Reviewerly/exHarmony huggingface dataset:
Human Action Recognition Based on Three-Stream Network with Frame Sequence Features

In the field of human action recognition (HAR), two-stream models have been widely employed. In recent years, traditional two-stream network models have disregarded the interframe sequence characteristics of video, resulting in a decrease in model robustness when local sequence information and long-term motion information interact. In light of this, a novel three-stream neural network is proposed by combining the long-term and short-term characteristics of a frame sequence with spatio-temporal information. Initially, the optical flow sequence image frames and RGB image frames in the video are extracted, the optical flow motion information and image space information in the video is obtained, the corresponding time network and space network are entered, and the spatial information is entered into the sequence feature processing network; the three networks are then pretrained. At the conclusion of training, the operation of feature extraction is executed, the features are incorporated with the parallel fusion algorithm by adding weights, and the behavior categories are classified using Multi-Layer Perception. Experimental results on the UCF11, UCF50, and HMDB51 datasets demonstrate that our model effectively integrates the spatial-temporal and frame-sequence information of human actions, resulting in a significant improvement in recognition accuracy. Its classification accuracy on the three datasets was 99.17%, 97.40%, and 96.88%, respectively, significantly enhancing the generalization capability and validity of conventional two-stream or three-stream models.
Anchor-free face detection in the wild with an application to facial expression recognition

Facial expression recognition (FER) has been received more attention in the field of affective computing. FER starts from the basic tasks of bounding box detection and landmark localization. For the sake of simplicity, we call bounding box detection and landmark localization as face detection. In this paper, we aim at proposing a new face detection method for FER system to improve the detect precision. More specifically, we present a new face localization based on a full convolution one-stage object detection algorithm, namely FCOSFace. It utilizes the advantages of Feature Pyramid Network and center-ness loss, which extract the shallow and high-level semantic features and suppress the influence of negative samples. Furthermore, based on FCOSFace, we used the landmark detection with DLib on the detected face. In order to evaluate the efficiency of the proposed system, experiments on both face localization and FER databases have been conducted. Experimental results demonstrate our proposed method has well-performance on face detection. Lastly, we extend our proposed method in facial expression recognition, especially in two challenging emotion databases including FER2013 and SFEW-SPI. Results on facial expression databases see the our designed system has promising results on facial expression recognition.
Cosmos Cash: Public Permissionless approach towards SSI and Use Cases

In the digital world, digital identity is essential as it represents users in the same way that physical identity represents people in the physical world. Traditional identity management systems (IDM) manage digital identities with dis-tinctly personal and sensitive qualities associated with identity owners. In most traditional systems, identity subjects do not control their own identity nor do they control personal data. In contrast, a new self-sovereign identity (SSI) protocol approach gives people ownership and control over their data. Cosmos Cash is an emerging open-source public permissionless protocol designed to be regulatory compliant with respect to Know Your Customer (KYC), anti-money laundering (AML) tracking, Financial Action Task Force (FATF) travel rule, and identity management. Cosmos Cash uses a novel SSI approach for identity management by leveraging World Wide Web Con-sortium (W3C) specifications for decentralized identifiers (DID) and verifiable credentials (VC). This paper presents the design and implementation of the Cosmos Cash protocol. First, the paper presents the modules implemented in the Cosmos Cash protocol. Second, it demon-strates the architecture and working of the Cosmos Cash identity management system and evaluates its operational services and efficiency. Finally, based on the architecture developed, the paper compares the Cosmos Cash protocol to existing protocols such as uPort and Sovrin.
CPNet: A Hybrid Neural Network for Identification of Carcinoma Pathological Slices

In the medical field, pathological carcinoma images look much more complicated than other medical images. Identifying carcinoma pathology images is a time-consuming and error-prone task for regular doctors and even for some specialists. Nowadays, deep learning has been widely applied in medicine, which could significantly reduce the time cost and improve accuracy. To save time and improve the accuracy of identifying pathological carcinoma slices, we propose a novel ViT-CNN hybrid neural network called CPNet, specially for the classification of different categories of carcinoma pathological slices. CPNet achieves the state-of-the-art performance in PatchCamelyon and our own dataset. We adopt a transfer learning method to identify degrees of malignancy using a few samples. Furthermore, we design and develop a fast medical decision system, where we deploy the CPNet in it. The system could effectively assist doctors in identifying the cancer pathology images with high accuracy and speed. The code of CPNet is in https://github.com/GuanRunwei/CPNet.
Breast Cancer Tumor Image Classification Using Deep Learning Image Data Generator

