Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Building Strategy with Knowledge Distillation for Small-Footprint Acoustic Models

In this paper, we propose a novel training strategy for deep neural network (DNN) based small-footprint acoustic models. The accuracy of DNN-based automatic speech recognition (ASR) systems can be greatly improved by leveraging large amounts of data to improve the level of expression. DNNs use many parameters to enhance recognition performance. Unfortunately, resource-constrained local devices are unable to run complex DNN-based ASR systems. For building compact acoustic models, the knowledge distillation (KD) approach is often used. KD uses a large, well-trained model that outputs target labels to train a compact model. However, the standard KD cannot fully utilize the large model outputs to train compact models because the soft logits provide only rough information. We assume that the large model must give more useful hints to the compact model. We propose an advanced KD that uses mean squared error to minimize the discrepancies between the final hidden layer outputs. We evaluate our proposal on recorded speech data sets assuming car-and home-use scenarios, and show that our models achieve lower character error rates than the conventional KD approach or from-scratch training on computation resource-constrained devices.
Common Knowledge Based Transfer Learning for Traffic Classification

Deep neural networks have been used for traffic classification, and promising results are obtained. However, most previous work confined to one specific classification task and restricted the classifiers potential performance and applications. As the traffic flows can be labeled from different perspectives, the performance of the classifier might be improved by exploring more meaningful latent features. For this purpose, we adopted a multi-output DNN model that simultaneously learns different traffic classification tasks. The common knowledge of traffic is exploited by the synergy among the tasks and boosts the individual performances of the tasks. Experiments show that this structure has the potential to meet new future demands and achieve the classification with advanced speeds and fair accuracies. Yet, due to the heavy training cost, the neural networks, though achieving good performance, are hard to implement in the real environment. We further show that few-shot learning could be a viable approach.
Efficient High Performance FPGA based NoSQL Caching System for Blockchain Scalability and Throughput Improvement

Blockchain inspires many people across the globe and has started getting many adoptions after realizing its promising benefits through successful trials. Applications of blockchain range from cryptocurrency, smart contract, banking, healthcare and identity management, insurance, land registry to IoT industry. However, scalability and throughput issues are major challenges of blockchain and are less researched. Many lightweight nodes especially IoT devices depend and put much workload on blockchain servers since they cannot store the full blockchain due to its huge size. This paper investigates performance bottlenecks in blockchain and presents efficient high performance system for caching the blockchain data in the FPGA network interface controller (NIC) for improving scalability and throughput of blockchain applications. We design a customized SHA-256 hash core specific for efficient blockchain caching to save hashing executions and improve the performance. We built and use Jansson and Curl libraries to interface our system with real Bitcoin core. We finally evaluate the throughput performance of the Bitcoin core. The overall result revealed that our system improves the throughput performance by 103 times when cache hit. The design also offers small work area utilization, low power consumption and higher performance.
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations

High quality face editing in videos is a growing concern and spreads distrust in video content. However, upon closer examination, many face editing algorithms exhibit artifacts that resemble classical computer vision issues that stem from face tracking and editing. As a consequence, we wonder how difficult it is to expose artificial faces from current generators? To this end, we review current facial editing methods and several characteristic artifacts from their processin pipelines. We also show that relatively simple visual artifacts can be already quite effective in exposing such manipulations, including Deepfakes and Face2Face. Since the methods are based on visual features, they are easily explicable also to non-technical experts. The methods are easy to implement and offer capabilities for rapid adjustment to new manipulation types with little data available. Despite their simplicity, the methods are able to achieve AUC values of up to 0.866.
VERB: VFCDM-Based Electrocardiogram Reconstruction and Beat Detection Algorithm

