Reviewerly/exHarmony huggingface dataset:
Outdoor image restoration based on belief propagation algorithm and formalized MTF

Single image dehazing technology can be applied to many fields. In order to solve this problem, an improved framework is proposed here, which can be used to estimate the light transmittance t in a given single input image and then the belief propagation (PB) algorithm is applied to image dehazing. At first, a coarse transmission map is calculated through Dark Channel Prior (DCP) knowledge. Next, the belief propagation methodology is introduced to correct the transmission map when the object in degraded image is similar to the airlight over a large local region. As a result, the transmission map can be estimated better and the important drawback of artifacts phenomenon can be avoid to some extent. At last, through this effective estimation to transmittance, the formalized MTF can be predicted to restore degraded images, then the haze-free scene contrasts can be better recovered. The experimental analysis results show that compared with defogged images acquired through DCP algorithms, our proposed algorithm can provide good results. Our method is more effective and robust.
The Use of New Information Technologies for the Development of Training Programs for the Training of Future Engineers. Experience SFedU and IITU

The article describes the use of modern trends and information technologies for the development of training programs for future engineers. The solutions of several countries are given along with the results of own projects of the authors. The work reflects the influence of new technologies on the way of learning and teaching. The work methodology is also given.
A Deep Learning Framework with Histogram Features for Online Writer Identification

This paper proposes a technique to identify the authorship of an online handwritten text using descriptors inspired by the Pattern of Local Gravitational Force (PLGF) from the area of computer vision. The strategy strives to leverage the spatial and temporal information in an online handwritten trace. A noteworthy aspect is that the spatial descriptors are based on histograms that are generated by incorporating the pressure and velocity information encapsulated in the sub-strokes of a writer. Thereafter, we utilize sequence modelling techniques to capture the temporal information present in the sequence of descriptors obtained from consecutive sub-strokes. By considering a sub-stroke as a basic temporal unit of a writer's trace, we propose a Long Short Term Memory (LSTM) autoencoder to compress the sequence data. The compressed representation is then used to train a Support Vector Machine (SVM) to obtain the final authorship of the handwritten text. The efficacy of our proposed approach is tested on the BIT CASIA Database. We achieve state-of-the art results at the text-line level.
Attentive Matrix Factorization for Recommender System

The traditional collaborative filtering algorithm is the most common algorithm in the recommender system. Among them, the matrix factorization technique is widely used because of its simplicity and effectiveness. Although matrix factorization effectively solves the problem of data matrix sparsity, it is not enough to use the inner product of the user feature vector and the item feature vector as the user's rating of the target item. This simple linear calculation ignores the user's attention to the different attributes of each item. Based on the matrix factorization, this paper adds an user attention to better analyze the user's attention to the item and obtain the user's preference more accurately. Using RMSE and MAE as evaluation indicators and conducting experiments on a real dataset, the experimental results show that this model is better than other comparison algorithms.
E-Banking Network Security Threats: An Analytic Study

This study aims at investigating the targets of network attacks, and the network assets that need to be protected. A survey has been prepared and administered to well-known banks in Sudan, such as Omdurman National Bank, Bank of Khartoum, and Bank of Sudan and theirs branches. The study shows that destruction of software turns out to be the most attacked targets. With relation to the assets targeted, it seems that hardware components should be protected most.
Stimulating Children’s Mother Tongue Development Through Animated Children Song

Early childhood in the modern era is very close to various technologies so that they are less familiar with local culture. It is has an impact on their mother's language skills, they are more familiar with the second language; Indonesian and English. Based on field observations that occurred in Kindergarten in Padang there was a low ability to discuss with children using their mother language. The observations show that: 1) 76.47% of children have not been able to speak fluently Minangkabau vocabulary which is meaningful so it is difficult to understand, this is seen when children do activities such as storytelling, recitation activities using Minangkabau language. It is not in accordance with the grammar in the Minangkabau regional language (Kato nan Ampek). 2) 80.35% of children have not been able to answer more complex questions. 3) 75.47% of children are less able to express various kinds of emotions that exist in feelings (angry, sad, happy, etc.) with appropriate expressions. 4) 88.25% of children have not been able to disclose information, objectives, messages that are felt when the child is less able to obtain a mother tongue (Minangkabau language) which has uniqueness in terms of pragmatics, children in Minangkabau are currently still lacking in upholding customs, manners. and politeness. This research design was carried out two years in a row. This research took the form of research and development using ADDIE model. Based on research data from the use of animation media; the results show the development of mother tongue skills in children. The development of mother tongue skills can be stimulated through animation media in Minangkabau songs to develop their imagination so that they are able to express and express ideas.
Feasibility Analysis of a Rule-Based Ontology Framework (ROF) for Auto-Generation of Requirements Specification

