Reviewerly/exHarmony huggingface dataset:
Content-Adaptive Memory for Viewer-Aware Energy-Quality Scalable Mobile Video Systems

Mobile devices are becoming ever more popular for streaming videos, which account for the majority of all data traffic on the internet. Memory is a critical component in mobile video processing systems, increasingly dominating power consumption. Today, memory designers are still focusing on hardware-level power optimization techniques, which usually come with significant implementation cost (e.g., silicon area overhead or performance penalty). In this paper, we propose a video content-aware memory technique for power-quality trade-off from viewer's perspectives. Based on the influence of video macroblock characteristics on the viewer's experience, we develop two simple and effective models - decision tree and logistic regression - to enable hardware adaptation. We have also implemented a novel viewer-aware bit-truncation technique which minimizes the impact on the viewer's experience, while introducing energy-quality adaptation to the video storage.
Adapting Sequence to Sequence Models for Text Normalization in Social Media

Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks. Off-theshelf tools are usually trained on formal text and cannot explicitly handle noise found in short online posts. Moreover, the variety of frequently occurring linguistic variations presents several challenges, even for humans who might not be able to comprehend the meaning of such posts, especially when they contain slang and abbreviations. Text Normalization aims to transform online user-generated text to a canonical form. Current text normalization systems rely on string or phonetic similarity and classification models that work on a local fashion. We argue that processing contextual information is crucial for this task and introduce a social media text normalization hybrid word-character attention-based encoder-decoder model that can serve as a pre-processing step for NLP applications to adapt to noisy text in social media. Our character-based component is trained on synthetic adversarial examples that are designed to capture errors commonly found in online user-generated text. Experiments show that our model surpasses neural architectures designed for text normalization and achieves comparable performance with state-of-the-art related work.
Examining and Enhancing the Illusory Touch Perception in Virtual Reality Using Non-Invasive Brain Stimulation

Virtual reality (VR) can be immersive to such a degree that users sometimes report feeling tactile sensations based on visualization of the touch, without any actual physical contact. This effect is not only interesting for studies of human perception, but can also be leveraged to improve the quality of VR by evoking tactile sensations without usage of specialized equipment. The aim of this paper is to study brain processing of the illusory touch and its enhancement for purposes of exploitation in VR scene design. To amplify the illusory touch, transcranial direct current stimulation (tDCS) was used. Participants attended two sessions with blinded stimulation and interacted with a virtual ball using tracked hands in VR. The effects were studied using electroencephalography (EEG), that allowed us to examine stimulation-induced changes in processing of the illusory touch in the brain, as well as to identify its neural correlates. Results confirm enhanced processing of the illusory touch after the stimulation, and some of these changes were correlated to subjective rating of its magnitude.
Bottom-up vs Top-down Model: The Perception of Reading Strategies among Thai University Students

Reading strategies have been declared as a vital role in reading comprehension among the students whose English is their second or foreign language. The use of reading strategies would help the learners to deeper understand the text so that they could achieve their tasks. The present study then investigates the use of reading strategies by focusing on the bottom-up and top-down models for both academic and business texts among Thai students in tertiary level. Two hundred-seventy non-simple random undergraduate students participated in this study. The forty-five-item questionnaire was delivered to the participants. The findings were that top-down reading strategies were used the most for both academic and business reading, whereas bottom-up reading strategies were reported the least on both kinds of text. The results suggest that larger numbers of participants, correction between the proficiency and the use of strategies, in-depth interview, and strategies used between low and high proficient readers should be examined.
HAUSDORFF DERIVATIVE LAPLACIAN OPERATOR FOR IMAGE SHARPENING

Image sharpening based on the partial differential equations plays an important role in the fields of image processing. It is an effective technique to clear and sharpen image features, and provides a higher resolution for the subsequent processing. This paper makes the first attempt to employ the Hausdorff derivative Laplacian operator to sharpen the images. In terms of the visual quality of details, contours and edges, the original images and noisy images were sharpened by using an appropriate Hausdorff derivative order. Numerical results indicate that the Hausdorff derivative Laplacian operator outperforms the high-pass filtering, the Roberts operator and the traditional integer-order Laplacian operator. In comparison with the existing methods for the image sharpening, the proposed new methodology could be considered as a competitive alternative.
Exploiting Augmented Reality to Display Technical Information on Industry 4.0 P&amp;ID