We have developed a novel method to accurately detect QRS complex peaks using the variable frequency complex demodulation (VFCDM) method. The approach’s novelty stems from reconstructing an ECG signal using only the frequency components associated with the QRS waveforms by VFCDM decomposition. After signal reconstruction, both top and bottom sides of the signal are used for peak detection, after which we compare the locations of the peaks detected from both sides to ensure that false peaks are minimized. Finally, we impose position-dependent adaptive thresholds to remove any remaining false peaks from the prior step. We applied the proposed method to the widely benchmarked MIT-BIH arrhythmia dataset and obtained among the best results compared with many of the recently published methods. Our approach resulted in 99.94% sensitivity, 99.95% positive predictive value, and a 0.11% detection error rate. Three other datasets—the MIMIC III database, University of Massachusetts atrial fibrillation data, and SCUBA diving in salt water ECG data—were used to further test the robustness of our proposed algorithm. For all these three datasets, our method retained consistently higher accuracy when compared with the BioSig Matlab toolbox, which is publicly available and known to be reliable for ECG peak detection.
Country Interest Analysis Based on Long-Term Short-Term Memory(LSTM) In Decentralized System

Social platform such as Facebook, Twitter and Instagram generates tremendous data these days. Researchers make use of these data to extract meaningful information and predict future. Especially twitter is the platform people can share their thought briefly on a certain topic and it provides real-time streaming data API for filtering data for a purpose. Over time a country has changed its interest in other countries. People can get a benefit to see a tendency of interest as well as prediction result from twitter streaming data. Capturing twitter data flow is connected to how people think and have an interest on the topic. We believe real-time twitter data reflect this change. Long-term Short-term Memory Unit (LSTM) is the widely used deep learning unit from recurrent neural network to learn the sequence. The purpose of this work is building prediction model "Country Interest Analysis based on LSTM (CIAL)" to forecast next interval of tweet counts when it comes to referring country on the tweet post. Additionally it's necessary to cluster for analyzing multiple countries twitter data over the remote nodes. In this paper we present how twitter streaming data can capture a tendency how a country attention shift to another rate with LSTM algorithm
Analysis of Explicit vs. Implicit Tasking in OpenMP Using Kripke

Dynamic task-based parallelism has become a widely-accepted paradigm in the quest for exascale computing. In this work, we deliver a non-trivial demonstration of the advantages of explicit over implicit tasking in OpenMP 4.5 in terms of both expressiveness and performance. We target the Kripke benchmark, a mini-application used to test the performance of discrete particle codes, and find that the dependence structure of the core “sweep” kernel is well-suited for dynamic task-based systems. Our results show that explicit tasking delivers a 31.7% and 8.1% speedup over a pure implicit implementation for a small and large problem, respectively, while a hybrid variant also underperforms the explicit variant by 13.1% and 5.8%, respectively.
A Coarse-to-Fine Model for Rail Surface Defect Detection

Computer vision systems have attracted much attention in recent years for use in detecting surface defects on rails; however, accurate and efficient recognition of possible defects remains challenging due to the variations shown by defects and also noise. This paper proposes a coarse-to-fine model (CTFM) to identify defects at different scales. The model works on three scales from coarse to fine: subimage level, region level, and pixel level. At the subimage level, the background subtraction model exploits row consistency in the longitudinal direction, and strongly filters the defect-free range, leaving roughly identified subimages within which defects may exist. At the next level, the region extraction model, inspired by visual saliency models, locates definite defect regions using phase-only Fourier transforms. At the finest level, the pixel subtraction model uses pixel consistency to refine the shape of each defect. The proposed method is evaluated using Type-I and Type-II rail surface defect detection data sets and an actual rail line. The experimental results show that CTFM outperforms state-of-the-art methods according to both the pixel-level index and the defect-level index.
$iCLAS$ : An Improved Certificateless Aggregate Signature Scheme for Healthcare Wireless Sensor Networks

As the world’s population gradually enters the aging stage, the smart healthcare demand has become extremely urgent and incomparably important. Healthcare wireless sensor networks (HWSNs) is one of the foundations of smart healthcare. Medical information is extremely sensitive information. However, HWSNs uses a common communication channel and is vulnerable to various types of security attacks. Therefore, ensuring the communication and data security of HWSNs and protecting patient privacy are the basis of smart healthcare. After analyzing the deficiencies of a certificateless aggregation signature scheme for HWSNs, an improved certificateless aggregation signature scheme (called iCLAS) is proposed in this paper. We present rigorous security proof that shows iCLAS can resist all kinds of security attacks and can ensure patient privacy protection. The detailed performance comparisons show iCLAS has clear advantages in computation and communication cost.
Finding Multi-granularity Community Structures in Social Networks Based on Significance of Community Partition

