Reviewerly/exHarmony huggingface dataset:
Protecting Location Privacy for Crowd Workers in Spatial Crowdsourcing Using a Novel Dummy-Based Mechanism

Spatial Crowdsourcing (SC) is a new valuable paradigm, relies on crowd workers to perform a set of spatial-temporal tasks at specific locations. It has garnered attention in collecting and processing social, environmental, and other spatio-temporal data by the contribution of individuals, communities and groups of workers in the physical world. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, which requires the workers to be physically traveling to the tasks' locations in order to perform them, i.e., taking photos or collecting real time weather information at pre-specified location. Existing solutions require crowd workers to disclose their precise locations to untrustworthy service providers. Location updates and tracking in spatial crowdsourcing raise several privacy concerns in that malicious parties could snoop on crowd workers' whereabouts. Thus, the crowd workers' privacy could be compromised by disclosing their locations to untrusted and possibly malicious parties. This paper provides a novel framework called Dummies' Centroid (DCentroid), which aims at preserving location privacy for crowd workers in SC. The framework adapts an anonymous communication technique using a dummy based approach to generate dummy locations, i.e. decoy locations, and send their centroid points (pseudolocations) to service providers for processing. This paper theoretically analyzes the DCentroid framework and guarantees the crowd workers' privacy, while preserving the functionality of SC, such as the success rate of task assignments, worker travel distances, and system overhead. Practical experimentation on real-world datasets shows that the DCentroid framework protects the crowd workers' location privacy without affecting the various performance parameters of task assignment.
LinggleWrite: a Coaching System for Essay Writing

This paper presents LinggleWrite, a writing coach that provides writing suggestions, assesses writing proficiency levels, detects grammatical errors, and offers corrective feedback in response to user's essay.The method involves extracting grammar patterns, training models for automated essay scoring (AES) and grammatical error detection (GED), and finally retrieving plausible corrections from a n-gram search engine.Experiments on public test sets indicate that both AES and GED models achieve state-of-the-art performance.These results show that LinggleWrite is potentially useful in helping learners improve their writing skills.
Influence of Video Delay on Quality, Presence, and Sickness in Viewport Adaptive Immersive Streaming

Current extended reality applications use omnidirectional video to boost users' immersion and sense of presence. As contents from distant video sources cannot be instantaneously delivered, the end-to-end delay becomes a key problem when user actions cannot be simultaneously matched by system reactions. Thus, we have designed and executed an experiment to assess its influence on the quality of experience, the sense of presence, and the sickness caused. To do it, we have developed a viewport adaptive simulator to render simultaneously two layers of immersive video to allow different adaptation schemes and delay values. Twenty observers have assessed 180 test videos from 9 sources. Our analysis shows a clear influence of the delay condition and the adaptation scheme on the perceived quality. Moreover, it also shows that the adaptation schemes and delay conditions have a small influence on the sense of presence and little effect on observers sickness.
Enhanced Insertion Sort by Threshold Swapping

Sorting is an essential operation that takes place in arranging data in a specific order, such as ascending or descending with numeric and alphabetic data.There are various sorting algorithms for each situation.For applications that have incremental data and require an adaptive sorting algorithm, the insertion sort algorithm is the most suitable choice, because it can deal with each element without the need to sort the whole dataset.Moreover, the Insertion sort algorithm can be the most popular sorting algorithm because of its simple and straightforward steps.Hence, the insertion sort algorithm performance decreases when it comes to large datasets.In this paper, an algorithm is designed to empirically improve the performance of the insertion sort algorithm, especially for large datasets.The new proposed approach is stable, adaptive and very simple to translate into programming code.Moreover, this proposed solution can be easily modified to obtain in-place variations of such an algorithm by maintaining their main features.From our experimental results, it turns out that the proposed algorithm is very competitive with the classic insertion sort algorithm.After applying the proposed algorithm and comparing it with the classic insertion sort, the time taken to sort a specific dataset was reduced by 23%, regardless of the dataset's size.Furthermore, the performance of the enhanced algorithm will increase along with the size of the dataset.This algorithm does not require additional resources nor the need to sort the whole dataset every time a new element is added.
DEVELOPING TEACHING MATERIAL OF POETRY APPRECIATION BASED ON STUDENTS� COMPETENCY ANALYSIS

