Reviewerly/exHarmony huggingface dataset:
A Hybrid Framework Combining Genetic Algorithm with Iterated Local Search for the Dominating Tree Problem

Given an undirected, connected and edge-weighted graph, the dominating tree problem consists of finding a tree with minimum total edge weight such that for each vertex is either in the tree or adjacent to a vertex in the tree. In this paper, we propose a hybrid framework combining genetic algorithm with iterated local search (GAITLS) for solving the dominating tree problem. The main components of our framework are as follows: (1) the score functions D s c o r e and W s c o r e applied in the initialization and local search phase; (2) the initialization procedure with restricted candidate list (RCL) by controlling the parameter to balance the greediness and randomness; (3) the iterated local search with three phases, which is used to intensify the individuals; (4) the mutation with high diversity proposed to perturb the population. The experimental results on the classical instances show that our method performs much better than the-state-of-art algorithms.
Multiple detection joint integrated track splitting for multiple extended target tracking

The extended target tracking problem, which occurs due to a high-resolution sensor resolving multiple scattering points of a target, is considered in this paper. Typical target tracking algorithms which use the point target assumption yield poor performances in extended target tracking scenarios, especially in the presence of closely spaced targets. This performance degeneration is mainly caused by a number of measurements that are shared among tracks when the extended targets are closely spaced. In order to achieve robust tracking performances in multiple extended target tracking environments, a novel tracker, named multiple detection joint integrated track splitting (MD-JITS), is proposed here. This tracker incorporates the measurement cells and the random matrix into a target existence based multitarget track splitting structure. In MD-JITS, each track builds a type of tree, and each branch of the tree is a track component corresponding to a sequence of data associations for multiple scans. The track kinematic state and extension state are obtained on the basis of the track components. Then, the proposed algorithm is tested using both Monte Carlo simulations and a real video surveillance application to demonstrate the effectiveness.
Sense-Based Topic Word Embedding Model for Item Recommendation

As a useful way to help users filter information and save time, item recommendation intends to recommend new items to users who tend to be interested. As the most common format related to items in online social networks, short texts have always been disregarded by previous research on item recommendation. The sparse features and insufficient information in short texts render the extraction of features from short texts difficult. To address the problems of short text feature extraction and item recommendation, we introduce a sense-based word embedding method to enrich word features and aid in item topic extraction. The sense is used to demonstrate the semantic meaning of a word since each word contains different senses, and each sense can be used to describe various words. Thus, the sense is befitting to enrich the information of short texts and address the problem of polysemy. By combining topic distribution, social relationships, and users' interests and interactions, we propose a time-aware probabilistic model to profile a user's preference score on items. By predicting the user preference score at a future time interval, the items with top scores can be regarded as the recommended items. Different experiments on real-world datasets are deployed to prove the feasibility and efficiency of sense-based short text topic assignment and item recommendation in multiple languages. The prediction results also demonstrate that the model substantially outperforms the compared methods.
Emotion Detection by Using Similarity Techniques

The analysis of emotions and opinions is a new research field that is aimed to identify opinions, feelings, assessments, behaviors, tendencies and emotions of people expressed in texts. The growing importance of emotion analysis has coincided with the growth of social media, such as surveys, forums, blogs, twitter and social networks. Emotional analysis systems are used in almost all businesses and social fields, because opinions and emotions are important for all human activities and have a serious impact on people's behaviours. In this paper, we discuss detecting and analyzing emotions based on similarity using VSM and STASIS methods. In comparison with keyword based method VSM has provided better results.
Prediction of Multi-target Dynamics Using Discrete Descriptors: an Interactive Approach

We propose a probabilistic method to track and interpret the interactions of moving objects. The proposed method is based on the analysis of location data from different moving objects that modify their dynamics according to rules of interactions, namely attractive and repulsive forces governing objects' motions in a scene. Our method uses a Bayesian structure to identify key elements of the interplay rules and facilitates the prediction of objects' dynamics as an interacting system. Such a prediction facilitates the detection of abnormalities by identifying unseen interaction effects in the scene.
Decision support system for considering the best teacher performance using MOORA method

