Reviewerly/exHarmony huggingface dataset:
Edge-based real-time face logging system for security applications

In this work, we have proposed a state-of-the-art face logging system that detects and logs high quality cropped face images of the people in real-time for security applications. Multiple strategies based on resolution, velocity and symmetry of faces have been applied to obtain best quality face images. The proposed system handles the issue of motion blur in the face images by determining the velocities of the detections. The output of the system is the face database, where four faces for each detected person are stored along with the time stamp and ID number tagged to it. The facial features are extracted by our system, which are used to search the person-of-interest instantly. The proposed system has been implemented in a docker container environment on two edge devices: the powerful NVIDIA Jetson TX2 and the cheaper NVIDIA Jetson N ano. The light and fast face detector (LFFD) used for detection, and ResN et50 used for facial feature extraction are optimized using TensorRT over these edge devices. In our experiments, the proposed system achieves the True Acceptance Rate (TAR) of 0.94 at False Acceptance Rate (FAR) of 0.01 while detecting the faces at 20–30 FPS on NVIDIA Jetson TX2 and about 8–10 FPS on NVIDIA Jetson N ano device. The advantage of our system is that it is easily deployable at multiple locations and also scalable based on application requirement. Thus it provides a realistic solution to face logging application as the query or suspect can be searched instantly, which may not only help in investigation of incidents but also in prevention of untoward incidents.
Quantum-Enhanced Machine Learning

Quantum Computing (QC) has come into view as an emerging technology. Machine Learning(ML) and QC have become ubiquitous in recent years. This study has focused on surveying the enhancement of ML with QC. Applications of QC in different fields are thoroughly discussed. The Biomedical/Healthcare field of research has potential with QC-enhanced ML (QML). Advancements in an amalgamation of ML with QC are further elaborated. QML concepts are introduced with current research advances. The survey ends with challenges in the new technology and future work considerations.
Research on Active Gravity Center Fault Tolerance Control of Fuel System

The active center of gravity control technology is to control the position of the center of gravity actively through reasonable fuel transmission in the course of flight, so as to realize the reasonable matching of the center of gravity and the pneumatic focus of the aircraft, so as to optimize the flight performance, reduce the maintenance cost give full play to the potential of the aircraft. This paper presents a method to change the relevant fuel control laws and implement the active center of gravity fault-tolerant control technology in fault mode. The active center of gravity fault-tolerant control technology realizes the design of fuel tank oil delivery scheme in fault mode by changing the fuel transmission logic in fault mode innovatively. The fault-tolerant control law can be verified by AMESIM and MATLAB simulation.
Design of Target Tracking Filter for Multi-rate System Based on Radar Measurement

To settle the multi-rate issues for multi-radar measurement system, an algorithm updated at the sampling points is deduced in this paper. Firstly, by introducing the principle of superposition, the state update model for linear system is transformed into a model updated at sampling points. Secondly, a novel filtering algorithm is designed at sampling points based on the projection theorem. Then, relying on the third-order spherical-radial rule, the numerical realization for the designed algorithm is provided. Finally, the simulation is carried out to illustrate the presented algorithm is valid.
A Memory-Efficient Hardware Architecture for Deformable Convolutional Networks

In recent years, deformable convolutional networks are widely adopted in object detection tasks and have achieved outstanding performance. Compared with conventional convolution, the deformable convolution has an irregular receptive field to adapt to objects with different sizes and shapes. However, the irregularity of the receptive field causes inefficient access to memory and increases the complexity of control logic. Toward hardware-friendly implementation, prior works change the characteristics of deformable convolution by restricting the receptive field, leading to accuracy degradation. In this paper, we develop a dedicated Sampling Core to sample and rearrange the input pixels, enabling the convolution array to access the inputs regularly. In addition, a memory-efficient dataflow is introduced to match the processing speed of the Sampling Core and convolutional array, which improves hardware utilization and reduces access to off-chip memory. Based on these optimizations, we propose a novel hardware architecture for the deformable convolution network, which is the first work to accelerate the original deformable convolution network. With the design of the memory-efficient architecture, the access to the off-chip memory is reduced significantly. We implement it on Xilinx Virtex-7 FPGA, and experiments show that the energy efficiency reaches 50.29 GOPS/W, which is 2.5 times higher compared with executing the same network on GPU.
Phishing Detection Techniques: A Comparative Study

Connectivity of services over the internet is increasing, allowing threat actors to experiment in new areas and domains. Data and reports show that cyber-attacks are on the rise. People get targeted by various luring techniques, which makes them susceptible to becoming a victim of fraud. In this paper, we have applied inclusion and exclusion criteria to select techniques of phishing prevention and detection for study. The goal of our study is to identify the challenges in prevention and detection of phishing websites which looms large today and requires an immediate solution to prevent such acts. In our study we have presented the existing work in this area, their technique used for phishing detection, analyzed their applicability to phishing detection and their accuracy rate. Our study reveals that researchers have used website properties like images, text, frames, and metadata for phishing detection and that their accuracy rate ranges between 92 to 99.95 percent. We feel that a browser-based plug-in may in the future be effective in preventing fraud to a larger extent.
An Infrastructure for Integrated Temperature Monitoring and Contact Tracing

The COVID-19 pandemic has urged national governments worldwide to recommend several health-safety measures, including social distancing, the use of face masks, and sanitization. In this paper, we focus on body temperature monitoring and contact tracing, and we introduce a novel infrastructure designed to provide National Healthcare Systems with a centralized repository for systematic data collection and analysis. To this end, our system enables aggregating body temperature monitoring and contact tracing information acquired from a distributed network of heterogeneous data collection nodes. By doing this, we aim at collecting more data using different types of existing measurement devices. Also, the information architecture of the proposed system supports analyzing data in a centralized fashion. In this regard, we highlight the components that render our approach more suitable than current solutions in supporting cohesive intervention protocols and we describe the advantages in terms of better response against the COVID-19 pandemic and other types of health-safety emergencies.
Video Watermarking Schemes Resistance Against Tampering Attacks

This paper reviews the video watermarking schemes resistance against tampering attacks. There are several transform methods which are used for Video Watermarking including Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete wavelet transform (DWT) and are discussed and compared in this paper. The results are presented in a table with a summary of their advantages.
Measuring Developers' Expertise Based on Version Control Data

Developers' fluctuation in the lifetime of a software product might deteriorate the understanding of the source code to a level where developer expertise of some modules drops to a dangerously low point. It is important for the project management to identify such critical modules to avoid complete knowledge loss. This paper presents a developer-centered static analysis tool that is intended to show individual expertise in large software projects. The expertise value is computed for each file through repository mining of the version control system of the project. The calculated value is based on the quality of commits per developer. The results of the proposed method have been validated on the CodeChecker open-source project, comparing against the findings of a user questionnaire filled by developers of the project on their expertise.
Classification of Idioms and Literals Using Support Vector Machine and Naïve Bayes Classifier

With the large amount of text data being populated on World Wide Web (WWW), Text classification becomes one of the most inevitable preprocessing techniques in any text processing application. There exist many text classification scenarios such as Sentiment analysis spam detection, Information extraction, and Intent detection. Intent detection is one of the most complex text classifications which have many facets such as using polysemy words, metaphors, idioms, and phrases. This paper attempts to classify idioms and literals. Idioms are those which convey an indirect meaning of the words present in them whereas, literals are those text which are identical to idioms but convey the direct meaning of the words in them. This paper has attempted to do this classification using Machine Learning algorithms, namely, SVM and Naïve Bayes classifier. The classifiers are tested using a dataset that is annotated by around 3 domain experts and consists of 735 idioms and 735 literals. SVM achieved an accuracy of 87.30% and Naïve Bayes achieved an accuracy of 82.09%.
Study on the application of intelligent security technology in ports

Ports are critical gateways to China’s foreign trade. Once attacked by terrorists, it will cause significant harm to the public security of the port area, the region, and even the national interest and people’s livelihood. Through the study of the existing intelligent security technology, this article designed the layout scheme of the port intelligent security system, and constructed the 3D prevention and control network of port intelligent security to initially establish the intelligent security system in a bid to realize the intelligent management of personnel, vehicles, buildings and structures. In this sense, the security work has changed from post-verification to active prevention, not only improving the ports’ management ability to deal with social security emergencies, but also maintaining its normal production order.
A Forward Error Compensation Approach for Fault Resilient Deep Neural Network Accelerator Design