The aim of this research was to develop the material of poetry appreciation at the Department of Indonesian Language and Literature Education of STKIP PGRI Lubuklinggau. The type of this research was Research and Development (R&amp;D) which was developed by the Dick, Carey, and Carey. Data collection was conducted through questionnaire, interviews, and tests. The data analysis and validity test were performed by using pretest and posttest of performance. The data is then processed by using a simple paired t test with the SPSS 20 program.� The result of this research can be measured Asym.Sig. (2-tailed) 0,000... &lt; 0,05. Therefore the value of t-hit 36,956 and t-tab 2,045 to (d) 17 and alpa 0,05, so it can be concluded� t-hit &lt;t-tab, so Ho has rejected, an Hi was accepted, its� meaning that the average value of students' ability to appreciate poetry after using instructional materials as a result of development was equal to 70 or more than 70. Thus, the model of instructional materials developed has been feasible and effective for lecturers
Parameter Training in MANET using Artificial Neural Network

The study of convenient methods of information dissemination has been a vital research area for years.Mobile ad hoc networks (MANET) have revolutionized our society due to their self-configuring, infrastructure-less decentralized modes of communication and thus researchers have focused on finding better and better ways to fully utilize the potential of MANETs.The recent advent of modern machine learning techniques has made it possible to apply artificial intelligence to develop better protocols for this purpose.In this paper, we expand our previous work which developed a clustering algorithm that used weight-based parameters to select cluster heads and use Artificial Neural Network to train a model to accurately predict the scale of the weights required for different network topologies.
Towards IT Peace Research: Challenges at the Intersection of Peace and Conflict Research and Computer Science

Advances in science and technology, including information technology (IT), play a crucial role in the context of peace and security. However, research on the intersection of peace and conflict research as well as computer science is not well established yet. This article highlights the need for further work in the area of research “IT peace research”, which includes both empirical research on the role of IT in peace and security, as well as technical research to design technologies and applications. Based on the elaboration of the disciplines, central challenges, such as insecurity, actors, attribution and laws, are outlined.
An ontological metamodeling framework for semantic simulation model engineering

Recently, the ontological metamodel plays an increas- ingly important role to specify systems in two forms: ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts, their interrelations, and constraints.On the other hand, metamodel is a more classical, but more powerful model in which concepts and relationships are represented in a prescriptive way.This study firstly clarifies the difference between the two approaches, then explains their advantages and limitations, and attempts to explore a general ontological metamodeling framework by integrating each characteristic, in order to implement semantic simulation model engineering.As a proof of concept, this paper takes the combat effectiveness simulation systems as a motivating case, uses the proposed framework to define a set of ontological composable modeling frameworks, and presents an underwater targets search scenario for running simulations and analyzing results.Finally, this paper expects that this framework will be generally used in other fields.
Data-Driven Decision-Support System for Speaker Identification Using E-Vector System

Recently, biometric authorizations using fingerprint, voiceprint, and facial features have garnered considerable attention from the public with the development of recognition techniques and popularization of the smartphone. Among such biometrics, voiceprint has a personal identity as high as that of fingerprint and also uses a noncontact mode to recognize similar faces. Speech signal-processing is one of the keys to accuracy in voice recognition. Most voice-identification systems still employ the mel-scale frequency cepstrum coefficient (MFCC) as the key vocal feature. The quality and accuracy of the MFCC are dependent on the prepared phrase, which belongs to text-dependent speaker identification. In contrast, several new features, such as d-vector, provide a black-box process in vocal feature learning. To address these aspects, a novel data-driven approach for vocal feature extraction based on a decision-support system (DSS) is proposed in this study. Each speech signal can be transformed into a vector representing the vocal features using this DSS. The establishment of this DSS involves three steps: (i) voice data preprocessing, (ii) hierarchical cluster analysis for the inverse discrete cosine transform cepstrum coefficient, and (iii) learning the E-vector through minimization of the Euclidean metric. We compare experiments to verify the E-vectors extracted by this DSS with other vocal features measures and apply them to both text-dependent and text-independent datasets. In the experiments containing one utterance of each speaker, the average accuracy of the E-vector is improved by approximately 1.5% over the MFCC. In the experiments containing multiple utterances of each speaker, the average micro-F1 score of the E-vector is also improved by approximately 2.1% over the MFCC. The results of the E-vector show remarkable advantages when applied to both the Texas Instruments/Massachusetts Institute of Technology corpus and LibriSpeech corpus. These improvements of the E-vector contribute to the capabilities of speaker identification and also enhance its usability for more real-world identification tasks.
NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency

Superpixels intuitively over-segment an image into small compact regions with homogeneity. Owing to its outstanding performance on region description, superpixels have been widely used in various computer vision tasks as the substitution for pixels. Therefore, efficient algorithms for generating superpixels are still important for advanced visual tasks. In this work, two strategies are presented on conventional simple non-iterative clustering (SNIC) framework, aiming to improve the computational efficiency as well as segmentation performance. Firstly, inter-pixel correlation is introduced to eliminate the redundant inspection of neighboring elements. In addition, it strengthens the color identity in complicated texture regions, thus providing a desirable trade-off between runtime and accuracy. As a result, superpixel centroids are evolved more efficiently and accurately. For further accelerating the framework, a recursive batch processing strategy is proposed to eliminate unnecessary sorting operations. Therefore, a large number of neighboring elements can be assigned directly. Finally, the two strategies result in a novel synergetic non-iterative clustering with efficiency (NICE) method based on SNIC. Experimental results verify that it works 40% faster than conventional framework, while generating comparable superpixels for several quantitative metrics—sometimes even better.
A Comparison of Data Sampling Techniques for Credit Card Fraud Detection

Credit Card fraud is a tough reality that continues to constrain the financial sector and its detrimental effects are felt across the entire financial market.Criminals are continuously on the lookout for ingenious methods for such fraudulent activities and are a real threat to security.Therefore, there is a need for early detection of fraudulent activity to preserve customer trust and safeguard their business.A major challenge faced in designing fraud detection systems is dealing with the class imbalance issue in the data since genuine transactions outnumber the fraudulent transactions typically account less than 1% of the total transactions.This is an important area of study as the positive case (fraudulent case) is hard to distinguish and becomes even harder with the inflow of data where the representation of such cases even decreases further.This study trained four predictive models, Artificial Neural Network (ANN), Gradient Boosting Machine (GBM) and Random Forest (RF) on different sampling methods.Random Under Sampling (RUS), Synthetic Minority Over-sampling Technique (SMOTE), Density-Based Synthetic Minority Over-Sampling Technique (DBSMOTE) and SMOTE combined with Edited Nearest Neighbour (SMOTEENN) was used for all models.The findings of this study indicate promising results with SMOTE based sampling techniques.The best recall score obtained was with SMOTE sampling strategy by DRF classifier at 0.81.The precision score for this classifier was observed to be 0.86.Stacked Ensemble was trained for all the sampled datasets and found to have the best average performance at 0.78.The Stacked Ensemble model has shown promise in the detection of fraudulent transactions across most of the sampling strategies.
Application of machine learning techniques and empirical mode decomposition for the classification of analog modulated signals

&lt;!--[if gte mso 11]&gt;&lt;w:PermStart w:id="205147274"&#x0D; w:edGrp="everyone"/&gt;&lt;![endif]--&gt;&lt;p class="Abstract"&gt;In this article, an automatic Analog Modulation Classifier based on Empirical mode decomposition and Machine learning approaches (AMC-EM) is proposed. The AMC-EM operates without a priori information and can recognise typical analog modulation schemes: amplitude modulation, phase modulation, frequency modulation, and single sideband modulation. The AMC-EM uses Empirical Mode Decomposition (EMD) to evaluate the features of the signal for the successive classification by using Machine Learning (ML). In the design of the AMC-EM, the selection of the specific ML technique is performed by comparing, with numerical tests, the performance of the (i) Support Vector Machine (SVM), (ii) k-nearest neighbor classifier, and (iii) adaptive boosting, since they are commonly used in the field of signal classification. The tests have highlighted that the SVM, specifically the quadratic SVM, permits the best possible performance concerning classification accuracy, by considering different noise intensities superimposed on the signal. To assess the advantages of the proposal, a comparison with other classifiers available in the literature has been undertaken through numerical tests. Finally, the AMC-EM is experimentally evaluated, and the experimental results agree with those of the simulation.&lt;/p&gt;&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;&lt;br /&gt;&lt;!--[if gte mso 11]&gt;&lt;w:PermEnd w:id="205147274"/&gt;&lt;![endif]--&gt;&lt;/span&gt;&lt;/p&gt;
AILA: A Question Answering System in the Legal Domain

