Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender Recognition Based on Social Networks for Multimedia Production

The proliferation of social media enables the development of innovative services for extracting insights about audience behaviour dynamics. Social media analytics allow the identification of demographics and interests of audiences that are valuable data considered by content producers, in principle driving the overall multimedia production process. This paper introduces and evaluates a lightweight and efficient mechanism for identifying Twitter users' gender that is coupled with content trend discovery and is exploited within multimedia production.
Attention Guiding Using Augmented Reality in Complex Environments

The localization of objects or locations in an environment is an essential task relevant for many work processes. An augmented reality (AR)-based assistance system may support users by guiding their attention towards the relevant targets. This will reduce the time needed for visual search and reduce errors, such as wrongly picked items or false placements. The design of proper attention guiding techniques is thus one area of research in augmented assistance. We developed a number of new attention guiding techniques and evaluated them in several experiments together with recent or classic existing techniques in varying picking scenarios. In our current work, we adapted a standardized assembly scenario to a more complex environment including occlusions to provide a scenario supporting reproducibility of our results. In the research demo, a number of visual and acoustic guiding techniques can be tested and combined. The demonstration is implemented on the Microsoft HoloLens.
On-Line Blind Unmixing For Hyperspectral Pushbroom Imaging Systems

In this paper, the on-line hyperspectral image blind unmixing is addressed. Inspired by the Incremental Non-negative Matrix Factorization (INMF) method [2], we propose an on-line NMF which is adapted to the acquisition scheme of a pushbroom imager. Because of the non-uniqueness of the NMF model, a minimum volume constraint on the endmembers is added allowing to reduce the set of admissible solutions. This results in a stable algorithm yielding results similar to those of standard off-line NMF methods, but drastically reducing the computation time. The algorithm is applied to wood hyperspectral images showing that such a technique is effective for the on-line prediction of wood piece rendering after finishing.
Family shopping recommendation system using behavior sequence data and user profile

With the arrival of the big data era, recommendation system has been a hot technology for enterprises to streamline their sales. Recommendation algorithms for individual users have been extensively studied over the past decade. Most existing recommendation systems also focus on individual user recommendations, however in many daily activities, items are recommended to the groups not one person. As an effective means to solve the problem of group recommendation problem, we extend the single user recommendation to group recommendation. Specifically we propose a novel approach for family-based shopping recommendation system. We use the dataset from the real shopping mall consisting of shopping records table, client-profile table and family relationship table. Our algorithm integrates user behavior similarity and user profile similarity to build the user based collaborative filtering model. We evaluate our approach on a real-world shopping mall dataset.
Prevalence of suicide attempt in individuals with major depressive disorder: a meta-analysis of observational surveys

Abstract Background Suicide attempt (SA), which is one of the strongest predictors of completed suicide, is common in major depressive disorder (MDD) but its prevalence across epidemiological studies has been mixed. The aim of this comprehensive meta-analysis was to examine the pooled prevalence of SA in individuals with MDD. Methods A systematic literature search was conducted in PubMed, Embase, PsycINFO, Web of Science and Cochrane Library from their commencement date until 27 December 2017. Original studies containing data on prevalence of SA in individuals with MDD were analyzed. Results In all, 65 studies with a total of 27 340 individuals with MDD were included. Using the random effects model, the pooled lifetime prevalence of SA was 31% [95% confidence interval (CI) 27–34%], 1-year prevalence was 8% (95% CI 3–14%) and 1-month prevalence was 24% (95% CI 15–34%). Subgroup analyses revealed that the lifetime prevalence of SA was significantly associated with the patient setting, study region and income level, while the 1-month prevalence of SA was associated with only the patient setting. Conclusion This meta-analysis confirmed that SA was common in individuals with MDD across the world. Careful screening and appropriate interventions should be implemented for SA in the MDD population.
Probability-Based Optimisation for a Multi-MHz IPT System with Variable Coupling

This paper presents the analysis and design of a dynamic inductive power transfer (IPT) system, in which coupling is treated as a stochastic variable and is therefore modelled as a probability distribution. The purpose of this formulation is to optimise the tuning of the inverter and the rectifier to the coupling value that achieves the highest charging energy-efficiency when operating at a broad range of coupling. The analysis is supported by a case study in which two rectifier designs, using the hybrid Class E topology, are tuned at different coupling values in order to verify which version achieves the highest charging efficiency. The load in the experiments is a wirelessly powered drone without a battery hovering randomly over the charging pad, and the range of motion is set by a nylon string tether. The experiments show lower energy consumption when the rectifier is tuned to present the optimal load of the link at the coupling value with the highest probability, as opposed to the first, which was designed to present the optimal load of the link at minimum coupling.
A PIR Scheme to Improve the Computation Cost on the Client-Side of Smartphone Applications