Community structure detection is an important and valuable task in social network studies as it is the base for many social network applications such as link prediction, recommendation, etc. Most social networks have an inherent multi-granular structure, which leads to different community structures at different granularities. However, few studies pay attention to such multi-granular characteristics of social networks. In this paper, a method called MGCD (Multi-Granularity Community Detection) is proposed for finding multi-granularity community structures of social networks. At first, a network embedding method is used to obtain the low-dimensional vector representation for each node. Then, an effective embedding-based strategy for weakening the detected community structures is proposed. Finally, a joint learning framework, which combines network embedding and community structure weakening is developed for identifying the multi-granularity community structures of social networks. Experimental results on real-world networks show that MGCD outperforms the state-of-the-art benchmark methods on finding multi-granularity community structure tasks.
Optimal Relay Selection for Wireless Relay Channel with External Eavesdropper: a NN-based Approach

In this paper, we exploit the potential benefits of machine learning in enhancing physical layer security in cooperative wireless networks. We focus on the case where multiple relays adopt amplify-and-forward (AF) relaying to forward information from the source to the destination. It is assumed that the global channel state information (CSI) of the legitimate links and wiretap links is available to the source. The optimal relay is selected to improve physical layer security against eavesdropping. By modeling the problem of the relay selection as a multi-class classification problem, a neural network (NN) based scheme is proposed to select the optimal relay which guarantees the perfect secrecy performance of the relay cooperative communication system. Compared with the conventional relay selection scheme, the simulation results show that our proposed scheme not only achieves almost the same secrecy performance, but also has advantage of relatively small feedback overhead. The work presented here provides insights into the new design of relay selection based on machine learning.
Intelligent Diagnosis of V-Type Marine Diesel Engines Based on Multifeatures Extracted From Instantaneous Crankshaft Speed

This paper presents an intelligent combustion fault diagnosis approach for V-type marine diesel engines with uneven firing intervals based on the instantaneous crankshaft speed (ICS). First, experimental ICSs of a 16-cylinder marine diesel engine under normal and different degrees of fault conditions are investigated to obtain the most sensitive fault features. A new polar representation method named cyclic-polar-map (CPM) is proposed to effectively demonstrate the fault features. The quantitative radii of the CPM are used to indicate the location and degree of fault. Then, a support vector machine (SVM)-based intelligent fault diagnosis system is developed for automatically detecting the combustion faults with the features extracted from the proposed CPM and the frequency analysis of ICS. In addition, for the SVM training set is not always available in practice, a dynamic model of the crankshaft system is developed to simulate different fault patterns. Finally, the efficiency of the proposed intelligent diagnosis approach is validated by experimental data. The results show that the proposed SVM-based intelligent diagnosis system using both CPM features and frequency domain features of the ICS can obtain good accuracies under different fault degrees at various speed and load conditions.
Flash Glucose Monitoring in Subjects with Diabetes on Hemodialysis: A Pilot Study

Background: In patients with diabetes related end-stage renal disease (ESRD) on hemodialysis, blood glucose management can be challenging due to the kinetics of glucose and insulin in addition to other factors. The glucose monitoring systems which measure glucose levels continuously may be useful to study the glucose profile of patients with diabetes undergoing hemodialysis. Our study is designed to use ambulatory glucose profile to study the glucose pattern – during, before, and after a session of hemodialysis. Materials and Methods: Ten patients with type 2 diabetes with ESRD undergoing hemodialysis were recruited. Forty-eight glucose readings were recorded in a 12-h period which included 4 h each prior, during, and after the dialysis session with a flash glucose monitor (FreeStyle Libre-pro). The same 12 h time frame was also monitored on a non-dialysis day. Results: On the day of dialysis, the mean glucose level was significantly lower (P = 0.013) compared to the day without dialysis (95 ± 12.7 mg/dl vs 194 ± 76.8 mg/dl). As compared to the pre-dialysis period, the mean blood glucose levels during dialysis were lower (P = 0.004). As compared to the dialysis period, the mean blood glucose levels in the post-dialysis period were higher but did not reach statistical significance. Conclusion: In our study, subjects with type 2 diabetes on hemodialysis had lower glucose levels on the day of dialysis compared to non-dialysis day. Glucose levels showed a fall during hemodialysis and then a rise to higher levels after dialysis.
Plug-and-Play for Fog: Dynamic Service Placement in Wireless Multimedia Networks