Breast cancer is often encountered in females and is a prominent causative factor in mortality in women. Pathology needs time, but since technologies are limited, it is vital to design a system to effectively clear the uncertainty of breast cancer. For the categorization of benign and malignant tumors, many Deep Machine Learnings have been applied. Several of these techniques have already been assessed and compared in aspects of precision and accuracy. All approaches are programmed in Python and executed by either Google Collab, Jupyter Notebook, or Spyder which are Scientific Python Development Environment. Our problem statement deals with Deep learning for breast cancer risk prediction. It defines the prediction of the type of breast cancer using CNN as a deep learning technique that strengthens forecasting accuracy. It also demonstrates how deep learning technology can be used to diagnose breast cancer using Python. Our proposal undoubtedly explicates whether an individual is affected by a cancerous tumor (malignant) or non-cancerous tissue (benign). CNN can accept an input image, assign learnable weights and biases to various objects in the picture, and be able to differentiate one from the other. SVM and Random Forest Classifier were shown to be the most accurate for predictive modeling, with a 95.87 and 94.92 percent accuracy rate, respectively. In the instance of CNN, the utmost accuracy rate gained is 97.3 percent. In terms of statistics, activation functions like ReLu, and Sigmoid have been exploited to determine the upshots.
Mobile Robot Path Planning Based on the Focused Heuristic Algorithm

Aiming at the problems of low search efficiency, high search cost, and redundant search range in the traditional D* Lite algorithm in solving the path planning problem, the Focused D* Lite (FDL) algorithm is proposed. The proposed algorithm optimizes and adjusts the node and line respectively. Firstly, based on the current coordinates of mobile robots, the feasibility judgment and information transmission of obstacle information in eight neighborhoods are carried out to enhance the search capability of each step and ensure the effectiveness of the subsequent search. Secondly, the weight assignment is provided for the planned path to improve the concentration of the planned path, so that the algorithm can focus on the key and leading path, reduce the divergence of the algorithm, reduce invalid search and improve the efficiency of the algorithm planning. Simulation results show that the FDL algorithm is more efficient and also could maintain the same level of path quality.
Relation Graph Reasoning for Image-Text Matching

Image-text matching is one of the research hotspots in cross-modal field. Most of the current methods focus on digging the semantic matching degree between image and text to depict the external relations. However, the extraction of implicit semantic features is still not accurate enough to effectively model the internal relationships between entities. To address this problem, we propose a novel Relation Graph Reasoning Network(RGRN). RGRN can not only learn the concepts of entities and attributes through content reasoning, but also its topological structure reasoning can better obtain the implicit action and orientation relationships between visual entities. Besides, we also integrate the overall semantics to obtain the final visual representation, helping to cross the semantic gap between image and text. We conduct experiments on Flickr30K and MS-COCOCO datasets to verify the effectiveness of our model.
FCOS-Mask: Fully Convolution Neural Network for Face Mask Detection

Since the end of 2019, the coronavirus disease 2019 (COVID-19) has spread globally, posing enormous challenges to the global society. Wearing a mask has been proven to be the easiest and the most effective way to limit the spread of COVID-19, and it has become the rule in many public areas. This has also led to a growing demand for automatic real-time mask detection services to replace manual reminders. However, current research on mask detection still has limitations, and both the accuracy and speed can be further improved. In this paper, we propose FCOS-Mask, a one-stage anchor-free object detection method for face mask detection. We add a bottom-up feature augmentation path to the model’s neck and conduct Mosaic to strengthen the ability to detect objects in an unusual context. Moreover, we adopt CIoU and Soft-NMS to improve the training speed and detection accuracy on occluded faces. FCOS-Mask is tested on the Face Mask Detection dataset and achieves a higher 2.9% mAP compared to baseline with a real-time speed of 20.6 FPS on RTX 2070.
Review of Human Violence Recognition Algorithms

Violent behavior recognition is a specific research direction of human behavior recognition. Published reviews mainly focuses on the development of deep learning in the field of behavior recognition, and the attention to violent behavior recognition is low. According to the different methods used, this paper analyzes various algorithms from the perspective of violence recognition based on manual descriptor and deep learning, and introduces the commonly used datasets. Finally, the characteristics of different models and algorithms are summarized, and discuss potential problems and future work in the field of violence recognition.
Multi-Object Tracking Based on Deep Path Aggregation Network

Multi-object tracking is an important task in the field of computer vision. With the rapid development of object detection and re-identification, object tracking performance has been improved accordingly. It has been proved that FairMOT jointly performs object detection and re-identification tasks in a single network and obtains a high level of detection and tracking accuracy. Based on the DLA model, this paper proposes a novel tracker equipped with a Deep Path Aggregation Network (DPANet) to effectively improve multi-object tracking accuracy. The network enhances the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation. In the final stage, we further concatenate multiple feature maps in parallel to maintain rich high-resolution representations. Experimental results show that the tracker outperforms the most previous state-of-the-art on several public datasets.
Two-Stage Market-Based Task Allocation for Blockchain-Based Cyber-Physical Production Systems

