Reviewerly/exHarmony huggingface dataset:
Improved Security in Multimedia Video Surveillance Using 2D Discrete Wavelet Transforms and Encryption Framework

As the rate of the video is increasing on the internet, the security of video data is considered as a critical issue. In case of video surveillance application, the multimedia video streams require the video to be transmitted in a more secure way to its corresponding monitoring site. The multimedia video security is improved in this paper using a compression based encryption module. 2D Discrete Wavelet Transforms method is used for the compression process. The wavelet transform eliminates the low visual information, and it is scrambled and rotated for the encryption process. The encryption is handled using a series of the permutation–diffusion framework that helps in encrypting each video frame for possible secured transmission. The diffusion is carried out using Logistic chaotic maps, and the permutated blocks are encrypted with block-wise encryption. The experimental results show that the proposed video security framework achieves an improved performance against the existing method in terms of PSNR, SSIM, key size, and compression ratio, error rate.
Highly secure image steganography approach using Arnold's Cat map and maximum image entropy

Media (image, text, sound, and video) becomes available in the digital form. The ability to copy or modify contents and access to these media easily lead to making these problems challenging when transferring them to the internet. Therefore, data hiding methods are developed to handle these problems. In addition, cryptography algorithms can be combined with data hiding to increase the security of secret data that are used during the hiding process. In this paper, a new method of image steganography is proposed. The suggested approach is based on using ACM and array of random bits generated using BBS CSPRBG to encrypt the secret logo that is used during the hiding process. In embedding process, the cover-image is divided into blocks and the entropy of each block is computed. Then, the block that has a maximum value of entropy is selected to hide secret logo in it using (1st-LSB) algorithm. According to experimental results, the suggested method has achieved the best quality for the stego-image. Also, the secrecy of the logo is enhanced.
PUF-PassSE: A PUF based Password Strength Enhancer for IoT Applications

Authentications and encryptions are urgently needed by the growing number of hardware devices for security applications of the Internet of Things (IoT). However, the existing cryptographic solutions may not be applicable to IoT devices because of the strict timing and power requirements of the devices. As a result, Physical Unclonable Function (PUF) is a promising hardware primitive to provide security in existing and future IoT applications due to it is lower hardware cost and energy efficient. In this paper, we propose an innovated application of PUF as an entropy pump to improve the entropy. Furthermore, we apply the PUF-based entropy pump in a password enhancement application for embedded IoT devices. We confirmed that an overall 48% significant improvement on the entropy of the human-generated passwords. The proposed design is demonstrated on the Nexys 4 DDR FPGA board with low hardware overhead, which ensures the feasibility for IoT applications.
Poirot

To better understand the issues designers face as they interact with developers and use developer tools to create websites, we conducted a formative investigation consisting of interviews, a survey, and an analysis of professional design documents. Based on insights gained from these efforts, we developed Poirot, a web inspection tool for designers that enables them to make style edits to websites using a familiar graphical interface. We compared Poirot to Chrome DevTools in a lab study with 16 design professionals. We observed common problems designers experience when using Chrome DevTools and found that when using Poirot, designers were more successful in accomplishing typical design tasks (97% to 63%). In addition, we found that Poirot had a significantly lower perceived cognitive load and was overwhelmingly preferred by the designers in our study.
Balanced quaternary sequences of even period with optimal autocorrelation

In many communication systems, sequences with low correlation can reduce interference and noise. Tang and Lindner presented a construction of low autocorrelation quaternary sequences of period p based on cyclotomy, where is a prime (f, y are integers and ). In this study, a class of quaternary sequences of period 2p are proposed via interleaving two Tang-Lindner sequences of period p. New quaternary sequences of period 2p are balanced and have optimal autocorrelation when .
Spatiotemporal scenario data-driven decision for the path planning of multiple UASs

Modern systems operate in spaiotemporally evolving environments, and similar spatiotemporal scenarios are likely to be tied with similar decision solutions. This paper develops a spatiotemporal scenario data-driven decision solution for the path planning of multiple unmanned aircraft systems (UASs) in wind fields. The solution utilities offline operations, online operations and sptaiotemporal scenario data queries to provide an efficient path planning decision for multiple UASs. The solution features the use of similarity between spatiotemporal scenarios to retrieve offline decisions as the initial solution for online fine tuning, which significantly shortens the online decision time. A fast query algorithm that exploits the correlation of spatiotemporal scenarios is utilized in the decision framework to quickly retrieve the best offline decisions. The solution is demonstrated using simulation studies, and can be utilized in other decision applications where spaiotemporal environments play a crucial role in the decision process and the allowed decision time window is short.
A Mimic Learning Method for Disease Risk Prediction with Incomplete Initial Data

