Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive beamforming based on theoretical interference-plus-noise covariance and direction-of-arrival estimation

A simple and effective adaptive beamforming technique is proposed for uniform linear arrays, which are based on projection processing for covariance matrix construction and desired-signal steering vector estimation. The optimal minimum variance distortion-less response beamformer is closely achieved through approximating the interference-plus-noise covariance matrix by utilising the eigenvalue decomposition of the received signal's covariance matrix. Moreover, the direction-of-arrival (DOA) of the desired signal is estimated by maximising the beamformer output power in a certain angular sector. In particular, the proposed beamformer utilises the aforementioned DOA in order to estimate the desired-signal's steering vector for general steering vector mismatches. Simulation results indicate the better performance of the proposed method in the presence of the different errors compared with some of the recently introduced techniques.
Scratchpad Memory Management For Multicore Real-Time Embedded Systems

Multicore systems will continue to spread in the domain of real-time embedded systems due to the increasing need for high-performance applications. This research discusses some of the challenges associated with employing multicore systems for safety critical real-time applications. Mainly, this work is concerned with providing: 1) efficient inter-core timing isolation for independent tasks, and 2) predictable task communication for communicating tasks. Principally, we introduce a new task execution model, based on the 3-phase execution model, that exploits the Direct Memory Access (DMA) controllers available in modern embedded platforms along with ScratchPad Memories (SPMs) to enforce strong timing isolation between tasks. The DMA and the SPMs are explicitly managed to preload tasks from main memory into the local (private) scratchpad memories. Tasks are then executed from the local SPMs without accessing main memory. This model allows CPU execution to be overlapped with DMA loading/unloading operations from and to main memory. We show that by co-scheduling task execution on CPUs and using DMA to access memory and I/O, we can efficiently hide access latency to physical resources. In turn, this leads to significant improvements in system schedulability, compared to both the case of unregulated contention for access to physical resources, and to previous cache and SPM management techniques for real-time systems. The presented SPM-centric scheduling algorithms and analyses cover single-core, partitioned, and global real-time systems. The proposed scheme is also extended to support large tasks that do not fit entirely into the local SPM. Moreover, the schedulability analysis considers the case of recovering from transient soft errors (bit flips caused by a single event upset) in several levels of memories, that cannot be automatically corrected in hardware by the ECC unit. The proposed SPM-centric scheduling is integrated at the OS level; thus it is transparent to applications. The proposed scheme is implemented and evaluated on a FPGA platform and a Commercial-Off-The-Shelf (COTS) platform. In regards to real-time task communication, two types of communication are considered. 1) Asynchronous inter-task communication, between either sequential tasks (singlethreaded) or parallel tasks (multi-threaded). 2) Intra-task communication, where parallel threads of the same application exchange data. A new task scheduling model for parallel tasks (Bundled Scheduling) is proposed to facilitate intra-task communication and reduce synchronization overheads. We show that the proposed bundled scheduling model can be applied to several parallel programming models, such as fork-join and DAG-based applications, leading to improved system schedulability. Finally, intra-task communication is governed by a predictable inter-core communication platform. Specifically, we propose HopliteRT, a lean and predictable Network-on-Chip that connects the private SPMs.
An analysis of Web image queries for search

This study examines the differences between Web image and textual queries, and attempts to develop an analytic model to investigate their implications for Web image retrieval systems. A large number of Web queries from image and textual search engines were analyzed and compared based on their factual characteristics, query types, and search interests. A feasible analytic model employing the concepts of uniqueness and refinement was adapted to categorize query types and analyze the characteristics of failed queries. Useful results include the findings that image requests may have higher specificity and contain more refined queries (especially among failed queries), and that the queries were refined more by interpretive attributes than by reactive and perceptual attributes. Current text retrieval technology is not capable of fulfilling such complex image requests. It is suggested that there is a need to increase the number of appropriate annotations for Web images and to utilize more advanced retrieval techniques for more effective Web image searching. Few previous large-scale studies have investigated visual information retrieval using image search engines. Thus, this study provides results that might enhance our understanding of Web image searching behavior and suggests implications for the improvement of current Web image search engines.
Measuring the Quality of 3D Visualizations using EEG: a Time-frequency Approach