Writing requirements specification documents plays an important role in determining the success of information system development. To compile documents that are consistent, complete and in accordance with standards, both from a technical and business perspective require enough knowledge. Some previous approaches, such as GUI-F framework, propose automated requirements specification document creation with a variety of different methods. However, most of them do not provide detailed guidance on how stakeholders can identify their needs to support the company's business needs. In addition, some methods only focus on documenting high level requirements specification, such as use case diagram. As for the code development process, this only represents very basic information and lack of technical aspects. In our previous work, we proposed a Rule-Based Ontology Framework (ROF) for Auto-Generating Requirements Specification. ROF covers 2 processes in requirements engineering, namely: elicitation and documentation. The output of the elicitation process is a list of final requirements that are stored in an ontology structure, called Requirements Ontology (RO). Using RO, the documentation process automatically generates 2 outputs: process model in the Business Process Model and Notation (BPMN) standard and Software Requirements Specification (SRS) documents in the IEEE standard. The aim of this paper is to conduct a feasibility analysis to prove that ROF is feasible to be implemented in an Information System (IS) projects. ROF is implemented in a case study, an IS project that calculates lecturer workload activity at a university in Indonesia. The feasibility analysis is carried out in stages for each output using qualitative and quantitative methods. The results of the analysis show that that the framework is feasible to be implemented in the IS project to minimize effort in generating requirements specification.
A Hardware-Accelerated Qubit Control System for Quantum Information Processing

In this paper we present a flexible, high-performance, integrated qubit control system using modular PXIe equipment. To manipulate qubits, a classical control system is required to generate and acquire a mix of baseband, radio frequency, and/or optical signals, with the specific mix different for each qubit technology. Typically, a quantum system requires many of these signals with a high degree of synchronization and phase coherence, and increasingly needs real-time data processing and on-the-fly sequencing capabilities for feedback experiments. The proposed system is scalable to hundreds of channels, all of them being phase coherent and fully time-synchronized.The system includes a programmable real-time sequencer for precise execution timing and decision making; and open FPGA capabilities for custom Digital Signal Processing (DSP) by hardware. Using the latter, we present FPGA IP to generate one-/two-qubit gates efficiently with the AWG, and to analyze high-frequency signals to measure the qubit states in real time on the digitizers. This IP features very low latencies for Quantum Error Correction (QEC) and Frequency Division Multiplexing (FDM) capabilities, among others.
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning

Few-Shot Learning (FSL) aims to improve a model’s generalization capability in low data regimes. Recent FSL works have made steady progress via metric learning, meta learning, representation learning, etc. However, FSL remains challenging due to the following longstanding difficulties. 1) The seen and unseen classes are disjoint, resulting in a distribution shift between training and testing. 2) During testing, labeled data of previously unseen classes is sparse, making it difficult to reliably extrapolate from labeled support examples to unlabeled query examples. To tackle the first challenge, we introduce Hybrid Consistency Training to jointly leverage two types of consistency: 1) interpolation consistency, which interpolates hidden features to imposes linear behavior locally, and 2) data augmentation consistency, which learns robust embeddings against sample variations. As for the second challenge, we use unlabeled examples to iteratively normalize features and adapt prototypes, as opposed to commonly used one-time update, for more reliable prototype-based transductive inference. We show that our method generates a 2% to 5% improvement over the state-of-the-art methods with similar backbones on five FSL datasets and, more notably, a 7% to 8% improvement for more challenging cross-domain FSL.
A Hybrid Path Planning Strategy of Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) play a unique role in many applications, including oceanographic research, country defense, ecosystem monitoring, to name a few. The autonomy of AUVs is utilized in automatically planning a feasible path/trajectory to a goal point. A robust planner of AUVs should be able to search a collision-free path/trajectory not only in a large-scale known static environment, but also in the environment with dynamic obstacles. This paper demonstrates a modified and combined Dynamic Window Approach (DWA) and Rapidly-exploring Random Tree (RRT*) to plan both the local trajectory and the global path for AUVs in environments where dynamic obstacles may appear. In case of dynamic obstacles, the planner automatically judges the risk of collision and switches from RRT* to DWA if necessary. Then the planner switches back after collision risk is dismissed. Hence, by switching between two algorithms, the balance of real-time computation and the globally optimal solution is achieved. The effectiveness of the proposed hybrid planning strategy is verified by simulation.
Virtual Reality Applications in the Context of Low-Vision Rehabilitation

