Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Musical Pieces with a Similar Emotional Distribution throughout the Same Composition

The aim of this work was to build a system that would enable to automatically compare various interpretations of the same composition in terms of the emotions, and to discover which of them are more and less similar. The stages of building such a computer system include such issues as annotating music data, building regressors, aligning different renditions, detecting and visualizing emotions over time, and result analysis. Analyzing the obtained results for Prelude No. 18 by Frédéric Chopin, the author found which performances of the same composition were more similar to each other and which were quite dissimilar in terms of the shaping of emotions over time.
Covariance Difference Matrix-based Sparse Bayesian learning for Off-grid DOA Estimation with Colored Noise

Most of the existing direction of arrival (DOA) estimation methods are applicable in the presence of Gaussian white noise situation. The precision of the traditional sparse representation based DOA estimation methods are limited to the grid interval. To address these problems, the covariance differencing method is applied and the colored noise component is eliminated by forming the difference of the original and the transformed covariance matrices of the observation data. And then an off-grid DOA estimation algorithm based on the eigenvectors corresponding to the positive eigenvalues of covariance difference matrix is proposed. The sparse Bayesian learning strategy is adopted to solve the off-grid DOA estimation model. The proposed method can suppress the colored noise and avoid the pseudo peaks effectively. Simulation results demonstrate the improvement of estimation accuracy.
A 29mW 5GS/s Time-interleaved SAR ADC achieving 48.5dB SNDR With Fully-Digital Timing-Skew Calibration Based on Digital-Mixing

This paper presents a 5GS/s 16-way Time-Interleaved SAR ADC in 28nm CMOS, proposing a fully-digital background timing-skew calibration based on digital mixing, without adding any extra analog circuits. We implement the sub-channel SAR with a splitting-combined monotonic switching procedure. The prototype ADC achieves 48.5dB SNDR at Nyquist rate, while the power consumption is 29mW leading to a Walden FOM of 26.7fJ/conv-step.
The Intrusion Detection Model based on Parallel Multi - Artificial Bee Colony and Support Vector Machine

In view of the problems existing in feature selection and support vector machine model parameter optimization in network intrusion detection, artificial bee colony algorithm is introduced. For the artificial bee colony algorithm, there are problems such as easy precocity, poor diversity of the solution, easy to fall into local optimum, and slow convergence in the later stage. In order to relieve these problems, we redesign the algorithm, including honey source coding scheme, the initialization of population, the construction of the fitness evaluation function, the neighborhood search method and so on. Then we propose the synchronization optimization model of characteristic parameters. It overcomes the above defects of the classical ABC algorithm. Finally, we propose an intrusion detection model based on the improved artificial bee colony algorithm and support vector machine model. The experimental results show that the detection performance of our model is far superior to the methods based on other feature selection and detection principles.
Towards a Methodology for Benchmarking Edge Processing Frameworks

With the spectacular growth of the Internet of Things, edge processing emerged as a relevant means to offload data processing and analytics from centralized Clouds to the devices that serve as data sources (often provided with some processing capabilities). While a large plethora of frameworks for edge processing were recently proposed, the distributed systems community has no clear means today to discriminate between them. Some preliminary surveys exist, focusing on a feature-based comparison. We claim that a step further is needed, to enable a performance-based comparison. To this purpose, the definition of a benchmark is a necessity. In this short paper, we make a step towards the definition of a methodology for benchmarking Edge processing frameworks.
Unsupervised Feature Learning With Graph Embedding for View-Based 3D Model Retrieval

3D model retrieval is becoming a hot research topic due to its wide applications such as computer-aided design, digital entertainment, and virtual reality. For this challenging task, feature learning and similarity measure are two critical problems. However, existing approaches usually learn discriminative visual features and develop a complex graph matching strategy to measure the similarity independently. In this paper, we propose an unsupervised method which can embed similarity measure into the feature space. The proposed method utilizes both similarity and dissimilarity information to better leverage the unsupervised problem and estimates the labels which are further used for metric learning. With the learned metric, we project the original features to more discriminative feature space and efficiently measure the similarity among models under the new feature space. We conduct extensive evaluations of three popular and challenging datasets. The experimental results demonstrate the superiority and effectiveness of the proposed method, competing against the state of the arts.
Influence of Cage Structure on Rotor Bar Current Waveform and Performance in Induction Machines

