Reviewerly/exHarmony huggingface dataset:
PCA-Based Robust Motion Data Recovery

Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work.
Multi-stage Attention Convolutional Neural Networks for HEVC In-Loop Filtering

In High Efficiency Video Coding (HEVC), lossy compression techniques bring various visual artifacts such as blocking, ringing, and blurring, etc. Recently, to explore convolutional neural networks (CNN) potential of removing such artifacts, researchers presented several CNN models. In this paper, we proposed a Multi-stage Attention Convolutional Neural Network (MACNN) to replace in-loop filters or postprocessing. Our proposed method has three characteristics of effective training and inferencing: employing a 3-stage design fashion with various loss criteria, adopting the revised Inception module, and utilizing Self-attention block. Such a design has several advantages. The 3-stage design clarifies the function of each stage, effectively alleviating the burden of the network; inception module along with self-attention block benefit our model having the ability to acquire global information with fewer layers. Hence, MACNN could reach the performance gain competitive to deeper models. The experimental results show that our MACNN achieves an average 6.5% BD-rate reduction compared to HEVC in all-intra configuration, beating state-of the-art methods.
Prediction of Twitter Message Deletion

Social media are a way for people to build their reputation or to promote an idea. Twitter, in contrast with other social media sources, is a generator of real-time textual information, and it is mainly used to share ideas, opinions and breaking news. It is meant for short, quick, compelling statements that reach out millions of users around the world. Posting something inappropriate may affect the public image, privacy of celebrities, politicians as well as ordinary Twitter users. If we could in advance alarm the user of the potential vulnerability in the message to be posted we could protect his/her identity from being compromised. So, automatic identification of the message with the content causing it to be deleted in the future is a promising area of research. In this paper, we are analyzing Twitter messages in English language with the objective to build a classifier to predict whether a particular post will be deleted by the user or not. We apply the Recurrent Neural Networks (RNN) model that relies on the context-based information of tweets while doing the classification. An additional contribution of the work is the construction of a rich set of features including twitter metadata, user information and tweets' text to train classical machine learning algorithms on Twitter data.
How to Reduce Confirmation Bias using Linked Open Data Knowledge Repository

In this research, we are focusing on the reduction of confirmation bias of provision of information and knowledge. Nowadays, it is very common to provide customized information that users need. This kind of a service is applied to everywhere such as e-commerce sites, YouTube social networks, search engines, news sites, online-contents services delivery sites and so on. However, this unfortunately puts the risk of being open only to narrow information. Thus, they may be separated from the general information. In an environment where custom information is common, we are exposed to vulnerability to acquire the balanced knowledge. Therefore, this study proposes a method to reduce possible confirmation bias using knowledge graph with semantic structured data. For the calculation of similarity of associative and contrast between comparatives, we adopt the MSSD and MDSD using Boolean logical processing.
Interactive Multiple-Target Tracking via Labeled Multi-Bernoulli Filters

In many cases, the multi-target tracking system is essential for realizing the current state of an environment. The standard multi-target tracking algorithms assume that each target state evolves independently and regardless of other targets' states. However, in a real scenario this assumption does not hold in that the motion of any target is dependent on other targets. This paper proposes a new mathematical solution for multi-target tracking system with interacting targets. In the proposed method the prediction operation of the labeled multi-Bernoulli filter is extended to incorporate all possible interactions between targets. The results show that in scenarios where the assumption of a standard motion model is violated, the proposed method achieves higher accuracy for the state estimation of the targets. Also, it shows better performance for estimating the identity of the targets.
Palo Catholic Churchyard Locator, Reservation and Manager

The growth of the internet is evident nowadays. Modern technology has made the information flow quick and readily available through the use of the internet; the typical example the internet has facilitated is making the communication cheap and fast. Where people choose to spend a significant part of their lives in social communities where they can chat, message, extract and share information. The internet is the center of usage for millions of users every day; thus, it also appears to be a medium for business.As a result, companies already have their advertisements and webpages to promote their services. To improve the traditional way of managing services is to create a web-based application for a cemetery; this is possible, and this is a system that could help to process their transactions.Palo Catholic Churchyard Locator, Reservation, and Manager is a powerful data manager that allows users to track all information about gravesites, owners, occupants, and more via a one-screen user interface. Then, designed to make the system as easy to learn as it is and to save time in locating gravesites through a map. Mapping has enabled the cemetery's recording with accuracy than the manual processes. The use of technology saves time, both in the office and on the site of the cemetery. And this is also beneficial to the public because it can provide an online map to search the graves of loved ones.
Bayesian Reinforcement Learning for Link-Level Throughput Maximization

