposed system uses simple and logical approach to detect the obstacles in the path of robotic wheelchair. The approach has been tested on self-captured images with different backgrounds, target obstacles, and light conditions. The experimental result shows that the proposed approach can precisely detect the obstacles.
A Method to Estimate Entity Performance from Mentions to Related Entities in Texts on the Web

Publications on the Web can influence the public opinion about certain entities (e.g., politicians, institutions). At the same time, a variety of indicators can be extracted from these publications and used to estimate entity performance (e.g., popularity, votes share). This work proposes an automatic method that employs state-of-the-art natural language processing tools to extract indicators about entities mentioned in texts, for estimating the performance of these entities or semantically related ones. Our method calculates performance metrics from performance indicators consolidated for semantically related entities, assess correlations of these consolidated metrics with ground true performance, and uses these metrics to predict certain fluctuations in entity performance. Experimental results in a case study on politics show that consolidated metrics for several interrelated entities are better correlated to observed real performance measures of some target entities and lead to better predictions, than metrics for just one entity.
The Use of Project Design Technology in Professionally-Oriented Educational Space

The subject of the research is the process of organizing and implementing the design and technological training of university students in the course of studying educational modules in a professionally-oriented educational space created on the basis of social partnership between the university and the enterprise-customer of trained personnel. The object of research is the technology of project-based training of students in the design of assemblies, parts and technological processes for their manufacture, implemented in a professionally-oriented educational space. The authors consider in detail such aspects of the topic as options for organizing a professionally-oriented educational space in the framework of social partnership through the use of real-life projects and simulation modeling of a specialist’s professional activity in students ’project activities. The authors consider a phased methodology for the implementation of project-based learning technology during the technological preparation of students, focused on the formation of professional competencies and labor functions of professional standards. The results of a pedagogical experiment on the formation of technological competencies during the implementation of project training in a professionally-oriented educational space are presented. The main research methods: theoretical analysis of the experience of organizing project training, the synthesis of didactic tools and teaching methods, a comparative pedagogical experiment. The main conclusions of the study are to justify the high pedagogical eff ectiveness of organized project training in a professionally-oriented educational space created on the basis of a social partnership between a university and an enterprise. The scientifi c novelty of the study lies in the application of technology for project training in the conditions of quasi-professional activity when students perform real projects. A special contribution of the authors to the study is the development of a model for the joint implementation of project-based learning technology in preparing students for a professionally-oriented educational environment.
CMM: A Combination-Based Mutation Method for SQL Injection

With the rapid development of Web applications, SQL injection (SQLi) has been a serious security threat for years. Many systems use superimposed rules to prevent SQLi like backlists filtering rules and filter functions. However, these methods can not completely eliminate SQLi vulnerabilities. Many researchers and security experts hope to find a way to find SQLi vulnerabilities efficiently. Among them, mutation-based fuzzing plays an important role in Web security testing, especially for SQLi. Although this approach expands the space for test cases and improves vulnerability coverage to some extent, there are still some problems such as mutation operators cannot be fully covered, test cases space explosions, etc. In this paper, we present a new technique Combinatorial Mutation Method (CMM) to generate test set for SQLi. The test set applies t-way and variable strength Combinatorial Testing. It makes the mutation progress more aggressive and automated and generates test cases with better F-measure Metric and Efficiency Metric. We apply our approach to three open source benchmarks and compare it with sqlmap, FuzzDB and ART4SQLi. The experiment results demonstrate that the approach is effective in finding SQLi vulnerabilities with multiple filtering rules.
DNA sequence alignment based on ants’ colony algorithm