The influences of different cage arrangements on the performance characteristics, particularly rotor bar current waveform are numerically studied in case of a 3-phase, 48-stator and 52-rotor slots, and 8-pole squirrel-cage induction machine (IM). The IMs have been designed by using the same geometric design and operating rates as the Toyota Prius 2010 interior permanent magnet (IPM) machine. The effect of multi-cage, cage shift angle, coupling of multi-cages, and bar shieling effect have been investigated in depth. It has been revealed that the number of cages, shifting of one cage according to other(s), and coupling of cages' end-rings have a significant effect on the bar current waveform and performance characteristics. It has also been shown that although inserting additional cages reduces the level of high order bar current harmonics, it also reduces the output power and hence efficiency. Moreover, some high and low order bar current harmonics can be simultaneously reduced by shifting one cage according to other cage(s).
Movement related activity in the μ band of the human EEG during a robot-based proprioceptive task

Innovative research in the fields of prosthetic, neurorehabilitation, motor control and human physiology has been focusing on the study of proprioception, the sense through which we perceive the position and movement of our body, and great achievements have been obtained regarding its assessment and characterization. However, how proprioceptive signals are combined with other sensory modalities and processed by the central nervous system to form a conscious body image, is still unknown. Such a crucial question was addressed in this study, which involved 23 healthy subjects, by combining a robot-based proprioceptive test with a specific analysis of electroencephalographic activity (EEG) in the $\mu$ frequency band (8-12 Hz). We observed important activation in the motor area contralateral to the moving hand, and besides, a substantial bias in brain activation and proprioceptive acuity when visual feedback was provided in addition to the proprioceptive information during movement execution. In details, brain activation and proprioceptive acuity were both higher in case of movements performed with visual feedback. Remarkably, we also found a correlation between the level of activation in the brain motor area contralateral to the moving hand and the value of proprioceptive acuity.
Stability Analysis of Islanded Microgrid with EVs

Renewable energy resources (RESs) and electric vehicles (EVs) have emerged as powerful concepts which can replace the conventional energy and transportation systems with more flexibility and efficiency. Due to low inertia of microgrid and intermittent nature of RESs, the rapid increase in the penetration level of RESs and EVs in smart grids may lead to frequency stability issue. However, EVs, as the mobile energy storage system, can contribute to the improvement of the frequency fluctuation and stability. This paper proposes a method to control the EVs integrated in an islanded microgrid so that they participate in load frequency control (LFC). On the contrary to the approaches proposed in the literature, the proposed control method enables the investigation of time delay effect on LFC and considers the communication latency on the LFC stability. The delay-dependent stability criterion of the proposed method is derived based on Lyapunov theory in the form of linear matrix inequality (LMI). The LMI is solved to find the parameters impacting the maximum allowable delay (MAD) and to obtain the MAD by which the stability of the LFC system is guaranteed. The effectiveness of the proposed method is validated through numerical simulation of a case study.
Assessment of human wrist rigidity and pain in post-traumatic patients*

The aim of this work is to present a novel robot-based method to assess the sources of a lack of functionality in patients with recent traumatic wrist injuries. Post-traumatic patients experience limited range of motion as well as strength and proprioceptive deficits. These dysfunctions are related to different complications that usually follow the injuries: pain, increased rigidity, lack of movement fluency and loss of stability could arise differently, according to the severity, site and kind of lesion. Their quantitative evaluation could be essential to target rehabilitation treatments to the specific problem and to optimize and speed up the functional recovery. The use of robotic devices for assessment not only ensures objectivity and repeatability, but could also help to estimate the goodness of the evaluation itself, in terms of reliability and patient’s engagement. Ten subjects with different types of wrist injuries were enrolled in this study and required to perform passive robot-guided reaching movements. Forces and angular positions were used to evaluate subject’s range of motion, rigidity and pain that, considered together, allowed a comprehensive characterization of the level of healing and functionality achieved by each subject.
A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results

Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback. In particular, the whole human-robot control system is concerned (i.e. myocontrol, robotic hand controller, vibrotactile feedback.) In order to evaluate the intuitiveness and force tracking performance provided by the proposed control architecture, an experiment was carried out involving four naïve able-bodied subjects in a grasping strength regulation task with a myocontrolled robotic hand (the University of Bologna Hand), requiring for grasping different objects with specific target force levels. The reported results show that the control architecture successfully allowed all subjects to achieve all grasping strength levels exploiting the vibrotactile feedback information. This preliminary demonstrates that, potentially, the proposed control interface can be profitably exploited in upper-limb prosthetic applications, as well as for non-rehabilitation uses, e.g. in ultra-light teleoperation for grasping devices.
High Power Wireless Power Transfer Efficiency and EMI Co-optimization Based on Fast Field-Circuit Co-simulation