One intrinsic property of neural networks is making confident decisions because they do not capture uncertainty in training data. As a result, when Neural Networks (NN) are used in Deep Reinforcement Learning (DRL), agents cannot explore the action-space effectively. Bayesian Neural Networks (BNN) is one alternative that, instead of one value, assigns a probability distribution to the weights of NN. Using BNN as the policy network of an RL agent, the RL agent will have natural exploration capability. Recent studies demonstrate high potential for the application of RL methods in wireless networks. The inefficient exploration capability, however, limits their use cases. In this letter, we show how Bayesian RL agents can be used to solve complex wireless resource allocation problems. We consider the link-level throughput maximization that needs simultaneous power and Modulation/Coding Scheme (MCS) assignment to each user. We show that due to the large and sparse action-space, only Bayes-by-Backprop Q-network (BBQN) agents can find proper assignments. Simulation results show the performance of the proposed scheme in different network settings.
Improving the Robustness of Threshold-Based Single Hidden Layer Neural Networks via Regularization

Edge computing can take full advantage of data-driven models only if the eventual inference function can be deployed on low-power, resource-constrained digital devices. In this regard, single layer feedforward neural networks (SLFNs) based on the threshold activation function represent a suitable option, as they can suitably balance generalization performances and computational costs. Their robustness to perturbations at the node level, though, is an issue: a small perturbation affecting the input to the activation might result in a sign inversion at the output of the neuron. This in turn may severely affect the accuracy of the inference function when implemented on hardware. This paper shows that the robustness of this class of SLFNs can be improved by introducing in the cost function a regularization term specifically designed to limit the impacts of perturbations at the node level. The novel cost function indeed admits a closed-form solution. Experimental validation involved six real world benchmarks.
QCKer-FPGA: An FPGA Implementation of Q- gram Counting Filter for DNA Sequence Alignment

Read mapping is a process in which DNA reads are mapped to a reference genome through filtering and verification using a predefined metric. Filtering is done by quickly eliminating incorrect regions when a DNA read is compared to the reference genome. Verification on the other hand is responsible for verifying these candidate regions which require mathematical and theoretical approaches. Due to large amounts of data produced by Next Generation Sequencing (NGS) platforms, a filter is needed to reduce various computational challenges introduced by the verification process. FPGAs are special purpose processors that are designed to handle compute-intensive applications, having a highly customizable fabric. In this paper, the q-gram counting filter is implemented that takes advantage of the flexibility and capabilities of FPGAs in parallel applications using the ZedBoard development board. The paper discusses the results of the filter with varying sizes of q, number of reads with various lengths, and different reference sequences. The results show an average of 34.02% lesser clock cycles with a q-gram length of 4 and 53.58% for q-gram of 8 when compared to an implementation in C.
A Perceptually Optimized Wavelet Foveation Based Embedded Image Coder and Quality Assessor Based Both on Human Visual System Tools

In this paper, we propose a visually optimized embedded foveation image coder thereafter called VOEFIC. It introduces a visual weighting model to wavelet coefficients prior to control SPIHT encoding algorithm. It aims reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate, observation distance and a fixation point that determines the region of interest ROI. Our VOEFIC coder incorporates a battery of vision models including various masking effects related to the human visual system (HVS) perception. Thus, the coder deploys the visual model to weight the original wavelet coefficients, reshape its frequency spectrum, adapts its contents, eliminates its redundancy and finally increases the perceptual quality with respect to given coding parameters. The visual weighting model within wavelet sub-bands uses successively the following visual models 1) foveation masking applied to remove or at least to reduce high frequencies around peripheral regions 2) Luminance and Contrast masking deployed to adapt lightening and correct the contrast with to the wavelet just noticeable distortion thresholds. The paper also introduces a new objective quality metric deploying a visual model that integrates the human visual properties. The visual quality assessor offers a score based on a foveation probability scale FPS evaluate objectively the coding quality. The new perceptually optimized codec has the same complexity as the standard SPIHT coder. However, its visual results show that VOEFIC coder exhibits very good visual performances in terms of quality assessment index.
Physical object interaction in first responder mixed reality training

