Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Recommendation With Large-Scale Group Decision-Making for Cyber-Enabled Online Service

Along with the development of several emerging computing paradigms and information communication technologies, it is said that cyber computing technology is playing an increasingly important role across cyber-related systems and applications. In this article, we focus on cyber–social computing and propose a computational model that integrates large-scale group decision-making (LSGDM) into social recommendations for cyber-enabled online services. As a concrete application example, a graph model is built to describe the LSGDM problem among researchers in scholarly big data environments. Following the basic profiling to describe decision-makers within scholarly networks, measures are defined to evaluate one researcher’s academic performance and research outcome and further quantify correlations between them based on their collaboration relationships in a constructed network model. A two-stage large-scale decision-making solution is then proposed for social recommendations: A network partition algorithm is developed based on the identification of experts along with their influence extending to a group of researchers, and a random walk with the restart-based algorithm is improved to calculate the weighted decisions for group decision aggregation and alternative ranking. Experiments using the real-world data demonstrate the usefulness and effectiveness of our proposed model and method, which can provide the target researcher with more reliable recommendations.
Multichannel Speech Enhancement Based On Time-Frequency Masking Using Subband Long Short-Term Memory

We propose a multichannel speech enhancement method using a long short-term memory (LSTM) recurrent neural network. The proposed method is developed in the short time Fourier transform (STFT) domain. An LSTM network common to all frequency bands is trained, which processes each frequency band individually by mapping the multichannel noisy STFT coefficient sequence to its corresponding STFT magnitude ratio mask sequence of one reference channel. This subband LSTM network exploits the differences between temporal/spatial characteristics of speech and noise, namely speech source is non-stationary and coherent, while noise is stationary and less spatially-correlated. Experiments with different types of noise show that the proposed method outperforms the baseline deep-learning-based full-band method and unsupervised method. In addition, since it does not learn the wideband spectral structure of either speech or noise, the proposed subband LSTM network generalizes very well to unseen speakers and noise types.
NATURAL LANGUAGE QUESTION ANSWERING SYSTEM OVER LINKED DATA

Linked Data project is aimed to give more details on any subject through the big knowledge bases defined on the web. In this context, knowledge bases offer endpoint service user interfaces to query their data. Because of the SPARQL query language limitation of these knowledge bases, a significant number of web users are unable to benefit from these services. In this paper, an English natural language question answering system over Linked Data is proposed in order to eliminate this limitation. The proposed system's main processes can be listed as follows: (1) Extracting Part-Of-Speech (POS) tags, (2) pattern extraction & preparing appropriate SPARQL queries, (3) executing user queries & displaying the results. The features which are not provided by the endpoint services of knowledge bases such as dynamic paging, voice search and answer vocalization which make the usage of the proposed system to be possible by the visually-impaired web users, question-answer caching, social media integration, and live spell checking are proposed. According to experimental results, the proposed system’s question answering performance is improved between 2 and 12 times through the type of natural language question thanks to the question-answer caching mechanism.
Radio and Computing Resource Allocation for Minimizing Total Processing Completion Time in Mobile Edge Computing

Due to explosive growth in mobile applications and services with different requirements, the concept of mobile edge computing (MEC) has emerged. For MEC, a mobile user (MU) and a MEC server need to exchange tasks using limited radio resources. Furthermore, when multiple MUs possess tasks, the MEC server has to handle multiple tasks simultaneously. Thus, the radio and computing resources need to be allocated to MUs by taking into account the wireless channel condition and the computing power of MUs and the MEC server. In this paper, a radio resource and computing resource allocation scheme is proposed to minimize the total processing completion time of all the tasks. Each task is assumed to be divided into local task and offload task. The local task is processed by each MU while the offload task is processed by a MEC server. We first formulate the optimization problem to minimize the total processing completion time of all tasks. To solve the formulated optimization problem, we propose a two-step radio and computing resources allocation scheme which iteratively performs bisection search method and Johnson’s algorithm. The numerical results elucidate that the proposed scheme can reduce the total processing completion time by about 25% on average compared to the conventional schemes when multiple MUs have divisible tasks.
A Novel ISAR Imaging Method for Maneuvering Target Based on AM-QFM Model Under Low SNR Environment

