Reviewerly/exHarmony huggingface dataset:
WISHED: Wavefront imaging sensor with high resolution and depth ranging

Phase-retrieval based wavefront sensors have been shown to reconstruct the complex field from an object with a high spatial resolution. Although the reconstructed complex field encodes the depth information of the object, it is impractical to be used as a depth sensor for macroscopic objects, since the unambiguous depth imaging range is limited by the optical wavelength. To improve the depth range of imaging and handle depth discontinuities, we propose a novel three-dimensional sensor by leveraging wavelength diversity and wavefront sensing. Complex fields at two optical wavelengths are recorded, and a synthetic wavelength can be generated by correlating those wavefronts. The proposed system achieves high lateral and depth resolutions. Our experimental prototype shows an unambiguous range of more than 1,000 x larger compared with the optical wavelengths, while the depth precision is up to 9µm for smooth objects and up to 69µm for rough objects. We experimentally demonstrate 3D reconstructions for transparent, translucent, and opaque objects with smooth and rough surfaces.
Recommending software features to designers: From the perspective of users

Summary With lots of public software descriptions emerging in the application market, it is significant to extract common software features from these descriptions and recommend them to new designers. However, existing approaches often recommend features according to their frequencies which reflect designers' preferences. In order to identify those users' favorite features and help design more popular software, this paper proposes to make use of the public data of users' ratings and products' downloads which reflect users' preferences to recommend extracted features. The proposed approach distinguishes users' perspective from designers' perspective and argues that users' perspective is better for recommending features because most products are designed for users and expect to be popular among users. Based on the lasso regression to estimate the relationship between the extracted features and the users' ratings, it first distinguishes the extracted features to identify those recommendable and undesirable features. By treating each download as a support from users to the product featurefeatures, it further mines the feature association rules from users' perspective for recommending features. By taking the public data on the market of SoftPedia.com for evaluation, our empirical studies indicate that: (i) selecting recommendable features by lasso regression is better than that by feature frequencies in terms of F 1 measure ; and (ii) recommending features based on the feature association rules mined from users' perspective is not only feasible but also has competitive performance compared with that based on the rules mined from designs' perspective in terms of F 1 measure .
Semi-static type, shape, and symbolic shape inference for dynamic computation graphs

The growing interest in deep learning has created a demand to compile computation graphs to accelerate execution and to deploy applications on various devices. Modern deep learning frameworks construct computation graphs dynamically. This gives rise to the problem of inferring types of dynamic computation graphs. Two approaches are known. One of them is a dynamic approach that constructs the computation graph from the execution trace of an actual input. While this approach is straightforward, the results of the shape inference will consist of only concrete values and is often difficult for users to interpret. The other one performs static analysis over the source program. This method can produce symbolic shape inference results but suffers from the dynamic nature of the host programming language Python.
Information sharing in cybersecurity : Enhancing security, trust and privacy by capacity building

This article focuses on the importance of information sharing practices for capacity building competences in the field of cybersecurity.It analyses needs and constraints of information sharing to produce security and cyberresilience that promote trust and confidence in cyberspace. It identifies the different types of information that can be shared as well as the main vectors for supporting information sharing. By proposing five applications of information sharing in cybersecurity, it focusses on education and warning in order to enforce people competencies.High-level recommendations are proposed in conclusion for the establishment of a platform for information sharing and analysis.
Does Improved Sound Rendering Increase Player Performance? A Graph-Based Spatial Audio Framework

Spatial audio is an important component of realistic simulations in virtual environments. However, there have been shortcomings in existing spatial audio rendering systems, due to the computational cost of rendering realistic sound propagation. In this article, we present a novel spatial audio rendering method using 3-D graphs to simulate sound propagation in real time. We then ask if such a distinction in accuracy makes a difference to task performance, and test this proposition in a 3-D game. We conclude that improvements in spatial audio rendering do increase task performance.
High-Speed Phase-Shifting 3D Profilometry on Human Face Assisted by Statistical Model