Virtual Reality (VR) systems can improve the training of first responders and soldiers in multi-domain operations in a number of ways. Realistic simulation of physical objects, however, is challenging. The huge variety of equipment pieces and other objects specialists in first responder units - and in particular CBRN-troops - interact with further increases the effort. In this paper, we present a novel and flexible Mixed Reality (MR) training system for first responders that enables the integration of physical objects by using Augmented Virtuality (AV) and &#98;inary tracking". A Head Mounted Display (HMD) immerses the user in VR, while augmenting the visualization with 3D imagery of real objects, captured by an RGB-D sensor. In addition, a RFID-reader at the user's hand detects the presence or absence (binary response) of certain equipment items. Our proposed MR system fuses this information with data of an inertial motion capture suit to an approximate global object pose and distributes it. Our solution provides a wide range of options for physical object interaction and collaboration in a multi-user MR environment. In addition, we demonstrate the training capabilities of our proposed system with a multi-user training scenario, simulating a CBRN crisis. Results from our technical and quantitative user evaluation with 13 experts in CBRN response from the Austrian Armed Forces (National Defense Academy and Competence Center NBC Defense) indicate strong applicability and user acceptance. Over 80% of the participants found it easy or very easy to interact with physical objects and liked the multi-user training much or very much.
Ontology-based sentimental knowledge in predicting the product recommendations: A data science approach

The effective item recommendations in the online business websites are largely based on the efficient study of product reviews. This paper describes the data science approach to predict product recommendations that are based on ontological sentiments calculated from consumer reviews. The recommendation strategy is based on the action taken on the discovered insight of deviated product recommendations obtained from the diagnosed machine learning model. It is revealed from the comparative results of Mean Absolute Error that there is vividness in the product recommendations in predicting the customer expected order of products when ontology support is used. This drives the customer purchase decisions on the product in a better manner.
Low Latency Big Data Processing without Prior Information

Job scheduling plays an important role in improving the overall system performance in big data processing frameworks. Simple job scheduling policies, such as Fair and FIFO scheduling, do not consider job sizes and may degrade the performance when jobs of varying sizes arrive. More elaborate job scheduling policies make the convenient assumption that jobs are recurring, and complete information about their sizes is available from their prior runs. In this paper, we design and implement an efficient and practical job scheduler for big data processing systems to achieve better performance even without prior information about job sizes. The superior performance of our job scheduler originates from the design of multiple level priority queues, where jobs are demoted to lower priority queues if the amount of service consumed so far reaches a certain threshold. In this case, jobs in need of a small amount of service can finish in the topmost several levels of queues, while jobs that need a large amount of service to complete are moved to lower priority queues to avoid head-of-line blocking. Our new job scheduler can effectively mimic the shortest job first scheduling policy without knowing the job sizes in advance. To demonstrate its performance, we have implemented our new job scheduler in YARN, a popular resource manager used by Hadoop/Spark, and validated its performance with experiments in both real testbeds including Amazon EC2 and large-scale trace-driven simulations. Our experimental and simulation results have strongly confirmed the effectiveness of our design: our new job scheduler can reduce the average job response time of the Fair scheduler by up to 45 percent and achieve better fairness at the same time.
Intelligent Navigation System for the Visually Impaired - A Deep Learning Approach

Visually impaired individuals have been gradually claiming a significant stake in the population demographics. The proposed autonomous device aims to provide a holistic solution by engineering a smart navigation system that relentlessly scans the environment, detects and classifies neighboring objects using a 4 layered Convolutional Neural Network (CNN) that has been trained on a data set containing 2513 permutations of various images of household objects that an individual may encounter in daily life. The CNN follows the 80-20 rule for testing and training the self-learning model enabling it to learn recursively from the error rate. The proposed system then calculates distances of neighboring objects from the user and provides adaptive solutions in real time to manoeuvre the user to safety by providing auditory input in a simplistic manner which considers 10-24 frames per second while drafting the kinematic response for the user. The device has achieved an unprecedented success rate of serving within a response time of less than 50 ms. The accuracy of the CNN algorithm being at 94.6%, also sets a distinguished benchmark as an object detection algorithm thereby contributing to the success in simulations of the proposed device in a constrained environment.
Methodologies for selection of Quality Web Services to Develop Efficient Web Service Composition