In this study we have assessed the quality of experience objectively when vertical disparity is introduced in the stereoscopic presentation of images. Four different conditions including a cube in 2D and the same cube in 3D with and without vertical disparities are compared based on the EEG signals recorded from 17 subjects. Two different vertical disparity levels are studied. Event-related potentials (ERPs) corresponding to four conditions are compared in time-frequency domain. The results show an increase in beta power of the occipital region for the images with vertical disparity compared to the 3D image with no vertical disparity. An increase in beta power in this region correlates with an increase in the level of attention which in our case is caused by the vertical disparity component in the stereoscopic images.
Dynasore protects the ocular surface against damaging oxidative stress

“Vital” dyes such as fluorescein and rose bengal are used clinically to evaluate ocular surface health; however, staining mechanisms remain poorly understood. Recent evidence suggests that sublethal cell damage stimulates fluorescein dye uptake. Since damage can also stimulate reparative plasma membrane remodeling, we hypothesized that dye uptake occurs via endocytic vesicles. Using an oxidative stress model, we show that damage to relatively undifferentiated monolayer cultures of human corneal epithelial cells stimulates uptake of fluorescein and rose bengal dyes and also stimulates endocytosis. Importantly, dye uptake was blocked by co-treatment with three different endocytosis inhibitors. Damage to stratified and differentiated corneal epithelial cell cultures, which are a better model of the ocular surface, also stimulated dye uptake; however, endocytosis was not stimulated in this case, and two of the inhibitors did not block dye uptake. The exception was the inhibitor Dynasore and its more potent analogue Dyngo-4a, small molecules that target dynamin family GTPases, but also have off-target effects on the plasma membrane. Significantly, while Dynasore blocked stress-stimulated dye uptake at the ocular surface of ex vivo mouse eyes when treatment was performed at the same time as eyes were stressed, it had no effect when used after stress was applied and the ocular surface was already damaged. Thus, Dynasore could not be working by inhibiting endocytosis. Employing cytotoxicity and western blotting assays, we demonstrate an alternative mechanism, showing that Dynasore is remarkably protective of cells and their surface glycocalyx, preventing damage due to oxidative stress, and thus precluding dye entry. These unexpected and novel findings provide greater insight into mechanisms of vital dye uptake and emphasize the importance of using a differentiated cell culture model for such studies. They also suggest that Dynasore and analogues might be used therapeutically to protect the ocular surface and to treat ocular surface disease.
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.
Communication-Efficient and Privacy-Preserving Data Aggregation without Trusted Authority

Privacy-preserving data aggregation has been extensively studied in the past decades. However, most of these works target at specific aggregation functions such as additive or multiplicative aggregation functions. Meanwhile, they assume there exists a trusted authority which facilitates the keys and other information distribution. In this paper, we aim to devise a communication efficient and privacy-preserving protocol that can exactly compute arbitrary data aggregation functions without trusted authority. In our model, there exist one untrusted aggregator and $n$ participants. We assume that all communication channels are insecure and are subject to eavesdropping attacks. Our protocol is designed under the semi-honest model, and it can also tolerate k (k ≤ $n-2$) collusive adversaries. Our protocol achieves (n - k) -source anonymity. That is, for the source of each collected data aparting from the colluded participants, what the aggregator learns is only from one of the (n - k) non-colluded ones. Compared with recent work [1] that computes arbitrary aggregation functions by collecting all the participants' data using the trusted authority, our protocol increases merely by at most a factor of $O((\frac{\log n}{\log\log n})^{2})$ in terms of computation time and communication cost. The key of our protocol is that we have designed algorithms that can efficiently assign unique sequence numbers to each participant without the trusted authority.
A young couple's grave found in the Rakhigarhi cemetery of the Harappan Civilization

The Harappan Civilization, one of the earliest complex societies in the world, flourished on the Indian subcontinent. Although many additional Harappan settlements and cemeteries have been discovered and investigated, no coupled burials at Harappan cemeteries have been reported to date. In 2013–2016, we excavated the cemetery of the Rakhigarhi site (Haryana), the largest city of the Harappan Civilization. At the site, we found a grave that turned out to be a coupled (joint) burial of the primary type. This report is the first anthropologically confirmed case of coupled burial from a Harappan cemetery.
Load balancing scheme for supporting real-time processing of big data in distributed in-memory systems

In this paper, we propose a new load balancing scheme which performs data migration or replication according to the loading conditions in heterogeneous distributed in-memory environments. The proposed scheme replicates hot data when the hot data occurs on the node where a load occurs. If the loading of the node increases in the absence of hot data, the data is migrated through an adjustment of the storage space. It was shown through various performance evaluations that the proposed load balancing scheme improved the overall load balancing performance.
Student information system satisfaction in higher education: the role of visual aesthetics