Question answering (QA) in the legal domain has gained increasing popularity for people to seek legal advice. However, existing QA systems struggle to comprehend the legal context and provide jurisdictionally relevant answers due to the lack of domain expertise. In this paper, we develop an Artificial Intelligence Law Assistant (AILA) for question answering in the domain of Chinese laws. AILA system automatically comprehends users' natural language queries with the help of the legal knowledge graph (KG) and provides the best matching answers for given queries. In addition, AILA provides visual cues to interpret the input queries and candidate answers based on the legal KG. Experimental results on a large-scale legal QA corpus show the effectiveness of AILA. To the best of our knowledge, AILA is the first Chinese legal QA system which integrates the domain knowledge from legal KG to comprehend the questions and answers for ranking QA pairs. AILA is available at http://bmilab.ticp.io:48478/.
Education Degree Fraud Detection and Student Certificate Verification using Blockchain

To verify the authenticity of an academic degree and certificates we propose a system which employs a digital signature scheme and timestamps using blockchain technology.As the number of universities and tertiary education students, the number of graduates is constantly increasing.Due to this verification process of these degree certificates generates a lot of new job opportunities.The sudden changes in the technology and development of new technologies like blockchain is booming, the implementation of blockchain using blockcerts software provides us a solution of plausible business models.In this paper we showcase two financial models balancing where the service rates is been balanced between graduates and employer as to main stakeholders of that service.A proof check of certificates for students is done at low cost and an easy check of the authenticity of the certificate is done from and trustable source while recruiting by the employer.
Flexible and attachable inertial measurement unit (IMU)-based motion capture instrumentation for the characterization of hand kinematics: A pilot study

(2021). Flexible and attachable inertial measurement unit (IMU)-based motion capture instrumentation for the characterization of hand kinematics: A pilot study. Instrumentation Science & Technology: Vol. 49, No. 2, pp. 125-145.
A DEEP AUTOENCODER-BASED REPRESENTATION FOR ARABIC TEXT CATEGORIZATION

Arabic text representation is a challenging assignment for several applications such as text categorization and clustering since the Arabic language is known for its variety, richness and complex morphology. Until recently, the Bag-of-Words remains the most common method for Arabic text representation. However, it suffers from several shortcomings such as semantics deficiency and high dimensionality of feature space. Moreover, most existing methods ignore the explicit knowledge contained in semantic vocabularies such as Arabic WordNet. To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. Our method allowed for the consideration of document semantics by combining both implicit and explicit semantics and reducing feature space dimensionality. To evaluate our method, we conducted several experiments on the standard Arabic dataset, OSAC. The obtained results showed the effectiveness of the proposed method compared to state-of-the-art ones.Arabic text representation
Accuracy Assessment of ARKit 2 Based Gaze Estimation

With the growing amount of mobile application usage, assuring a high quality of experience became more and more important. Besides traditional subjective methods to test and prototype new developments, eye tracking is a prominent tool to assess quality and UX of a software product. Although portable eye trackers exist, the technology is still mostly associated with expensive laboratory equipment. To change that and to run quick and cheap eye-tracking studies in the field, attempts have been made to turn everyday hardware like smartphone cameras and webcams into eye trackers. This study explores the possibility of using a standard library of iOS to tackle the vast technical complexity usually coming with such approaches. The accuracy of an eye-tracking system purely based on the ARKit APIs of iOS is evaluated in two user studies (N = 9 & N = 8). The results indicate that an ARKit based gaze tracker provides comparable performance in terms of accuracy ($$3.18^\circ $$, or 1.44 cm on screen), while at the same time, it uses far fewer hardware resources and provides a higher sample-rate than any other smartphone eye tracker. Especially the easy to use API is the main advantage over the technical complex systems which rely on their own image analysis for gaze estimation. Privacy implications are discussed.
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (CAM), the field has focused on how to expand the attention regions to cover objects more broadly and localize them better. However, these strategies rely on full localization supervision for validating hyperparameters and model selection, which is in principle prohibited under the WSOL setup. In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set. We observe that, under our protocol, the five most recent WSOL methods have not made a major improvement over the CAM baseline. Moreover, we report that existing WSOL methods have not reached the few-shot learning baseline, where the full-supervision at validation time is used for model training instead. Based on our findings, we discuss some future directions for WSOL. Source code and dataset are available at https://github.com/clovaai/wsolevaluation https://github.com/clovaai/wsolevaluation.