Huge amounts of electronic health records (EHRs) accumulated in recent years have provided a rich foundation for disease risk prediction. However, the challenging problems of incompletion in raw data and interpretability of prediction model are not solved very well so far. In this study, we present a mimic learning approach for disease risk prediction with large ratio of missing values, called SR-DF, as one of the early attempts. Specifically, we adopt spectral regularization for incomplete medical data learning, on which the missingness among raw data can be more accurately measured and imputed. Moreover, by utilizing deep forest, we get an effective method that takes advantages of interpretable and reliable model for disease risk prediction, which requires far fewer parameters and is less sensitive to parameter settings. As we will report in the experiments, the proposed method outperforms the baselines and achieves relatively consistent and stable results.
Composer4Everyone: Automatic Music Generation with Audio Motif

We introduct Composer4Everyone, a wechat-based application that automatically generates music for a piece of audio recording. A user with little composition experience would also be able to create his own work by a simple interaction of humming, singing or even speaking. The input audio is taken as a motif and developed into a piece of piano music of 20-30 seconds. A rearranging system is provided for users to change the piano music into a different style.
A Marine Environment Early Warning Algorithm Based on Marine Data Sampled by Multiple Underwater Gliders

This study analyzes and summarizes seven main characteristics of the marine data sampled by multiple underwater gliders. These characteristics such as the big data volume and data sparseness make it extremely difficult to do some meaningful applications like early warning of marine environment. In order to make full use of the sea trial data, this paper gives the definition of two types of marine data cube which can integrate the big marine data sampled by multiple underwater gliders along saw-tooth paths, and proposes a data fitting algorithm based on time extraction and space compression (DFTS) to construct the temperature and conductivity data cubes. This research also presents an early warning algorithm based on data cube (EWDC) to realize the early warning of a new sampled data file. Experiments results show that the proposed methods are reasonable and effective. Our work is the first study to do some realistic applications on the data sampled by multiple underwater vehicles, and it provides a research framework for processing and analyzing the big marine data oriented to the applications of underwater gliders.
A Quantum Representation of the Homogeneous 5D Manifold and the Perelman Mappings of 5D onto Non-Homogeneous Lorentz 4D Manifolds

The expression of the Maxwell magnetic monopole was employed to correlate the space to space projection that gives rise to the Gell-Mann standard model, and space to time projection which gives the leptons; and how does it correlate to the Perelman mappings from the homogeneous 5D manifold to the Lorentz 4D manifold, together with correlating the physical consequences caused by the breaking of the Diagonal Long Range Order [DLRO] of the monopoles quantum states affected by the motion of massive particles in the Lorentz 4D boundary of the 5D manifold, which leads to gravitons and the gravity field via the General Relativity covariant Riemannian 4D curvatures metric equation.
BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation

Matrix Approximation (MA) is a powerful technique in recommendation systems. There are two main problems in the prevalent MA framework. First, the latent factor is out of explanation and hampers the understanding of the reasons behind recommendations. Besides, traditional MA methods produce user/item factors globally, which fails to capture the idiosyncrasies of users/items. In this paper, we propose a model called Boosted Local rank-One Matrix Approximation (BLOMA). The core idea is to locally and sequentially approximate the residual matrix (which represents the unexplained part obtained from the previous stage) by rank-one sub-matrix factorization. The result factors are distinct and explainable by leveraging social networks and item attributes.
A Case History of Applying In situ Analysis of the Sulfur Isotopic Compositions of Sulfides from Ores of the Largest Deposits in the Bodaibo Synclinorium (Eastern Siberia)

The experience of applying the laser ablation technique to study the sulfur isotopic compositions of pyrite and arsenopyrite samples from gold deposits of the Baikal–Patom highland is discussed. The experimental procedure is described. All studied samples were found to display δ34S and δ33S values in strict compliance with the law of mass-dependent sulfur isotope fractionation. Regular lightening of the sulfur isotope composition in the course of crystal growth for pyrites from the Sukhoi Log deposit and the isotopic homogeneity of pyrite and pyrrhotite from the Golets Vysochaishii deposit were established. The conclusion that the sulfur isotopic homogeneity of sulfides in the ores occurs probably owing to metamorphism within the Mama–Oron belt was made.
Data Based Construction of Kernels for Semi-Supervised Learning With Less Labels