Purpose The purpose of this study is to explore the role of visual aesthetics as a key in generating satisfaction in student information system (SIS) users and to discover relationships to other antecedents. This work has also studied how gender discriminates those relationships. Design/methodology/approach DeLone and McLean’s model of information systems success, visual aesthetic and gender socialisation theory are used as a theoretical framework for the study. An explanatory model was proposed based on the previous literature, and then this model was validated using a sample of undergraduate students. Partial Least Squares was chosen as the approach to conduct the statistical analysis. Findings First, the findings show that the variance explained by the proposed model in user satisfaction of SIS is 67 per cent. Second, visual aesthetics has a significant direct effect on user satisfaction of SIS, but the indirect effect is much higher, through its relationship to system quality and information quality. Third, the results indicate that there are significant differences between men and women in the direct relationship between visual aesthetics and user satisfaction of SIS, and this relationship being much more intense in the case of women. Originality/value The study highlights the role of visual aesthetics as a variable that explains user satisfaction in information systems; this fact emphasises on the importance of taking into account aesthetics when designing information systems. In addition, the findings support the existence of a novel relationship between visual aesthetics and information quality. Finally, new evidence on gender differences concerning user satisfaction in information systems is presented; this result indicates in the opposite direction to recent studies that imply a certain homogenisation of gender in relation to the perceptions associated with the technologies of the information.
Comparison of a Bioelectrical Impedance Device against the Reference Method Dual Energy X-Ray Absorptiometry and Anthropometry for the Evaluation of Body Composition in Adults

This study aimed to compare the use of the bioelectrical impedance device (BIA) seca® mBCA 515 using dual X-ray absorptiometry (DXA) as a reference method, for body composition assessment in adults across the spectrum of body mass indices. It explores the utility of simple anthropometric measures (the waist height ratio (WHtR) and waist circumference (WC)) for the assessment of obesity. In the morning after an overnight fast (10 h), 30 participants underwent a body composition DXA (GE iDXA) scan, BIA (seca 515), and anthropometric measures. Compared to the DXA reference measure, the BIA underestimated fat mass (FM) by 0.32 kg (limits of agreement −3.8 kg, 4.4 kg); overestimated fat free mass (FFM) by 0.43 kg (limits of agreement −8.2 kg, 4.3 kg). Some of the variation was explained by body mass index (BMI), as for FM, the mean difference of the normal range BMI group was smaller than for the overweight/obese group (0.25 kg and 0.35 kg, respectively) with wider limits of agreement (−4.30 kg, 4.81 kg, and −3.61 kg, 4.30 kg, respectively). There were significant differences in visceral adipose tissue (VAT) volume measurements between methods with BIA systematically overestimating VAT compared to DXA. WC was more strongly correlated with DXA FM (rho = 0.90, p < 0.001) than WHtR (rho = 0.83, p < 0.001). BIA had some agreement with DXA; however, they are not equivalent measures for the range of BMIs explored, with DXA remaining the more informative tool. WC is a useful and simple assessment tool for obesity.
Light Field Image Compression Based on Deep Learning

In this paper, we propose a novel light field image compression scheme by exploiting the intrinsic similarity of light field images with deep learning. In particular, instead of conveying all LF sub-views, only sparsely sampled LF sub-views are compressed and the remaining sub-views are reconstructed from the coded sub-views in the neighbourhood with convolutional neural network (CNN). To jointly suppress the artifacts induced in compression and reconstruct the un-coded views with high geometric accuracy, a multi-view joint enhancement network is introduced to improve the coding performance. Extensive experiments show the superior compression performance of our scheme compared with the state-of-the-art methods.
Proactive Eavesdropping With Full-Duplex Relay and Cooperative Jamming

In this paper, we consider a proactive eavesdropping scenario, where a central monitor tries to intercept the information exchanged between a pair of suspicious entities through amplify-and-forward full-duplex relays and a cooperative jammer. Specifically, the eavesdropping relays simultaneously listen to the suspicious transmitter and forward the eavesdropped information to the central monitor. At the same time, the jammer broadcasts jamming signals to maintain the data rate of the suspicious users below the channel capacity of the central monitor so that the eavesdropped information can be successfully decoded by the central monitor. In this system, we jointly design the receive combining vector at the central monitor, the relay precoders at the eavesdropping relays and the transmit covariance matrix at the jammer to maximize the eavesdropping rate. First, we examine the case of a single eavesdropping relay equipped with a single antenna and provide some useful insights. Also, an effective two-layer optimization method is proposed to obtain the globally optimal solution. Then, we study a general case of multiple eavesdropping relays with multiple antennas, and solve a semi-definite relaxation problem. Numerical results verify the efficiency of our proposed solutions for both single and multiple relay cases and show considerable performance gains over conventional schemes.
Context-Aware Mouse Behavior Recognition Using Hidden Markov Models