Phase-shifting 3D profilometry is of high precision, but its performance could be far below expectations due to its long exposure time in the measurement of dynamic objects, especially human faces. In this article, a new idea is proposed to improve the measuring speed by providing reference derived from the statistical characteristics of human faces for the reconstruction of faces measured. During the measurement, no Gray code or low-frequency fringe is required, which reduces the number of patterns projected and sharply shortens the measuring time. Assisted by face detection and point cloud clustering, several candidate faces of different orders can be generated from the wrapped phase. Afterwards, the most probable candidate is selected according to the probability model of human face to obtain the correct result. Furthermore, the applicable conditions of our method have been analyzed, and the validity has been proved by experiments on actual human faces as well.
Multi-level Recognition on Falls from Activities of Daily Living

The falling accident is one of the largest threats to human health, which leads to broken bones, head injury, or even death. Therefore, automatic human fall recognition is vital for the Activities of Daily Living (ADL). In this paper, we try to define multi-level computer vision tasks for the visually observed fall recognition problem and study the methods and pipeline. We make frame-level labels for the fall action on several ADL datasets to test the methods and support the analysis. While current deep-learning fall recognition methods usually work on the sequence-level input, we propose a novel Dynamic Pose Motion (DPM) representation to go a step further, which can be captured by a flexible motion extraction module. Besides, a sequence-level fall recognition pipeline is proposed, which has an explicit two-branch structure for the appearance and motion feature, and has canonical LSTM to make temporal modeling and fall prediction. Finally, while current research only makes a binary classification on the fall and ADL, we further study how to detect the start time and the end time of a fall action in a video-level task. We conduct analysis experiments and ablation studies on both the simulated and real-life fall datasets. The relabelled datasets and extensive experiments form a new baseline on the recognition of falls and ADL.
Two Stage Semantic Segmentation by SEEDS and Fork Net

Semantic segmentation of image is one of the most challenging and researched topic in the field of computer vision. Statistical methods can be employed for the task with low computational resources, but in a diverse natural environment, it fails to label many complicated objects. Deep learning methods are quite popular now for high accuracy but dense semantic segmentation at pixel level accuracy is very resource-intensive and not suitable for robot vision. Proposed methodology merges the best of both worlds to semantically label superpixels computed by a statistical method, with a deep net. The deep convolution network is novel in its use of superpixels in different fields of vision. The methodology is tested on the Pascal VOC dataset and compared with recent popular approaches. The results show that the proposed methodology is on par with the best results.
A New Global Routing Optimization Algorithm based on Pigeon Inspired Optimization

During the last few decades, the world has witnessed a surge in the use of electronic goods. However, this surfeit has introduced a new challenge related to the energy and cost optimization of the electronic devices. As a result, researchers primarily tried to solve this problem through algorithmic approaches. This proposed work focuses on solving the Rectilinear Steiner Minimal Tree (RSMT) problem with the help of a metaheuristic algorithm, namely, the Pigeon Inspired Optimization (PIO) algorithm. The performance of the algorithm has been compared with the pre-existing algorithms like PSO and some of its variants on the basis of efficiency, accuracy and execution time of the algorithm. It was observed that the algorithm besides maintaining high stability and accuracy, required lower execution time to descend to the optimal solution within the search space.
Efficient &amp; secure image availability and content protection

Digital images are among the most communicated multimedia data types. Many of these images include private data that require a high level of security. The traditional image security schemes rely on cryptographic solutions to ensure the confidentiality or the authentication of image contents, and to ensure that the encryption key is not compromised. However, the continuous evolution of the attackers’ capabilities is making it harder than ever to achieve the goal of safeguarding the private data against breaches. Moreover, the centralization aspect of images’ storage makes them prone to availability attacks. In this paper, we propose a distributed and secure storage scheme for images, based on the Modified Information Dispersal Algorithm (MIDA), and taking into consideration the trade-off between the high security level and the associated computational overhead. The proposed solution applies block permutation on the image to ensure data confidentiality and then, divides it into k fragments that are encoded using the proposed parallel modified IDA. The output consists of n encoded fragments, instead of k, to ensure data availability. Next, each encoded fragment is authenticated using a lightweight Message Authentication Algorithm (MAA) to ensure data integrity with source authentication. Finally, the encoded fragments are distributed over n storage nodes (or multi-cloud providers). The resilience degree of such redundancy is (n − k), since only k fragments are required to reconstruct the original images. All the cryptographic steps such as permutation, IDA encoding and MAA consist of simple operations and they are based on a dynamic key. This ensures a high level of security since in each session, a new key is used to produce different cryptographic primitives as well as the update primitives, which are used to update the permutation and selection tables. The implementation results show that the proposed scheme meets the desired cryptographic properties to guard against different attacks. Finally, the performance tests show that the proposed scheme is lightweight with low overhead in terms of computations, communication and storage.
Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks

In this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach can jointly learn from C- and L-band data and improve the single band classification accuracy. To the best of our knowledge, this is the first study that introduces 1D-CNNs to land use/land cover classification domain using PolSAR data. The proposed approach aims to achieve maximum classification accuracy by one-time training over multiple frequency bands with limited labelled data. Moreover, the proposed dual-band 1D-CNN approach yields a superior computational efficiency compared to the deep 2D-CNN based approaches. The performed experiments using AIRSAR PolSAR image over San Diego region at C- and L-bands have shown that the proposed approach is able to simultaneously learn from the C- and L-band SAR data and achieves an elegant classification performance with minimal complexity.
Intelligent Detection of Information Outliers Using Linguistic Summaries with Non-monotonic Quantifiers

In the processing of imprecise information, principally in big data analysis, it is very advantageous to transform numerical values into the standard form of linguistic statements. This paper deals with a novel method of outlier detection using linguistic summaries. Particular attention is devoted to examining the usefulness of non-monotonic quantifiers, which represent a fuzzy determination of the amount of analyzed data. The answer is positive. The use of non-monotonic quantifiers in the detection of outliers can provide a more significant value of the degree of truth of a linguistic summary. At the end, this paper provides a computational example of practical importance.
The Use of Mobile Learning in Grades K–12: A Literature Review of Current Trends and Practices

This article presents a literature review of mobile learning (m-learning) in the K–12 classroom and explores the impact of tablets in primary education. Additionally, it shows the impact of mobile learning on K–12 online learning as well as the need for professional development of educators utilizing mobile technologies. Further, the article reviews challenges with m-learning and provides potential recommendations and guidelines for utilizing mobile technologies in the future of educational curriculum development.
A survey on RDBMS and NoSQL Databases MySQL vs MongoDB

The breakneck development in the Internet showcase and the rising of the new web innovations with another challenges, fresh concepts and modern applicationsfor example, NoSQL databases which is as of late turns out to be well known as an option in contrast to the social databases particularly in managing huge information which is one of the most widely recognized highlights of the web today. This provideshigh accessibility and adaptability to the circulated frameworks when compared to RDBMS. Commonly distributed system needsquick access time and can't endure any vacation during disappointments and has been utilized vigorously by enormous endeavors and web organizations. Every new technology faced many challenges like effective access such as responses with high speed. This paper compares the concepts of NoSQL and Relational databases then it’s limitations. And address the advantages and types of NoSQL databases.
DehazeGlasses

We present DehazeGlasses, a see-through visual haze removal system that optically dehazes the user's field of vision. Human vision suffers from a degraded view due to aspects of the scene environment, such as haze. Such degradation may interfere with our behavior or judgement in daily tasks. We focus on hazy scenes as one common degradation source, which whitens the view due to certain atmospheric conditions. Unlike typical computer vision systems that process recorded images, we aim to realize a see-through glasses system that can optically manipulate our field of view to dehaze the perceived scene. Our system selectively modulates the intensity of the light entering the eyes via occlusion-capable optical see-through head-mounted displays (OST-HMD). We built a proof-of-concept system to evaluate the feasibility of our haze removal method by combining a digital micromirror device (DMD) and an OST-HMD, and tested it with a user-perspective viewpoint camera. A quantitative evaluation with 80 scenes from a haze removal dataset shows that our system realizes a dehazed view that is significantly closer to the ground truth scene compared to the native view under a perceptual image similarity metric. This evaluation shows that our system achieves perceptually natural haze removal while maintaining the see-through view of actual scenes.
Video Steganography: Using Scrambling- AES Encryption and DCT, DST Steganography

Steganography is a creative work of concealing secret data or message within an ordinary, non-secret file. This helps to prevent detection of the existence secret data. Steganography takes information hiding one step ahead than data encryption. Data encryption hides the data, but Steganography hides the existence of the data. In this paper, we combine both Data Steganography and Data Encryption, thus providing double layer security. The paper deals with Multimedia Steganography which enables working successfully with video files, without obscuring the quality of the resultant video. The proposed technique deals with Steganography in Transform domain, thus overcoming the drawbacks of the spatial domain techniques. An important drawback of spatial domain technique is that data is prone to different attacks, whereas Transform domain provides a robust aftermath. Applying the advantages of Transform domain, we have used DCT and DST technique on the cover video whilst using Encryption algorithm of Scrambling- AES on the message image, ensuring double layer security.
Bee-Bots: A Blockchain Based Decentralised Swarm Robotic System