In this work, we present an Augmented Reality framework for handheld devices that enhance users in the comprehension of plant information traditionally conveyed through printed Piping and Instrumentation Diagrams (P&ID). The proposed framework augments on the P&ID of a plant some virtual interactive graphics (hotspots) referenced to specific components drawn on the P&ID. In this way, it is possible to easily find all the components belonging to the same category (e.g., all the pumps). By tapping, on the tablet screen, on a single hotspot further multimedia information can be displayed: technical data, 3D CAD model of the component, and 360° images of the plant section. The application is connected to the factory database where all the information associated with the plant components is stored. We used, as a case study, the cleaning section of a milling plant. With the tool presented in this work, technicians will be able to find information updated and in less time, so reducing the intervention time and increasing the accuracy of the operations. Furthermore, the cognitive load associated with the task of understanding the plant is highly reduced through the use of virtual information displayed using Augmented Reality.
VIKOR multi-criteria decision making with AHP reliable weighting for article acceptance recommendation

DSS is built to support the solution recommendation of a problem. AHP and VIKOR are examples of DSS method. Due to VIKOR’s subjective weighting, this study combines the AHP and VIKOR approach to create a better and more reliable decision support system. The DSS is used to recommend article acceptance using five criteria: originality, quality, clarity, significance, and relevance. The results showed that AHP-VIKOR outperforms the performance of VIKOR. AHP weighting reliably replaces the subjective VIKOR’s initial weighting. The AHP-VIKOR result is more accurate and steadier than VIKOR. Thus, AHP-VIKOR can be presented as a proposed approach for creating a recommendation of scientific article acceptance.
Online Data Preprocessing: A Case Study Approach

&lt;span lang="EN-US"&gt;Besides the Internet search facility and e-mails, social networking is now one of the three best uses of the Internet. A tremendous number of volunteers every day write articles, share photos, videos and links at a scope and scale never imagined before. However, because social network data are huge and come from heterogeneous sources, the data are highly susceptible to inconsistency, redundancy, noise, and loss. For data scientists, preparing the data and getting it into a standard format is critical because the quality of data is going to directly affect the performance of mining algorithms that are going to be applied next. Low-quality data will certainly limit the analysis and lower the quality of mining results. To this end, the goal of this study is to provide an overview of the different phases involved in data preprocessing, with a focus on social network data. As a case study, we will show how we applied preprocessing to the data that we collected for the Malaysian Flight MH370 that disappeared in 2014.&lt;/span&gt;
Privacy-Preserving Ontology Publishing for $$\mathcal {EL} $$ Instance Stores

We make a first step towards adapting an existing approach for privacy-preserving publishing of linked data to Description Logic (DL) ontologies. We consider the case where both the knowledge about individuals and the privacy policies are expressed using concepts of the DL $$\mathcal {EL} $$ , which corresponds to the setting where the ontology is an $$\mathcal {EL} $$ instance store. We introduce the notions of compliance of a concept with a policy and of safety of a concept for a policy, and show how optimal compliant (safe) generalizations of a given $$\mathcal {EL}$$ concept can be computed. In addition, we investigate the complexity of the optimality problem.
Island-based Cuckoo Search with Highly Disruptive Polynomial Mutation

