Reviewerly/exHarmony huggingface dataset:
Covert Channel Construction Method Based on HTTP Composite Protocols

Aiming at the problems of low concealment of existing storage-type covert channels, high bit error rate, and low transmission rate of time-type covert channels, this paper proposes a method of constructing covert channels based on HTTP protocol combination. The method simulates browser application to send HTTP requests, dynamically distributes HTTP requests to different browsers, embeds hidden information by mathematical combination, and dynamically adjusts access objects, data packet time interval, and data packet length, thus improving the concealment of the channel. At the same time, the channel is based on the reliable transmission inside TCP protocol so that it is not affected by network jitter, thus ensuring the reliability of the channel. Experimental results show that this method can resist the detection method based on application signature, protocol fingerprint detection method, and combination model detection method, and has strong concealment. It can adjust the concealment and channel capacity according to the application scenario.
Metacompetences-Building for Students of Humanities Department on the Basis of Project Activity

Nowadays particular attention is paid to self-education and the development of skills that help a person to perceive information faster and more efficiently. A change in the learning paradigm implies the development of metaskills and metaknowledge. Implementing Federal State Educational Standards (FSES) of the third generation also reinforces the need to study and develop metacompetences and metaskills for students in order to be competitive in the labor market and in demand as specialists. Metacompetences as some new qualities that develop on the basis of the basic professional and personal qualities necessary to improve efficiency, work capacity or learning ability are inextricably linked with metacognitive processes. The development of the ability to manage one’s cognitive processes can be implemented using teaching methods aimed at the student’s independent activity. The project method is one of these teaching methods. Project activity in higher education is a part of innovation activity. Implemented in different directions, project activity at the university contribute to the development of the required competences by students in terms of the ability to design and innovative activity, and can also be the basis for innovation in the areas of specialization of the university.
An Improved Image Processing Based on Deep Learning Backpropagation Technique

In terms of image processing, encryption plays the main role in the field of image transmission. Using one algorithm of deep learning (DL), such as neural network backpropagation, increases the performance of encryption by learning the parameters and weights derived from the image itself. The use of more than one layer in the neural network improves the performance of the algorithm. Also, in the process of image encryption, randomness is an important component, especially when used by smart learning methods. Deep neural networks are related to pixels used to manipulate position and value according to the predicted new value given from a variable neural system. It also includes messy encrypted images used via applying randomness and increasing the key space in addition to using the logistic and Henon map for complexity. The main goal of any encryption method is to increase the complexity of the encrypted image to be difficult or impossible to decrypt the image without the proposed key. One of the important measurements for image encryption is the histogram and how it can be uniformed by the proposed method. Variables of randomness are used as features for the deep learning system, with feedback during iteration. An ideal image processing encryption yields high messy images by keeping the quality. Experimental results showed the backpropagation algorithm achieved better results than other algorithms.
Social Media Application Development in Android with Firebase

Sharing thoughts and information through internet has become crucial these days. An online writing enables the users to communicate with other people in a fast and convenient way. Considering this, the online writing application must be able to share the content or any other useful text in a faster way with minimum delay or with no delay. Firebase is one of the platform that provides a real-time database and cloud services which allows the developer to create these applications with ease. Instant writing can be considered as a platform to maintain communication. Android offers a better platform to develop various applications for instant writing compared to other platforms such as iOS. The main objective of this paper is to present a software application where people across the globe with similar interests can connect with each other and share their thoughts, insight, and emotions. The application developed will enable the writers to share their amazing content with the readers and also with the fellow writers. This application will be based on Android with the backend provided by Google Firebase.
Likelihood ratio method for the interpretation of iPhone health app data in digital forensics

This study presents a method to derive numerical LRs for distance data from the iPhone Health app. The LR method provides a probability model for hypotheses that specify a walking distance in meters. The method is demonstrated using a hypothetical case example in which a suspected arsonist's walking route is disputed. The performance of the LR method is assessed through studying the discrimination, calibration, accuracy, and through performing a sensitivity analysis. The proposed method is balanced and transparent, yet its performance is highly case-dependent. Nevertheless, the method and validation procedure are straightforward and can therefore easily be repeated for different data obtained from the iPhone Health app but also for other data within and outside the field of digital forensics. By illustrating the LR method through the example of the comparison of walking routes, this study advocates the application of an LR approach to further relevant questions in digital forensics.
The differential fault analysis on block cipher KLEIN-96

