Reviewerly/exHarmony huggingface dataset:
A Multi-Tenant Framework for Cloud Container Services

Container technologies have been evolving rapidly in the cloud-native era. Kubernetes, as a production-grade container orchestration platform, has been proven to be successful at managing containerized applications in on-premises datacenters. However, Kubernetes lacks sufficient multi-tenant supports by design, meaning in cloud environments, dedicated clusters are required to serve multiple users, i.e., tenants. This limitation significantly diminishes the benefits of cloud computing, and makes it difficult to build multi-tenant software as a service (SaaS) products using Kubernetes. In this paper, we propose Virtual-Cluster, a new multi-tenant framework that extends Kubernetes with adequate multi-tenant supports. Basically, Virtual Cluster provides both control plane and data plane isolations while sharing the underlying compute resources among tenants. The new framework preserves the API compatibility by avoiding modifying the Kubernetes core components. Hence, it can be easily integrated with existing Kubernetes use cases. Our experimental results show that the overheads introduced by VirtualCluster, in terms of latency and throughput, is moderate.
Comparative Analysis of Image Security Using DCT, LSB and XOR Techniques

In current technology, the majority of data is transferred over the internet, and the most useful data type is image. As a result, image security is necessary for safe transmission. Image security methods are used to safeguard the image. Low MSE and high PSNR values are necessary for improved hiding. This paper shows the comparative analysis of DCT, LSB and XOR image security techniques and obtained the MSE and PSNR values from these techniques and shown techniques, which provided the better image security.
A Fall Detection Alert System Based on Lightweight Openpose and Spatial-Temporal Graph Convolution Network

To solve the home safety problem of the solitary group, we have developed a system based on Lightweight Openpose and Spatial-Temporal Graph Convolution Network (ST-GCN). We use Lightweight Openpose to extract the human skeleton sequence from the video stream in real-time, and use ST-GCN to extract the action time and space features from it to obtain an efficient fall detection model. For further application, we develop an email alert system using STMP protocol. This work integrates fall detection into the surveillance camera to realize real-time intelligent monitoring and detection at home. When the family member falls, the picture will be captured, and the alarm email will be sent to the owner or guardian, to provide a safer guarantee for the family member, especially the empty-nesters and young people living alone.
MandiPass: Secure and Usable User Authentication via Earphone IMU

Biometric plays an important role in user authentication. However, the most widely used biometrics, such as facial feature and fingerprint, are easy to capture or record, and thus vulnerable to spoofing attacks. On the contrary, intracorporal biometrics, such as electrocardiography and electroencephalography, are hard to collect, and hence more secure for authentication. Unfortunately, adopting them is not user-friendly due to their complicated collection methods and inconvenient constraints on users. In this paper, we propose a novel biometric-based authentication system, namely MandiPass. MandiPass leverages inertial measurement units (IMU), which have been widely deployed in portable devices, to collect intracorporal biometric from the vibration of user's mandible. The authentication merely requires user to voice a short ‘EMM’ for generating the vibration. In this way, MandiPass enables a secure and user-friendly biometric-based authentication. We theoretically validate the feasibility of MandiPass and develop a two-branch deep neural network for effective biometric extraction. We also utilize a Gaussian matrix to defend against replay attacks. Extensive experiment results with 34 volunteers show that MandiPass can achieve an equal error rate of 1.28%, even under various harsh environments.
Development of Multi-Robot Systems Using Particle Swarm Optimization Algorithm for Task Allocation

Task allocation is a problem of finding targets and distributing agents to these targets to achieve certain goals. One algorithm to solve this problem is Particle Swarm Optimization (PSO). This paper investigates the possible implementation of PSO using a real multi-robot system. Here, three E-Puck robots were used and a camera was placed on top of the environment to locate the robots using image processing technique. The three robots’ locations were sent to the server and PSO calculation was performed on the server to navigate each robot to its next position. Experiment results showed that the designed multi-robot system in the real world can mimic the behavior of multi-robot system in the simulation.
On the Potential of Large-scale Extended Reality Interaction for Industrial Environments

