Reviewerly/exHarmony huggingface dataset:
Privacy preservation for social networks sequential publishing

The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contain much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support, etc). Most of the anonymization works in social networks focus on publishing one instance while not considering the need for anonymizing sequential releases. However, many cases show that sequential releases may infer private information even though individual instances are anonymized. This paper studies the privacy issues of sequential releases and proposes a privacy preserving solution for this case. The proposed solution ensures three privacy requirements (users’ privacy, groups’ privacy and edges’ privacy), and it considers the case where many users and groups may share the same profiles. Some experiments over some complex queries show that the utility of the released data is better preserved than other solutions, with regard to the privacy of users, groups and edges.
Fast scalable construction of ([compressed] static | minimal perfect hash) functions

Recent advances in the analysis of random linear systems on finite fields have paved the way for the construction of constant-time data structures representing static functions and minimal perfect hash functions using less space with respect to existing techniques. The main obstacle for any practical application of these results is the time required to solve such linear systems: despite they can be made very small, the computation is still too slow to be feasible. In this paper, we describe in detail a number of heuristics and programming techniques to speed up the solution of these systems by orders of magnitude, making the overall construction competitive with the standard and widely used MWHC technique, which is based on hypergraph peeling. In particular, we introduce broadword programming techniques for fast equation manipulation and a lazy Gaussian elimination algorithm. We also describe a number of technical improvements to the data structure which further reduce space usage and improve lookup speed. Our implementation of these techniques yields a minimal perfect hash function data structure occupying 2.24 bits per element, compared to 2.68 for MWHC-based ones, and a static function data structure which reduces the multiplicative overhead from 1.23 to 1.024. For functions whose output has low entropy, we are able to implement feasibly for the first time the Hreinsson–Krøyer–Pagh approach, which makes it possible, for example, to store a function with an output of 106 values distributed following a power law of exponent 2 in just 2.76 bits per key instead of 20.
A Localized Learning Approach Applied to Human Activity Recognition

The recognition of human physical activities and postures based on sensor data has received much research attention in several human health and biomedical engineering applications.In this study, the challenges of class-imbalance and ambiguity (or confusion) are discussed that frequently arise in data from human activity recognition (HAR) systems.In order to reduce the influence of imbalance and ambiguity in HAR problems, a novel hybrid localised learning approach of K-nearest neighbours leastsquares support vector machine (KNN-LS-SVM) is proposed.The classifier is applied to different synthetic and real-world datasets where imbalance and ambiguity are present.In this study, it is novel to apply a hybrid localised learning algorithm to the HAR problem.When compared to different global and local approaches, higher classification performances could be obtained by using the proposed localised learning approach.Furthermore, the computational effort could be reduced in an online learning mode.
Fog-Assisted Virtual Reality MMOG with Ultra Low Latency

In this paper, we propose a method to realize a virtual reality MMOG with low latency. The basic idea of the proposed method is to introduce an intermediate layer consisting of several fog nodes to offload the rendering of game view from the cloud server which conducts the rendering in conventional cloud games. In addition, we propose three techniques to further reduce the latency: 1) To maintain the overall game space in a consistent manner, the collision detection of virtual objects is conducted by the cloud server in a centralized manner; 2) To reflect subtle changes of the line of sight of a player to the resulting game view, such a change is acquired by the HMD worn by the player, and is directly sent to the corresponding fog node to alleviate the VR sickness; and 3) To overcome the overload of fog nodes concerned with the rendering, we separate the rendering of the background view from that of the foreground view, and offload the former to other nodes including the cloud server. The performance of the proposed method is evaluated through experiments conducted on a prototype system.
Learning optics with android-assisted comics: the impacts on students critical thinking

Abstract The purpose of this study is to improve students’ critical thinking skills with the assisted of android comics learning media on optical material. The study was conducted at SMA Negeri 1Kalasan, Yogyakarta with a data sample of 27 students. The study was conducted using the type of pre-experimental research. The data is taken using a cluster random sampling technique and the research design is one group pretest-posttest design. The test used is a critical thinking ability test in the form of essays in optical material, and it’s validated by expert judgment. Test data were analyzed using n-gain analysis, the results of the analysis showed that the increase in students’ critical thinking skills is in the average value of N-gain included in the moderate and high categories. Students who get the moderate category have an N-gain value of 0.53 with a percentage of many students at 44%. Meanwhile, students who get the high category have an N-gain value of 0.79 with the percentage of many students at 56%. This shows that android comics learning media is effectively used to improve students’ critical thinking skills.
Boosting Real-Time Driving Scene Parsing With Shared Semantics

