Reviewerly/exHarmony huggingface dataset:
Data Hiding in Symmetric Circular String Art

Creative digital artwork is usually the outcome of a long period of intellectual creation and labor of an artist. Similarly, computer-created digital artwork is an outcome of a large amount of machine time and computational resources. However, such intellectual properties can be easily copied by illegal users. Copyright protection of digital art is increasingly more important than before. Recently, using a computational approach to generate string art tends to be popular and attractive. To protect the illegal usage of the digital form of string art, we propose a data hiding algorithm specifically designed for string art. A digital string art image consists of a sequence of string lines, each specified by two nails fixed at the two ends of that line. The encrypted secret data (the watermark) is embedded into the list of line segments by odd–even modulation, where a bit ‘1’ is embedded by forcing the next node to be an odd node, and a bit ‘0’ is embedded by forcing the next node to be an even node. To minimize the impact of data embedding on the quality of the original string art image, a local optimization algorithm is developed to select the nodes that produce minimal distortion. To quantify the embedding distortion, we introduce a smoothing filter model for the human vision system (HVS) specifically tailored to string art image. Experimental results show that using the proposed algorithm, the distortion between the original string art image and the watermarked string art image is unnoticeable. The modified string art image is statistically indistinguishable from the original string art, and hence is secure under steganalysis. To our best knowledge, this is the first work towards data hiding and copyright protection of digital string art.
Investigating the Relationship between Co-occurring Technical Debt in Python

Technical debt (TD) reflects issues that may negatively affect software maintenance and evolution. There is currently little evidence on how the different types of TD co-occur; for example, how code smells and design smells affect the same part of the system. This paper investigates how different types of TD co-occur, as well as the time period of the co-occurrence. To that end, we analyzed the co-occurring associations between five types of TD, captured in 42 SonarQube rules, in 3862 files of 20 Python projects from the Apache Software Foundation. We found that this phenomenon is dominant, affecting more than 90% of Python files. We also found that Documentation Debt and Test Debt appear in the majority of the files, although it seems to be mostly by coincidence. Finally, we noticed that co-occurrence of TD seems to happen very quickly: co-occurring issues tend to be introduced within the same week. But once it does happen, it is hard to get rid of. These results can benefit both researchers and practitioners by: aiding the prioritization of TD remediation; leading to novel tools for detecting co-occurring TD and warning potential issues; shedding further light on the explanation of how TD is introduced and can be mitigated.
Differential Privacy for Context-Aware Recommender Systems

How to prevent the individual privacy from being disclosed and incorporate contextual information into recommendations process is an urgent problem that needs to be solved in recommendation systems. Challenged by the above, a context-aware recommendation method that integrates Differential Privacy and Bayesian Network technologies is proposed. Firstly, in order to alleviate sparsity of the rating matrix, the paper adopts k-means algorithm to cluster items. And then add noises to ratings to protect users' privacy. Finally, the probability that a user likes a certain type of project in contextual information is calculated by Bayesian formula. Experimental evaluations show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.
An intelligent teaching assistant system using deep learning technologies

In this paper, we describe an intelligent teaching assistant system using deep learning technologies. A few works had been done on building intelligent assistant for teachers before and our job is novel. The main challenge in this area is the distraction detection under various surroundings of the students. We invent a creative way to embed human prior knowledge of distraction judgment. Our system takes multiple sequential images as input, runs through a prior feature image extraction component using image segmentation and face detection technologies base on deep learning, and then a distraction detection component which uses AlexNet to do the classification, and finally outputs the evaluation of the online lesson. Our system has achieved 85.8% precision on student distraction detection and the evaluation generated by the system can serve as an indication to notify the teacher when specific teaching methods should be taken so as to enhance the lesson effectiveness.
KSF-ST: Video Captioning Based on Key Semantic Frames Extraction and Spatio-Temporal Attention Mechanism