KLEIN is a new family of lightweight block ciphers designed for resource-constrained devices. Compared to other schemes, it also has great advantages in both software and hardware performances. In recent works, many researchers have studied its security against differential fault analysis (DFA). Note that all the works only focused on the scheme KLEIN-64, which only has 64 bits key length. In fact, the 64-bit's security is obviously not enough for the current ciphers. In this paper, we investigate the differential fault attack on the key schedule of KLEIN-96, which has 96 bits key length. More specifically, by deeply developing the inner-relationship of input-output differentials for its S-box, we reduce the complexity of exhaustive searching from the original 296 to an acceptable boundary by injecting a certain number of byte-faults. Finally, we also demonstrate the efficiency of our proposed attack by simulations, which show that our method has great advantages over other cryptanalysis on KLEIN cipher.
Power IoT System Architecture Integrating Trusted Computing and Blockchain

With the construction of power Internet of Things, the network scale is constantly expanding, the network structure is increasingly complex, and the number of terminals is increasing rapidly, resulting in huge pressure on network security protection, and it is urgent to build a credible operation environment to ensure that the Internet of Things runs safely and reliably. This paper proposes a power Internet of Things architecture combining trusted computing and blockchain. The architecture ensures the credibility and security of the terminal based on remote integrity verification and access control policy verification. A method for malicious node detection based on blockchain (MMNDB) is placed in acquisition layer. It is used to monitor the status of each acquisition devices in time, detect the malicious terminal in time, isolate the suspicious terminal with low credibility from the power Internet of Things, and ensure credibility of the acquisition terminal and the data collected. Under premise of ensuring the communication efficiency of the power communication network, this architecture can greatly ensure the stable working of the power Internet of Things.
Machine Learning based Criticality Estimation Algorithm for Search &amp; Rescue Operations in Collapsed Infrastructures

Disasters cause disruptions to human life, damage public properties, and hinder the economic growth of the country. Building collapse is one of the most common disasters and causes severe loss to humans. Advanced innovative technologies such as the Internet of Things (IoT), image detection and machine learning algorithms are employed to minimize post-disaster risk factors and support rescue management. In this paper, we summarise the state of the art in rescue management and the role of advanced technologies in rescue assistance. We also propose a machine learning algorithm for first responders to safely evacuate people trapped under debris from collapsed buildings. This paper summarises the identified machine learning algorithms for this application and compares their performances with the data that we generated from the simulation setup at our laboratory.
End-to-End Speech Recognition for Low Resource Language Sanskrit using Self-Supervised Learning

We are presenting the work on building a speaker independent, continuous speech recognition system for Samskruta (also called Sanskrit) using self-supervised learning. We have used a Pre-trained model from the Vakyansh team where the model is trained using 10,000 Hrs of data with 23 Indic languages and Fine-tuned it using a data-set containing nearly 78 Hrs of Samskruta audio along with their transcription taken from Vaksancaya - Sanskrit Speech Corpus from IIT Bombay. Acoustic representations are learned in an end-to-end deep learning approach using the wav2vec2.0 architecture from Fairseq. On top of this acoustic model, a language model is used to increase the overall performance. Our system provides a word error rate (WER) of 5.1 &#x0025; on test data and 2.4&#x0025; on train data. Meanwhile we built a graphical user interface in the form of a web page using the Flask framework, which provides an interactive platform for the user to record audio and see the transcription in real-time. To the best of our knowledge, our approach using self-supervised learning, gives better performance compared to the state of the art methods.
Optimization and Application of Image Defogging Algorithm Based on Deep Learning Network

