Reviewerly/exHarmony huggingface dataset:
Proportionally Fair approach for Tor’s Circuits Scheduling

The number of users adopting Tor to protect their online privacy is increasing rapidly. With a limited number of volunteered relays in the network, the number of clients' connections sharing the same relays is increasing to the extent that it is starting to affect the performance. Recently, Tor's resource allocation among circuits has been studied as one cause of poor Tor network performance. In this paper, we propose two scheduling approaches that guarantee proportional fairness between circuits that are sharing the same connection. In our evaluation, we show that the average-rate-base scheduler allocates Tor's resources in an optimal fair scheme, increasing the total throughput achieved by Tor's relays. However, our second proposed approach, an optimization-based scheduler, maintains acceptable fairness while reducing the latency experienced by Tor's clients.
Mizo Spoken Query System Enhanced with Prosodic Information

We report the development of a Mizo spoken language interface to an online database of commodity prices in various agricultural markets of Mizoram province. The spoken query system is designed to be used over mobile phone networks. It is inspired by earlier such systems developed for languages such as Assamese and Marathi. However, considering the manifold increase in the performance of the current Mizo language spoken query system, we were motivated to report the whole system implementation in detail. The average word error rate of the DNN-HMM speech recognition system in a 5-fold cross validation experiment is 2.2%. The word error rate of the Mizo commodity name recognition system is 2.75 times smaller than that reported for a similar system for Assamese language. The reduction in error rate is attributed to data collection from diverse environmental situations with different noise levels and further processing of the data. The word error rate of the Mizo ASR system increases to 8.5% in field trials conducted with several users.
Modeling Eye-Gaze Behavior of Electric Wheelchair Drivers via Inverse Reinforcement Learning

It is intuitively obvious that eye-gaze behaviors of experienced drivers are different from those of novice drivers. However, it is not easy to understand the difference in their behavior quantitatively. In this work, we present an explainable eye-gaze behavior modeling method for electric wheelchair drivers based on Inverse Reinforcement Learning (IRL). We first create feature maps that represent risk factors during driving. These feature maps are able to represent not only to what but also from where drivers pay attention. IRL uses the feature maps to learn the reward representing the eyegaze behaviors and allows us to see important features via the automatic acquisition of the reward. Through analysis of the learned model, we show quantitative evidence that eye-gaze behaviors of experienced drivers are better-balanced by paying attention to multiple risks.
A Study on Distance Measure for Effective Anomaly Detection using AutoEncoder

Anomaly detection is a popular application in various areas. One challenging issue is to build an anomaly detection model using normal data because collecting potential abnormal data is quite difficult. In this paper, we build an anomaly detection model using just normal data based on adversarial autoencoder for acoustic data. After extracting features using the trained model, we apply a distance-based method for calculating a threshold to be used for anomaly detection. In particular, we propose a method for reflecting differences in dimensions in calculating distance. Through experiments, we show that the proposed dimension-aware distance measure improves anomaly detection accuracy by up to 7% compared to existing distance measure methods.
Development of Multiple Behaviors in Evolving Robots

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered.
A Lightweight Model For Real-time Traffic Sign Recognition

Traffic sign detection (TSR) is a hot topic in the field of computer vision with lots of applications such as autonomous vehicles, path planning, robot navigation etc. Especially, it also can be applied in advanced driver assistance system (ADAS) which can help drivers get more useful information on the road to make decision exactly. However, most of the developed systems cannot be used in real-time environment. Therefore, in this paper, a light-weight model has been proposed for real-time traffic sign recognition. Our system is embedded on a 1:7 RC vehicle and tested on our small driving environment. The experimental results demonstrate the effectiveness and robustness of the proposed model in many challenging situations.
Deep Blind Video Quality Assessment for User Generated Videos

As short video industry grows up, quality assessment of user generated videos has become a hot issue. Existing no reference video quality assessment methods are not suitable for this type of application scenario since they are aimed at synthetic videos. In this paper, we propose a novel deep blind quality assessment model for user generated videos according to content variety and temporal memory effect. Content-aware features of frames are extracted through deep neural network, and a patch-based method is adopted to obtain frame quality score. Moreover, we propose a temporal memory-based pooling model considering temporal memory effect to predict video quality. Experimental results conducted on KoNViD-1k and LIVE-VQC databases demonstrate that the performance of our proposed method outperforms other state-of-the-art ones, and the comparative analysis proves the efficiency o f our temporal pooling model.
Learning to Learn in a Semi-supervised Fashion

To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval. Our learning scheme exploits the idea of leveraging information from labeled to unlabeled data. Instead of fitting the associated class-wise similarity scores as most meta-learning algorithms do, we propose to derive semantics-oriented similarity representations from labeled data, and transfer such representation to unlabeled ones. Thus, our strategy can be viewed as a self-supervised learning scheme, which can be applied to fully supervised learning tasks for improved performance. Our experiments on various tasks and settings confirm the effectiveness of our proposed approach and its superiority over the state-of-the-art methods.
Quantitative Sørensen–Dice Indexed Damgård–Jurik Cryptosystem For Secured Data Access Control In Cloud

Abstract Data Access Control has become a demanding issue in cloud storage systems. Access control is the protection method to control who can view or access the information in computing scenarios. Some techniques have been designed formost of the security strategiesprovidedtotheclients accessingtheuploadeddata. AQuantitative Sørensen–Dice Indexing Damgård–Jurik Cryptosystem based Data Access Control (QSDIDJC-DAC) method is introduced to avoid the illegitimate data access in the cloud server. Initially, the QSDIDJC-DAC method comprises five processes, namely registration, key generation, authentication, encryption and decryption for data access. At first, the clientsregister their information to the cloud server. After that, the cloud server generates the key pairs (i.e., public key, private key). Then the client encrypts the data with the general public key and sends it to the cloud server for storing the data. During the data access, the user transmits the request to the cloud server. Upon receiving the request, the authentication server verifies the cloud user is a legitimate user using the Quantitative Sørensen–Dice similarity coefficient with higher authentication accuracy. The Similarity Coefficient matches the requested user with user information stored in the cloud server on the time of registration. Based on the similarity value, the legitimate and illegitimate users are correctly identified with minimum time consumption. After performing the verification process, the cloud server allows legitimate users to access the data. Subsequently, the client decrypts the data with the help of their private key. This helps to enhance the data access control in the cloud server with a better security level. Experimental assessment is carried out on factors such as authentication accuracy, computation time and data confidentiality rate with recognize to some of the cloud users and thedata.
Noisy Intermediate Scale Quantum Computers: on the Co-Simulation of Qubits and Control Electronics

In this paper, we discuss the challenges in modeling of Noisy Intermediate Scale Quantum Computers, in particular, those implementations that rely on integrated interface and control electronics. We begin with an overview of the current state of quantum computers and highlight the emergence of quantum engineering and its relation to electrical and electronic engineering. We then discuss qubit modeling and existing solutions. The particular example we focus on in this paper is related to semiconductor CMOS charge qubits. For this reason, we overview the physical models and equivalent circuit model of this type of qubits.
A software tool to generate a Model-View-Controller architecture based on the Entity-Relationship Model

Most web projects that are developed by software engineering students have at least one form and a controller, which contains a model of the data related to the manipulation of the forms, as well as a database. In other words, for each entity in the database, a view, a controller and a model must be created. Allowing to standardize the communication between the web project and the database. The main problem is that students do not know how to implement the Model-View-Controller (MVC) design pattern, due to this, their projects lack scalability, understanding, and include bad development practices. Therefore, this proposal consists of a CASE tool (Computer Aided Software Engineering) capable of generating an application based on an Entity-Relationship diagram, generating the model, the controller, and the user interface. The experiment with the application consists of measuring its usability through the Systems Usability Scale (SUS). For this, the Results indicate a good and acceptable evaluation according to the scales used (72.64 SUS points). The developed software allows students to understand the MVC design pattern clearly by being able to interact with the inputs and outputs of the system.
Entity and Verb Semantic Role Labelling for Tamil Biomedicine

The primary task of Semantic Role Labelling (SRL) is to indicate exactly what semantic relations hold among a predicate and its associated participants. This type of role labelling yields a first level semantic representation of the text in question. Since the field of computation in Tamil Biomedicine is rather unexplored, SRL is introduced to label the named entities with specific roles in the given domain. In contrast to many state-of-the-art SRL systems, we devise a new approach to define roles to predicate terms along with its constituent terms. In order to achieve this, a MEM based classifier model is built using the features obtained from parsed input sentences. The parsing is done on a syntactic level and a dependency parse tree is built. The classifier model is further strengthened by verb frame training, as their probability give an extra edge to determine verb roles. The MEM model is compared with linear classifiers such as SVM and Linear Regression classifier and is found to perform better than the others.
Native and Non-Native Marathi Numerals Recognition using LPC and ANN