Islamic Boarding School is a school where students stay, live and study about formal educations and Islamic religion educations in the dormitory. The best teacher performance in Islamic boarding school is assessed using two competences, hard and soft competency. Each of these competences has many criteria. The assessment is conducted in every semester. The assessment aims to support and improve the quality of teacher's performance because its result become as baseline to determine incentive level and warning letter for teachers. These assessments can lead tedious, human error and subjectively decision. To avoid the wrong decision, it is necessary a decision support system for helping to decide the best teacher performance. This application was using Multi Objective Optimization method on the Basis of Ratio Analysis (MOORA). This research produced a web-based decision support system that was used to help decision maker to decide the best teacher performance.
Retrieval effectiveness of image search engines

Purpose The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is. Design/methodology/approach The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall. Findings Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others. Research limitations/implications The study only takes into consideration basic image search feature, i.e. text-based search. Practical implications The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use. Originality/value The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.
Phoneme Specific Modelling and Scoring Techniques for Anti Spoofing System

Replay attack refers to the use of recorded speech in an attempt to spoof an automatic speaker verification system and the development of countermeasures that can detect these attacks is an active area of research. This paper investigates the effect of phoneme specific information on replay attack detection. It then develops a replay detection system that employs phoneme specific genuine and spoof models and compares novel scoring methods that take into account phonetic information obtained from a suitable phoneme recogniser. Experiment result on the ASVSpoof 2017 V2.0 corpus indicated that replayed speech may be easier to detect from speech corresponding to some phonemes compared to others and consequently judicious use of phoneme specific models can improve replay detection systems.
Ballot-Marking Devices (BMDs) Cannot Assure the Will of the Voters

Computers, including all modern voting systems, can be hacked and misprogrammed. The scale and complexity of U.S. elections may require the use of computers to count ballots, but election integrity requires a paper-ballot voting system in which, regardless of how they are initially counted, ballots can be re- counted by hand to check whether election outcomes have been altered by buggy or hacked software. Furthermore, secure voting systems must be able to recover from any errors that might have occurred.However, paper ballots provide no assurance unless they accurately record the vote as the voter expresses it. Voters can express their intent by hand-marking a ballot with a pen, or using a computer called a ballot-marking device (BMD), which generally has a touchscreen and assistive interfaces. Voters can make mistakes in expressing their intent in either technology, but only the BMD is also subject to systematic error from computer hacking or bugs in the process of recording the vote on paper, after the voter has expressed it. A hacked BMD can print a vote on the paper ballot that differs from what the voter expressed, or can omit a vote that the voter expressed.It is not easy to check whether BMD output accurately reflects how one voted in every contest. Research shows that most voters do not review paper ballots printed by BMDs, even when clearly instructed to check for errors. Furthermore, most voters who do review their ballots do not check carefully enough to notice errors that would change how their votes were counted. Finally, voters who detect BMD errors before casting their ballots, can correct only their own ballots, not systematic errors, bugs, or hacking. There is no action that a voter can take to demonstrate to election officials that a BMD altered their expressed votes, and thus no way voters can help deter, detect, contain, and correct computer hacking in elections. That is, not only is it inappropriate to rely on voters to check whether BMDs alter expressed votes, it doesn’t work.Risk-limiting audits of a trustworthy paper trail can check whether errors in tabulating the votes as recorded altered election outcomes, but there is no way to check whether errors in how BMDs record expressed votes altered election out- comes. The outcomes of elections conducted on current BMDs therefore cannot be confirmed by audits. This paper identifies two properties of voting systems, contestability and defensibility, that are necessary conditions for any audit to con- firm election outcomes. No commercially available EAC-certified BMD is contestable or defensible.To reduce the risk that computers undetectably alter election results by printing erroneous votes on the official paper audit trail, the use of BMDs should be limited to voters who require assistive technology to vote independently.
Privacy Protection of Patient Medical Images using Digital Watermarking Technique for E-healthcare System

Privacy protection has been a critical issue in the delivery of medical images for telemedicine, e-health care and other remote medical systems.The aim of this proposed work is to implement a secure, reversible, digital watermarking technique for the transmission of medical data remotely in health care systems.In this research work, we employed a novel optimized digital watermarking scheme using discrete wavelet transform and singular value decomposition using cuckoo search algorithm based on Lévy flight for embedding watermark into the grayscale medical images of the patient. The performance of our proposed algorithm is evaluated on four different 256 × 256 grayscale host medical images and a 32 × 32 binary logo image.The performance of the proposed scheme in terms of peak signal to noise ratio was remarkably high, with an average of 55.022dB compared to other methods.Experimental results reveal that the proposed method is capable of achieving superior performance compared to some of the state-of-art schemes in terms of robustness, security and high embedding capacity which is required in the field of telemedicine and e-health care system.
A Novel Semi-Supervised Classification Method Based on Soft Evidential Label Propagation