Real-time scene parsing is a fundamental feature for autonomous driving vehicles with multiple cameras.In this letter we demonstrate that sharing semantics between cameras with different perspectives and overlapped views can boost the parsing performance when compared with traditional methods, which individually process the frames from each camera.Our framework is based on a deep neural network for semantic segmentation but with two kinds of additional modules for sharing and fusing semantics.On the one hand, a semantics sharing module is designed to establish the pixel-wise mapping between the input images.Features as well as semantics are shared by the map to reduce duplicated workload which leads to more efficient computation.On the other hand, feature fusion modules are designed to combine different modal of semantic features, which leverage the information from both inputs for better accuracy.To evaluate the effectiveness of the proposed framework, we have applied our network to a dual-camera vision system for driving scene parsing.Experimental results show that our network outperforms the baseline method on the parsing accuracy with comparable computations.
Towards Industrial IoT-AR Systems using Deep Learning-Based Object Pose Estimation

Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target's pose parameters. Quantitative and qualitative experiments prove this system's efficacy and show potential for fusing these emerging technologies in real-world applications.
Creative AI: A new avenue for the Semantic Web?

Computational Creativity (or artificial creativity) is a multidisciplinary field, researching how to construct computer programs that model, simulate, exhibit or enhance creative behaviour. This vision paper explores a potential of the Semantic Web and its technologies for creative AI. Possible use s of the Semantic Web and semantic technologies are discussed, regarding three types of creativity: i) exploratory creativity, ii) combinational creativity, and iii) transformational creativity and relevant research questions. For exploratory creativity, how can we explore the limits of what is possible, while remaining bound by a set of existing domain axioms, templates, and rules, expressed with semantic technologies? To achieve a combinational creativity, how can we combine or blend existing concepts, frames, ontology design patterns, and other constructs, and benefit from cross-fertilization? Ultimately, can we use ontologies and knowledge graphs, which describe an existing domain with its constraints and, applying a meta-rule for transformational creativity, start dropping constraints and adding new constraints to produce novel artifacts? Together with these new challenges, the paper also provides pointers to emerging and growing application domains of Semantic Web related to computational creativity: from recipe generation to scientific discovery and creative design.
Action Description From 2D Human Postures in Care Facilities

This article describes a novel approach to classification of whole-body motions from estimated human postures in 2D camera images and subsequent generation of their relevant descriptions. The motions are encoded into stochastic motion models referred to as motion primitives. Words are connected to the motion primitives, and word n-grams are represented stochastically. More specifically, the motion observation is classified into the motion primitive, from which its relevant words are generated. These words are arranged in a grammatically correct order to make the descriptions for the observation. This approach was tested on actions performed by older adults in the care facility and its validity was demonstrated.
The Tempest in a Cubic Millimeter: Image-Based Refinements Necessitate the Reconstruction of 3D Microvasculature From a Large Series of Damaged Alternately-Stained Histological Sections

This work presents two methods that facilitate a 3D reconstruction of microscopic blood vessels in the volume slightly larger than 1 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The source of the data are histological serial sections, i.e., microscopic images of probes, stained with immunohistochemistry. Odd and even sections have different stainings in our primary data set. Thus, firstly, an approach to register an alternately-stained series is presented. With image filtering and a feature-detection-based registration we obtain a registered stack of 148 serial sections. The series has missing sections, locally damaged sections, artifacts from acquisition. All these hinder correct connectivity of blood vessels. With our second approach we interpolate the missing information while maintaining the connectivity. We achieve this with deformations based on dense optical flow. The presented methodology is applicable to further histological series. A combination of both approaches allows us to reconstruct more than 76% larger volumes. An important detail was the composition mode of images. Summarizing, we use methods from image processing and computer vision to create large-scale 3D models from immunostained histological serial sections.
Removing Rain from Single Image Based on Details Preservation and Background Enhancement