DNA sequence alignment is the basis of analyzing biological evolution. This paper proposes the plan of DNA sequence alignment based on genetic algorithm and the method of self-adjusting pheromone to solve the problem which exists in traditional ant colony genetic algorithm of sticking into local optimal solutions. Self-adjusting pheromone dynamically adjusts the assignment of pheromones according to the search of solutions to realize the optimal situation as a whole. In the experimental validation, the algorithm in this paper is simple and easy to calculate compared to traditional dot-matrix analysis and dynamic programming.
Understanding the Relationship Between Cybercrime and Human Behavior Through Criminological Theories and Social Networking Sites

This article seeks to gain a better understanding of how to address some of the challenges in the digital world. In order to do this, the authors presented some of the emerging issues in the psychology of human behavior and the ever changing nature of cyber threats in the digital world. They reviewed both the theories of crime (i.e., self-control and rational choice theories) and the empirical studies that have examined user behavior on social networking sites leading to victimization. Importantly, they mentioned the role of social engineering as the entry point of many of these sophisticated attacks. They went on to examine the relevance of the human element as the starting point of implementing cyber security programs in organizations as well as securing individual online behavior. Furthermore, issues that are associated with the emerging trends in human behavior research as well as ethics were also discussed. They acknowledge that much more academic attention is needed in this area to avoid the exponential growth of future information breaches.
Super Long Interval Time-Lapse Image Generation for Proactive Preservation of Cultural Heritage Using Crowdsourcing

To establish advanced analytical methods for preserving cultural heritage, this research proposes a method to generate a time-lapse image with a super-long temporal interval. The key issue is to realize an image collection method using crowdsourcing and a method to improve the matching accuracy between images of cultural heritage buildings captured 50 to 100 years ago and current images. As degradation and damage to the appearance of cultural heritage buildings occurs due to ageing, rebuilding, and renovation, image features of the timed images are changed. This decreases the accuracy of the matching process that uses the appearance of patch-region. In addition, we need to give more consideration to incorrect feature correspondence that is prominent in buildings with considerable symmetry. We aim to solve these difficulties by applying an Autoencoder and a guided matching method. Our method involves utilizing the function of crowdsourcing, which can easily obtain the current image captured at the same position and orientation as the past image. We propose this method to address the inability to obtain the correspondence points between two images when observation times are significantly different.
Towards Fast and Unified Transfer Learning Architectures for Sequence Labeling

Sequence labeling systems have advanced continuously using neural architectures over the past several years. However, these tasks require large sets of annotated data to achieve such performance. In particular, we focus on the Named Entity Recognition (NER) task on clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards low-resource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework, the Tunable Transfer Network (TTN) consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. Our best TTN model achieves 2-5% improvement over pre-trained language model BERT as well as its multi task extension MT-DNN in low resource settings. However, our proposed sharing scheme requires an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for an exponential search.
Targeted display advertising: the case of preferential attachment

An average adult is exposed to hundreds of digital advertisements daily <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , making the digital advertisement industry a classic example of a big-data-driven platform. As such, the ad-tech industry relies on historical engagement logs (clicks or purchases) to identify potentially interested users for the advertisement campaign of a partner (a seller who wants to target users for its products). The number of advertisements that are shown for a partner, and hence the historical campaign data available for a partner depends upon the budget constraints of the partner. Thus, enough data can be collected for the high-budget partners to make accurate predictions, while this is not the case with the low-budget partners. This skewed distribution of the data leads to preferential attachment of the targeted display advertising platforms towards the high-budget partners. In this paper, we develop domain-adaptation approaches to address the challenge of predicting interested users for the partners with insufficient data, i.e., the tail partners. Specifically, we develop simple yet effective approaches that leverage the similarity among the partners to transfer information from the partners with sufficient data to cold-start partners, i.e., partners without any campaign data. Our approaches readily adapt to the new campaign data by incremental fine-tuning, and hence work at varying points of a campaign, and not just the cold-start. We present an experimental analysis on the historical logs of a major display advertising platform <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Specifically, we evaluate our approaches across 149 partners, at varying points of their campaigns. Experimental results show that the proposed approaches outperform the other domain-adaptation approaches at different time points of the campaigns.
Analyzing the human behavior using pervasive sensing system