Nowadays factories are highly flexible and adaptive industrial environments backed by digital models. To ease the interaction of human workers with these environments and represent hidden information and contexts, we propose a large-scale extended reality system for industrial settings. The huge application potential is demonstrated with various interaction methods like trigger zones, guide paths, ergonomics guidance, and restricted areas.
Optimizing Winograd-Based Convolution with Tensor Cores

Convolution computing is one of the primary time consuming part of convolutional neural networks (CNNs). State of the art convolutional neural networks use samll, 3 × 3 filters. Recent work on Winograd convolution can reduce the computational complexity a lot, making the convolution computing fast. But existing implementations of Winograd convolution is limited to small tiles, i.e. F(4 × 4, 3 × 3) and F(2 × 2, 3 × 3) where 4 × 4 and 2 × 2 are tile sizes of output channels and 3 × 3 is the filter size, and single precision data. In this paper, we propose an optimized mixed precision F(6 × 6, 3 × 3) Winograd convolution implementation on NVIDIA Ampere GPUs using Tensor Cores. Our experiments show that the accuracy of mixed precision F(6 × 6, 3 × 3) Winograd convolution is sufficient to infer the convolutional neural networks. Besides, our method achieves up to 15.71x and 2.41x speedup on NVIDIA Ampere A100, compared with the state of the art Winograd based convolution and GEMM based convolution in cuDNN 8.1.0, respectively. Moreover, we integrate our F(6 × 6, 3 × 3) Winograd convolution implementation into NVIDIA TensorRT, which is a C++ inference library on GPUs provided by NVIDIA, as custom layer plugins. And we build the whole VGG network model using our custom Winograd convolution layers and other layers supported by TensorRT. The experiments show that the accuracy of the whole VGG network using our F(6 × 6, 3 × 3) Winograd convolution is 71.24%, while the accuracy of using FP32 computing for the VGG network is 71.22%.
Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.
Efficient Violence Detection Using Transfer Learning

In recent years several applications, namely in surveillance, human-computer interaction and video recovery based on its content has studied the detection and recognition of violence []. The purpose of violence detection is to automatically and effectively determine whether or not violence occurs in a short time. So, it is a crucial area since it will automatically enable the necessary means to stop the violence. To quickly solve this problem, we used models trained to solve general activity recognition problems such as Kinetics-400 to learn to extract general patterns that are very important to detect violent behaviour in videos. Our approach consists of using a state of the art pre-trained model in general activity recognition tasks (e.g. Kinetics-400) and then fine-tuning it to violence detection. We applied this approach in two violence datasets and achieved state-of-the-art results using only four input frames.
Argumentation-Based Online Incremental Learning

The environment around general-purpose service robots has a dynamic nature. Accordingly, even the robot’s programmer cannot predict all the possible external failures which the robot may confront. This research proposes an online incremental learning method that can be further used to autonomously handle external failures originating from a change in the environment. Existing research typically offers special-purpose solutions. Furthermore, the current incremental online learning algorithms cannot generalize well with just a few observations. In contrast, our method extracts a set of hypotheses, which can then be used for finding the best recovery behavior at each failure state. The proposed argumentation-based online incremental learning approach uses an abstract and bipolar argumentation framework to extract the most relevant hypotheses and model the defeasibility relation between them. This leads to a novel online incremental learning approach that overcomes the addressed problems and can be used in different domains including robotic applications. We have compared our proposed approach with state-of-the-art online incremental learning approaches, an approximation-based reinforcement learning method, and several online contextual bandit algorithms. The experimental results show that our approach learns more quickly with a lower number of observations and also has higher final precision than the other methods. Note to Practitioners—This work proposes an online incremental learning method that learns faster by using a lower number of failure states than other state-of-the-art approaches. The resulting technique also has higher final learning precision than other methods. Argumentation-based online incremental learning generates an explainable set of rules which can be further used for human-robot interaction. Moreover, testing the proposed method using a publicly available dataset suggests wider applicability of the proposed incremental learning method outside the robotics field wherever an online incremental learner is required. The limitation of the proposed method is that it aims for handling discrete feature values.
Restoration Algorithm of Blurred UAV Aerial Image Based on Generative Adversarial Network

