Reviewerly/exHarmony huggingface dataset:
Privacy-Enhanced Data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

A significant number of cloud storage environments are already implementing deduplication technology. Due to the nature of the cloud environment, a storage server capable of accommodating large-capacity storage is required. As storage capacity increases, additional storage solutions are required. By leveraging deduplication, you can fundamentally solve the cost problem. However, deduplication poses privacy concerns due to the structure itself. In this paper, we point out the privacy infringement problem and propose a new deduplication technique to solve it. In the proposed technique, since the user’s map structure and files are not stored on the server, the file uploader list cannot be obtained through the server’s meta-information analysis, so the user’s privacy is maintained. In addition, the personal identification number (PIN) can be used to solve the file ownership problem and provides advantages such as safety against insider breaches and sniffing attacks. The proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication, and for smaller file sizes, the time required for additional operations is similar to the operation time, but relatively less time as the file’s capacity grows.
Pedestrian Detection Based on Center, Temperature, Scale and Ratio Prediction in Thermal Imagery

Pedestrian detection has become an important branch in the field of object detection. However, pedestrian detection algorithms based on visible imagery meet many challenges under low lighting or no lighting conditions. Thermal imagery can effectively compensate the information loss of visible imagery in the night environment. In order to solve the problem of pedestrian detection at night or in other bad conditions with limited vision, a pedestrian detection method based on center, temperature, scale and ratio prediction in thermal imagery is proposed. The proposed method contains two parts: (1) feature extraction part; and (2) center, temperature, scale and ratio prediction part. The feature extraction part uses ResNet-101 network to extract high-level semantic features from input thermal imagery. In the center, temperature, scale and ratio prediction part, the center branch is to determine whether there is a target center point in each position of feature map, which is a binary classification problem. The temperature branch is to determine whether the center point is a pedestrian target radiating heat or the background. Scale prediction branch and aspect ratio prediction branch are to determine the size of the target, which is a regression problem. We test the proposed detection method in FLIR advanced driver assistance system (ADAS) dataset. Experimental results show that the proposed method can effectively deal with pedestrian detection in low light and dark environment, and its detection performance is better than that of the baseline of nighttime pedestrian detection methods.
Implementing Secure Reporting of Sexual Misconduct - Revisiting WhoToo

Reporting sexual assault or harassment is notoriously difficult, and even though more victims are coming forward every year, a significant percentage of victims do not formally report it (Morgan and Oudekerk - U.S. Department of Justice). Studies have shown that most sexual assault episodes occur by repeat perpetrators and that people are more likely to report if they know that other victims of the same aggressor exist (Callisto Homepage). Recently, the WhoToo protocol (Kuykendall, Krawczyk and Rabin - POPETS 2019) presented a system in which the identities of the accuser and the accused are protected until a certain pre-specified number (quorum) of victims reports the same perpetrator. We revisit this protocol from an implementation perspective, shedding light on necessary clarifications and optimizations.
Innovative Research of Blockchain Technology in the field of Computer Monitoring of Hydropower Station

Early power stations in China have been in operation for a long time. Due to the limitations of design, construction materials and technological level at that time, many hydroelectric generators have some defects. The computer monitoring and control system of a hydropower station refers to the measurement, control, protection and monitoring of the entire equipment of a hydropower station completed and realized by the computer system. The computer monitoring and control system of hydropower station replaces the traditional control equipment, fault recording equipment, monitoring and measuring equipment and relay protection equipment. This paper studies the full homomorphic encryption algorithm, introduces the zero-knowledge proof technology into the block chain trading protocol based on the full homomorphic encryption, and constructs the block chain that protects data privacy.
Visual gesture recognition based on hand key points

Abstract In recent years, computers have become a part of people's daily life, and the interaction between people and computers has increasingly become a hot spot in the field of scientific research. Gesture recognition based on vision is an indispensable part of the new generation of human-computer interaction. This paper presents a visual gesture recognition method based on hand key points, which realizes the detection of hand key points in the current input image and the recognition of defined gestures. It provides a new technical scheme for human-computer interaction application. Compared with the current mainstream solutions that directly train the defined gestures and obtain the gestures in the input image through template comparison detection, this paper first uses the rigid characteristics of the palm to design a palm detection model instead of directly detecting the entire hand. After detecting the presence of hand in the picture, the hand key point model locates 21 3D hand key point coordinates in the previously detected hand area through direct coordinate prediction. Finally, the meaning of gestures in the input image is obtained through the positional relationship between the key nodes. This solution achieves 95.7% of the accuracy of detection, and improves the FPS from 21-23 to 30-31.
Valid inferential models for prediction in supervised learning problems

