Reviewerly/exHarmony huggingface dataset:
Video Anomaly Detection by Fusion of Middle Layer Features of U-Net Discriminator in Spatio-Temporal Generative Adversarial Networks

In recent years, attempts to detect video anomalies in daily life, manufacturing sites, etc. using deep learning have been investigated. In particular, generative adversarial networks (GANs) have been widely used. However, they are inefficient, and simple difference-based methods are affected by noise. This paper proposes a new unsupervised learning method for video anomaly detection using spatio-temporal generative adversarial networks. Since the proposed method utilizes the middle layer features of the U-Net Discriminator, it is able to detect anomalies efficiently and accurately in the region of interest of the Discriminator.
Assessment of the level of drainage services in Damai settlement area (Ampal River Watershed), Balikpapan, Indonesia

Abstract Flood is a major disaster in Balikpapan City. One of the locations experiencing high flooding is in the Damai settlement area (Ampal River Watershed), especially in Mayor Polisi Zainal Arifin Street. One of the causes of this flood-prone area is the suboptimal drainage conditions. This study aimed to assess the drainage services in the Damai settlement area (Ampal River Watershed), of Mayor Polisi Zainal Arifin Street to observe the drainage services and overcome flood problems. Achieving such goals was done by analyzing the service level of the drainage channel by using the scoring and weighting method. The scoring and weighting method produced classifications of drainage services in the Damai settlement area (Ampal River Watershed) on Mayor Polisi Zainal Arifin Street, namely, medium and bad drainage services. There is segment 2 channel 2A, segment 3 channel 3A, segment 4 channel 4A, and segment 5 on channel 5A and 5B classified as having bad service level and there are 13 channels classified as having moderate service levels.
Anuvaad

Machine Translation is best alternative to traditional manual translation. The corpus of Sanskrit literature includes a rich tradition of philosophical and religious texts as well as poetry, music, drama, scientific, technical and other texts. Due to the modernization of tradition and languages, Sanskrit is not on everyone's lips. Translation makes it convenient for users to understand the unknown text. This paper presents a language Machine Translation System from Hindi to Sanskrit and Sanskrit to Hindi using a rule-based technique. We developed a machine translation tool 'anuvaad' which translates Sanskrit prose text into Hindi &amp; vice versa. We also developed bi-lingual corpora to deal with Sanskrit and Hindi grammar rules and text applied rule based method to perform the translation. The experimental results on different 110 examples show that the proposed anuvaad tool achieves overall 93% accuracy for both types of translations. The objective of our work is to ensure confidentiality and multilingual support, which can be tedious and time consuming in case of manual translation.
A Deep Learning Based Cockroach Swarm Optimization Approach for Segmenting Brain MRI Images

Segmentation of the biomedical images is essential to determine and discover the most significant parts in the images that represent the particular disease in diagnosis and prognosis issues. In this work, an optimized deep learning approach based on cockroach swarm optimization is proposed for segmenting brain MRI scans. Moreover, the U-net architecture created and inspired the proposed approach, with 2003 training images in testing and validation phases. The motivation of this proposed work is to develop robust architecture and apply some restrictions on the size of the dataset to avoid human manual segmentation. We present a robust machine learning technique that can segment both gray and white matter and investigate the cerebrospinal fluid of brain MRI scans by using images from OASIS and ABIDE datasets. This assures saving time from specialists, allowing them to focus on fields that require their experience. Therefore, Cockroach Swarm Optimizer (CSO) implemented optimization of the hyper-parameters to ensure the proposed architecture reliability based on the accuracy achieved. In this work, we built an architecture that acquired an overall 92% accuracy.
An Efficient Policy Improvement in Human Interactive Learning Using Entropy

Human knowledge is used in reinforcement learning (RL), which reduces the amount of time taken by the learning agent to achieve its goal. The TAMER (Training an Agent Manually via Evaluative Reinforcements) algorithm allows a human to provide a reward to an autonomous agent through a manual interface while watching the agent performs the action. Because a policy, the agent have, is updated based on human rewards, it approximates how a human trainer gives rewards to the agent. For policy update, events that occurred during learning were selected. Furthermore, while selecting events, the temporal distance from the event to the human reward is considered. Thus, the events that only occurred in a certain time interval before the human trainer gives a reward are selected. However, this approach of considering only the time factor demands quite many human rewards for the policy. The policy update with high complexity make the human trainer exhausted during improvement of policy. Therefore, we propose a new method of selecting events, which considers the entropy value over the distribution of Q-values, in addition to the time factor. For the policy update in our proposed event selection method, we reuse the events despite of long temporal distance since human reward when their each human reward is negative and entropy value (over the distribution of Q-values) is low. To compare the effectiveness of the proposed method with the classic TAMER, we implement an experiment with the policy initialized to an incorrect weight. The results show that the TAMER algorithm, using our proposed selection of events, efficiently improves the policy.
Interactive screen-shooting resilient ciphertext watermarking protocol between the cloud and the user