In the process of using visual devices to perform tasks, the image information acquired by unmanned aerial vehicle (UAV) often becomes blurred due to the jitter of its own camera or the movement of objects in the scene, which seriously affects the use of images and subsequent processing. To solve this problem, the paper proposed a single blurred image restoration in an end-to-end method. This method is based on generative adversarial network (GAN), using the improved scale-recurrent network (SRN) as generator and the critic in DeblurGAN as discriminator. Entire network structure is relatively simple, and the trained model has strong generalization ability. Results from numerical experiments show that compared with the Deep-Deblur, SRN and DeblurGAN, the proposed method improves the recovery efficiency of blurred image.
DeepFake Creation and Detection:A Survey

DeepFake has become very popular of late. The term DeepFake refers to any multimedia content generated using deep learning technology appearing realistic to people. Despite the beneficial advances of DeepFake, it has been a major cause of threats to a person's privacy, where one person's face can be swapped with another in an indistinguishable way without consent. Also, it is easy for malicious parties to take over public events like elections by spreading misinformation and leaving a negative impact on national security. Thus, detection of such DeepFake is a crucial yet challenging problem. Human-eye-based segregation of DeepFaked contents from real ones has always been a difficult task; but recent works have shown the use of different technologies recording good results for the same, although with some limitations. The paper thus explores different algorithms used for DeepFake creation and detection; presenting a comprehensive overview of the techniques used and aimed at identifying their pros and cons.
A Study on the Measurement Method of Educational Capability of High School Teachers

The primary role of a teacher from a technical high school in Japan is to teach the students the specialized knowledge and skills for the target industry such as manufacturing. At the same time, it is nothing to say to enhance the humanity of the students. The society of teachers is aging same as the aging and the birth rate decline society as the whole environment of Japan. From those backgrounds, teachers in the technical high school with educational capability who can teach students appropriately are required. One of the methods to improve the educational capability of teachers more than ever, we propose a method to quantify the educational capability in this paper. The educational capability of each teacher can be confirmed objectively at the same time by our proposed method. From the result of analyses, the educational capability of teachers is obtained from the results of the questionnaire. Based on our proposed method, the optimal composition of teachers by each subject is generated. This is research in progress.
Unified curiosity-Driven learning with smoothed intrinsic reward estimation

In reinforcement learning (RL), the intrinsic reward estimation is necessary for policy learning when the extrinsic reward is sparse or absent. To this end, Unified Curiosity-driven Learning with Smoothed intrinsic reward Estimation (UCLSE) is proposed to address the sparse extrinsic reward problem from the perspective of completeness of intrinsic reward estimation. We further propose state distribution-aware weighting method and policy-aware weighting method to dynamically unify two mainstream intrinsic reward estimation methods. In this way, the agent can explore the environment more effectively and efficiently. Under this framework, we propose to employ an attention module to extract task-relevant features for a more precise estimation of intrinsic reward. Moreover, we propose to improve the robustness of policy learning by smoothing the intrinsic reward with a batch of transitions close to the current transition. Extensive experimental results on Atari games demonstrate that our method outperforms the state-of-the-art approaches in terms of both score and training efficiency.
Hearing loss classification via AlexNet and extreme learning machine