Speech recognition is gaining an increasing research interest in the last five decades. Speech processing is considered as an interdisciplinary branch of electronics and computer science domain. It considers speech as an input and converts it into the corresponding text. This paper describes the design and development of Marathi Numeric Speech Dataset. Marathi Numbers (Ank) ranging from Shunya(0) to Nau(9) and are taken into consideration for recording. Speech samples are collected from 50 native and 50 non-native speakers of Marathi language. The dataset remains as a gender balanced since it is recorded from 50 females and 50 male speakers. The age of speakers will affect the speech. Therefore, 5 different age groups such as 11-20, 21-30, 31-40, 41-50 and 51-60 are considered. Native and non-native speakers are selected to obtain ample amount of variations in the pronunciation of Marathi numerals. Feature extraction and feature matching technique plays a vital role for speech recognition and here LPC (Linear Predictive Coding) is used for extracting features from samples, whereas ANN (Artificial neural network) is used to classify them. Experimental specifications and results are also discussed. This research work has attempted to design and develop a speech recognition system, which can understand Marathi Ank (Numbers) and identify them accurately.
A Smoothed LASSO-Based DNN Sparsification Technique

Deep Neural Networks (DNNs) are increasingly being used in a variety of applications. However, DNNs have huge computational and memory requirements. One way to reduce these requirements is to sparsify DNNs by using smoothed LASSO (Least Absolute Shrinkage and Selection Operator) functions. In this paper, we show that irrespective of error profile, the sparsity values obtained using various smoothed LASSO functions are similar, provided the maximum error of these functions with respect to the LASSO function is the same. We also propose a layer-wise DNN pruning algorithm, where the layers are pruned based on their individual allocated accuracy loss budget, determined by estimates of the reduction in number of multiply-accumulate operations (in convolutional layers) and weights (in fully connected layers). Further, the structured LASSO variants in both convolutional and fully connected layers are explored within the smoothed LASSO framework and the tradeoffs involved are discussed. The efficacy of proposed algorithm in enhancing the sparsity within the allowed degradation in DNN accuracy and results obtained on structured LASSO variants are shown on MNIST, SVHN, CIFAR-10, and Imagenette datasets and on larger networks such as ResNet-50 and Mobilenet.
AR Mini-Games for Supermarkets

We present an Augmented Reality (AR) application intended for use in supermarkets, with the primary goal to bring fun and digital engagement through AR mini-games to the customers while shopping. We believe that our approach can be extended and scaled up by integrating mini-games into existing shopping apps in the future.
Use of field-portable XRF in exploration of PGE-enriched zones in the Pilanesberg PGE deposit, Bushveld Complex, South Africa

The field-portable X-ray fluorescence (FPXRF) spectrometer has evolved significantly in the last decade and has become one of the most innovative tools for field geologists. Portability and ease of use of FPXRF systems have opened up new and unique applications for even novice technicians. Application of FPXRF in precious metals exploration and mining appears to be challenging due to their low concentrations (lower than detection limit by FPXRF) in nature and even in most ore deposits. This case study shows the success of FPXRF in identifying anomalous zones of platinum group elements (PGE) and Au (target elements) using pathfinder elements in the Pilanesberg PGE deposit, Bushveld Complex, South Africa. Sixty-three core samples were analysed using both FPXRF and laboratory methods. In these samples, Pt &lt;8 ppm, Pd &lt;5 ppm and Au &lt;1 ppm, which were not detected by FPXRF; however, Ni and Cu are up to 6540 and 3560 ppm, respectively, which were easily detected by the same method. These elements show positive correlation with the precious metals indicating that they can be used as pathfinders. Both direct shot analyses of core samples and their pulverized specimen assays show correlation with lab assay data suggesting that both methods can be used in the field; however, the accuracy of direct shot data is lower due to the heterogeneity of samples.
Inferring the Logic of Collective Information Processors