The current cloud storage services generally launch the function of browsing files, which brings greatconvenience for users to steal information by shooting files on the screen, and also increases the risk of information leakage. As a result, users taking photos to steal information poses a potential security risk for cloud storage services.The existing method is for the cloud to embed a screen-shooting resilient watermark associated with the user's identityin an image before sending it to the user. Then the cloud can detect the user's watermark from the captured image and discover the user's identity. Both the cloud and the user have the image embedded with the user's watermark, so the leaked images cannot be confirmed to be from the cloud or from the user. To eliminate the ambiguity of the leakage source, ciphertext watermark is utilized, that is, the user's ciphertext watermark is embedded into a plaintext image in the cloud to generate the ciphertext image containing the user's watermark, which can only be decrypted by the user to obtain theplaintext image. Once this image is secretly shot and leaked, it must flow out from the user. However, the current screen-shooting resilient watermarking algorithm does not support the above functions and only supports the embedding of the plaintext watermark. This paper proposes a screen-shooting resilient ciphertext watermarkingprotocol between the cloud and users, which aims to protect the copyright users' ownership of their images and prevent other userswho use the images from stealing images by taking pictures secretly. This paper verifies the security and effectiveness of the proposed protocol through theoretical analysis and simulationexperiments.
LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images

This study proposed a novel Local Reference Semantic Code (LRSC) network for automatic breast ultrasound image classification with few labeled data.In the proposed network, the local structure extractor is firstly developed to learn the local reference which describes common local characteristics of tumors. After that, a two-stage hierarchical encoder is developed to encode the local structures of lesion into the high-level semantic code. Based on the learned semantic code, the self-matching layer is proposed for the final classification.In the experiment, the proposed method outperformed traditional classification methods and AUC (Area Under Curve), ACC (Accuracy), Sen (Sensitivity), Spec (Specificity), PPV (Positive Predictive Values), and NPV(Negative Predictive Values) are 0.9540, 0.9776, 0.9629, 0.93, 0.9774 and 0.9090, respectively. In addition, the proposed method also improved matching speed.LRSC-network is proposed for breast ultrasound images classification with few labeled data. In the proposed network, a two-stage hierarchical encoder is introduced to learn high-level semantic code. The learned code contains more effective high-level classification information and is simpler, leading to better generalization ability.
A Literature Review on Secured Data Management using Block Chain Technology in Healthcare Sector

The block chain technology is one of the important revolutionary innovations as well as the enhancements of the Information Technology (IT) industry. The current technological enhancements permit for a main improvement in the healthcare sectors. The accessibility as well as the information security is the crucial considerations for the communication as well as the integration with the Electronic Healthcare Record (EHR) systems when the private medical information is shared. Therefore, this paper plans to provide an appropriate literature review on existing secured data management using the block chain technology in the health care sector. In the initial step, related works as well as the chronological review of the considered contributions are explained. Next, the different datasets used in the considered works, simulation platform, as well as the performance measures are also explained briefly. In the final step, several research challenges is defined on the basis of advanced techniques that considered the unresolved challenges with respect to the secured data management using the block chain technology in the healthcare sector.
The Algorithm and VLSI Architecture of an Efficient Image Sharpening Scheme Based on the Frequency Domain Analysis

Feature augmentation of the images plays a big role in the image recognition system and using a sharper image can enhance the accuracy of the image recognition. This paper presents a non-iterative image sharpening algorithm based on the frequency-domain analysis. This algorithm greatly improves the noise sensitive issue in classical unsharp masking technique. Furthermore, the non-iterative property is conducive to the hardware acceleration design. Considering the design of hardware acceleration, we focus on the three parts with the highest computation load of the algorithm, including FFT/IFFT unit, Wiener deconvolution process, and the least square method. Through the hardware acceleration, this algorithm is more suitable for applying to the real-time image recognition system. The experiment results show that this algorithm enhances the feature and contrast of image with effective noise reduction and achieves a throughput 30fps.
A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and Swap Operator