Inverse synthetic aperture radar (ISAR) imaging for long range maneuvering small/stealth target plays an important role in the field of national defense surveillance. Due to low signal-to-noise (SNR) of received echo signal and the nonstationary Doppler frequency modulated (DFM) during the coherent processing intervals (CPI), the conventional range-Doppler (RD) algorithm is unsuitable to generate a well-focused ISAR image. Furthermore, the dihedral or trihedral components of the maneuvering target or range cell migration caused by the target’s complex motion generate the time-varying amplitudes for the received signal, which brings a new challenge for ISAR imaging algorithm design. To overcome those issues, in this work, we propose a new method to obtain the well-focused ISAR image of long range maneuvering small/stealth target. This method consists of two main steps. First, the received signal in a range bin is characterized as multicomponent amplitude modulated-quadratic frequency modulated (AM-QFM) signal with time-varying amplitudes after the translational compensation. After that, a novel coherently integrated general cubic phase function (CIGCPF) is developed to estimate the parameters of multi-component AM-QFM signal. Compared with the existing ISAR imaging methods based on the constant amplitude signal model during the CPI, the well-focused ISAR image can be obtained by the proposed method with time-varying amplitude in a low SNR environment. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.
Influence of Cleat Design on Knee Joint Moments During Cutting Maneuver and Instep Kick in Soccer

LEVEL OF EVIDENCE: 4 SUMMARY Background. This study aimed to compare the influence of different cleat designs on specific soccer movements. Methods. The sample consisted of 10 recreational players who performed cutting and kicking movements using soccer shoes with three different cleat shapes: C1 (Circular), C2 (Rectangular) and C3 (Mixed). Kinematic data were collected using the Vicon system and an AMTI force platform. The moments were calculated by the Nexus System and the peaks extracted in Matlab at every 10% of the stance phase. Repeated measures ANOVA and the Bonferroni post-hoc test were conducted at α<0.05 for statistical analysis. Results. C1 exhibited a higher external rotation moment (P<0.05) at the onset of the stance phase than C2 and C3. In the kicking movement, C3 showed a higher valgus moment (P<0.05) than C2 and C1 in the first 10% of stance. C3 also obtained a higher internal rotation moment (P<005) at 60% of stance. Conclusions. These results suggest that C1 may offer greater stability at the onset of the stance phase. In kicking, the different physical traits of C3 allow faster transition and deeper soil penetration. Soccer cleats influence the knee joint moment during cutting movements and kicking. These results are important form to reduce the risk of injury.
Theory of Mind as a Mediator of Reading Comprehension Differences between Chinese School-Age Children with Autism and Typically Developing Peers

ABSTRACT This study examined the relation between Theory of Mind (ToM) and reading comprehension in 42 7- to 9-year-old Hong Kong Chinese children with autism and 55 typically developing peers (TD) who were comparable in age, nonverbal intelligence, and working memory. Relative to their TD peers, children with autism exhibited difficulties with reading comprehension and advanced ToM tasks, but not word reading and basic ToM tasks. After controlling for nonverbal intelligence, working memory, word reading, and vocabulary knowledge, ToM partially mediated the relation between group status (autistic or not) and reading comprehension. This mediation was significant for non-literal comprehension skills (i.e., those involving inference, evaluation, and mentalization) but not for literal comprehension skills (i.e., those involving simple recall and recognition of textually explicit information). These findings indicate that ToM partially explains the differences in non-literal reading comprehension between children with autism and their TD peers.
Hemodynamic interaction between portal vien and hepatic artery flow in small‐for‐size split liver transplantation

Abstract In split‐liver transplantation, the entire portal flow is redirected through relatively small‐for‐size grafts. It has been postulated that excessive portal blood flow leads to graft injury. In order to elucidate the mechanisms of this injury, we studied the hemodynamic interactions between portal vein‐and hepatic artery flow in an experimental model in pigs. Six whole pig liver grafts were implanted in Group 1 (n=6) and six whole liver grafts were split into right and left grafts and transplanted to Groups 2 (n=6) and 3 (n=6), respectively. The graft‐to‐recipient liver volume ratio was 1:1, 2:3 and 1:3 in Groups 1, 2 and 3, respectively. Portal vein‐and hepatic artery flows were measured with an ultrasonic flow meter at 60,120 and 180min after graft reperfusion. Portal vein pressure was also recorded at the same time intervals. Graft function was assessed at 3,6h and 12h, and morphological changes at 12h after reperfusion. Following reperfusion, portal vein flow showed an inverse relationship to graft size, while hepatic artery flow was reduced proportionately to graft size. The difference was significant among the three groups (P<0.05). Portal vein pressure was significantly higher in group 3, compared to groups 1 and 2 (P<0.05). Hepatic artery buffer response was significantly higher in Group 3, compared to Groups 1 and 2 in relation to preocclusion values (P<0.05). Split‐liver transplantation, when resulting in small‐for‐size grafts, is associated with portal hypertension, diminished arterial flow, and graft dysfunction. Arterial flow impairment appears to be related to increased portal vein flow.
Increasing Visibility of IEC 104 Communication in the Smart Grid