In the foggy environment, the images collected outdoors are prone to problems, such as low contrast and loss of details. In order to solve this problem, this paper proposes an algorithm based on multiscale parallel-depth separable convolutional neural network (MSP-DSCNN) to remove fog from foggy images and improve image quality. The multiscale feature extraction module extracts texture feature details from fog images at different scales and extracts high-dimensional and low-dimensional features from fog images by using parallel depth and shallow channels. In order to further optimize the network model, a split convolution method is proposed, which can split the feature graph into two categories, one is the main feature and the other is the secondary feature. The key information is extracted from the main features with high complexity, and the compensation information is extracted from the minor features with low complexity. Experiments show that compared with other algorithms, the model constructed in this paper has obvious advantages in defogging effect, natural color of restored images, good detail retention, and dominant indicators. It effectively solves the problems of incomplete haze, color offset, and poor visibility of detail maintenance in the current image.
The Role of Sample Size in Multilayer Neural Networks

The Fourier spectra technics was used to investigation of data set size influence to the learning rate and the learning error for Multilayer neural networks. Optimal values were determined for two different data sets. The program was created on Python environment.
Two-Stage AES Encryption Method Based on Stochastic Error of a Neural Network

This paper proposes a new two-stage encryption method to increase the cryptographic strength of the AES algorithm, which is based on stochastic error of a neural network. The composite encryption key in AES neural network cryptosystem are the weight matrices of synaptic connections between neurons and the metadata about the architecture of the neural network. The stochastic nature of the prediction error of the neural network provides an ever-changing pair key-ciphertext. Different topologies of the neural networks and the use of various activation functions increase the number of variations of the AES neural network decryption algorithm. The ciphertext is created by the forward propagation process. The encryption result is reversed back to plaintext by the reverse neural network functional operator.
Vehicle Detection and Counting using Deep Learning basedYOLO and Deep SORT Algorithm for Urban Traffic Management System

Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.
The Data-Oriented Design Process for Game Development

Data-oriented design is a growing software development process for games that has not been well studied in academia. It seeks to subtract complicated design methods from problem solving and leverage the simplicity of what computer architecture is designed to do: input, transform, and output data.
A Motion Deblurring Disentangled Representation Network

We present a Motion Deblurring Disentangled Representation Network (MDDRNet), an end-to-end learned method for motion deblurring. There are three main parts in MDDRNet, Blur Loss Function, Disentangled Representation Network (DRN) module, and Structural Convolutional Neural Network (SCNN) module. By converting matched Gram matrix into minimized Maximum Mean Discrepancy (MMD), the Blur Loss Function is obtained for extracting the motion blur features. And by means of novel convolution and pooling layers, the DRN module is designed for motion deblurring. Furthermore, by the SCNN module, the deblurred image is further corrected and restored. Experiment results show that the MDDRNet has best performance compare with five methods, under three kinds of datasets.
A BNN Accelerator Based on Edge-skip-calculation Strategy and Consolidation Compressed Tree

Binarized neural networks (BNNs) and batch normalization (BN) have already become typical techniques in artificial intelligence today. Unfortunately, the massive accumulation and multiplication in BNN models bring challenges to field-programmable gate array (FPGA) implementations, because complex arithmetics in BN consume too much computing resources. To relax FPGA resource limitations and speed up the computing process, we propose a BNN accelerator architecture based on consolidation compressed tree scheme by combining both XNOR and accumulation operation of the low bit into a systematic one. During the compression process, we adopt 0-padding (not ±1) to achieve no-accuracy-loss from software modeling to hardware implementation. Moreover, we introduce shift-addition-BN free binarization technique to shorten the delay path and optimize on-chip storage. To sum up, we drastically cut down the hardware consumption while maintaining great speed performance with the same model complexity as the previous design. We evaluate our accelerator on MNIST and CIFAR-10 dataset and implement the whole system on the ARTIX-7 100T FPGA with speed performance of 2052.65 GOP/s and area efficiency of 70.15 GOPS/KLUT.
An infrared image dehazing method based on modified dark channel prior