Low-vision rehabilitation represents the primary intervention for individuals with chronic, disabling visual impairment. Novel head-mounted displays using virtual reality have become available and are now a viable alternative to traditional handheld or stand magnifiers. More recently, simulator displays have been applied as a tool in visual performance assessment and training for individuals with low vision. As a result, public health education and promotion have shown interest to enhance the knowledge of sighted individuals when trying to understand the challenges experienced by people living with low vision while completing daily activities. Technology development will need to focus on, and further enhance topics related to, binocular vision, gaze control, cybersickness, as well mainstream and personalized devices. Insights into the lived experiences of persons with low vision will enable researchers to better understand the social benefits of these emergent technologies. This understanding will guide our efforts to further improve functional independence and social participation of individuals with low vision.
Improved Indistinguishability for Searchable Symmetric Encryption

This work presents a new experiment defining indistinguishability for searchable symmetric encryption to include security against published practical attacks. The proposed experiment allows the adversaries to use their prior knowledge about the stored documents to win. We solve the problem of modelling the adversaries with prior knowledge using the interacting split adversary technique. This new indistinguishability definition is aligned with the security goals and adversary capabilities listed in [4]. The correctness of the indistinguishability experiment is demonstrated by presenting proofs of strength and vulnerabilities of $$\varSigma {o} \phi {o} \varsigma $$ -B and one of its variant. We write the security proofs based on the indistinguishability experiment with an adversary without any prior knowledge and adversary with full knowledge of the document set. We show how to win the indistinguishability experiment using the count attack by an adversary who knows the document distribution, and the file injection attack by an adversary without prior knowledge.
Underwater image quality assessment based on human visual system

With the increasing demand of underwater vision, more and more attention is paid to underwater image processing and it becomes very significant to improve the image perception effect Aiming at degradation and color bias of underwater images, a method for underwater image quality assessment (IQA) is proposed. The paper selected three indexes: sharpness, contrast and chroma, which are related to human vision system. Sharpness is calculated from the gradient values of pixels in the transverse, vertical and diagonal directions of the image. Contrast is calculated by the relative standard deviation of RGB three channels. Chroma is calculated through the relative value of red channel to green channel and blue channel. The weighted sum of the three indexes constituted the underwater image quality evaluation method in this paper. The proposed algorithm is compared with BRISQUE, NIQE, UIQM and UCIQE. Its performance is obtained. The experimental results show that the proposed underwater IQA method is better consistent with human visual perception, and can better reflect the quality of underwater image.
MODSiam: Moving Object Detection using Siamese Networks

Moving object detection is a challenging task in computer vision. A class agnostic model is learned to detect moving objects in a video despite their category. This is done using the proposed MODSiam that takes a single background image of the scene and the current frame as input, then the model extracts features from both inputs and merges then to output the foreground objects. A comparison of using this model with three different backbone convolutional neural networks is presented. The evaluation is done using the metrics precision, recall, F1-measure, false-positive rate, false-negative rate, specificity, accuracy, and the number of frames per second. All models are tested on the benchmark dataset CDNet, which is a dataset of videos for moving objects under different conditions like low frame rate, shadows, and dynamic background. The results show that using ResNet as a backbone produced promising results compared to other models with respect to most of evaluation metrics.
Siamese Networks Based People Tracking for 360-degree Videos with Equi-angular Cubemap Format

This paper proposes a deep learning based pedestrian tracking scheme for 360-degree videos using equiangular cubemap (EAC) format. To be robust against content discontinuity of EAC images, this paper proposes an efficient face stitching scheme such that the tracker keeps tracking across adjacent faces and avoids raising geometric deformation simultaneously. By referring to statistics of score maps from efficient fully-convolutional siamese networks, the proposed mechanism of template update determines the timing of update. Experimental results show that the proposed tracker operates at 60 fps and outperforms the fully convolutional siamese networks based tracker on 360-degree videos with EAC format both in precision plots and success plots.
A Robust Audio Authentication Scheme Using (11,7) Hamming Error Correcting Code