We present an approach to retrieve data from the database by using Private Information Retrieval (PIR). PIR is a cryptographic database technique that solves the seemingly impossible problem of allowing the user to query a database while the content of the user's query is protected from the database server. Different types of PIR-based approaches have been proposed during the last two decades. The common criticism against PIR approaches is that the computational overhead is not suitable for the smartphones' hardware with limited resources and therefore, they are not practical. The main focus of this paper is to reduce the computation cost of decoding the received response(s) from the database on the client side to make PIR more practical for the smartphone applications.
A Module for Protecting Data Location Privacy on Mobile Devices

when downloading an application by a smartphone user, the application asks the user to accept a set of permissions allowing it to access sensitive information on the phone. It is mostly unclear how, when, and why such data will be used. openPDS is a framework that was proposed to limit such privacy invasion. Although openPDS protects the user’s raw data, sensitive personal information, such as location trace, can still be inferred by the service provider by analyzing the accumulated answers. In this paper, we aim to append openPDS with a module that prevents the service provider from reconstructing the trace of users. This module abides with the QoS requirements necessitated by the provider, and defined as the tolerance of the application to inaccurate answers. Our approach was tested on 10 users whose locations traces were recorded for 10 months. Results show that no user trace was successfully reconstructed even when high QoS levels were required.
Ladder-Type Mid-Range Wireless Power Transfer

In order to improve the power transfer efficiency and ensure the transfer distance as long as possible in the resonance wireless power transfer system, a new ladder-type pattern is proposed. Based on the mutual inductance coupling theory, a new methodology for ensuring the maximum transfer efficiency is presented. The theoretical proof and the conditions for meeting the objective are derived and practically verified in a practical prototype. The experimental results have confirmed the mid-range and high-efficiency power transfer capability.
Design and Dynamic Modeling of Variable Stiffness Joint Actuator Based on Archimedes Spiral

Traditional rigid actuators are not applicable to robot applications involving explosive actions. Solutions suggested by previous studies have mainly focused on the concept of stiffness variation, its design and verification, and the explosive actions of variable stiffness joint actuators. This paper proposes a variable stiffness actuator that features a leaf spring as the elastic element and a pair of disks with two Archimedes spiral grooves as the stiffness adjusting mechanism, maintaining $\phi 114\,\,\text {mm}\times 130$ mm overall dimensions and a full range (i.e., 127–2095 Nm/rad) of stiffness variation driven by a 7.2-W motor and, therefore, creating a competitive advantage in both compactness and energy saving capacity. The proposed actuator is intended as a dynamics model to predict the exact kicking speed under any given initial conditions of joint stiffness, kicking time, and load mass and for uses in the joints of industrial and/or service robots that frequently involve explosive actions.
Algorithm and Realization of the Key Modules for IQ-Separated Receiver Structure

The satellite communication systems continually increase the bandwidth to support high-throughput applications. However, the sampling rate of the analog-digital converter (ADC) limits the performance of the system. A dedicated receiver structure is proposed for IQ-separated sampling to support ultra-high sampling rate. This paper designs the key modules of the structure, inclnding frequency error estimation, frame location and timing synchronization. The algorithm for the modules is descripted and the details of realization are focused. The simulation results demonstrate reasonable performance of the designed modules.
Product and Service Popularity Analysis on Instagram

Social media has changed the world greatly and become the significant part of our modern civilization. Investigating information on SNS can help us to understand customers' preferences. For instance, we can analyze customers' experiences of certain products or services, how they are shared, how they affect other customers, etc. In this paper, we analyze how certain products or services are posted onto Instagram, one of the largest social networks, and how they evolve by analyzing tags and image features. We also analyze temporal evolution of certain tags, which can provide different perspectives when analyzing certain products.
Modeling EV fleet Load in Distribution Grids: A Data-Driven Approach

This paper proposes a modeling method for electric vehicle (EV) charging loads in the distribution grids. Different from previous work that modeling under general car travel distance based statistics, we advocate using real world power consumption data, which collected from the charging port meters. The essential charging behavior characteristics were retrieved from such high-resolution data. The vehicle behavior indicates that the distribution of the initial SOC when EV put into charge is not necessarily Iognormal type. Possible causes for such non-ideality are discussed. The new model proposed here can incorporate the stochastic nature of EV charging to improve researchers’ analysis. An explicit description of the model along with its operating dynamics and a practice to analyze the total power load of a mid-sized EV fleet is provided. We demonstrate that the proposed model can more correctly reflect the total power need of a fleet and can be adopted as a load-forecasting tool of EV fleet charging load.
Personalized Pricing for Efficient User-Centric Multi-Resource Control in 5G Wireless Networks