The island model is one of the most well-known structured population strategies used to control the diversity in evolutionary algorithms. The population of an island-based evolutionary algorithm is normally partitioned into several sub-populations (islands). An evolutionaryalgorithm is then applied to each island independently. After a number of predefined generations, a migration process takes place to exchange specific candidate solutions between the islands. Recently, the Cuckoo search (CS) algorithm has been proposed as a population-based algorithm that mimics the nesting and parasitic reproduction behaviors of some cuckoo species. The main drawback of the CS algorithm is that its evolutionary operators may not adequately preserve the diversity of its population during the evolution process which may cause it to converge earlier than expected to suboptimal solutions. This paper introduces an improved variation of CS called island-based CS with polynomial mutation (iCSPM) that adapts two improvements to CS. First, the strategy of island model is incorporated into the CS algorithm to empower its capability in controlling the diversity of its population. Second, the L´evy flight method in CS is replaced with the highly disruptive polynomial mutation method in an attempt to enhance the exploration of CS. The iCSPM algorithm was evaluated using 15 standard benchmark functions in terms of the accuracy and reliability of the obtained results over multiple simulations. The sensitivity analysis of the main parameters of iCSPM was carried out to show their effect on the convergence behavior of iCSPM. The experimental results suggest that iCSPM provides more accurate and reliable results than 3 competitive methods. The source code of iCSPM is available at https://www.dropbox.com/sh/99x5374fiz2e390/AAC_6Eb9VFrDEdvt6wAmJstSa?dl=0
Learned versus Handcrafted Features for Person Re-identification

Person re-identification is one of the indispensable elements for visual surveillance. It assigns consistent labeling for the same person within the field of view of the same camera or even across multiple cameras. While handcrafted feature extraction is certainly one way of approaching this problem, in many cases, these features are becoming more and more complex. Besides, training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. This paper explores the following three main strategies for solving the person re-identification problem: (i) using handcrafted features, (ii) using transfer learning based on a pre-trained deep CNN (trained for object categorization) and (iii) training a deep CNN from scratch. Our experiments consistently demonstrated that: (1) The handcrafted features may still have favorable characteristics and benefits especially in cases where the learning database is not sufficient to train a deep network. (2) A fully trained Siamese CNN outperforms handcrafted approaches and the combination of pre-trained CNN with different re-identification processes. (3) Moreover, our experiments demonstrated that pre-trained features and handcrafted features perform equally well. These experiments have also revealed the most discriminative parts in the human body.
Retrieving Textual Evidence for Knowledge Graph Facts

Knowledge graphs have become vital resources for semantic search and provide users with precise answers to their information needs. Knowledge graphs often consist of billions of facts, typically encoded in the form of RDF triples. In most cases, these facts are extracted automatically and can thus be susceptible to errors. For many applications, it can therefore be very useful to complement knowledge graph facts with textual evidence. For instance, it can help users make informed decisions about the validity of the facts that are returned as part of an answer to a query. In this paper, we therefore propose , an approach that given a knowledge graph and a text corpus, retrieves the top-k most relevant textual passages for a given set of facts. Since our goal is to retrieve short passages, we develop a set of IR models combining exact matching through the Okapi BM25 model with semantic matching using word embeddings. To evaluate our approach, we built an extensive benchmark consisting of facts extracted from YAGO and text passages retrieved from Wikipedia. Our experimental results demonstrate the effectiveness of our approach in retrieving textual evidence for knowledge graph facts.
EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities

Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.
Role-play in Virtual Reality Game for the Senior

The games market is growing, in the current generation, no matter what age or gender you are, everyone plays games.
Self-moving robots and pulverized urban displays

In this paper, we investigate current approaches to the design of pervasive urban displays through two dimensions: increasing levels of physical integration of content into the surrounding environment (attached, blended, physicalized), and increasing levels of mobility of the display technology (fixed, portable, self-moving). We provide a classification of pervasive displays along these two dimensions and introduce a new class of pervasive display, which we call pulverized urban displays (PUDs). These displays represent content in a physical form, entangled with the built and natural environment, and are capable of autonomously changing their position. Drawing on urban robotic devices and their capability to sense and manipulate the environment, the paper lays out five characteristics of future forms of PUDs.
A YOLOv2 Convolutional Neural Network-Based Human–Machine Interface for the Control of Assistive Robotic Manipulators