This paper presents a fast, general-purpose power transfer efficiency and electromagnetic interference co-optimization method for high power wireless power transfer systems. Constraints considering inverter zero voltage switching, total harmonic distortion of the rectifier current, inverter power factor and magnetic field leakage are included in the disciplined optimization problem. In order to reduce time consumption, fast calculation of electromagnetic field is derived by leveraging the linearity of the coil structure. An application using SAE-J2954 Recommended Practice shows that optimization time can be reduced from 24 hours to 2 hours.
A New Method for Road Element Extraction Based on Fully Convolutional Network

Aiming at the problem of extracting road elements from high-resolution satellite photos, a new method based on deep learning techniques is proposed and implemented in this paper. Unlike the traditional road extraction algorithms, the new method regards road elements as semantic objects and classifies them using a well trained fully convolution network, which is constructed by adding deconvolution layers to a VGG16 network model. The experimental result indicated that the trained network model is not only able to extract roads from satellite photos, but also segment major road elements at a high accuracy rate. Especially, the method has shown better robustness in dealing with the road edges which are partially blocked by vehicles or trees than that of traditional road extraction algorithms.
An Adaptive Socket Attaches onto Residual Limb Using Smart Polymers for Upper Limb Prosthesis

A major challenge for upper limb amputees is discomfort due to improper socket fit on the residual limb during daily use of their prosthesis. Our work introduces the implementation of soft robotic actuators into a prosthetic socket. The soft actuators are a type of electrically-powered actuator. The actuator is driven through changes in internal temperature causing actuation due to vapor pressure, which results in high and reliable force outputs. A regression fit was generated to model how the smart polymer’s temperature relates to force output, and the model was cross-validated based on training data collected from each actuator. A proportional integral (PI) controller regulated the force exerted by the actuators based off of tactile and temperature feedback. Results showed that a socket system can be integrated with smart polymers and sensors, and demonstrated the ability to control two actuators and reach desired forces from set temperatures.
A Beam-Steering Dielectric Resonator Antenna without Beamforming Network

A novel beam-steering dielectric resonator antenna (DRA) without beamforming network is presented in this paper. Instead of adopting the most common way of combining array antennas with beamforming networks to achieve beam steering, the proposed DRA has a very simple structure, with only a single rectangular dielectric resonator (DR) as its radiator. Three microstrip lines are employed to feed the DRA through three slots etched on the ground plane respectively. Between every two microstrip lines lies a decoupling network to improve the isolation of the ports. When one of the three ports is excited and the other two are terminated to 50 Ω impedance, the radiator is fed accordingly at different positions and can generate three different tilted radiation beams in total. As a result, beam steering can be achieved using a single DRA without beamforming network. For demonstration, a single beam-steering DRA without beamforming network working at 5.25 GHz is designed and simulated. The proposed antenna has a bandwidth of 5% and an isolation higher than 15 dB at the center frequency. The three main beams are tilted to -40°, 0° and 40°, with an antenna gain of around 7.45 dBi.
A Systematic Review – Intrusion Detection Algorithms Optimisation for Network Forensic Analysis and Investigation

As the digital world is growing widespread crime in the cyberspace is also increasing. Knowledge sharing and utilization of services attracts to use the digital devices, but the concern here is malicious usage of the system. If the crime takes place over the network how to collect it, analyze it and investigate based on the evidences. So, the role of forensic and incident response is crucial here. Digital forensics is categorized in Disk, Live, Network and Mobile Forensics. Anomaly or attack over the network comes under network forensic branch. In this paper extensive literature review is performed to compare the latest intrusion detection systems and based on the learning’s a system is proposed which covers the peer to peer architecture system and utilization of web robots to trace the attack and log it in a form which will be a useful input for forensic investigation and analysis work.
EEDAC-WSN: Energy Efficient Data Aggregation in Clustered WSN

One of the primary challenges in Wireless Sensor Networks (WSNs) is energy efficiency. In the last decade WSNs have found its application in wide range of applications producing huge amount of data. Energy efficiency of a clustered WSNs can be improved, if size of data being transmitted in the network is reduced. In this paper, we present an energy efficient data aggregation scheme for clustered wireless sensor network (EEDAC-WSN). It reduces intra-cluster communications by allowing cluster member nodes to send small sized control frames followed by relatively detailed frames from nodes selected by the cluster head node. The proposed work can dovetailed with any clustering scheme, we have used it with LEACH for the purpose of simulation. The results obtained are substantial in terms of network stability period and lifetime.
A Lock-Free Skiplist for Integrated Graphics Processing Units