In this paper an analytical framework for the joint allocation of multiple physical resources under a dynamic personalized pricing setting, in a NOMA wireless network, is designed. Under our proposed user-centric paradigm, price is treated as a resource itself rather than simply being a control parameter and represents the user willingness to pay towards obtaining certain QoS levels. Each user expresses her satisfaction with respect to her and other users choices through a specifically designed utility function based on her unique characteristics and preferences. The resource allocation problem under consideration, becomes a distributed utility maximization problem, where each user updates her controllable parameters in an autonomous manner targeting at her satisfaction maximization. The problem is modeled and solved as a multivariable non cooperative game, admitting a unique Nash Equilibrium (NE), whose convergence is reached via a distributed and low complexity algorithm. Detailed numerical results, clearly demonstrate that the proposed framework allows the users to better exploit the system's resources, and therefore improve their overall satisfaction, while achieving significant improvements in the system operation in terms of power savings and achievable data rate.
Noise and Vibration Performance in Fractional Slot Permanent Magnet Synchronous Machines Using Stator Bridge

This research analyzes the use of stator bridges to reduce noise and vibration (NV) in fractional slot surface mounted permanent magnet synchronous machines (PMSM) having 12-stator slots and 10-rotor poles (12s/10p). Several stator bridge designs in the stator geometry are proposed and investigated for NV reduction. Then structural-harmonic and acoustic analysis were used to quantify the comparative vibration and acoustic noise performance of the investigated design features.
The Human SLC1A5 (ASCT2) Amino Acid Transporter: From Function to Structure and Role in Cell Biology

SLC1A5, known as ASCT2, is a neutral amino acid transporter belonging to the SLC1 family and localized in the plasma membrane of several body districts. ASCT2 is an acronym standing for Alanine, Serine, Cysteine Transporter 2 even if the preferred substrate is the conditionally essential amino acid glutamine, with cysteine being a modulator and not a substrate. The studies around amino acid transport in cells and tissues began in the ‘60s by using radiolabeled compounds and competition assays. After identification of murine and human genes, the function of the coded protein has been studied in cell system and in proteoliposomes revealing that this transporter is a Na+ dependent antiporter of neutral amino acids, some of which are only inwardly transported and others are bi-directionally exchanged. The functional asymmetry merged with the kinetic asymmetry in line with the physiological role of amino acid pool harmonization. An intriguing function has been described for ASCT2 that is exploited as a receptor by a group of retroviruses to infect human cells. Interactions with scaffold proteins and post-translational modifications regulate ASCT2 stability, trafficking and transport activity. Two asparagine residues, namely N163 and N212, are the sites of glycosylation that is responsible for the definitive localization into the plasma membrane. ASCT2 expression increases in highly proliferative cells such as inflammatory and stem cells to fulfill the augmented glutamine demand. Interestingly, for the same reason, the expression of ASCT2 is greatly enhanced in many human cancers. This finding has generated interest in its candidacy as a pharmacological target for new anticancer drugs. The recently solved 3D structure of ASCT2 will aid in the rational design of such therapeutic compounds.
Achieving effective and efficient attributed graph data management using lucene

How to manage graph data which have complex graph structure reasonably and efficiently is a big challenge. Graph database becomes a new choice due to its natural graph processing ability. Nevertheless, it is still in the development stage with some problems that need to be solved. For instance, the formal definition of attributed graph model, the query efficiency and effectiveness remain to be perfected. In this paper, we make a formal definition of the attributed graph model, and implement an attributed graph database MyGraphDB based on Lucene which is a high-performance, full-featured text search engine library. The introduction of Lucene not only brings the improvement of query efficiency, but also takes advantage of the abundant attribute information of the nodes and edges. We compare our MyGraphDB with another two graph databases SparkSee and Neo4j. The results show that MyGraphDB has obvious advantages in terms of efficiency and the diversity of operations.
A Study on Robot Motions Inducing Awareness for Elderly Care

In recent years, there are many applications based on IT for watching the elderly due to the increase of elderly population. Watching and medical care for the elderly is one of the promising application fields in IoT. Among them, detecting and preventing the risk of indoor heat stroke is an important topic. It is a crucial issue on how effectively notice the danger of heat stroke to the elderly. In this paper, we describe our trial for alerting the risk of heat stroke to the elderly people using a communication robot. We design and verify what kinds of robot motions are effective to solve the risk warning problem.
Graphless: Using Statistical Analysis and Heuristics for Visualizing Large Datasets

Data visualisation over semantic datasets and Knowledge Graphs becomes a challenging task as the data volume increases. The amount of nodes and edges linking them produce overwhelming diagrams in which understanding the information displayed is cumbersome. In this work we introduce Graphless, a visualisation interface that exploits the statistical information of the usage of instances and relations to produce graphical summaries of entities. The system supports several heuristics, providing different visualisations, according to configurable parameters. 2
A Distributed Social-Aware Clustering Approach in D2D Multicast Communications