This paper deals with the problem of semi-supervised learning using a small number of training samples. Traditional kernel based methods utilize either a fixed kernel or a combination of judiciously chosen kernels from a fixed dictionary. In contrast, we construct a data-dependent kernel utilizing the Mercer components of different kernels constructed using ideas from diffusion geometry, and use a regularization technique with this kernel with adaptively chosen parameters. Our algorithm is illustrated using a few well-known data sets as well as a data set for automatic gender identification. For some of these data sets, we obtain a zero test error using only a minimal number of training samples.
Enhancement of panoramic musculoskeletal ultrasound image based on fuzzy technique

Panoramic Musculoskeletal Ultrasound Images (PMUI) is a developed version of ultrasound images. However, low contrast is a concrete problem which impact negatively on the interpretation of important details of PMUI. Therefore, in this paper a new automated contrast enhancement method was presented and examined on the PMUI. A fuzzy technique is the main tool underpinning this method, and it consists of three steps: fuzzification, modification of membership equation and defuzzification. Maximum fuzzy entropy of PMUI was used to optimize parameters of the membership function. The quality of results was examined using quantitative metrics. Based on these assessment metrics, the performance of the fuzzy technique outperforms the performance of other method with 21%. The results achieved are very effective and could be used for preprocessing in computer vision applications.
Improved Division Property Based Cube Attacks Exploiting Algebraic Properties of Superpoly

At CRYPTO 2017 and IEEE Transactions on Computers in 2018, Todo et al. proposed the division property based cube attack method making it possible to launch cube attacks with cubes of dimensions far beyond practical reach. However, assumptions are made to validate their attacks. In this paper, we further formulate the algebraic properties of the superpoly in one framework to facilitate cube attacks in more successful applications: we propose the “flag” technique to enhance the precision of MILP models, which enable us to identify proper non-cube IV assignments; a degree evaluation algorithm is presented to upper bound the degree of the superpoly s.t. the superpoly can be recovered without constructing its whole truth table and overall complexity of the attack can be largely reduced; we provide a divide-and-conquer strategy to Trivium-like stream ciphers namely Trivium, Kreyvium, TriviA-SC1/2 so that the large scale MILP models can be split into several small solvable ones enabling us to analyze Trivium-like primitives with more than 1000 initialization rounds; finally, we provide a term enumeration algorithm for finding the monomials of the superpoly, so that the complexity of many attacks can be further reduced. We apply our techniques to attack the initialization of several ciphers namely 839-round Trivium, 891-round Kreyvium, 1009-round TriviA-SC1, 1004-round TriviA-SC2, 184-round Grain-128a and 750-round Acorn respectively.
Jekyll RDF: Template-Based Linked Data Publication with Minimized Effort and Maximum Scalability

Over the last decades the Web has evolved from a human–human communication network to a network of complex human–machine interactions. An increasing amount of data is available as Linked Data which allows machines to "understand" the data, but RDF is not meant to be understood by humans. With Jekyll RDF we present a method to close the gap between structured data and human accessible exploration interfaces by publishing RDF datasets as customizable static HTML sites. It consists of an RDF resource mapping system to serve the resources under their respective IRI, a template mapping based on schema classes, and a markup language to define templates to render customized resource pages. Using the template system, it is possible to create domain specific browsing interfaces for RDF data next to the Linked Data resources. This enables content management and knowledge management systems to serve datasets in a highly customizable, low effort, and scalable way to be consumed by machines as well as humans.
A relation-aware multiparty access control

Privacy of data is a basic requirement for all types of applications but is especially relevant to online social networks. These systems allow users to share personal data with their friends and thus rightfully desire to have enriched social interactions. Current access control models fulfill basi c requirements of individual users but struggle to meet needs of social interactions such as multiparty privacy, politeness and asymmetric, non-reciprocal and intransitive nature of relationships. A personalized access control model, based on the social relations model and the socio-technical design paradigm, is proposed in this paper to satisfy these requirements. The model is formulated through extreme formal modeling and implemented through Facebook APIs. The model's expressiveness is examined for overlapping policies of multiple users, its consistency is analyzed for conflicting and redundant policies and user acceptance testing is performed for validity. The results suggest that the proposed technique outperformed the existing one to secure data in multiparty interactions.
Distributed Reachability Queries on Massive Graphs

Reachability querying is one of the most fundamental graph problems, and it has many applications in graph analytics and processing. Although a substantial number of algorithms are proposed, most of them are centralized and cannot scale to massive graphs that are distributed across multiple data centers. In this paper, we study the problem of distributed reachability queries, and present an efficient distributed reachability index, called Parallel Vertex Labeling (PVL), together with two lemmas to accelerate the query. Using real datasets, we demonstrate that our solutions achieve better indexing performance and significantly outperform the state-of-the-art distributed techniques.
Optimizing the A* search algorithm for mobile robotic devices