Swarm robotics is a biologically inspired approach that has been popularly used for tasks that require miniaturisation and fault tolerance at low cost. Constituents in a swarm communicate by means of stigmergy, a mechanism where common information is stored in the environment by the individuals. We model the storage and retrieval of this communication in swarm robotic systems with decentralisation using Blockchain Technology. Blockchain lets a group of agents reach consensus as well as exchange data without the need for a central authority, just like in a swarm. Construction of structures is a common problem that can be solved by using swarm robotics. In this paper, we apply the blockchain approach to decentralisation of swarm systems to construct 2-D structures with blocks in a multi-bot environment as a Proof of Concept.
Combining object detection with generative adversarial networks for in-component anomaly detection

Present inspection techniques in place at the Joint European Torus (JET), as well as some of those planned for ITER make use of robotically deployed inspection systems, which typically collect data for offline analysis. This can be a slow, laborious process with subjective or error-prone results. There are significant benefits to be gained through automation or user assistance, for example through prioritisation of samples for analysis. Automated visual anomaly detection is a highly challenging problem due to high dimensionality of the input data, meaning that the normal statistical distribution cannot be directly modelled. We provide a robotic and algorithmic framework that utilizes Generative Adversarial Ngenerative adversarial networks (GANs) to indirectly model this distribution, and hence provide a mechanism to quantify the anomalousness of given image data samples from a tokamak environment. This paper presents an approach to visual anomaly detection that combines multiple deep neural network architectures in order to extract individual components and then classify anomalies. An overview of the architecture and algorithms employed as well as quantitative and qualitative assessments of the performance against data from both a benchmark dataset, and real data gathered from JET components is provided.
QoE-driven HAS Live Video Channel Placement in the Media Cloud

HTTP adaptive streaming (HAS) technology has been increasingly employed by video service providers (VSPs) due to its prominent benefits such as reducing interruptions of video playback and achieving higher bandwidth utilization and outstanding quality of experience (QoE). And many VSPs have deployed HAS applications in the media cloud to provide large-scale video streaming services. At present, research into the media cloud typically focuses on the management and optimization of cloud resources, such as the placement and migration of virtual machines in media cloud data centers. However, considering the HAS <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">live video streaming</i> service, existing related works have not adequately discussed the specific impact of the consumption of computing and bandwidth resources of media cloud servers on the user experience (QoE), particularly under the resource constraints in the media cloud. In this paper, we first investigate and formulate the computing and bandwidth resource consumption characteristics of HAS live video streaming with different frame rates and resolutions, and we further establish a resources-aware QoE model to quantify the user experience of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">live video channels</i> (i.e., programs). Then, based on the model, we present a QoE-driven HAS live video channel placement approach (including a placement algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HCP</i> and a rescheduling algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HCR</i> ) to optimize the channel allocation in media cloud servers, aiming to maximize the average user QoE. We abstract the maximization problem into an MMKP problem, and employ a heuristic solution to address this problem. The experimental results demonstrate the effectiveness of our proposed approach compared with benchmark solutions.
Implementation of iterative k-means-+ and ant colony optimization (ACO) in portfolio optimization problem

Portfolio optimization aims to provide investors with the highest returns and the least amount of risk. To that end, investors diversify to improve a portfolio’s effectiveness by reducing risks. In this research, we used the iterative k-means -+ algorithm as a clustering method and ant colony optimization (ACO). Clustering was used to diversify a portfolio based on the financial ratio of each stock. Iterative k-means -+ improves the solution obtained using k-means by removing 1 cluster (minus), dividing another cluster (plus) and re-clustering with each iteration. After clustering, some of the stocks are chosen and their weights are determined using a metaheuristic method, ant colony optimization (ACO). The numerical result of this method is evaluated with the data. This research yielded the results in which the performance of the iterative k-means -+ and ACO methods yields better returns and Sharpe ratios compared to those of the S&P 500 index data used.