Prediction, where observed data is used to quantify uncertainty about a future observation, is a fundamental problem in statistics. Prediction sets with coverage probability guarantees are a common solution, but these do not provide probabilistic uncertainty quantification in the sense of assigning beliefs to relevant assertions about the future observable. Alternatively, we recommend the use of a {\em probabilistic predictor}, a data-dependent (imprecise) probability distribution for the to-be-predicted observation given the observed data. It is essential that the probabilistic predictor be reliable or valid, and here we offer a notion of validity and explore its behavioral and statistical implications. In particular, we show that valid probabilistic predictors must be imprecise, that they avoid sure loss, and that they lead to prediction procedures with desirable frequentist error rate control properties. We provide a general construction of a provably valid probabilistic predictor, which has close connections to the powerful conformal prediction machinery, and we illustrate this construction in regression and classification applications.
Disentangled representation learning GANs for generalized and stable font fusion network

Automatic generation of calligraphy fonts has attracted broad attention of researchers. However, previous font generation research mainly focused on the known font style imitation based on image to image translation. For poor interpretability, it is hard for deep learning to create new fonts with various font styles and features according to human understanding. To address this issue, the font fusion network based on generative adversarial networks (GANs) and disentangled representation learning is proposed in this paper to generate brand new fonts. It separates font into two understandable disentangled features: stroke style and skeleton shape. According to personal preferences, various new fonts with multiple styles can be generated by fusing the stroke style and skeleton shape of different fonts. First, this task improves the interpretability of deep learning, and is more challenging than simply imitating font styles. Second, considering the robustness of the network, a fuzzy supervised learning skill is proposed to enhance the stability of the fusion of two fonts with considerable discrepancy. Finally, instead of retraining, the authors' trained model can be quickly transferred to other font fusion samples. It improves the efficiency of the model. Qualitative and quantitative results demonstrate that the proposed method is capable of efficiently and stably generating the new font images with multiple styles. The source code and the implementation details of our model are available at https://github.com/Qinmengxi/Fontfusion.
Wearable IoT based diagnosis of prostate cancer using GLCM-multiclass SVM and SIFT-multiclass SVM feature extraction strategies

Purpose This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life. Design/methodology/approach The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer. Findings The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set. Originality/value The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.
Application of SSD core detection algorithm in intelligent visual monitoring of examination room

In the examination room monitoring, the traditional mode relies heavily on human resources, and only uses the surveillance camera as an auxiliary tool. In order to solve the problem of intelligent visual monitoring in the examination room, this paper proposes to use SSD core detection algorithm to realize real-time invigilation based on Deep-learning, and verifies the detection performance of the algorithm through data set test. The results show that the average accuracy of SSD300 algorithm is 79.8%, the detection frame rate is 46, and the algorithm performance is the best; at the same time, the accuracy and recall rate of SSD300 algorithm are improved by 3.0% and 3.4% respectively. This study has achieved good results, which shows that the proposed detection algorithm and optimization ideas have a certain application value.
Image Enhancement and Object Recognition for Night Vision Traffic Surveillance

Image processing is a vital task. Image processing is very useful in analyzing, detecting, surveillance systems, etc. The most challenging task in detection and surveillance systems is capturing and enhancing images during dark light. Nighttime low-illumination image enhancement is desired for outdoor computing vision applications. Nighttime image and video processing are crucial tasks because of the color density, low brightness, low contrast, and in addition to the high amount of noise available in the captured image or video. Hence, to overcome this challenge the Dark Channel Prior (DCP) filter is used in the proposed work. This filter is used to improve the visibility, color contrast, and brightness of the image or video captured during nighttime. Another challenge in real-time applications is the processing speed, which can be improved by implementing the algorithms on a Field-Programmable Gate Array (FPGA). In this study, an Alveo board was used to improve the processing speed. The board was officially introduced by Xilinx.
A Survey of Preprocessing Methods for Marine Ship Target Detection Based on Video Surveillance

Ship target detection based on video surveillance has an important application in maritime security. In order to reduce the interference caused by the complex marine environment, it is usually necessary to preprocess the captured images. Based on the current research results of ship video detection at sea, this paper summarizes the commonly used ship target detection preprocessing methods under visible light detection conditions, including: image format conversion, video image anti jitter, defogging processing, image filtering, binary processing and morphological filtering. On this basis, the development of pretreatment technology and the research and development strategy of ship target detection system are discussed.
Deep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review

In recent years, deep learning as a subfield of machine learning has gained increasing attention due to its potential advantages in empowering autonomous systems with the ability to automatically learn underlying features in data at different levels of abstractions, to build complex concepts out of simpler ones and to get better with experience without being explicitly programmed. This book chapter provides a comprehensive review on the applications of deep learning in unmanned autonomous vehicles. We focus on particular research efforts that employ deep learning techniques to endow autonomous vehicles with different cognitive functionality, following the cognitive cycle of autonomous vehicles. This cognitive cycle of Sense-Aware-Decide-Act-Adapt-Learn extends the deliberative cycle of Sense-Decide-Act by adding situation awareness, adaptation and learning capabilities to autonomous vehicles. Potential applications of deep learning and major challenges are highlighted in this chapter.
Ranking of Teaching Evaluation Index System of Public Physical Education Curriculum Based on Management by Objectives Using Analytic Hierarchy Process