To improve the effectiveness of the infrared image dehazing algorithm, we modify the DCP based on the observation of several outdoor haze-free images: in these images, pixels of most local patches no longer have low intensity but are close to high intensity. The transmission map is estimated through the MDCP, and the guided filter and CLAHE achieve enhancement. The haze infrared images can be recovered completely. Moreover, to thrive the infrared dehazing research, we introduce the IR Dense-haze dataset, which contains 13 pairs of synthetic haze infrared images and corresponding haze-free infrared images and six extra haze infrared images captured in natural scenes. Experiments on the IR Densehaze dataset show that our method achieves dominant performance. The average PSNR of the infrared image is 39.02% higher than the original DCP method, and the average SSIM is 28.65% higher than the original DCP method.
Machine-Learning Object Detection and Recognition for Surveillance System using YoloV3

Intelligent Video surveillance is one of the most emerging technologies in Computer vision, used for object detection and locating within the video or image. Majority of the research are carried on the Yolo algorithm on vehicle tracking, monitoring vehicles, and medical science. The main objective of this paper is to develop an optimal solution to detect, locate multiple objects such as person and vehicles in a single frame using Kitti dataset. Usually the kitti data set focuses on foreground vehicle detection; in the proposed algorithm it detects person, vehicle and also the background objects. The output obtained for every image includes the information of object such as probability, classification of the object, bonding box, object center (x, y coordinates), height, width using Non-Maximum Suppression (NMS) algorithm. The Kitti dataset of 350 images is used and it is observed that classification rate is 80% at 0.3 confidence threshold value over bounding boxes pixel area for vehicle and person detection. This work can be further carried out in detecting and tracking of objects at different weather conditions like rainy, winter and also during night.
A Proposed Framework for Cloud Computing Adoption

This paper presents a review related to Cloud Computing focusing on Cloud business requirements. From the review the author recommends a number of methods managing Cloud services and evaluating its service performance, including the use of a pair of the Hexagon Models. Three organizational challenges of Cloud adoption are identified: (i) Organizational Sustainability; (ii) Portability and (iii) Linkage. The Cloud Computing Adoption Framework (CCAF) is designed to deal with these challenges by helping organizations to achieve good Cloud designs, deployment and services. How these three challenges are addressed by the CCAF is demonstrated using case studies. Services implemented by CCAF are reviewed using the Hexagon Models for comparison. This paper provides recommendations to help organizations, researchers and practitioners to understand Cloud business context, to measure their risk and return analysis, to migrate their services to Cloud from all types and to connect and integrate different services as a single service.
Cloud-Based Healthcare Portal in Virtual Private Cloud

Healthcare system providing cloud-based storage makes possible to store the patient’s therapeutic records to the remote server than keeping the files and radiological images on a hard drive or local storage device which enables the patient to access their medical records at any place from the Internet through a web-based application. The structure of cloud application guarantees the privacy and security of health-related data to preserve the sensitive health information. The architectural design of cloud computing is to alleviate the privacy concern and to fulfill the confidence and trust of the cloud-based healthcare organization. This work proposes a structure for a cloud-based healthcare system to allow patients get to their medical images and reports from the cloud, ensuring that the data are available when it meets the prerequisite of a particular contract that is authorized. The requirements, architecture design, software components, and validation methods of cloud-based healthcare system are introduced.
Gaussian Process Based Robust Trajectory Tracking of Autonomous Underwater Vehicle

This paper develops a novel robust control method for trajectory tracking of autonomous underwater vehicles by addressing significant modeling uncertainties and external disturbances. Given a prior dynamic model, we apply sparse online Gaussian process technique to acquire nonparametric estimates of the unknown dynamics along with their probabilistic confidence intervals. The employed Gaussian process regression approach has a predefined limited budget for the training data set, enabling computation efficiency and online learning. By explicitly incorporating Gaussian process posterior means and variances into a sliding mode control framework, the proposed method guarantees asymptotic stabilization of a vehicle to the desired trajectory with a specific probability. Simulation results demonstrate the effectiveness of the proposed control method.
Hybrid Approach of Modified AES

Advanced Encryption Standard is one of the most popular symmetric key encryption algorithms to many works, which have employed to implement modified AES. In this paper, the modification that has been proposed on AES algorithm that has been developed to decrease its time complexity on bulky data and increased security will be included using the image as input data. The modification proposed itself including alteration in the mix column and shift rows transformation of AES encryption algorithm, embedding confusion-diffusion. This work has been implemented on the most recent Xilinx Spartan FPGA.