With the advent of computing systems with on-die integrated graphics processing unit (iGPU), new general-purpose GPU programming challenges have emerged from these heterogeneous processors. We propose a lock-free skiplist for Intel's integrated graphics processor that is optimized to achieve the best performance using the C for Media framework. To the best of our knowledge, this is the first implementation of a lock-free data structure for iGPU. Experimental results show that our proposal is more compute-efficient than an existing discrete GPU implementation and outperforms state-of-the-art lock-free and lock-based skiplists for multi-core CPU, achieving up to 3.5x speedup. Additionally, energy savings of up to 300% are obtained when running different skiplist workloads on iGPU instead of CPU cores, hence further improving energy efficiency.
Parallel Processing on FPGA Combining Computation and Communication in OpenCL Programming

In recent years, Field Programmable Gate Array (FPGA) has been a topic of interest in High Performance Computing (HPC) research. Although the biggest problem in utilizing FPGAs for HPC applications is in the difficulty of developing FPGAs, this problem is being solved by High Level Synthesis (HLS). We focus on very high-performance inter-FPGA communication capabilities. The absolute floating-point performance of an FPGA is lower than that of other common accelerators such as GPUs. However, we consider that we can apply FPGAs to a wide variety of HPC applications if we can combine computations and communications on an FPGA. The purpose of this paper is to implement a parallel processing system running applications implemented by HLS combining computations and communications in FPGAs. We propose the Channel over Ethernet (CoE) system that connects multiple FPGAs directly for OpenCL parallel programming. "Channel"' is one of the new extensions provided by the Intel OpenCL environment. They are ordinally used for intra-kernel communication inside an FPGA, but we extend them to external communication through the CoE system. In this paper, we introduce two benchmarks as demonstration of the CoE system. We achieved 29.77 Gbps in throughput (approximately 75% of the theoretical peak of 40Gbps) and 950 ns in latency on our system using the pingpong benchmark, which was implemented on Intel Arria10 FPGA. In addition, we evaluated the Himeno benchmark which is a sort of 3D-Computational Fluid Dynamics (CFD) on the system, and we achieved 23689MFLOPS with 4 FPGAs on a problem of size M. We also notice strong scalability, with a 3.93 times speedup compared to a single FPGA run, on the same problem size.
Pareto Optimal Design Space Exploration for Accelerated CNN on FPGA

Convolutional Neural Networks (CNNs) are at the base of many applications, both in embedded and in server-class contexts. While Graphics Processing Units (GPUs) are predominantly used for training, solutions for inference often rely on Field Programmable Gate Arrays (FPGAs) since they are more flexible and cost-efficient in many scenarios. However, existing approaches fall short to accomplish several conflicting goals, like efficiently using resources on multiple platforms while retaining deep configurability and allowing a quick Design Space Exploration (DSE) towards the best solution. This paper proposes a solution composed of highly configurable kernels designed for resources time-sharing with an analytical model of their resource/performance characteristics. Building on such models, we propose an Integer Linear Programming (ILP)-based approach to effectively identify pareto optimal kernel configurations in terms of throughput and resource consumption. We evaluate our DSE on two state-of-the-art CNNs, showing how it identifies hundreds of pareto optimal solutions in less than a minute. Guided from the DSE configurations of the AlexNet network, we quickly identified a candidate design for a Xilinx Virtex-7 XC7VX485T FPGA and achieved a peak throughput of 4.05 ms per image, while we measured a maximum estimation error of 6.69% with respect to the proposed analytical models.
Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities

Educational data mining techniques are widely used in academic prediction on student performance in classroom education. However most of the existing researches were studied and evaluated student coursework performance against the passing grade of the exam. In this paper, we performed analysis to identify the significant and impact of student background, student social activities and student coursework achievement in predicting student academic performance. Supervised educational data mining techniques, namely Naïve Bayesian, Multilayer Perceptron, Decision Tree J48 and Random Forest were used in predicting mathematic performance in secondary school. The prediction was performed on 2-level classification and 5-level classification on final grade. The experimental results have shown that student background and student social activities were significant in predicting student performance on 2-level classification. The model can be used for early predicting student performance to help in improving student performance on the subject.