Life is often accompanied by the bad weather. In the rainy days, the quality of images and videos acquired will be greatly degraded, affecting human observation and target detection and recognition in computer vision systems. In this paper, a single image rain removal algorithm is proposed based on details preservation and background enhancement. We first decompose the rainy image into low-frequency part and high-frequency part by low pass smoothing filter. Then, edge detection is performed on the low-frequency part to extract a mask, which will be used to capture rain-free image details from the high-frequency part. Next, the rain-free image details are superimposed on the low-frequency part to obtain an image without rains but well preserved details. Finally, the dark channel prior method is utilized to further alleviate the blur due to raining. Experiments on both synthetic and real rainy images demonstrate the effectiveness and efficiency of the proposed method.
Evaluating User Preferences for Augmented Reality Interactions with the Internet of Things

We investigate user preferences for controlling IoT devices with headset-based Augment Reality (AR), comparing gestural control and voice control. An elicitation study is performed with 16 participants to gather their preferred voice commands and gestures for a set of referents. We analyzed 784 inputs (392 gestures and 392 voice) as well as observations and interviews to develop an empirical basis for design recommendations that form a guideline for future designers and implementors of such voice commands and gestures for interacting with the IoT via headset-based AR.
Imminent Warning Communication: Earthquake Early Warning and Short-Term Forecasting in Japan and the US

The ability of scientists to warn communities of the occurrence of potentially damaging earthquakes is evolving, though accurate prediction of the time, location, and magnitude of such earthquakes remains a distant goal. Anticipation of major earthquakes has taken the form of long-term forecasts, earthquake hazard mapping, and scenario development accompanied by societal impact assessments based on loss estimation methodologies. More recently, earthquake early warning systems (EEW), pioneered and first implemented in Japan, facilitated the communication of very short-term alerts that ground motion from an earthquake would arrive in a matter of seconds to tens of seconds. Currently, a nationwide EEW system exists only in Japan, but regional systems are becoming available in many earthquake vulnerable nations, including the US. A second means of anticipating an earthquake is operational earthquake forecasting (OEF) based on the occurrence of seismic activity of various kinds that raises the short-term probability that additional earthquakes, including damaging earthquakes, could occur in hours to a few days. OEF has been implemented in the US and has its most vocal champions among American scientists. These two means of anticipating potentially damaging earthquakes and the communication of information to communities at risk are the subject of this chapter.
Turbo: Dynamic and Decentralized Global Analytics via Machine Learning

Big data analytics are practiced in many fields to extract insights from massive amounts of data. With exponential growth in both the volume and variety of data, analytic queries have expanded from those executed in a single datacenter to those requiring inputs from multiple datacenters that are geographically separate or even globally distributed. Unfortunately, the software stack that supports data analytics is designed originally for a cluster environment and is not tailored to execute global analytic queries, where resources such as inter-datacenter networks may vary on the fly. Existing optimization strategies that determine the query execution plan before its execution are not able to adapt to resource variations at query runtime. In this article, we present Turbo, a lightweight and non-intrusive global analytics system that can dynamically adjust query execution plans for geo-distributed analytics in the presence of time-varying resources and network bandwidth across datacenters. Turbo uses machine learning to accurately predict the time cost of a query execution plan so that dynamic adjustments can be made to it when necessary. Turbo is non-intrusive in the sense that it does not require modifications to the existing software stack for data analytics. We have implemented a real-world prototype of Turbo, and evaluated it on a cluster of 33 instances across eight regions in the Google Cloud Platform. Our experimental results have shown that Turbo can achieve an accuracy of 95 percent for estimating time costs, and can reduce the query completion time by 41 percent.
Prediction of Received Signal Power in Mobile Communications Using Different Machine Learning Algorithms:A Comparative Study

In this paper, we apply and compare various machine learning techniques to predict the received signal strength (RSS) in cellular communications. We generate the training set using experimental measurements from an unmanned aerial vehicle (UAV). We make a prediction model for the RSS using five base learners. We create a new ensemble method that averages the results from these five base learners. The proposed model outperforms all the original base learners. The obtained numerical results are compared with the original data from the test dataset using representative performance indicators and exhibit good precision.
Remote Sensing Image Aircraft Detection Technology Based on Deep Learning