Deep learning accelerator is a key enabler of a variety of safety-critical applications such as self-driving car and video surveillance. However, recently reported hardware-oriented attack vectors, e.g., fault injection attacks, have extended the threats on deployed deep neural network (DNN) systems beyond the software attack boundary by input data perturbation. Existing fault mitigation schemes including data masking, zeroing-on-error and circuit level time-borrowing techniques exploit the noise-tolerance of neural network models to resist random and sparse errors. Such noise tolerant-based schemes are not sufficiently effective to suppress intensive transient errors if a DNN accelerator is blasted with malicious and deliberate faults. In this paper, we conduct comprehensive investigations on reported resilient designs and propose a more robust countermeasure to fault injection attacks. The proposed design utilizes shadow flip flops for error detection and lightweight circuit for timely error correction. Our forward error compensation scheme rectifies the incorrect partial sum of the multiply-accumulation operation by estimating the difference between the correct and error-inflicted computation. The difference is added back to the final accumulated result at a later cycle without stalling the execution pipeline. We implemented our proposed design and the existing fault-mitigation schemes on the same Intel FPGA-based DNN accelerator to demonstrate its substantially enhanced resiliency against deliberate fault attacks on two popular DNN models, ResNet50 and VGG16, trained with ImageNet.
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation

While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency. In this paper, we propose a tiny deep neural network of which partial layers are recursively exploited for refining its previous estimations. During its iterative refinements, we employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. Our network is trained to be aware of the uncertainty in its current predictions to efficiently gate at each iteration, estimating variances after each loop for its keypoint estimates. Additionally, we investigate the effectiveness of end-to-end and progressive training protocols for our recursive structure on maximizing the model capacity. With the proposed setting, our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.
Sentiment Analysis From Machine Learning to Deep Learning

Sentiment analysis has been considered as a vital method to analyze a huge amount of documents in digital forms that are widespread and continuously increasing. In general, sentiment analysis plays an important role in social media analysis and business analysis. This paper illustrates sentiment analysis from machine learning methods (TF-IDF, Naïve Bayes, SVM) to deep learning methods (Embedding, LSTM). We explain how the technique developed from sentence level to document level and analyze their advantages and disadvantages.
Privacy-Preserving Medical Image Segmentation via Hybrid Trusted Execution Environment

Recently, it is reported that the-state-of-the-art secure protocol is able to segment a three-dimensional heart CT scan in roughly 3,000 seconds, without revealing any sensitive information related to the parties involved in the computation. In this work, building upon the existing mix-protocol approach, we make use of the trusted execution environment (TEE) to implement a more efficient privacy-preserving medical image segmentation protocol. In the experiment, we show that by offloading the computations of single-party operators to trusted hardware, the latency for a round of privacy-preserving segmentation can be further reduced by 25×.
Research on Face Recognition Technology Based on Improved YOLO Deep Convolution Neural Network

Abstract With the development of information technology, deep learning has become a research hots pot in the field of computer vision, among which face recognition technology, as one of its applications, has received extensive attention in recent years. A face recognition method based on improved YOLOv3 deep convolution neural network is proposed. The feature pyramid network is used to obtain the four scale features of the target to fuse the shallow features and the deep features, and the influence weight of the loss function is adjusted according to the size of the detected target, so as to enhance the detection effect of small targets and mutually occluded objects, and the Softmax function is used for classification and recognition. Experimental results on mixed data sets show that this method can improve the real-time performance of face detection.
Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets

Untrimmed videos on social media or those captured by robots and surveillance cameras are of varied aspect ratios. However, 3D CNNs usually require as input a square-shaped video, whose spatial dimension is smaller than the original. Random- or center-cropping may leave out the video's subject altogether. To address this, we propose an unsupervised video cropping approach by shaping this as a retargeting and video-to-video synthesis problem. The synthesized video maintains a 1:1 aspect ratio, is smaller in size and is targeted at video-subject(s) throughout the entire duration. First, action localization is performed on each frame by identifying patches with homogeneous motion patterns. Thus, a single salient patch is pinpointed per frame. But to avoid viewpoint jitters and flickering, any inter-frame scale or position changes among the patches should be performed gradually over time. This issue is addressed with a polyBézier fitting in 3D space that passes through some chosen pivot timestamps and whose shape is influenced by the in-between control timestamps. To corroborate the effectiveness of the proposed method, we evaluate the video classification task by comparing our dynamic cropping technique with random cropping on three benchmark datasets, viz. UCF-101, HMDB-51 and ActivityNet v1.3. The clip and top-1 accuracy for video classification after our cropping, outperform 3D CNN performances for same-sized random-crop inputs, also surpassing some larger random-crop sizes.
The Application Status and Development Prospects of Artificial Intelligence and Industrial Robots in the Manufacturing Industry

Abstract China has achieved quite good results in the research and application of information technology and other fields. The continuous advancement of technologies such as AI, 5G communications, and cloud computing have played a key role in the development of intelligence, convenience, and innovation in various industries in China. The application of AI as a technical means in the Internet industry has been relatively mature and has achieved relatively good results. However, AI has not yet achieved satisfactory results in the development and upgrading of the manufacturing industry. As the most critical industry in the real economy, the manufacturing industry has played a decisive role in the sustainable development of China’s real economy. Therefore, it is necessary to promote the deep integration of AI technology and manufacturing and to further advance the process of intelligent manufacturing. Accordingly, this article discusses the application status and development prospects of AI and industrial robots in the manufacturing industry.
Security Model using Intrusion Detection System on Cloud Computing Security Management

The use of Cloud Computing has advantages that have the potential to be an extraordinary service in supporting work procedures that can be carried out anywhere via the internet. Through the internet, we can carry out activities anywhere, but it will also cause other problems that will arise because of access via the internet, one of which is from the security side, system and security management on a computer is very crucial for securing a lot of data on it. The purpose of this research is to provide suggestions related to security management in Cloud Computing, especially in the sectors of Integrity, Authority, Privacy, and Confidentiality, also propose the proposed Concepts and Recommendations which are expected to improving security for Cloud Computing security and detection intruders who break into cloud computing systems using Intrusion Detection System and networks using Intrusion Detection System methods. Further research can be implemented and performed for improving security on cloud computing.
Assisted Counterexample-Guided Inductive Optimization for Robot Path Planning

This paper presents and evaluates a novel offline mobile robot path planning algorithm based on the Assisted Counterexample-Guided Inductive optimization (ACEGIO) technique. ACEGIO employs a technique to assist the Counterexample-Guided Inductive optimization (CEGIO) technique by requesting counterexamples from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers to improve its efficiency and effectiveness. In particular, Gradient Descent (GD) technique is implemented as an auxiliary technique to CEGIO. GD-Assisted Counterexample-Guided Inductive optimization (ACEGIO-GD) has been successfully applied to obtain two-dimensional paths for autonomous mobile robots using off-the-shelf SAT and SMT solvers. Experimental results demonstrate that the ACEGIO-based path planning algorithm has substantial improvements in efficiency and effectiveness compared to the traditional CEGIO-based path planning algorithm, which allows generating the optimal paths for autonomous mobile robots with much less execution time. If compared to other traditional path planning optimization techniques (e.g., GA and PSO), the execution time of the proposed algorithm is relatively high, whereas its performance is stable, reliable and robust.
Reliability Aware Mechanism to ensure increased Fault Tolerance using Throttle Load Balancer

Virtual Machines are created to provide sharable resources to the physical machines with limited resources with the help of Cloud Computing. With the advancement in the technology, the cloud users are increasing rapidly. Even though Cloud resources are infinite but they are not enough to fulfill the increasing demands of the users. This paper proposed the mechanism of handling multiple requests from multiple users to the cloud. It has two parts: in the first part all the VM's are monitored and the optimal VM with sufficient resources to handle the requests is selected and in the second part the tasks are allocated the required resources. It also checks the load on each virtual machine by continuous monitoring and if the load is increased, migration is done to the next VM using the phenomenon of Throttle Load Balancer. This complete system handles the faults even before it is occurred, so it falls under the proactive fault tolerance. The simulation is conducted within Netbeans IDE and Cloudsim 4.0 and evaluation parameters are load balancing degree, throughput and execution cost.