Video captioning is one of research hotspots in computer vision. At present, video captioning algorithms mainly have following problems: First, traditional algorithms use equal-interval sampling to extract video features, which causes the loss of key frames containing a large amount of semantic information, thus leading to the inaccuracy of video captioning. Moreover, equal-interval sampling method results in lots of redundant frames, thereby increasing the amount of computation of algorithms extremely. Second, traditional algorithms only consider temporal information when extracting features. However, for the image and video, the spatial features also contain rich latent semantic information. Only extracting temporal features will lead to inaccurate natural language descriptions. To address these problems, we propose the video captioning method based on key semantic frames extraction and spatio-temporal attention mechanism (KSF-ST) in this paper. In order to extract key semantic frames, knowledge graph is adopted to obtain key semantic information of video frames, and knowledge reasoning is used to obtain the correlation among entities in the knowledge graph. In order to extract spatial latent semantic information of video frames, spatial attention mechanism is combined with temporal features to generate accurate natural language descriptions. We evaluate KSF-ST on two benchmark datasets. Extensive experiments have been conducted and the results demonstrate that our algorithm could achieve better video captioning performance than the state-of-the-art algorithms.
MEDIA LIBRARY AS A PROGRESSIVE MULTIFUNCTIONAL SPACE

The article considers the evolution of the public library, the modern functional zones of the library and their importance in the life of society.The international experience of developing public libraries was analyzed.It reveals that the library had turned into a multimedia library under the influence of technology, and later, under the influence of society, turned into a multimedia library center.A fourspace model for the functional purpose of the space of the modern library is considered.The model is used to build new libraries, modify existing libraries, and design the interiors of individual library spaces.Studies have found that the model embodies four common goals for the public library: Experience, Empowerment, Engagement and Innovation.A term for defining a "multimedia library center» was proposed.
CEN: Thomas Ströhlein’s Endgame Tables, a 50th Anniversary

In 1970, Thomas Strohlein gained his doctorate with the thesis 'Research on Combinatorial Games'. This included a graph-theoretic definition of an algorithm for computing endgame tables for chess. The computation was carried out on the LRZ AEG TR4 computer in Munich. The results spanned KRk, KQk, KRkb, KRkn and the iconic 'hard endgame' KQkr, and included maximal depths (to conversion), lists of maximal-depth positions and optimal moves. Here, we celebrate the 50th anniversary of this achievement and put his results in a broader context.
Robust Network Flow Classification against Malicious Feature Manipulation

Network flow classification is essential to proper provisioning of Quality of Service (QoS). Conventional machine-learning based flow classification methods assume reliable knowledge of the flow features. However, in practice, malicious flow generators can manipulate the flow features to increase the likelihood of certain learning outcomes, e.g., in terms of the QoS requirement label. Training a classifier that is robust to such feature manipulation is imperative. In this work, we present a study on robust flow classification against malicious feature manipulation. We leverage a detailed system model to capture the relation between the classifier and malicious flow generators and propose a Stackelberggame based solution framework to train a robust classifier. We conduct extensive experimentation using real-world traces. For flows with manipulated features, the Stackelberg classifier trained by our solution framework significantly outperforms a non-robust classifier that is oblivious to manipulation, achieving accuracy close to that of the non-robust classifier on unmanipulated flows. Furthermore, the Stackelberg classifier on manipulated test flows is no worse than the non-robust classifier on unmanipulated flows.
Proactive Information Dissemination in WebRTC-based Live Video Distribution

Live video distribution represents a significant fraction of the video traffic in the Internet. Its distribution induces heavy costs to serve clients during peak hours. To tackle this issue, the hybrid Viewer-to-Viewer (V2V) mode - where peers cooperate with each other in addition to receiving data from a Content Distribution Network (CDN) - has been proposed to both improve the Quality of Experience (QoE) and reduce the distribution cost. In this paper, we present the improvements we have introduced in a commercial V2V Web live streaming system based on the Web Real-Time Communication (WebRTC) standard. A key challenge faced when designing such a system is the algorithm that governs the client's behavior and should maximize the amount of a data exchanged in V2V mode. A crucial component in V2V Web live streaming is the fast distribution of the video chunks availability in the V2V network. We present in this work our initial reactive approach to distribute the information concerning chunks availability and contrast it with our new proactive approach. We demonstrate the efficiency of the proactive approach on a testbed featuring light clients (browsers) separated using Linux network namespaces, as well as with live experiments. The fraction of V2V traffic for the production environment also improved from 19% in the reactive approach to 37% in the proactive one. Furthermore, we demonstrate that the buffer length of the clients both in the controlled environment and in production is improved, implying a better QoE.
Image Contrast Enhancement Using Histogram Equalization-Based Grey Wolf Optimizer (GWO)