During the last years, the mobility of people with upper limb disabilities and constrained on power wheelchairs is empowered by robotic arms. Nowadays, even though modern manipulators offer a high number of functionalities, some users cannot exploit all those potentialities due to their reduced manual skills, even if capable of driving the wheelchair by means of proper Human–Machine Interface (HMI). Owing to that, this work proposes a low-cost manipulator realizing only simple tasks and controllable by three different graphical HMI. The latter are empowered using a You Only Look Once (YOLO) v2 Convolutional Neural Network that analyzes the video stream generated by a camera placed on the robotic arm end-effector and recognizes the objects with which the user can interact. Such objects are shown to the user in the HMI surrounded by a bounding box. When the user selects one of the recognized objects, the target position information is exploited by an automatic close-feedback algorithm which leads the manipulator to automatically perform the desired task. A test procedure showed that the accuracy in reaching the desired target is 78%. The produced HMIs were appreciated by different user categories, obtaining a mean score of 8.13/10.
Security in Plain TXT

The Domain Name System is a critical piece of infrastructure that has expanded into use cases beyond its original intent. DNS TXT records are intentionally very permissive in what information can be stored there, and as a result are often used in broad and undocumented ways to support Internet security and networked applications. In this paper, we identified and categorized the patterns in TXT record use from a representative collection of resource record sets. We obtained the records from a data set containing 1.4 billion TXT records collected over a 2 year period and used pattern matching to identify record use cases present across multiple domains. We found that 92% of these records generally fall into 3 categories; protocol enhancement, domain verification, and resource location. While some of these records are required to remain public, we discovered many examples that unnecessarily reveal domain information or present other security threats (e.g., amplification attacks) in conflict with best practices in security.
Unifying Messaging, Queuing, Streaming and Light Weight Compute for Online Event Processing

Online event processing are abound ranging from web and mobile applications to data processing. Such event processing applications often require the ability to ingest, store, dispatch and process events. Until now, supporting all of these needs has required different systems for each task -- stream processing engines, messaging queuing middleware, and pub/sub messaging systems. This has led to the unnecessary complexity for the development of such applications and operations leading to increased barrier to adoption in the enterprises.
Individual Patient-Level Data Sharing for Continuous Learning: A Strategy for Trial Data Sharing

Initiated in 2013, the YODA Project is a voluntary, industry-supported eff ort to promote open science and data sharing.This
Recognition of static hand gesture with using ANN and SVM

Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally.There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language.However, there is no clue to understand the meaning of gesture through the computers directly.So this calls for definitions that generalize models in a computer.That is why the machine-learning approaches are implemented in recognition systems.There are generally two types of hand gestures recognition systems which researches have concentrated on.These include static and dynamic Hand gesture recognition systems.However, in building Hand gesture recognition systems, various machine learning approaches have been used.For implementing the proposed system, MS Kinect depth sensor was used as a hardware.The Kinect depth sensor is composed of an infrared camera.This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance.So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives.In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks.We use of the newly launched Microsoft Kinect sensor for image extraction.The sensor helps us to extract the hand images.We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations.In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM).From this dataset, we can generate the generalized gesture model for each alphabet image.For the proposed system, the classification with ANN proves a higher performance then SVM.
Correction of keystone distortion in tilted imaging systems using a digital micro-mirror

To reduce keystone distortion in tilted imaging systems when the object area is much larger than the lens diameter, an optical system design using a digital micro-mirror device (DMD) is proposed. Keystone distortion is analyzed, establishing the relationships between keystone distortion and object height, tilt angle of the object plane, focal length, and object distance. A design with a DMD in a reimaging setup is analyzed, providing a solution that eliminates keystone distortion in tilted imaging systems. The method is validated by simulation and experiment; it provides an innovative method to correct keystone distortion without digital signal processing.
A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach for fraud detection in online e-commerce transactions is proposed with majorly four logical modules, which uses big data analytics and machine learning algorithms to parallelize the processing of the data from a Chinese e-commerce company. Groups of experimental results show that the approach is more accurate and efficient to detect frauds in online e-commerce transactions and scalable for big data processing to obtain real-time property.