Teaching evaluation is an important means to ensure the teaching quality of public physical education in colleges and universities. At present, physical education in colleges and universities is constantly changing, and the evaluation system of public physical education has also changed greatly. Based on the perspective of management by objectives, this paper makes a comprehensive evaluation of physical education teaching from two aspects: teachers’ teaching preparation and practical teaching. It takes the cultivation of students’ sports habits and lifelong sports consciousness as the ultimate goal, establishes 22 indexes to form an index system, and follows the requirements of the new curriculum teaching reform for the development of physical education. In order to continuously improve the level of physical education teaching as the guiding concept, the indexes are refined and decomposed. To determine the weight score of the three-level indexes and the index weight ranking, AHP is used, which is a formal method used to derive ranking from pairwise comparison technique, in accordance with a certain logical relationship, and provide reference for the evaluation of public physical education teaching in colleges and universities. In order to achieve the multiple objectives of public physical education curriculum as the starting point, attention should paid to the teaching focus of public physical education curriculum, and a comprehensive reform strategy should be put forward.
Methodologies in Chat Bot Learning

Chatbots, or conversational interfaces as they are furthermore known, present another way for individuals to participate with PC systems. By and large, to get a request answered by an item program included using a web searcher, or adjusting a structure. A chatbot empowers a customer to simply present request along these lines that they would address a human. The most outstanding chatbots starting at now are voice chatbots: Alexa and Siri. Nevertheless, chatbots are starting at now being gotten at a high rate on PC talk stages.
Morphological Analysis of Histopathological Images Using Deep Learning

In this study, we introduce a morphological analysis of segmented tumour cells from histopathology images concerning the recognition of cell overlapping. The main research problem considered is to distinguish how many cells are located in a structure, which is composed of overlapping cells. In our experiments, we used convolutional neural network models to provide recognition of the number of cells. For the medical data used: Ki-67 histopathology images, we achieved a high f1-score result. Therefore, our research proves the assumption to use convolutional neural networks for morphological analysis of segmented objects derived from medical images.
Age-Based Automatic Voice Conversion Using Blood Relation for Voice Impaired

The present work presents a statistical method to translate human voices across age groups, based on commonalities in voices of blood relations. The age-translated voices have been naturalized extracting the blood relation features e.g., pitch, duration, energy, using Mel Frequency Cepstrum Coefficients (MFCC), for social compatibility of the voice-impaired. The system has been demonstrated using standard English and an Indian language. The voice samples for resynthesis were derived from 12 families, with member ages ranging from 8–80 years. The voice-age translation, performed using the Pitch synchronous overlap and add (PSOLA) approach, by modulation of extracted voice features, was validated by perception test. The translated and resynthesized voices were correlated using Linde, Buzo, Gray (LBG), and Kekre's Fast Codebook generation (KFCG) algorithms. For translated voice targets, a strong (θ >~93% and θ >~96%) correlation was found with blood relatives, whereas, a weak (θ
An Impetus to Swap from Traditional to Blockchain Environment in Indian Banks

This article provides a methodical overview of investigations blockchain applications in different areas. The ground of research is to examine the current state of blockchain innovation and its applications, and to envision how the clear attributes of this problematic innovation can change “the same old things.” In this paper we recognize the blockchain commitments to different areas and study the construction of how proficiently blockchains are embraced in Indian banks and feature the solid pointers that change the customary financial practices received in Indian banks. On one hand, the banking in India goes via the impact of interest rate liberalization and income decline because of the narrowing interest-rate spread. On the alternative hand, it is also tormented by economic amendment, digitalization, and monetary innovations. Hence, the banking agency requires urgent revision and is in seek of recent increase route. As such, blockchains may also need to transfigure the captioned generation of the price clearing and credit score rating records systems in banks, thus elevating and transforming them. Blockchain applications moreover promote the formation of “multi-center, weakly intervened” frameworks, so you can enhance the overall performance of the banking agency. However, in spite of permission tons less and autonomous nature of blockchains, the regulation and actual execution of a decentralized device are issues that need to be committed to. In addition, we highlighted the obvious flaws in the applicable writing, especially the limitations of blockchain innovation and how these obstacles appear in different fields and between different domains. Also, recommend the actions for profound entrance of blockchain in Indian banks.
High-Rate Storage Codes on Triangle-Free Graphs

Consider an assignment of bits to the vertices of a connected graph $G(V,E)$ with the property that the value of each vertex is a function of the values of its neighbors. A collection of such assignments is called a {\em storage code} of length $|V|$ on $G$. The storage code problem can be equivalently formulated as maximizing the probability of success in a {\em guessing game} on graphs, or constructing {\em index codes} of small rate. If $G$ contains many cliques, it is easy to construct codes of rate close to 1, so a natural problem is to construct high-rate codes on triangle-free graphs, where constructing codes of rate $>1/2$ is a nontrivial task, with few known results. In this work we construct infinite families of linear storage codes with high rate relying on coset graphs of binary linear codes. We also derive necessary conditions for such codes to have high rate, and even rate potentially close to one. We also address correction of multiple erasures in the codeword, deriving recovery guarantees based on expansion properties of the graph. Finally, we point out connections between linear storage codes and quantum CSS codes, a link to bootstrap percolation and contagion spread in graphs, and formulate a number of open problems.