Image contrast enhancement is a common image enhancement method in various image applications. Histogram equalization (HE) is a popular conventional and improved way to enhance the image contrast. This conventional HE and its updated versions try to maintain the original image characteristics especially in original image brightness distribution during contrast enhancement. But it suffers various restrictions including false contouring, lack of original image features, etc. Optimization can be a possible solution to overcome such problems and to maintain desired image characteristics as well as enhanced image contrast. Grey wolf optimizer (GWO) is a newly high potential algorithm in the computational intelligence field to solve this type of problem. This paper presents an application of HE-based contrast enhancement using GWO. The objective function has been designed using different parameters to preserve original image characteristics. The results have been taken using the standard database of both gray and colour images. The results have also been compared against various renowned techniques by standard image quality evaluation metrics. The visual as well as parametric presentation indicates the distinguished prospective of GWO for image contrast enhancement.
Exploiting Privacy-preserving Voice Query in Healthcare-based Voice Assistant System

Voice Assistant Systems (VAS) such as Amazon Echo and Google Home are becoming a popular technology for medical health systems among patients and caregivers. The VAS devices allow patients and caregivers to interact with them via voice commands. In most cases, the users' private voice data is fully disclosed to the VAS server, which may raise severe privacy concerns, especially in case of medical information which are clearly sensitive. In this paper, we propose a privacy-preserving voice query scheme in the healthcare-based voice assistant system, which enables the users to use voice commands for uploading medical data and later retrieving them. The VAS server in this case has no access to the original voice command or the data stored but it can accurately respond to user's query. Our scheme consists of two voice matching techniques with weak and strong privacy levels, where the former discloses only the voice feature, and not the original voice to the server. The latter further uses an obfuscation function to hide the voice features, thus the data is fully protected. We evaluate the performance of our proposed scheme by conducting experiments on self-generated voice data set, from three different languages, English, Chinese, and Arabic. We prove that our proposed scheme can achieve the privacy preservation of the voice data, and up to 98% accuracy in responding to voice queries.
Sentiment Analysis of Democratic Presidential Primaries Debate Tweets Using Machine Learning Models

Primary election debates play a significant role in the outcome of the general presidential election. This is more so when the candidate from the voter’s affiliated party is running against an incumbent. This research focuses on the sentiment analysis of tweets posted during the third Democratic primary debate. The goal of this research is to determine whom the Twitter users believe won or lost the debate. We evaluated the sentiment of tweets by using natural language processing tools and building machine learning models. This paper presents our approach to determining which candidate garnered the most support during the debate.
Autonomous line follower robot with fuzzy based hybrid controller

The autonomous navigation problem is currently a popular research topic encountered in many different areas, from smart cars to automated warehouse operations. Autonomous navigation of robotic systems is examined in two basic areas, with and without external supports. Even if the robot uses external supports (like GPS, road signs, ground surface lines, barcodes etc.) or positioning itself with internal sensors (like gyro, IMU, etc.), it independently needs a control method to follow the desired route. When the widely used control approaches in the literature are examined, PID and fuzzy-based approaches are frequently encountered. They both offer advantages and disadvantages. Within the scope of this study, the positive aspects of both of control approaches are utilized and the hybrid control approach for a robot that can autonomously follow the ground line presented as an external reference is mentioned. The success of the approach was tested comparatively on an exemplary mobile platform. The applied results obtained experimentally showed that a more efficient fuzzy logic-based hybrid control method could be developed. In addition, the effect of the number of conditions in the rule table on the success of the hybrid control technique is shown in the study. It has also been experimentally demonstrated that the success of hybrid control approach with a limited low power consuming ARM based processor that is suitable to control most of lightweight mobile platforms.
Video Cloud Services for Hospitals: Designing an End-to-End Cloud Service Platform for Medical Video Storage and Secure Access

The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem. We propose a generic architecture for a holistic, end-to-end recording and storage platform for hospitals, define crucial components, and identify existing and future solutions to address all parts of the system. This paper focuses mostly on the recording part of the system by introducing the major challenges in the area of bioinformatics, with particular focus on three major areas: video encoding, video quality, and video metadata.
Development and Application of Matrix Variate Restricted Boltzmann Machine

Matrix Variate Restricted Boltzmann Machine (MVRBM) model could maintain valuable spatial information using much fewer model parameters than the classic RBM, thus it has been used in image analysis, pattern recognition and machine learning for data analysis. This paper introduces two extensions of MVRBM: Matrix Variate Deep Belief Network (MVDBN) and Multimodal MVRBM (MMVRBM), both of them are composed of two or more MVRBMs whose input and latent variables are in matrix form. The MVDBN and MMVRBM have much fewer model parameters and deeper layer with better features to avoid overfitting and more accurate model easier to be learned. We test their efficiency on two real-world applications: image super-resolution and handwritten digit recognition. The experiments show the feasibility and superiority of the proposed models.
On‐line partitioning of the sample space in the regional adaptive algorithm