The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. The SKF utilizes a Kalman filter process that includes prediction, measurement, and estimation to determine the global optimum. However, the SKF can only operate in the numerical search space. Numerous approaches and modifications have been used in the literature to enable numerical meta-heuristic algorithms to operate in a discrete search space. This paper presents modifications to measurement and estimation in the SKF by utilizing mutation and Hamming distance technique to accommodate the discrete search space. The modified algorithm is called Discrete Simulated Kalman Filter Optimizer (DSKFO). Additionally, the DSKFO algorithm incorporates the swap operator as an extension to improve the solution in solving the travelling salesman problem (TSP). The DSKFO algorithm was compared against four other combinatorial SKF algorithms and outperformed them all.
Model Based Coupling of Context Aware Middleware and Ambient Assisted Healthcare Systems

In this paper we present an approach to couple context aware middleware, which is already existing in a user’s smart home environment to his or her ambient assisted living (AAL) solutions in a flexible and user friendly way based on runtime-models. This approach enables the interoperability between arbitrary context aware middleware systems and AAL solutions, if they expose a REST API interface. After introducing the process of model-based coupling, we present CAAMIL, a domain specific modeling language to design the coupling in form of a corresponding runtime-model. We demonstrate our modeling tool to support the user-friendly creation of runtime-models and to transform them into a machine-readable runtime-model representation. This runtime-model representation is used then by our transformation engine at runtime to control the designed data exchange. We demonstrate our approach by coupling the middleware FIWARE and the AAL solution AYUDO, an innovative personal health management system.
Application of Internet of Things Technology in the Field of Environmental Engineering

With the vigorous development of social economy, the environmental problems facing human beings are getting more and more serious. Land degradation, water shortage and pollution, air pollution, low quality of urban environmental infrastructure, rural environmental degradation, increasing frequency of environmental accidents, climate change and other global environmental issues. These environmental problems seriously threaten people's health and are not conducive to the sustainable development of society. Moreover, the current level of informatization in the field of environmental protection in China is still very low, and the sharing of environmental protection data and services in various regions is not open to the outside world, so many investments have been made in different regions. Therefore, the focus of environmental protection work in the future will be to promote the development of environmental protection in the direction of automation, intelligence, and networking. Informatization of environmental protection is imperative. In addition, in recent years, the application of the Internet of Things (IoT) in the field of environmental protection is an inevitable result of the development of information and communication technology to a certain stage and it is an inevitable trend of informatization in the field of environmental protection.
Home Video Behavior Recognition based on Convolutional Residual Network with Visual Privacy Protection Mechanism and Temporal Adaptive Module

To address the privacy leakage issue that exists in home surveillance video, and considering that 3D Convolutional Neural Network (3D CNN) simply uses a fixed number of video invariant cores, it does not realize the time difference between videos, this paper proposes a home behavior video recognition algorithm based on computer vision considering privacy protection. The system combines compressed sensing (CS) theory with convolutional residual network of temporal adaptive module (TAM). Firstly, compressed sensing technology is used to make video obtain the required "visual masking domain", and then TAM is inserted into 2D Deep Residual Network (2D ResNet). TAM is composed of two modules cascaded together before and after, which give the temporal information in the video sequence into a mapping based on important sensitive locations and a mapping based on invariant locations, using this system to enable the effective identification of behaviors in compressed video.
Derivation and Formal Proof of Binary Tree Depth Non-Recursive Algorithm

The development of loop invariants for recursive problems of nonlinear data structures are always difficult problems in formal development. The paper studies the derivation and formal proof of binary tree non-recursive algorithm. The non-recursive Apla algorithm of binary tree depth and its exact and simple loop invariant are derived. Then, Dijkstra-Gries’ standard proving technique is used to prove the correctness of the algorithm. In the end, Apla to C++ program automatic generation system automatically generates C++ code, which realizes the complete refinement process from abstract specification to concrete executable program. The experimental results of the example simplify the derivation and proof of the algorithm program and point out the direction for the exploration of loop invariant of non-recursive algorithm for recursive problems. It has guiding significance for the formal proof of algorithm program for nonlinear data structure.
Analysis of approaches to assessing the quality of the results of student training in the system of vocational education abroad

In this article an attempt to systematize the existing approaches to the assessment of quality of training students of technical universities in various countries is made; features of the assessment result of students' learning common for the whole system of higher education in Europe and the United States are identified; differences in the quality assessment result of training technical university students in foreign countries are also discussed. The relevance of the research is determined by the features of the organization and trends in the development of quality assessment of student training results, which is one of the most important aspects of managing the development of the higher education system. The choice of methods for its implementation directly affects all aspects of university activities in general, and largely determines the quality of educational services. The purpose of the article is to study and analyze existing approaches to assessing the quality of training results of technical university students abroad. It is established that the assessment of the quality of students' training results in Russia requires transformation of our mentality in determining the content of education. The presented results and conclusions of the study make it possible to formulate recommendations for assessing the quality of student training results in the system of professional education in Russia and promote the creation of a unified system for assessing the quality of higher education, which contributes to increasing the prestige of Russian higher education and its integration into the international scientific and educational space.
Towards Developing Uniform Lexicon Based Sorting Algorithm for Three Prominent Indo-Aryan Languages

Three different Indic/Indo-Aryan languages - Bengali, Hindi and Nepali have been explored here in character level to find out similarities and dissimilarities. Having shared the same root, the Sanskrit, Indic languages bear common characteristics. That is why computer and language scientists can take the opportunity to develop common Natural Language Processing (NLP) techniques or algorithms. Bearing the concept in mind, we compare and analyze these three languages character by character. As an application of the hypothesis, we also developed a uniform sorting algorithm in two steps, first for the Bengali and Nepali languages only and then extended it for Hindi in the second step. Our thorough investigation with more than 30,000 words from each language suggests that, the algorithm maintains total accuracy as set by the local language authorities of the respective languages and good efficiency.
Research on intelligent troubleshooting of power communication equipment based on RPA technology

With the continuous evolution of domestic power grid construction technology, my country's power grid has built a diversified composite power transmission and communication network that integrates information networks, computer networks and power networks. The existing electronic information detection technology is mainly for power generation, transmission and transformation. , Lack of technical means to detect power communication equipment. Therefore, this paper proposes an intelligent troubleshooting method for power communication equipment based on PRA technology, analyzes key technologies such as equipment abnormal locations, failure causes, and power outage restarts, and establishes an effective guarantee for protecting power communication equipment based on PRA, and realizes key technologies Autonomous and controllable, improve work efficiency and quality, make digital technology In-depth integration with the business to help the digital transformation of enterprises.
Investigation of the Transition Mechanism to Chaos in Multilayer Neural Networks

Multilayer neural networks were considered. The program in the Python software environment was developed. The transition of the network to an unpredictable (chaotic) mode was described by a cascade of bifurcations that follow one another. The chaos was considered as consequence of the increase in the number of local minima in the area of the global minimum. Increasing of the number of layers of the neural network as well as the number of neurons in the hidden layer leads to an increase in the number of local minima when approaching the global minimum, and thus narrows the range of optimal solutions. Fractal structures on bifurcation diagram were observed. Therefore, the solution to the problem of automatic selection of the learning rate is related to the number of existing local minima.
The Implementation of a Red Object Tracking Algorithm with a Single, Static Camera

In recent years, activity recognition and object tracking are receiving extensive attention due to the increasing demand for adaptable surveillance systems. Activity recognition is guided by the parameters such as the shape, size, and color of the object. This article purposes an examination of the performance of existing color-based object detection and tracking algorithms using thermal/visual camera-based video steaming in MATLAB. A framework is developed to detect and track red moving objects in real time. Detection is carried out based on the location information acquired from an adaptive image processing algorithm. Coordinate extraction is followed by tracking and locking the object with the help of a laser barrel. The movement of the laser barrel is controlled with the help of an 8051 microcontroller. Location information is communicated from the image-processing algorithm to the microcontroller serially. During implementation, a single static camera is used that provides 30 frames per second. For each frame, 88 ms are required to complete all three steps from detection to tracking, to locking, so a processing speed of 12 frames per second is implemented. This repetition makes the setup adaptive to the environment despite the presence of a single static camera. This setup can handle multiple objects with shades of red and has demonstrated equally good results in varying outdoor conditions. Currently, the setup can lock only single targets, but the capacity of the system can be increased with the installation of multiple cameras and laser barrels.