Smartphones are becoming an integral part of the Internet of Things-(IoT) paradigm with their built-in sensor ecosystem providing a convenient platform to study human behavior. Examples include activity monitoring, transportation mode detection, travel behaviour and so on. All of these examples are connected in one way or the other to the IoT domain such as smart cities, smart health and so on.
Efficiency of legal and regulatory framework in combating cybercrime in Malaysia

This article examines the increase of cybercrime in Malaysia and how this issue should be resolved. Cybercrime, which is also known as computer crime, involves the use of a computer as an instrument to further illegal ends, such as committing fraud, trafficking in child pornography, intellectual property violations, cyberbullying, identity theft, and privacy violations. There are several legislations covering cybercrime, but statistics show that cybercrime has been increasing in Malaysia. Thus, the objective of this article is to investigate and examine the efficiency of the existing legal and regulatory framework in combating cybercrime and to recommend whether to enact a new legislation to address all types of cybercrime including cyberbullying, which is a serious crime committed by students. Qualitative analyses were used and selected interviews were conducted with relevant agencies.
Predictive Data Analytics for Breast Cancer Prognosis

The leading healthcare explosion in datasets has created furious interest among the researchers and scientists to determine automated technology for clinical future implications. Hence, data mining in the past decade has resolved and discovered hidden information and knowledge from large-scale databases. Data mining tends to be widely anticipated technology which is utilized in varied application domain for retrieval of patterns for future decision-making. In the past several research studies are based on the prevalence and prognosis of breast cancer in females, but our proposed discuss the interception of disease for both the genders. Hence, we integrate the cancer data with predictive data analytics to determine the incidence and mortality rate of breast cancer both in males and females from the year 1990 to 2014.
Design of Efficient Convolutional Neural Module Based on An Improved Module

In order to further improve the feature extraction performance of the convolutional neural networks, we focus on the selection and reorganization of key features and suspect that simple changes in the pooling layers can cause changes in the performance of neural networks.According to the conjecture, we design a funnel convolution module, which can filter out the key features and perform multi-scale convolution of key features.And we apply this module to the design of high performance small neural networks.The experiments are carried out on 101_food and caltech-256 benchmark datasets.Experiments show that the module has higher performance than classical module, and the small convolution networks based on the module has less parameters and more excellent performance.
Efficient Free Keyword Detection Based on CNN and End-to-End Continuous DP-Matching

For continuous keyword detection, the advantage of dynamic programming (DP) matching is that it can detect any keyword without re-training the system. In previous research, higher detection accuracy was reported using 2D-RNN based DP matching than using conventional DP and embedding methods. However, 2D-RNN based DP matching has a high computational cost. In order to address this problem, we combine a convolutional neural network (CNN) and 2D-RNN based DP matching into a unified framework which, based on the kernel size and the number of CNN layers, has a polynomial order effect on reducing the computational cost. Experimental results, using Google Speech Commands Dataset and the CHiME-3 challenge's noise data, demonstrate that our proposed model improves open keyword detection performance, compared to the embedding-based baseline system, while it is nine times faster than previous 2D-RNN DP matching.
Argument Identification in a Language Without Labeled Data

This paper addresses the issue of how to obtain processing tools for argument identification for the vast majority of the languages that, differently from English, have little to no relevant labeled data. This issue is addressed by taking an under-resourced language as a case study, namely Portuguese, and by experimenting with three techniques to cope with the scarceness of data: to obtain labelled data by machine translating data sets from another language labelled with respect to argument identification; to transfer to the argument identifier the language knowledge captured in distributional semantic models obtained during the resolution of other tasks for which more data exist; to expand data for argument identification with text augmenting techniques. The results obtained demonstrate that it is possible to develop argument identification tools for under-resourced languages with a level of performance that is competitive to the ones for languages with relevant language resources.