Abstract The regional adaptive (RAPT) algorithm is particularly useful in sampling from multimodal distributions. We propose an adaptive partitioning of the sample space, to be used in conjunction with the RAPT sampler and its variants. The adaptive partitioning consists in defining a hyperplane that is orthogonal to the line joining averaged coordinates in two separate regions and that goes through a point such that both averaged coordinates are equally Mahalanobis‐distant from this point. This yields an adaptive process that is robust to the choice of initial partition, stabilizes rapidly and is implemented at a marginal computational cost. The ergodicity of the sampler is verified through the simultaneous uniform ergodicity and diminishing adaptation conditions. The approach is compared to the RAPT algorithm with fixed regions and to the RAPT with online recursion (RAPTOR) through various examples, including a real data application. In short, our main contribution is the development of an alternative version of RAPTOR that seems to have no obvious downside and runs 15–35% faster in the examples considered.
Evaluation of maker space index system based on machine learning and intelligent interactive system

Machine learning technology is the core of artificial intelligence and the basis of computer intelligence. In recent years, machine learning technology has integrated and developed different learning methods, and the research of integrated learning system with more flexible and efficient form is also emerging. In this paper, the authors analyze the maker space index system based on machine learning and intelligent interactive system. As a comprehensive innovation and entrepreneurship platform, mass innovation space has the characteristics of both existing entrepreneurship service system and knowledge innovation driven. Through the index score calculation, the related evaluation system is constructed, the final score of social support system is 61.4.Multi-factor performance evaluation system based on machine learning and artificial intelligence,this paper reveals the development and change law of maker space, and provides theoretical basis for the future operation and decision-making of maker space.
Dynamic Block-Based HTTP Adaptive Streaming Scheme in Multiple Wireless Networks

Multipath-based hypertext transfer protocol (HTTP) adaptive streaming (MPHAS) has been studied due to the increasing use of mobile devices equipped with multiple network interfaces and the prevalence of high-quality video streaming services.However, the existing MPHAS schemes undergo a degraded quality of experience (QoE) in multiple wireless networks.The segment request-based MPHAS (SR-MPHAS) encounters frequent quality switching, and the block request-based MPHAS (BR-MPHAS) causes results in playback stalling and inefficient bandwidth utilization, according to slow responsiveness.In this paper, we propose a dynamic block-based MPHAS (DBAS) to improve the QoE by overcoming the limitations of the existing schemes in multiple wireless networks.We propose buffer-based block length adaptation and QoE-based block quality adaptation to reduce the frequency of quality switching and to improve the responsiveness to network changes.Moreover, we propose a partial request-based block scheduler to mitigate segment reordering.The experimental results show that the DBAS improves the QoE by reducing the quality switching frequency and rapidly adapting to abrupt changes in the bandwidth.
ACETA: Accelerating Encrypted Traffic Analytics on Network Edge

Applying machine learning techniques to detect malicious encrypted network traffic has become a challenging research topic. Traditional approaches based on studying network patterns fail to operate on encrypted data, especially without compromising the integrity of encryption. In addition, the requirement of rendering network-wide intelligent protection in a timely manner further exacerbates the problem. In this paper, we propose to leverage ×86 multicore platforms provisioned at enterprises' network edge with the software accelerators to design an encrypted traffic analytics (ETA) system with accelerated speed. Specifically, we explore a suite of data features and machine learning models with an open dataset. Then we show that by using Intel DAAL and OpenVINO libraries in model training and inference, we are able to reduce the training and inference time by a maximum order of 31× and 46× respectively while retaining the model accuracy.
SVM Based Hiragana and Katakana Recognition Algorithm with Neural Network Based Segmentation

A Japanese writing system, unlike the European system, is complex. It contains three types of signs: hiragana, katakana and Kanji. For daily use, more than 2000 characters are used, and each symbol can consist of 6 or more strokes. That is why it seems possible to recognise each sign by using a similar approach to fingerprint recognition. Authors are using the minutiae-finding algorithm to find three types of characteristic points. For preprocessing and classification, machine learning algorithms were used. The presented system uses the image of a single sign as an input.