How to use high-resolution remote sensing images to quickly and accurately obtain high-value target information such as aircraft and ships has become a hot topic in the research of automatic target detection. This paper uses Google Earth image to make aircraft data set, then detect the aircraft targets respectively based on the two deep learning models YOLOv3 and Faster_R_CNN. At the same time, in order to significantly improve the aircraft detection accuracy, this paper proposes a shadow processing algorithm with double threshold random sampling to conduct data preprocessing for aircraft targets in remote sensing images. The experimental results show that both deep learning models can effectively detect aircraft targets and have great application potential in automatic detection of remote sensing image targets. The shadow processing algorithm can effectively eliminate the projected shadow of the aircraft, restore the texture characteristics of the shaded area, greatly improve the overall quality of the remote sensing image, and lay a good data foundation for subsequent aircraft detection.
SignAR: A Sign Language Translator Application with Augmented Reality using Text and Image Recognition

Studies have shown that hearing impaired students face lots of difficulties in reading comprehension mostly because they cannot develop a fluent system of communication required to become proficient readers. This in turn can have an impact on their communication and social skills, leading to an overall depressed academic achievement, affecting their future prospects. SignAR will help hearing impaired children learn English as well as sign language and hearing people can also use it to learn sign language. So, basically, once a word has been captured by the phone camera, the corresponding sign animation will be augmented on the user's screen. Each word will be mapped to its corresponding animation and shown on the phone's screen. By using SignAR, the hearing impaired children will be able to learn English using Signs while hearing users will learn Signs using English. In this way, the communication barriers between the hearing and non-hearing will hopefully be greatly diminished.
AG codes from the second generalization of the GK maximal curve

Let q be a prime-power, and n≥3 an odd integer. We determine the structure of the Weierstrass semigroup H(P) where P is an arbitrary Fq2-rational point of GK2,n where GK2,n stands for the Fq2n-maximal curve of Beelen and Montanucci. We prove that these points are Weierstrass points, and we compute the Frobenius dimension of GK2,n. Using these results, we also show that GK2,n is isomorphic to the Güneri–Garcìa–Stichtenoth only for n=3. Furthermore, AG codes and AG quantum codes from the GK2,n are constructed and discussed. In some cases, they have better parameters compared with those of the known linear codes.
JavaScript Development Environment for Programming Education Using Smartphones

A development environment that allows programming at any time or place is required for supporting programming education. Currently, smartphones are more popular than personal computers. Therefore, if a development environment for smartphones is available then students can learn to program in a normal room, with no personal computer, or on the way to school. A browser-based development environment can be realized using JavaScript as a learning language. In this paper, we describe the design, implementation, and evaluation of a JavaScript development environment. Our development environment is suitable for smartphones and is easy to use even with a small screen.
Impact of Video Streaming Delay on User Experience with Head-Mounted Displays

Delays in the delivery of immersive video to Head Mounted Devices (HMDs) are considered in the paper. The goal of this paper is to analyze the impact of video streaming latency on user experience with HMDs. The paper reports the results of the subjective quality assessment as a function of delay. The possible practical solutions are identified and, on this base, an experimental model of the considered systems is proposed. In order to properly test the influence of the streaming delay on the quality, the extensive subjective tests have been performed. Interesting conclusions have been drawn, showing that human acceptance of delay of translation of virtual point of viewing is much stronger than for delay of rotation of virtual viewing direction in immersive video. Finally, the influence of such observations on the system architectures is concluded.
MCENN: A variant of extended nearest neighbor method for pattern recognition

Recent studies have shown that extended nearest neighbor (ENN) method is able to improve the classification performance over traditional k-nearest neighbor (KNN) methods, due to its novel “two-way communication” decision making process. The ENN method classifies a test data sample by maximizing the intra-class coherence gain over the whole data set. However, the original ENN method, like KNN, is a type of instance-based learning algorithm without a training stage. This paper presents a variant of ENN method, called Maximum intra-class Coherence Extended Nearest Neighbor (MCENN), which incorporates distances between individual data sample and its nearest neighbors into the decision making process and introduces a novel distance metric learning algorithm as a training process to learn an optimal linear transformation that maximizes the overall intra-class coherence of training data. We demonstrate that the proposed MCENN approach is able to improve the discriminative performance for pattern recognition. Experimental results on real-life data sets demonstrate the effectiveness of the proposed approaches.