For semi-supervised learning, we propose the label propagation method in the framework of evidence theory. The proposed method can deal with two problems which are the classification tasks with extremely few labeled data and identification of outlier samples. By incorporating the basic belief assignment functions as the soft evidential labels, both the uncertainty of samples’ class assignments and the degree of samples’ abnormality can be measured. On the basis of the designed label update mechanism, the soft evidential label of each initially unlabeled sample will be iteratively updated by absorbing the label information from its neighbors. Once the partition of the dataset is stable, the samples with high confidences of abnormality will be identified as outliers and others will be assigned to their most supported classes. The experiment results on several UCI and benchmark datasets validate the effectiveness of the proposed method in comparison with several state-of-the-art methods. In addition, tests on noisy spiral datasets show the robustness and feasibility of detecting outliers.
Identification and Location Method of Microseismic Event Based on Improved STA/LTA Algorithm and Four-Cell-Square-Array in Plane Algorithm

The key issue of microseismic (MS) monitoring technology is to detect MS events and accurately locate the MS source. On the basis of the traditional short-term average/long-term average (STA/LTA) algorithm, a new characteristic function has been proposed that can reflect the signal energy and dispersion. The results reveal that the improved method can accurately pick up the first arrival of MS events. A new monitoring network of the four-cell-square-array in plane algorithm is proposed, which can overcome the ill-conditioned problem of the objective equation. Taking into account the actual project, we designed the monitoring arrangement scheme for the surrounding rock around the underground cavern. The improved MS monitoring scheme can envelop the front of the cave and the dome and floor areas of the working face.
Probability hypothesis density filter using determinantal point processes for multi object tracking

Multi Object Tracking (MOT) has many applications such as video surveillance and event recognition among others. In this paper, we present a novel multi object tracking method using the Probability Density Hypothesis (PHD) filter and Determinantal Point Processes (DPP). The PHD filter is an algorithm for jointly estimating an unknown number of targets and their states from a sequence of observations in the presence of data association uncertainty, noise and false alarms. A tractable implementation of the PHD filter is based on a Gaussian Mixture approximation. However, the Gaussian Mixture PHD suffers from computational problems due to an increasing number of Gaussian components as time progresses. In this paper, we propose a novel pruning method based on Determinantal Point Process which handles the overestimation problem on the number of tracks. The DPP-PHD filter promotes diversity in the resulting Gaussian components and leads to improved tracking results.
Interactive Repair of Tables Extracted from PDF Documents on Mobile Devices

PDF documents often contain rich data tables that offer opportunities for dynamic reuse in new interactive applications. We describe a pipeline for extracting, analyzing, and parsing PDF tables based on existing machine learning and rule-based techniques. Implementing and deploying this pipeline on a corpus of 447 documents with 1,171 tables results in only 11 tables that are correctly extracted and parsed. To improve the results of automatic table analysis, we first present a taxonomy of errors that arise in the analysis pipeline and discuss the implications of cascading errors on the user experience. We then contribute a system with two sets of lightweight interaction techniques (gesture and toolbar), for viewing and repairing extraction errors in PDF tables on mobile devices. In an evaluation with 17 users involving both a phone and a tablet, participants effectively repaired common errors in 10 tables, with an average time of about 2 minutes per table.
A Combined Approach for Eliciting Relationships for Educational Ontologies Using General-Purpose Knowledge Bases