Web Services are materialized as a major technology carried out for automated interaction between distributed and miscellaneous applications. It is defined as a software service that provides business solutions consumed by different service requester. It can be accessed by a standard web protocol. Service composition is the mechanism used for selecting, reusing and combining existing web services to build new web services. With immense increase in web services, quality assessment plays an essential role in the selection approach. QoS is defined as the ability to guarantee the requirements like latency, reliability, bandwidth, etc. in order to satisfy a service level agreement between an application provider and end-user. QoS based dynamic service composition leads to the upward growth of an organization that implements multiple services to provide its business solution. The proposed research work elaborates the research work on different methodologies of QoS prediction with its implications to provide a proper road map for future research on efficient service composition.
RGB-D Camera Based Anthropometric Measuring System for Barong Tagalog Tailoring

In this paper, we present the development and evaluation of a low-cost RGB-D camera based anthropometric measurement system for Barong Tagalog tailoring shops. Although anthropometric scanners exist in the market today, most of these current systems have high cost and complex installation process. The proposed barong anthropometric measurement system utilizes only frontal and side-view images from off-the-shelf RGB-D sensor, i.e. Kinect camera, which is inexpensive and readily available. The proposed system measures the linear body distances, i.e. arms, shoulder, and upper body lengths based on RGB-D skeleton tracking joints. We also propose three different methods for estimating the circumferential barong measurements for the hips, chest, and waist. The proposed system was evaluated by comparing the measured and actual values for absolute and relative error metrics in every measurements from 200 randomly selected participants - 100 males and 100 females. The average relative errors (%) for the male arm, body, shoulder, chest, waist, and hip girth measurements were 4.09, 3.06, 2.80, 4.51, 4.14, and 3.05, respectively, while generally higher for females, 3.93, 6.53, 5.19, 5.23, 4.37, and 5.27, respectively. The acquired body measurements were utilized to produce barong tailoring patterns and assign standard barong sizes, i.e. Extra Small (XS), Small (S), Medium (M), Large (L), and Extra Large (XL) sizes, to 130 male participants. Notably, only one participant was misallocated by the proposed system as shown in the confusion matrix results. Finally, a website was developed as a proof-of concept for e-commerce potential. Overall, the results illustrated the feasibility of the proposed system.
Malaria Parasite Classification Employing Chan–Vese Algorithm and SVM for Healthcare

Malaria a contagious disease that spreads through Anopheles mosquito bite, it’s deathly infection which if not treated in time can take lives and become an epidemic. According to WHO, it is already one of the major reasons of death in tropical and sub-tropical regions across the globe. Early detection and diagnosis of malaria can help to prevent lot of casualties; however, gold test till date is by microscopy. Different species of plasmodium parasite have evolved over generations, five main that can be listed are: P. falciparum, P. vivax, P. ovale, P. malariae, and P. knowlesi, these parasites affect human adversely. Deadliest among these is P. falciparum which contributes to majority of deaths and has also developed resistances to various drugs employed to prevent the infection. Accurate evaluation of parasite density and, hence, identification of infection till date are done by an expert; which is an exhaustive process and lack accuracy. Paper presents an automated algorithm based on SVM for accurate and early determination of parasite density and infection in red blood cells, the algorithm was designed for automated detection and was tested on sample of 100 images obtained from online UCI repository. An efficiency of 91% percent was achieved on sample infected images utilizing SVM classifier.
Copy-Move Image Forgery Detection Using DCT and ORB Feature Set

The unprecedented use of digital images and videos for communication, the ease of access and use of graphic editing applications have consequently led to the increased importance of detecting copy-move forgery. The proposed copy-move forgery detection (CMFD) technique relies on DCT and ORB feature extraction and distance-based clustering approach. Extracted DCT features are matched based on Euclidean distance. Extracted key-points using ORB are matched using k-NN procedure based on Hamming distances. To improve accuracy, false matches are removed with the help of a distance-based clustering technique. The proposed technique is applied for testing on CoMoFoD small dataset. Results on experimentation showcase that the technique is efficient in detecting copy-move forged regions and also robust towards brightness and contrast change, noise addition, geometric transformations like scaling and rotation and several forgeries. The proposed technique is compared with two state-of-the art techniques.