The purposes of this article are to analyze the pathfinding algorithms used to search and calculate the trajectory of movement of mobile autonomous robotic devices and to search for optimization methods in order to reduce the load on computing and memory units of the mobile devices. This paper analyzes the basic pathfinding algorithms including breadth-first search, depth-first search, Dijkstra's algorithm, greedy best-first search, and the A*algorithm, considers principles of their work, and proposes optimization methods. This work results in a new approach for using the A* algorithm for solving problems associated with pathfinding in real dynamic environments. The proposed approach speeds up the process of path calculation and the choice of the movement direction and reduces the requirements for computing power of autonomous mobile devices.
Adaptive Attention-Aware Gated Recurrent Unit for Sequential Recommendation

Due to the dynamic and evolutionary characteristics of user interests, sequential recommendation plays a significant role in recommender systems. A fundamental problem in the sequential recommendation is modeling dynamic user preference. Recurrent Neural Networks (RNNs) are widely adopted in the sequential recommendation, especially attention-based RNN becomes the state-of-the-art solution. However the existing fixed attention mechanism is insufficient to model the dynamic and evolutionary characteristics of user sequential preferences. In this work, we propose a novel solution, Adaptive Attention-Aware Gated Recurrent Unit (3AGRU), to learn adaptive user sequential representations for sequential recommendation. Specifically, we adopt an attention mechanism to adapt the representation of user sequential preference, and learn the interaction between steps and items from data. Moreover, in the first level of 3AGRU, we construct adaptive attention network to describe the relevance between input and the candidate item. In this way, a new input based on adaptive attention can reflect users' diverse interests. Then, the second level of 3AGRU applies adaptive attention network to hidden state level to learn a deep user representation which is able to express diverse interests of the user. Finally, we evaluate the proposed model using three real-world datasets from various application scenarios. Our experimental results show that our model significantly outperforms the state-of-the-art approaches on sequential recommendation.
Self-Organizing Maps with Convolutional Layers

Self-organizing maps (SOMs) are well appropriate for visualizing high-dimensional data sets. Training SOMs on raw high-dimensional data with classic metrics often leads to problems arising from the curse-of-dimensionality effect. To achieve more valuable semantic maps of high-dimensional data sets, we assume that higher-level features are necessary. We propose to gather such higher-level features from pre-trained convolutional layers, i.e., filter banks of convolutional neural networks (CNNs). Appropriately pre-trained CNNs are required, e.g., from the same or related domains, or in semi-supervised scenarios. We introduce SOM quality measures and analyze the new approach on two benchmark image data sets considering different convolutional network levels.
Smooth hyperbolic wavelet deconvolution with anisotropic structure

This paper considers a deconvolution regression problem in a multivariate setting with anisotropic structure and constructs an estimator of the function of interest using the hyperbolic wavelet basis.The deconvolution structure assumed is an anisotropic version of the smooth type (either regular-smooth or super-smooth).The function of interest is assumed to belong to a Besov space with anisotropic smoothness.Global performances of the presented hyperbolic wavelet estimators is measured by obtaining upper bounds on convergence rates in the L p -risk with 1 ≤ p ≤ 2 and 1 ≤ p < ∞ in the regular-smooth and super-smooth cases respectively.The results are compared and contrasted with existing convergence results in the literature.
Content Delivery on IP Network: Service Providers and TV Broadcasters Business Repositioning

This paper examines the main evolution drivers for the content delivery market, as a consequence of the rapid increase in the UBB access networks coverage. The bandwidth increase has a strong impact on the IP network architecture. It will induce new players to be directly involved in Quality of Experience improvement, so pushing the advent of two-sided business models. We consider both cases of VoD and live streaming (linear TV) over IP networks and that of future MoVAR (Mobile Virtual and Augmented Reality) services, pointing out how Edge Cloud Computing can address some main cost and performance challenges in both fixed and mobile networks.
Learning to Multi-Target Tracking in Dense Clutter Environment with JPDA-Recurrent Neural Networks

Multi-target tracking in dense clutter environment is an open problem and still involves many challenges. Different from the classical tracking methods involving complex models and accurate prior knowledge, deep learning methods have been researched for this problem in recent years. In this paper, a novel JPDA-recurrent neural networks (RNNs) based tracking approach is proposed. Three stages which can address data association, state prediction and track management are included in our approach. For the prediction stage, a model for modeling temporal sequence based on long-short term memory (LSTM) is employed to learn the targets motion parameters from radar noisy measurements. Moreover, an RNN-based track existence probability model is proposed to assess the track quality and to automatically initialize, maintain and terminate the tracks. This approach is demonstrated over a simulation scenario, with promising results.