Initially as an extension of cloud computing, fog computing has been inspiring new ideas about moving computing tasks to the edge of a network. In fog, we often repeat the procedure of placing service because of the geographical distribution of mobile users. We may not expect a fixed demand and supply relationship between users and service providers since users always prefer nearby service with less time delay and transmission consumption. That is, a plug-and-play service mode is what we need in fog. In this paper, we put forward a dynamic placement strategy for fog service in a three-tier wireless multimedia network to guarantee the normal service provision and optimize the Quality of Service (QoS). The simulation results show that our strategy can achieve better performance under metrics including energy consumption and end-to-end latency compared with existed methods.
Distributed constrained cooperative control of discrete-time high-speed train systems

In this paper, we study a cooperative control problem for discrete-time high-speed train systems with states constraints in the presence of incomplete exactly known parameters. A distributed control algorithm is adopted to enable all trains to converge to a position and also to keep a safe distance from its neighbours by using local communication. Through multiple model transformations, the system matrix is turned into a stochastic matrix for analysis. The lower bound of each effective element of the system matrix is given to guarantee the convergence of the system.
Inhibition of cell proliferation, migration and colony formation of LS174T Cells by carbonic anhydrase inhibitor

Background: Metastasis is the leading cause of cancer deaths. Migration of tumor cells is an important stage in metastasis. Therefore, recent studies have focused on clarifying migration and migration-dependent cell functions such as angiogenesis, wound healing, and invasion. Objectives: In the present study, we aimed to investigate the effect of acetazolamide, which is a classical carbonic anhydrase inhibitor, on the cell viability, migration, and colony forming capacity of human LS174T colorectal cancer cells. Methods: Three different cell culture techniques (MTT test, wound healing and clonogenic assay) were performed in this in vitro study on colorectal cancer cells. Results: Acetazolamide reduced the cell viability, migration and colony formation ability of cells depending on dose. There was no significant difference between the cells treated with acetazolamide with 1 μM dose and the control. However, it can be concluded that acetazolamide exerts its effect on human colorectal cancer cells at 10-1000 μM concentrations. Conclusion: Acetazolamide was observed to significantly inhibit the cell viability, colony forming capacity, and migration ability in the culture medium of LS174T cells. This inhibitor effect of acetazolamide was observed to be dependent on the concentration in medium.
Varactor-based Rectifier with Adaptive Matching Network for Wireless Power Transfer system

This work presents an adaptive matching network for rectifier circuit in wireless power transfer system by using varactor diode. By setting appropriate biasing voltages, the changes in the capacitances of the varactor enable the rectifier to reconfigure in response to the fluctuations in operating conditions, such as input power level and frequency. This allows the system to remain efficient despite changes in the external factors. Furthermore, integration with microcontroller unit (MCU) is without consuming excessive power. This would allow the rectifier circuit to become optimizable in real time.
Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing

Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases.
Normal Estimation of Surface in PointCloud Data for 3D Parts Segmentation

3D object parts segmentation is a fundamental task and essential for many higher level tasks, such as object detection, human pose estimation and environment recognition. For 3D object parts segmentation, there are methods using local 3D concave/convex relationships. Those relationships are distinguished by using normals in local adjacent regions of 3D object surface. To obtain these relationships, precise estimation of normals are very important. However, the normal estimation method for a whole surface of an object which are represented by PointCloud is not discussed sufficiently. Therefore, in this research, we propose an automatic normal estimation method. In our method, the number of times that a normal go through surfaces of the object is used to determine the direction of the normal, inside or outside of the object. As results of experiments on the benchmark which consists of 380 3D objects in 19 categories, we obtained the average normal estimation accuracy of 99%