As Multi-Agent Systems, Cyber-Physical Production Systems (CPPS) require mechanisms for the allocation of tasks to multiple agents such as workstations or mobile robots. One approach is market-based task allocation (TA) which faces challenges related to fraud and intransparency. Integrating blockchain technology (BCT) into TA enables transparency and tamper resistance. It also allows for the downstream integration of financial transactions in CPPS. This paper proposes a method for two-stage market-based TA in CPPS enabled by BCT. A formal definition for a reverse first-price-sealed-bid auction including the payment process is presented for the blockchain-based two-stage auctioning in a CPPS. The proposed method is implemented in a prototype and tested with a focus on scalability. Transaction times and costs are considered within the experiments. The evaluation reveals that the proposed process is functionable and that financial transactions can be completely automated within CPPS. The time and gas usage of transactions scale in most scenarios and only take a few seconds for completion.
Fairledger: a Fair Proof-of-Sequential-Work based Lightweight Distributed Ledger for IoT Networks

Blockchain has been recognized as a promising solution to construct a tamper-proof and trust-free decentralized framework for Internet of Things (loT) systems. However, directly applying cryptocurrency-oriented blockchains in loT networks still meets tremendous limitations. Proof-of-Work (PoW) consensus protocol enables a blockchain to achieve pseudonymity, scalability and probabilistic finality in an asynchronous and open-access network environment. The compute-intensive PoW favors nodes possessing more computing power, but fairness is an important requirement in highly heterogeneous loT networks. A blockchain designed for loT edge environments must consider devices with various constraints on computation power. This paper proposes Fairledger, a fair Proof-of-Sequential-Work (PoSW) based lightweight distributed ledger for small-scale, permissioned loT networks. By combining efficient verifiable delay function (eVDF) and Proof-of-Credit (PoC) puzzle, PoSW consensus protocol guarantees fairness by requiring all the miners perform a fixed sequential computing steps to represent PoW for block generation despite their hardware resources. Due to the virtual mining manner of PoSW, Fairledger demonstrates computing efficiency compared to traditional PoW blockchains. In addition, Fairledger is less susceptible to “long-range” and “nothing-at-stake” attacks than Proof-of-Stake (PoS) protocols are. A proof-of-concept prototype is implemented, and the experimental results verify the feasibility of running Fairledger in a physical loT network with higher throughput, less computation and communication cost, and better security guarantees.
Edge-Aware Fusion for Camouflaged Object Detection

Camouflaged object detection (COD) is more challenging than traditional object detection because of the high similarity in color and texture between the camouflaged object and the background. Current COD models employ deep neural networks and focus more on the deep semantic information, ignoring the edge boundaries and not making good use of the information in high-resolution feature maps. To this end, we propose an edge fusion network which fuses low-resolution but high-level semantic information with high-resolution but low-level spatial information. The low resolution features are first fused at multiple scales, which enhances the high resolution feature map. The two maps are fused by an edge-aware fusion module that can better exploit the semantic and spatial information to obtain the final prediction map. Our models achieve competitive results on public datasets.
Audio Based Action Recognition for Monitoring Elderly Dementia Patients

Dementia is a disease that causes a decrease in memory and thinking. This condition has an impact on their lifestyle, social skills, and their daily activities. Generally, people with dementia live with care without a family companion. Treatment for dementia patients can be in the form of therapy for activities of daily living. Due to the limitations of the medical side in supervision, one way to find out the activities carried out by patients can be done by using voice. The system that will be used is Audio Based Action Recognition which can monitor the activities of dementia patients through voice data. The voice data is processed by sound signal processing using a deep learning Convolutional neural network (CNN) method to get the output about activities carried out by dementia patients. The system will record the voice of the dementia patient every 5 seconds. Then do feature extraction from audio data to get spectrogram data. Spectrogram data will be labeled according to the action wanted to identify. Then the system performs training and action classification using CNN. Action data of dementia patients recognized will be sent to the server to be used as a habit analysis of dementia patients. In testing, the system recognized the action with the pre-trained model for ResNet101 getting 69% accuracy, DenseNet 87%, InceptionV3 89%, MobileNet 95%, and the author's design model got 98% accuracy in a noise level of 30db.
Block Pruning with UTXO Aggregation