In the advancement of high-speed internet technology and vast use of social media, people are sending audio and video message for their daily communication in case of textual information. Due to unprotected nature of communication channel and increasing unauthorized users or adversary, it is essential to measure some protection of valuable multimedia messages which is one of a popular research issue for various human centric applications, especially in law enforcement and military applications. In this context, an effective audio authentication scheme has been proposed using (11,7) Hamming codes which are employed on an audio file. Now, for the first round, any arbitrary position (say nth position) bit will be considered as a secret key, and for rest of the cases data embedded position will be treated as a secret key position for the immediate round. For the first round, secret message bit will be embedded in the LSB position (if possible), otherwise next bit will be checked and for the remaining cases message bit will be embedded in a position where (LSB position bit ! = secret message bit) and (secret key position bit ! = secret message bit). Continue this process to embed all secret data bits within the cover audio file and produce stego audio. Various private, public and government sector will be benefited from this scheme.
Short-Term Sky Image Prediction using LSTMs from the Past Frames of Sky Images and Synthesized Images by Optical Flow Extrapolation1

This paper describes a short-term sky image prediction method. Predicting cloud flow in the sky image is a useful technique that can be applied to forecasting solar power generation and forecasting sudden downpours. We propose LSTM based predicting method which uses optical flow vectors as input for the network. The flow obtained from sky images masked around sun are to cope with extreme high-brightness object (the sun). We implemented two way to merge the flow information with the sky image for inputs of the LSTMs and compared with baseline method which inputs past frames of sky images for the LSTMs.
Towards the Machine Learning Algorithms in Telecommunications Business Environment

We live in times where companies and individuals are dealing with extremely large amounts of data coming from all different kind of sources. This data includes a lot of very valuable information, which, most of the time, cannot be inferred at first sight. Therefore, in today’s businesses there is a growing necessity of discovering efficient and useful information out of the data that has been gathered. This is the reason why Machine Learning, a technology that has been developed since mid-20th century, is one of the biggest growing technologies in this last decade, being one of its most popular applications in the field of data. The paper presents an analysis what techniques are available for starting with a Data Science project, how easy they are to implement, and how they can be applied in a real world case. The data that was worked with for this project was gathered from a telecommunications company.
A Simple AI-Powered Video Analytics Framework for Human Motion Imitation

In recent years, a great amount of video data is generated by surveillance cameras in cities and industries, and social media, and internet sites. It seems that this trend will continue with the video data produced from various sources. Consequently, there is a request for automatic processing and analysis of large-scale video data. Deep learning-powered video analytics can help make these unstructured videos understandable and make the video analysis process faster and more efficient. On the other hand, the reproduction of the human movement has long been the inspiration for robotics. This project introduces the field of deep learning-powered human motion imitation via motion primitives. This work overviews the data processing pipeline, starting from human observation in videos and progressing through analyzing motion via deep learning-powered video analytics, motion modeling through motion primitives, and reproducing it by V-REP robotic simulator. The proposed framework is an early version of deep learning video analytics for human motion imitation with motion primitive approaches. It ensures controlling the robot in the simulator environment to replicate the desired movement based on overviewed human activity recognition in the video.
Digital Communication and the Digital Divide: Cultural Aspects of Human-Computer Interactions

This paper focuses on specific aspects of human-computer communication, i.e., the interculturality in information and communication technologies in the global world of interconnectedness. The current world of communication can be described by two aspects, namely, the vast proportion of communication is done electronically and it is more or less always intercultural because it is conducted through modern electronic means of communication (emails, social media, presentations, eLearning, blended learning, mLearning, etc.) in the global world of interconnectedness (the World Wide Web). These two aspects are at the foundation of basically all processes both IT and business and must not be neglected. The paper attempts to highlight the fact that a vast part of information can be lost during the information transfer through communication, and it is due to the aforementioned combination of interculturality and interconnectedness. The research was conducted into the awareness of these two intertwined aspects in foreign (Asian) and Czech university students of business and IT. The results clearly show the lack of information and knowledge about the current situation.
Poisson-Gamma mixture processes and applications to premium calculation

In the paper, Poisson-Gamma mixture process is first brought forward, which is dynamically expanded from the well-known Poisson-Gamma mixture model. Some properties on Poisson-Gamma mixture process are presented, including the distribution of increment, Markov property, infinitesimal generator, joint density function of jump/waiting times, and the limit distribution of compound Poisson-Gamma mixture process, etc., which provide a thorough grounding in application of Poisson-Gamma mixture process. At last, some premium calculation principles are presented to show the application of Poisson-Gamma mixture process, which include expected value premium, stop-loss premium, mean-variance premium, and exponential premium.