Device-to-Device (D2D) multicast communication is becoming a promising technology to improve local area service. In this paper, we present a distributed social-aware clustering method to group the content requesters (CRs) into clusters. Specifically, a cluster head (CH) selection algorithm based on social maximum weight (SMW) is firstly proposed to select the CHs from the original CRs. After that, we propose a cluster formation optimization framework via a non-transferable utility coalition formation game (CFG), and a distributed coalition formation algorithm for clustering is proposed based on preference relationship and switch operations. Moreover, the final coalition structure is proved to be Nash-stable. Numerical results are presented to verify the effectiveness of our proposed schemes.
Perceptual Hashing of Cyclostationary Signal with Sparse Coding

Cyclostationarity is the most common property displayed in the monitored signals during a machine condition monitoring process, especially for rotating and reciprocating machines. Redundancy in cyclostationary signals is also obvious, not only because these signals are usually sampled with high frequency, but also the machine health information remains the same and stable for a period of time. Thus, the transmission, storage and computation with these signals would unnecessarily consume more resources, power and costs. In order to reduce the redundancy, optimize the condition monitoring process, a new framework is introduced based on perceptual hashing, and sparse coding is proposed as an effective method to generate machine condition hash (MCH). The verification with bearing vibration signals shows that the data dimensionality can be reduced while diagnostic information can be retrieved. Finally, bandwidth consumption and data storage space can be reduced, while the diagnostic accuracy can still be guaranteed.
Run-Time Detection of Protocol Bugs in Storage I/O Device Drivers

Protocol violation bugs in storage device drivers are a critical threat for data integrity, since these bugs can silently corrupt the commands and data flowing between the OS and storage devices. Due to their nature, these bugs are notoriously difficult to find by traditional testing. In this paper, we propose a run-time monitoring approach for storage device drivers, in order to detect I/O protocol violations that would otherwise silently escalate in corruptions of users’ data. The monitoring approach detects violations of I/O protocols by automatically learning a reference model from failure-free execution traces. The approach focuses on selected portions of the storage controller interface, in order to achieve a good tradeoff in terms of low performance overhead and high coverage and accuracy of failure detection. We assess these properties on three real-world storage device drivers from the Linux kernel, through fault injection and stress tests. Moreover, we show that the monitoring approach only requires few minutes of training workload, and that it is robust to differences between the operational and the training workloads.
A Study on the Learning Based Human Pose Recognition

Human pose recognition is considered a well-known process of estimating the human body pose from a single image or a series of video frames. There exist many applications that can benefit from human pose technology e.g. activity recognition, human tracking, 3D gaming, character animation, clinical analysis of human gait and other HCI applications. Due to its many challenges, such as illumination, occlusion, outdoor environment and clothing, it is considered one of the active areas in computer vision. For the last 15 years, Human pose recognition problem significantly gained interest of many researchers and therefore, many techniques were proposed in order to address the challenges of human pose recognition. In this study, we review the recently progressed work in human pose recognition using computer vision feature extraction and machine learning classification techniques. Accordingly, we identify gaps in existing work and give direction for future work.
The Fluorescent D-Amino Acid NADA as a Tool to Study the Conditional Activity of Transpeptidases in Escherichia coli

The enzymes responsible for the synthesis of the peptidoglycan (PG) layer constitute a fundamental target for a large group of antibiotics. The family of β-lactam antibiotics inhibits the DD-transpeptidase (TPase) activity of the penicillin binding proteins (PBPs), whereas its subgroup of carbapenems can also block the TPase activity of the LD-TPases. D-Ala fluorescent probes, such as NADA, are incorporated into the PG presumably by TPases in Escherichia coli and can be used to study conditions that are required for their function. Of all LD-TPases of E. coli, only LdtD was able to incorporate NADA during exponential growth. Overproduction of LdtD caused NADA to be especially inserted at mid cell in the presence of LpoB-activated PBP1b and the class C PBP5. Using the NADA assay, we could confirm that LpoB activates PBP1b at mid cell and that CpoB regulates the activity of PBP1b in vivo. Overproduction of LdtD was able to partly compensate for the inhibition of the cell division specific class B PBP3 by aztreonam. We showed that class A PBP1c and the class C PBP6b cooperated with LdtD for NADA incorporation when PBP1b and PBP5 were absent, respectively. Besides, we proved that LdtD is active at pH 7.0 whereas LdtE and LdtF are more active in cells growing at pH 5.0 and they seem to cooperate synergistically. The NADA assay proved to be a useful tool for the analysis of the in vivo activities of the proteins involved in PG synthesis and our results provide additional evidence that the LD-TPases are involved in PG maintenance at different conditions.