Abstract From neurons to insects to societies, across biology we see impressive feats of collective information processing. What strategies do these systems use to perform useful computations? Moving toward an answer to this question, this chapter focuses on common challenges in inferring models of complicated distributed systems and how the perspective of information theory and statistical physics is useful for understanding collective behavior.
Towards an RDF Knowledge Graph of Scholars from Early Modern History

The use of Semantic Web Technologies supports research in the field of digital humanities. In this paper we focus on the creation of semantic independent online databases such as those of historical prosopography. These databases contain biographical information of historical persons. We focus on this information with an interest in German professorial career patterns from the 16th to the 18th century. In that respect, we describe the process of building an Early Modern Scholarly Career RDF Knowledge Graph from two existing prosopography online databases: the Catalogus Professorum Lipsiensium and the Catalogus Professorum Helmstadiensium. Further, we provide an insight in how to query the information using KBox to answer research questions.
Prediction of Dengue Infected Areas using A Novel Blockchain based Crowdsourcing Framework

The impact of global transformation due to mosquito-borne diseases like dengue is noticeable and according to the World Health Organization, approximately 96 million people are infected by dengue per year. Moreover, the climate of tropical countries, e.g., Bangladesh is highly in favor of dengue. The initiatives taken by different organizations every year are not enough to face the challenges of dengue. To mitigate the effect of dengue, we propose a distributed crowdsourcing framework, the Dengue Tracker System in which the infected patients and the conscious citizen can submit the possible infectious locations. With the submitted data, two separate heatmaps can be generated so that the people and the concerned authority can get ready to face the challenges of dengue. Moreover, the system is deployed on the Ethereum-blockchain to enhance the security of the system. To prevent fake location data, different token generation methods are implemented.
Bernoulli Filters for Multiple Correlated Sensors

The Bernoulli filter is a general, Bayes-optimal solution for tracking a single disappearing and reappearing target, using a single sensor whose observations are corrupted by missed detections and a general, known clutter process. Like virtually all target-tracking algorithms it presumes a hidden Markov model (HMM) structure on the sensor and target. Pieczynski's pairwise Markov model (PMM) relaxes this assumption, thereby addressing correlated sensor noise and non-Markovian target motion. In an earlier paper, we derived a “PMM Bernoulli filter” that obeys PMM rather than restrictive HMM sensor/target statistics. This paper generalizes both the HMM and PMM Bernoulli filters to the case of multiple, possibly correlated sensors, resulting in a general, Bayes-optimal single-target tracker for complexly correlated sensors.
Portrait of Higher Education in The Covid-19 Period in A Digital Literacy Perspective: A Reflection on The Online Lecture Process Experience

The Covid-19 pandemic catastrophic event has devastated the world of education in various parts of the world, including Indonesia.Without adequate preparation, all education units are forced to conduct distance education.The diversity of abilities and literacy of both educators and students is an obstacle to the effective and efficient learning process.Apart from the unequal digital literacy maturity of these stakeholders, the teaching and learning process must continue.This article is the result of a six-month study of the teaching and learning process from home.The evaluation focuses on phenomena that occur due to different levels of digital literacy.
A Maximum Likelihood Approach to Speed Estimation of Foreground Objects in Video Signals

Motion and speed estimation play a key role in computer vision and video processing for various application scenarios. Existing algorithms are mainly based on projected and apparent motion models and are currently used in many contexts, such as automotive security and driver assistance, industrial automation and inspection systems, video surveillance, human activity tracking and biomedical solutions, including monitoring of vital signs. In this paper, a general Maximum Likelihood (ML) approach to speed estimation of foreground objects in video streams is proposed. Application examples are presented and the performance of the proposed algorithms is discussed and compared with more conventional solutions.
Question Answering for Estimation of Seen Image Contents from Multi-subject fMRI Responses

Estimating seen image contents based on the analysis of functional magnetic resonance imaging (fMRI) data has been actively researched. So far, it has been necessary to train a model with individual fMRI data and construct models for each task. In this paper, we propose an estimation method via Visual Question Answering (VQA) from fMRI data from multi-subjects. The task of VQA in the field of computer vision is generating answers when given an image and questions about its contents. The proposed method enables generation accurate answers via the VQA model when given fMRI data and questions about the seen images. Besides, we newly introduce training with multi-subject fMRI data since fMRI datasets are generally small due to the burden for subjects. Then we can realize the estimation of the contents of seen images from fMRI data of a subject whose data are not used in the training phase.