The ledger of a blockchain is designed to be an append-only data structure, which means that its size only increases. Therefore, to run a node, it is a burden to the node operator to store the entire blockchain ledger. We propose a new method, UTXO aggregation, to reduce the high occupancy of storage. In this method, UTXOs are periodically merged, and a special block including all merged UTXOs is generated. Unlike Bitcoin's block file pruning, this method can allow other nodes to bootstrap and does not require a separate database that stores only UTXOs. This method also has the effect of reducing UTXOs with low economic value. By applying this method to Bitcoin, the storage occupancy of running a node can be immediately reduced to approximately 1/100, and an economic effect of up to 14120 BTC can be expected.
Zero-DCE with HSV loss for Low-Light Image Enhancement

Many surveillance or reconnaissance images are taken at night without auxiliary light. The images are usually very dim and need to be enhanced. Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. However, Zero-DCE can enhance the images and perform well, color compensation is required. The RGB and HSV color space are compared and analyzed, and the differences, advantages and disadvantages between the two are compared. The HVS loss algorithm is considered to be converted from RGB to HSV color field, and the HSV algorithm is constructed in order to obtain new effects in color compensation. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively.
Automated Database Management System using Artificial Intelligence

Face recognition from an image or video is a promising biometrics research topic. Video surveillance cameras are popular in public places, and they provide important safety benefits. Since the entity does not require any assistance, the face recognition in a surveillance system has become a critical research topic. It is well-known all over the world. Face recognition outperforms other biometric recognition techniques in terms of uniqueness and validity. The human face recognition is a challenging topic in the computer vision due to its diverse and dynamic nature. Precision and recognition speed are considered as the critical challenges in this industry. The primary objective of the proposed work is to examine various face detection and identification methodologies in order to deliver a comprehensive solution for facial recognition and detection, which is more effective and has a great response in an image-based way. This gives a solution based on facial rich dataset research by incorporating imagery, attitudes, ethnicity, and colour.
DeSAT: Towards Transparent and Decentralized University Counselling Process

The admission process in academic institutions (universities, colleges, etc.) is more digitized than ever. Starting from standardized tests to application processing, to shortlisting on the basis of merit, to even document verification, everything is carried out through online processes now. However, in spite of having huge benefits in terms of convenience, existing admission processes severely lack transparency. The entire process is dependent on certain central authoritative entities such as the testing authorities followed by the institutes themselves. Moreover, critical tasks such as verifying educational and identity-related documents of students is a tedious affair and the effort is duplicated across all institutions. In this work, we attempt to overcome these limitations of the existing workflow of academic institutes' admission process by designing a distributed ledger based framework that involves the academic institutes, testing authorities, document and credential validators, as well as the students. Our framework DeSAT uses verifiable credentials together with a permissioned ledger to remove the duplicate efforts in verification of test scores as well as validation of students' documents. In addition, it makes the entire process transparent and auditable while enforcing fair merit-based seat allotment through smart contracts. Through a prototype implementation using Hyperledger Fabric, Indy, and Aries, we demonstrate the practicality of DeSAT and show that our system offers acceptable performance while scaling with the number of participating institutions.
Blockchain-based Authenticated Stego-Channels: A Security Framework and Construction

While blockchain has been an innovative and disruptive technology across many different domains, one area in which is has proven to be particularly game changing is in steganography. Steganography offers data privacy by hiding the existence of data, a property that is distinct from confidentiality (i.e. data existence is known but access is restricted) and authenticity (data existence is known but manipulation is restricted). Combinations of the latter two properties are common in analyses such as Authenticated Encryption with Associated Data (AEAD), yet there is a lack of research combining such guarantees with steganography. In this work, we introduce the security definition of Authenticated Stegotext with Associated Data (ASAD) which captures steganographic properties even when there is contextual information provided alongside the hidden data. Furthermore, we present a real-world stego-embedding scheme, Authenticated SteGotex with Associated tRansaction Data (ASGARD), that leverages a blockchain-based application as a medium for sending hidden data. We analyze ASGARD in our framework, demonstrating that it meets Level-4 ASAD security.
CASC: Content Addressed Smart Contracts

Blockchains (BC) depend on content-based addressing, where data is identified and retrieved over its hash. Location-based addressing is used when addressing Smart Contract (SC) code. Using location-based addressing to refer to SC code risks that the code at the location is not the one expected, and it may even be manipulated. Furthermore, addresses for the same SC are different when deployed on multiple BCs. Code referring to other SCs must be adapted upon deploying them to another BC. Besides preventing these risks, content-based code addressing shows additional benefits, like storing the SC code outside the BC state. Therefore, this work developed a concept to use and benefit from content-based addressing during the development and operation of SCs, while retaining the convenience of location-based addressing.