Automated recognition of mouse behaviors is crucial in studying psychiatric and neurologic diseases. To achieve this objective, it is very important to analyze the temporal dynamics of mouse behaviors. In particular, the change between mouse neighboring actions is swift in a short period. In this paper, we develop and implement a novel hidden Markov model (HMM) algorithm to describe the temporal characteristics of mouse behaviors. In particular, we here propose a hybrid deep learning architecture, where the first unsupervised layer relies on an advanced spatial-temporal segment Fisher vector encoding both visual and contextual features. Subsequent supervised layers based on our segment aggregate network are trained to estimate the state-dependent observation probabilities of the HMM. The proposed architecture shows the ability to discriminate between visually similar behaviors and results in high recognition rates with the strength of processing imbalanced mouse behavior datasets. Finally, we evaluate our approach using JHuang’s and our own datasets, and the results show that our method outperforms other state-of-the-art approaches.
mSNP: A Massively Parallel Algorithm for Large-Scale SNP Detection

Single Nucleotide Polymorphism (SNP) detection is a fundamental procedure of whole genome analysis. SOAPsnp, a classic tool for detection, would take more than one week to analyze one typical human genome, which limits the efficiency of downstream analyses. In this paper, we present mSNP, an optimized version of SOAPsnp, which leverages Intel Xeon Phi coprocessors for large-scale SNP detection. Firstly, we redesigned the essential data structures of SOAPsnp, which significantly reduces memory footprint and improves computing efficiency. Then we developed a coordinated parallel framework for a higher hardware utilization of both CPU and Xeon Phi. Also, we tailored the data structures and operations to utilize the wide VPU of Xeon Phi to improve data throughput. Last but not the least, we proposeed a read-based window division strategy to improve throughput and obtain better load balance. mSNP is the first SNP detection tool empowered by Xeon Phi. We achieved a 38x single thread speedup on CPU, without any loss in precision. Moreover, mSNP successfully scaled to 4,096 nodes on Tianhe-2. Our experiments demonstrate that mSNP is efficient and scalable for large-scale human genome SNP detection.
StaCover: Mobile Energy-Sharing Cabinets Deployment with Public Bike System

To enhance environmental sustainability and fulfill metropolitan public traffic demands, some metropolises are developing a kind of new Bike-Energy System (BES) including the existing Public Bike System (PBS) and Mobile Energy-sharing System (MES), which consists of some batteries-loaded cabinets. Cabinet location selection is expected to facilitate users to access battery in nearby cabinet. Meanwhile, the limited budget cannot afford establish a cabinet for each station. It thus brings up a problem: which bike stations should be selected to deploy the cabinets so as to minimize the deployment cost and satisfy that all users can rent and return battery conveniently from at least one nearby bike station on the way riding. This paper formulates the bike station selection problem as the Set Cover Problem and proposes a novel data-driven method—StaCover accordingly. StaCover presents the Density-Based Stations Clustering algorithm (DBSC) to select the candidate stations and then designs a Greedy Heuristic Selection algorithm (GHS) to determine the final stations to deploy the energy-sharing cabinets. Our experiments adopt diversified parameters to demonstrate the effectiveness of StaCover over the other methods
Feature Extraction and Classification of Speech Signal Using Hidden Markov-Gaussian Mixture Model (HM-GMM) for Driving the Rehabilitative Aids

For the last few decade, an increase in experimental research has been carried-out to help the people with different-level of disabilities, who seek help towards the mobility or movement or transport aids. Hence, in our study, we have developed a system which has the ability to classify the human speech signal into corresponding commands for triggering a rehabilitative aid such as wheelchair for helping the people seek for a movement from one place to other. For this purpose, we have introduced a classification model which uses hidden Markov-Gaussian mixture, for classifying a speech signal especially the words for driving a wheelchair. To improve the efficiency of the proposed classification model, the classification was performed based on the features extracted from the speech signal. Ten volunteers were used in this study to validate the result and it is found to have an accuracy of about 98% to classify the word into their corresponding command. Hence, we propose this model as a good tool for helping the people with differently-disabled people.