Educational Ontologies describe learning domains by specifying the topics to be learned together with the relationships among the topics. Any technology supported learning system could take advantage of an educational ontology in the process of guiding students during their learning processes. This paper presents LiReWi, a system for the elicitation of relationships for educational ontologies from electronic textbooks. LiReWi combines grammar-based, co-occurrence-based and taxonomy-based methods together with several knowledge bases, such as Wikipedia, WordNet, WikiTaxonomy, WibiTaxonomy, and WikiRelations to elicit isA, partOf, prerequisite and pedagogicallyClose relationships between learning domain topics. LiReWi performs a three-step procedure to fulfill its task: first, all the topics are mapped to the diverse knowledge bases that will be used to identify the relationships; then, several relationship extractors, each using a different approach, are concurrently run to elicit candidate relationships; and, finally, the results are combined and filtered to obtain the final set of pedagogical relationships. LiReWi is designed following a modular approach that enables the inclusion of new relationship extractors. The evaluation conducted to validate the proposal is also reported in this paper.
Deep spatiotemporal LSTM network with temporal pattern feature for 3D human action recognition

Abstract With the rapid development of RGB‐D cameras and pose estimation techniques, action recognition based on three‐dimensional skeleton data has gained significant attention in the artificial intelligence community. In this paper, we incorporate temporal pattern descriptors of joint positions with the currently popular long short‐term memory (LSTM)–based learning scheme to obtain accurate and robust action recognition. Considering that actions are essentially formed by small subactions, we first utilize a two‐dimensional wavelet transform to extract temporal pattern descriptors in the frequency domain for each subaction. Afterward, we design a novel LSTM structure to extract deep features, which model a long‐term spatiotemporal correlation between body parts. Since temporal pattern descriptors and LSTM deep features can be regarded as multimodal representations for actions, we fuse them with an autoencoder network to achieve a more effective feature descriptor for action recognition. Experimental results on three challenging data sets with several comparative methods demonstrate the effectiveness of the proposed method for three‐dimensional action recognition.
Deep CNN-Assisted Personalized Recommendation over Big Data for Mobile Wireless Networks

At present, to improve the accuracy and performance for personalized recommendation in mobile wireless networks, deep learning has been widely concerned and employed with social and mobile trajectory big data. However, it is still challenging to implement increasingly complex personalized recommendation applications over big data. In view of this challenge, a hybrid recommendation framework, i.e., deep CNN-assisted personalized recommendation, named DCAPR, is proposed for mobile users. Technically, DCAPR integrates multisource heterogeneous data through convolutional neural network, as well as inputs various features, including image features, text semantic features, and mobile social user trajectories, to construct a deep prediction model. Specifically, we acquire the location information and moving trajectory sequence in the mobile wireless network first. Then, the similarity of users is calculated according to the sequence of moving trajectories to pick the neighboring users. Furthermore, we recommend the potential visiting locations for mobile users through the deep learning CNN network with the social and mobile trajectory big data. Finally, a real-word large-scale dataset, collected from Gowalla, is leveraged to verify the accuracy and effectiveness of our proposed DCAPR model.
Stereo Matching Methods for Imperfectly Rectified Stereo Images

Stereo matching has been under development for decades and is an important process for many applications. Difficulties in stereo matching include textureless regions, occlusion, illumination variation, the fattening effect, and discontinuity. These challenges are effectively solved in recently developed stereo matching algorithms. A new imperfect rectification problem has recently been encountered in stereo matching, and the problem results from the high resolution of stereo images. State-of-the-art stereo matching algorithms fail to exactly reconstruct the depth information using stereo images with imperfect rectification, as the imperfectly rectified image problems are not explicitly taken into account. In this paper, we solve the imperfect rectification problems, and propose matching stereo matching methods that based on absolute differences, square differences, normalized cross correlation, zero-mean normalized cross correlation, and rank and census transforms. Finally, we conduct experiments to evaluate these stereo matching methods using the Middlebury datasets. The experimental results show the proposed stereo matching methods can reduce error rate significantly for stereo images with imperfect rectification.
Egocentric Smaller-person Experience through a Change in Visual Perspective

This paper explores how human perceptions, actions, and interactions can be changed through an embodied and active experience of being a smaller person in a real-world environment, which we call an egocentric smaller person experience. We developed a wearable visual translator that provides the perspective of a smaller person by shifting the wearer's eyesight level down to their waist using a head-mounted display and a stereo camera module, while allowing for field of view control through head movements. In this study, we investigated how the developed device can modify the wearer's body representation and experiences based on a field study conducted at a nursing school and museums, and through lab studies. It was observed that the participants changed their perceptions, actions, and interactions because they are considered to have perceived themselves as being smaller. Using this device, designers and teachers can understand the perspectives of other people in an existing environment.