Energy systems like smart grids are part of critical infrastructure and their interruption or blackout may have fatal consequences on energy production, distribution, and eventually the life of individual people. In order to secure communication in Industrial Control Systems (ICS) and detect cyber attacks on smart grids, we need to increase visibility of ICS communication so that an operator can see what commands are sent between ICS devices. Security monitoring of ICS transmission requires (i) retrieving monitoring data from ICS packets, (ii) processing and analyzing extracted data, (iii) visualizing the ongoing communication to the operator. The proposed work presents a concept of ICS flow monitoring system that extracts meta data from ICS packet headers and creates ICS flow records similarly to Netflow/IPFIX system. ICS flows represent communication in the smart grid network that is further visualized using dashboard and communication charts. Unlike traditional monitoring approach that works with network and transport layer data only, we extend flow monitoring to application layer with focus on ICS protocols. The proposed approach is demonstrated on monitoring IEC 60870-5-104 communication.
Optimization and Machine Learning

Problem 1 (20%) Consider the function f(x1, x2) = (x1 + x 2 2) 2 At the point xk = [1, 0] T , find (a) the gradient descent direction (b) xk+1 by exact line search on the gradient descent direction (c) the Newton direction (d) xk+1 by exact line search on the Newton direction Problem 2 (40%) Consider the following quadratic function: f(x) = 1 2 xQx− bx Assume Q is symmetric and positive definite. (a) What’s the gradient of f(x)?
Learning Analytics as AI Extenders in Education: Multimodal Machine Learning versus Multimodal Learning Analytics

The nature of the appropriate role for AI is a topic of great interest in many disciplines, and Education is no exception. Perhaps, the initial focus of AI in Education research was on attempts to create systems that are as perceptive as human teachers [1]. Therefore, the majority of early research in the field focused on designing autonomous intelligent tutoring systems. However, more recently, there have been AI technologies embedded in non-autonomous systems, used by educators to support their practice. Non-autonomous systems, in which AI is used to extend human cognition and enhance teacher and learner capabilities, differ significantly from approaches that aim to create fully automated AI systems. These non-autonomous approaches might even be considered as ‘incomplete’ or ‘inadequate’ in AI research. Here, I argue that, in educational contexts, AI systems should be considered a continuum with regards to the extent they are decoupled from humans, rather than only an approach to provide full-automation. AI can be used to externalize, internalize or extend human cognition [2], and these different conceptualizations and implementations of AI can each have a valuable role to play in the support of learning and teaching. In this paper, I will briefly describe the distinctions between AI as a fully autonomous system versus AI as part of a non-autonomous supportive system, and present examples of both in educational contexts. I will particularly focus on multimodal learning analytics where the human cognition is internalised or extended with AI tools, rather than externalised. For educational research, where the ultimate purpose is to improve education rather than improving the state-of-the-field in AI, AI extenders as exemplified in learning analytics research should be distinct from research on fully autonomous AI designs; and they require attention from the field at least in equal measure.
Robust watermarking technique using different wavelet decomposition levels for signature image protection

This paper proposed a non-blind watermarking technique based on different wavelet decomposition levels for biometric image protection.In this technique, a biometric image is used as a watermark instead of a standard image, logo or random noise pattern type watermark.For watermark embedding, the original host image and the watermark biometric image are transformed into various levels of wavelet coefficients.The watermark biometric image is embedded into the host image by modifying the values of the wavelet coefficients of the host image using the values of wavelet coefficients of the watermark biometric image.Experimental results demonstrated that the proposed technique was able to withstand various watermarking attacks. The novelty of the proposed technique is that it is used to transform coefficients of the watermark biometric image instead of the Pseudo Noise sequences or any other feature extraction technique.
Theoretical and Experimental Evaluation of the Two-Level Processor Sharing Discipline for TCP Flows

Size-based scheduling policies have been widely studied in the literature, and their interest in networking applications has been huge in the last decade. These policies consist in deciding the priority of the packets belonging to a certain flow based on the service time that the flow has received up to a certain epoch or, when possible, on the remaining service time. The scientific literature has devoted many efforts to the comparison and analysis of these disciplines either by simulation or by analytical models although real-world implementations can have different performance for numerous reasons. In this paper, we consider the two-level processor sharing discipline (2LPS), i.e., packets are served with high priority if they belong to a flow that has required less than a work up to that moment, where a is a threshold-parameter of the model. The goal is that of assessing the performance of this discipline once implemented in a real router with a real-world network traffic and compare these measurements with the performance indices obtained by the queueing model. To characterise the networks traffic, we fit two datasets with an acyclic phase-type distribution thanks to an existing tool and then transform the resulting distribution into a generalised hypergeometric distribution. Our experiments confirm that the 2LPS improves the flow expected response time with respect to the standard scheduling by taking advantage of the heavily tailed distribution characterising the TCP flow sizes, but this improvement seems slightly smaller than what predicted by the analytical models.
Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization

This technical report investigates the potential of Convolutional Neural Networks to post-process images from primary atomization. Three tasks are investigated. First, the detection and segmentation of liquid droplets in degraded optical conditions. Second, the detection of overlapping ellipses and the prediction of their geometrical characteristics. This task corresponds to extrapolate the hidden contour of an ellipse with reduced visual information. Third, several features of the liquid surface during primary breakup (ligaments, bags, rims) are manually annotated on 15 experimental images. The detector is trained on this minimal database using simple data augmentation and then applied to other images from numerical simulation and from other experiment. In these three tasks, models from the literature based on Convolutional Neural Networks showed very promising results, thus demonstrating the high potential of Deep Learning to post-process liquid atomization. The next step is to embed these models into a unified framework DeepSpray.
The Use of Humanoid Robots with Multilingual Interaction Skills in Teaching a Foreign Language: Opportunities, Research Challenges and Future Research Directions

Since a humanoid robot does not get tired regardless of how many mistakes a student makes, and because it can be equipped with novel teaching techniques and updated with the most current knowledge, it can be useful for achieving many educational goals. The rationale behind this is that it has been shown in the literature that robots are more useful as teaching aids than computers or other instructional tools, as they can mimic human responses. Furthermore, humans, especially children, prefer robot interaction to other interaction types. Adults generally struggle to learn a foreign language, but this is true of some students, too. With their multilingual interaction capabilities, their ability to provide real-time feedback, and their humanlike physical shape, some types of humanoid robots can be of great assistance to students in learning a foreign language. The use of humanoid robots leads to a personal connection with the students, and this can help overcome issues related to shyness, reluctance, frustration and lack of confidence that may emerge in dealing with a human teacher. Moreover, as humanoid robots can be programmed to know specifically what each individual student needs to learn, they can be quite useful for one-on-one speaking activities. Considering the many possibilities that can be offered by information and communication technology tools, particularly by humanoid robots, this paper reviews the roles and functions of humanoid robots in teaching a foreign language, presents novel approaches in this domain, provides research challenges, and finally outlines future research directions.
A compensation method for the consistency of multi-channel mixing circuit for solar radio observation system

The observing frequency of Ground-based Solar Radio Observation Systems (SROS) ranges from tens of MHz (10-m-wave) to tens of GHz (millimeter-wave). Due to the limitation of the ADC input bandwidth and sampling rate, the broadband solar radio signals acquired by the SROS will be segmented by mixing circuit and then processed the analog-to-digital conversion. The analog device inconsistency leads to the inconsistencies of the amplitude and phase between different channels of mixing circuits, which affects the accuracy of the receiver. The compensation coefficient of each frequency point is first calculated by measuring the deviation of the amplitude and phase between different channels, and then stored in FPGA of the receiver. The data achieved by ADC are proceed by FFT and then compensated by the compensation coefficients. The validation experiments are carried out using 150–500 MHz SROS located in Chashan Solar Radio Observatory (CSO). The amplitude difference between different channels can be as small as 0.5 dBm. The phase consistency can also be significantly improved. The practical application shows that this method can improve the consistency of the wideband mixing circuit, and can be used to correct of multi-channel consistency of the digital beam forming (DBF) for the low-frequency log-periodic antenna array and interference of the multiple antennas.
Removal of Power Line Interference From ECG Signals Using Adaptive Notch Filters of Sharp Resolution

The noise cancellation in electrocardiogram (ECG) signal is very influential to distinguish the essential signal features masked by noises. The power line interference (PLI) is the main source of noise in most of bio-electric signals. Digital notch filters can be used to suppress the PLI in ECG signals. However, the problems of transient interferences and the ringing effect occur, especially when the digitization of PLI does not meet the condition of full period sampling. In this paper, to obtain a better cancellation of the PLI, a designing approach, generating adaptive notch filter (ANF) of sharp resolution, is proposed. The proposed method is concise in algorithm and achieves a more comprehensive reduction of the PLI. It only requires on one fast Fourier transform on the input signal. The spectrum correction method, based on the information from the FFT spectrum of the corrupted signal, is utilized to estimate the harmonic parameters of the PLI. The information of a few main lobe spectral bins in the FFT spectrum is merged such that a compensation signal can be synthesized. By subtracting the compensational signal from the original measurement, the PLI within the investigated signal can substantially reduced. A distinguished advantage of the proposed ANF lies in the fact that no parameters are required to be specified, making the algorithm easier to be implemented. The proposed ANF outperforms conventional notch filters because it not only alleviates the undesirable effects but also better preserves the QRS-complex features in the filtered signal.