Hearing is a very important feeling for humans. It helps to perceive the environment around us and can warn us of any imminent dangers around us. Hearing loss is an impaired ability to hear sound. Sensorineural hearing loss (SNHL) is very common in today's society. Classification of hearing loss is helpful for clinical diagnosis, finding suitable treatment methods and allocating appropriate medical interventions. This paper presents a novel method for hearing loss classification based on magnetic resonance images, which can automatically recognize tissue specific features in a given MRI image. The study involved magnetic resonance images (MRI) of 60 participants in three categories of balance: left SNHL, right SNHL, and a healthy control group. Our method uses AlexNet to extract the features of a single category, and uses Extreme Learning Machine to form a triclassifier for automatic SNHL classification. From the experimental results and the practicability of the algorithm, the classification performance is obviously better than the standard deep learning method and other traditional methods.
Linguistic-Coupled Age-to-Age Voice Translation to Improve Speech Recognition Performance in Real Environments

We address a low-performance problem of the elderly in automatic speech recognition (ASR) through feature adaptation agnostic to the ASR. Most of the datasets for speech recognition models consist of datasets collected from adult speakers. Consequently, the majority of commercial speech recognition systems typically tend to perform well on adult speakers. In other words, the limited diversity of speakers in the training datasets yields unreliable performance for minority (e.g., elderly) speakers due to the infeasible acquisition of training data. In response, this paper suggests a neural network-based voice conversion framework to enhance speech recognition of the minority. To this end, we propose a voice translation model including an unsupervised phonology clustering to extract linguistic information to fit the minority’s speech to a current acoustic model frame. Our proposal is a spectral feature adaptation method that can be placed in front of any commercial or open ASR system, avoiding directly modifying the speech recognizer. The experimental results and analysis demonstrate the effectiveness of our proposed method through improvement in elderly speech recognition accuracy.
Trustworthy Blockchain Gateways for Resource-Constrained Clients and IoT Devices

Resource-constrained blockchain clients and the Internet of Things (IoT) devices pose limitations in terms of processing and storing the entire blockchain ledger and mining blocks.Such clients and devices rely on a view of the blockchain provided by full nodes acting as gateways.However, gateway nodes sometimes can provide a distorted view of the blockchain that makes lightweight clients vulnerable to the eclipse attack.When under such an attack, a client cannot differentiate between a forked view of the blockchain and the legitimate blockchain ledger leading to fatal consequences and huge losses incurred.In this paper, we propose a data attestation solution that employs full nodes as validators to attest the responses reported by gateways of lightweight nodes.We leverage smart contracts to give lightweight clients confidence in the data reported as they are unable to validate it from the blockchain network themselves.The system governs the attestation process that comprises submitting attestation requests, approving them, recording the response of validators, and manage payments.Clients can, thereafter, provide their feedback about the validator/gateway performance in the form of a reputation score.We present the proposed system architecture and describe its implementation on the Ethereum blockchain network.We evaluate the proposed solution with respect to functionality testing, cost of execution, and security analysis of the developed smart contracts.We make our smart contracts code and testing scripts publicly available.
Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches.
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift

Recently proposed neural architecture search (NAS) methods co-train billions of architectures in a supernet and estimate their potential accuracy using the network weights detached from the supernet. However, the ranking correlation between the architectures' predicted accuracy and their actual capability is incorrect, which causes the existing NAS methods' dilemma. We attribute this ranking correlation problem to the supernet training consistency shift, including feature shift and parameter shift. Feature shift is identified as dynamic input distributions of a hidden layer due to random path sampling. The input distribution dynamic affects the loss descent and finally affects architecture ranking. Parameter shift is identified as contradictory parameter updates for a shared layer lay in different paths in different training steps. The rapidly-changing parameter could not preserve architecture ranking. We address these two shifts simultaneously using a nontrivial supernet-∏ model, called ∏-NAS. Specifically, we employ a supernet-∏ model that contains cross-path learning to reduce the feature consistency shift between different paths. Meanwhile, we adopt a novel nontrivial mean teacher containing negative samples to overcome parameter shift and model collision. Furthermore, our ∏-NAS runs in an unsupervised manner, which can search for more transferable architectures. Extensive experiments on ImageNet and a wide range of downstream tasks (e.g., COCO 2017, ADE20K, and Cityscapes) demonstrate the effectiveness and universality of our ∏-NAS compared to supervised NAS. See Codes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .