Reviewerly/exHarmony huggingface dataset:
Feature extraction method based on point pair hierarchical clustering

Conventional feature detection algorithms are largely based on clustered two-dimensional (2D) blocks of information. However, corners located at the centre of gradually greying blocks of information cannot be extracted using these algorithms. The edge feature points described by the algorithms are often affected by background changes, leading to significant differences in the descriptors for the same feature. These issues are detrimental to subsequent matching processes. Therefore, we propose a new feature detection method that will provide more useful corner information for subsequent tracking and detection processes, particularly for edge features. The edge information of corners is used to search for points that satisfy the requirements for inner greyscale consistency. The points are then used to construct point-symmetric structures. The zeroth-order inner greyscale data, first-order gradient orientation differences, and angular directions of the point-symmetric connections of the structures are considered structural attributes, which help search for feature points. Similar feature points are then clustered using a hierarchical clustering algorithm, followed by extracting the feature points from point pair features of the same type. It was experimentally demonstrated that the proposed point-symmetric structural features would help increase the number of valid feature points that can be extracted from an image.
BlocklySQL

This paper introduces BlocklySQL, a new block-based editor for SQL. BlocklySQL was developed on the basis of Google's JavaScript library Blockly, a tool for developing own block-based editors, which comes with several technological and pedagogical advantages. BlocklySQL is also integrated into the learning platform dbup2date, which provides daily updated databases for the use in CS classrooms.
Automated mineralogical profiling of soils as an indicator of local bedrock lithology: a tool for predictive forensic geolocation

The use of soil evidence to identify an unknown location is a powerful tool to determine the provenance of an item in an investigation.We are particularly interested in the use of these indicators in nuclear forensic cases, whereby identification of locations associated with for example, a smuggled nuclear material, may be used to indicate the provenance of a find.The use of soil evidence to identify an unknown location relies on understanding and predicting how soils vary in composition depending on their geological / geographical setting.In this study, compositional links between the mineralogy of forty soils and the underlying bedrock geology were established.The soil samples were collected from locations with broadly similar climate and land use across a range of geological settings in a 'test bed' 3500 km 2 area of South West England.In this region, the soils formed through chemical weathering of the bedrock, representing a worst case for OFFICIAL
Coordination of Mobile Agents for Simultaneous Coverage

Simultaneous environment coverage represents a challenging multi-agent application, in which mobile agents (drones) must cover surfaces by simultaneously capturing images from different viewpoints. It constitutes a complex optimization problem with potentially conflicting criteria, such as mission time and coverage quality, and requires dynamic coordination of agent tasks. In this paper, we introduce a decentralized coordination method, adaptive to a dynamic and a priori unknown 3D environment. Our approach selects the role an agent should take on and coordinates the assignment of agents to their computed viewpoints. Our main goal is to cover all detected objects in the environment at a certain quality as soon as possible. We evaluate the methods in AirSim in different setups and assess how the proposed methods respond to dynamic changes in the environment.
Interactive Multi-camera Soccer Video Analysis System

Automatic sports video analysis is an active field of research, and accurate player & ball tracking is essential for soccer video analysis and visualization. However, the variations over frames and the scarceness of large-scale well-annotated datasets make it difficult to perform supervised learning using pre-trained models, especially for Multi-Camera Multi-Target Tracking (MCMT). In this paper, we introduce an end-to-end system for multi-camera soccer video analysis that makes heavy use of parallel processing for optimization of the processing workflow. The proposed thread-level parallelism speeds up our system by more than 15 times while maintaining the level of accuracy. The system tracks the trajectories of the ball and the players in a world coordinate system based on soccer videos captured by a set of synchronized cameras. Based on these trajectories, various player-, ball-, and team-related statistics are computed, and the resulting data and visualizations can be interactively explored by the user.
Super Resolution Using Dual Path Connections

\beginabstract Deep convolutional neural networks (CNNs) have been demonstrated to be effective for singe-image super-resolution (SISR) recently. Inspired by Chen et al. \citechen2017dual, we propose a novel method for SISR by introducing dual path connections into a deep convolutional neural network, we call it SRDPN. SRDPN consists of three parts, which are feature extraction block, multiple stacked dual path blocks and reconstruction block. Each dual path block is made of one transition unit and several cascading dual path units. Dual path unit, the core component of the proposed SRDRN, is a specially designed network unit which uses both residual connection and dense connections for convolution layer to exploit common features and explore new features layer-wise. The transition unit in each dual path block is used to fuse the residual and dense features in previous dual path block to keep computation and memory cost under control. Finally, we concatenate outputs of all the dual path blocks for reconstruction of a residual between high-resolution (HR) image and low-resolution (LR) image, both making information forward-propagation direct and alleviating gradient vanishing/exploding problem. Experiments show the proposed SRDPN has superior performance over the state-of-the-art methods. \endabstract
Navigation System for the Visually Challenged Using Internet of Things

In daily life, visually challenged peoples face many difficulties in life. As visually impaired find difficult to travel from one place to another. Our main concern is to develop a smart walking stick using ultrasonic sensor which is used as navigational tool that will detect manhole and staircase in an environment. This paper reviews the performance of several methods, techniques and algorithms (GCF, ML) used for manhole, pitfall detection and recognition of staircase and its working efficiency. IOT sensors (UV, Infrared, Temperature) are used to detect the obstacles and provide the alternate way to the visually impaired through GSM and also calculates the shortest distant to the destination for their convenience. The efficiency and performance measure of various algorithms (three class and binary prediction,) for image reorganization is reviewed and accuracy shown.
Security Driven Design Space Exploration for Embedded Systems

With the advent of the Internet of Things (IoT) and Cyber Physical Systems (CPS), embedded devices have been gaining importance in our daily lives, as well as industrial processes. Independent of their usage, be it within an IoT system or a CPS, embedded devices are always an attractive target for security attacks, largely due to their continuous network availability and the importance of the data they handle. Thus, the design of such systems requires a thorough consideration of the various security constraints they are liable to. Introducing these security constraints, next to other requirements (e.g. power consumption, performance, etc.), increases the number of design choices that must be taken. As the various constraints are often conflicting each other, designers are faced with the complex task of balancing them. To support a system designer in this job, Design Space Exploration (DSE) tools can be facilitated. However, available DSE tools only offer a limited way of considering security constraints during the design process. In this paper we introduce a novel DSE framework, which allows the consideration of security constraints, in the form of attack scenarios, and attack mitigations, in the form of security tasks. Based on the descriptions of the system's functionality and architecture, possible attacks, and known mitigation techniques, the framework finds the optimal design for an secure IoT device or CPS. Our framework's functionality and its benefits are shown based on the design of a secure sensor system.
Implementation of K-Means Algorithm with Distance of Euclidean Proximity in Clustering Cases of Violence Against Women and Children

Cases of violence that befell women and children in Tasikmalaya area are rampant, as well as low awareness of victims to follow up on their cases, it is necessary to recognize patterns in characteristics cases of violence that have ever existed. To obtain this pattern, data on cases violence was carried out on women and children using the clustering method, k-means algorithm, and using Euclidean proximity distance. The steps were taken following the CRIPS-DM model, starting from the process of understanding problem, understanding data, pre-processing data, modelling, evaluation, and pattern deployment. The results of this data extraction process are 4 clusters that have a DBI value of 0.657 and purity of 72%. To further facilitate the authorities cases of violence against women and children, an application was built using PHP programming language, MySQL as its database and software design methods using the waterfall model. With this application, it can be useful to facilitate the process of introducing further patterns of violence, so as to minimize emergence cases of violence against women and children in Tasikmalaya area.
SNaP 2: The Evolution of a Board Game for Smart Nature Environments

SNaP is a collaborative board game for children. In the game, children become Designers and the protagonists of the ideation of smart nature ecosystems, reflecting on them. This paper presents the evolution of the game and its usage in a recent workshop with 11-years old children.
On the Logical Ways to Counter an Argument: A Typology and Some Theoretical Consequences

My aim here is to give an adequate definition of counterargument, establishing a suitable typology of counterarguments grounded on Toulmin’s model. An argument structure is a combination of arguments with an explicit argumentative orientation, positive, negative or neutral. Counterarguing involves conjoining the criticized and the critical argument into a single argumentation. Therefore, counterargumentation produces argument structures, and a classification of counterarguments is by itself a classification of argument structures.
Application of RC5 for IoT devices in Smart Transportation System

Nowadays Internet of Thing (IoT) technology has accessed all the aspects of our life. One of the IoT applications is the smart transportation. Smart transportation gains a lot of attention, as it is part of the smart cities. The data transmission in smart transportation is done through the wireless communication and especially through the Vehicular ad hoc networks (VANETs) which made the data vulnerable to many issues concerning security. Therefore, security techniques should be applied to secure vehicle's user data. In this paper, the RC5 encryption algorithm will be used as a security technique to protect users' privacy in smart transportation. The simulation of RC5 will be implemented with FPGA using Quartus Prime Lite Edition version 18. The purpose is to check hardware resources of RC5 and make sure it is suitable for smart transportation.
Using Spectral Graph Theory to Map Qubits onto Connectivity-limited Devices

We propose an efficient heuristic for mapping the logical qubits of quantum algorithms to the physical qubits of connectivity-limited devices, adding a minimal number of connectivity-compliant SWAP gates. In particular, given a quantum circuit, we construct an undirected graph with edge weights a function of the two-qubit gates of the quantum circuit. Taking inspiration from spectral graph drawing, we use an eigenvector of the graph Laplacian to place logical qubits at coordinate locations. These placements are then mapped to physical qubits for a given connectivity. We primarily focus on one-dimensional connectivities, and sketch how the general principles of our heuristic can be extended for use in more general connectivities.
Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

Organizations that preserve and promote heritage must meet the expectatives of sophisticated visitors who, far from wanting simply to be informed, desire to explore engaging and innovative technology-driven experiences which consider their particular interests and encourage them to discover more. We describe an approach based on quiz games that can be exploited in the deployment of such challenging experiences. The game consists of raising multiple-choice questions about a particular theme which is introduced by a Humanities expert through a brief narrative. Given the input text, a question and its right answer, our strategy provides the expert with a set of wrong alternatives (called distractors). These options are chosen from a (semi)automatically-built tailor-made corpus of documents by considering each player’s level of knowledge on the game theme and exploiting Linked Open Data initiatives and natural language processing. On the one hand, automatic selection of distractors assists the Humanities expert to create games about very diverse topics without needing to be a specialist in all of them. On the other one, distractors are related to the right answer of each question in an appealing and meaningful way, which contributes to arouse the visitors’ curiosity and their possible interest in exploring similar experiences in future visits. The work has been experimentally validated, achieving better results than a previous distractor identification strategy.
FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for a while. Cloud computing seen as a promising technology, offers usage-based payment, scalable and on-demand computing resources. However, during the past decade, the growing complexity of the IT world has resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, the fair allocation of resources in the cloud becomes particularly interesting when many users submit several tasks which require multiple resources. Research in this area has been increasing since 2012 by introducing the Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the fair resource allocation problem in the cloud. Although DRF meets a sort of desirable fairness properties, it has been proven to be inefficient in certain conditions. Noticeably, DRF and other works in its extension are not intuitively fair after all. Those implementations have been unable to utilize all the resources in the system, leaving the system in an imbalanced situation with respect to each specific system resource. In order to address those issues, we propose in this paper a novel algorithm namely a Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments (FFMRA) which allocates resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments conducted in CloudSim show that FFMRA provides approximately 100% recourse utilization, and distributing them fairly among the users while meeting desirable fairness features.
openEyeTrack - A high speed multi-threaded eye tracker for head-fixed applications

When faced with a decision, an organism uses information gathered by its senses in order to determine the best course of action.Vision is one of the primary senses, and tracking eye gaze can offer insight into the cues that affect decision-making behavior.Thus, to study decision-making and other cognitive processes, it is fundamentally necessary to track eye position accurately.However, commercial eye trackers are often very expensive, and incorporate proprietary software to detect the movement of the eye.Closed source solutions limit the researcher's ability to be fully informed regarding the algorithms used to track the eye and to incorporate modifications tailored to their needs.Here, we present our software solution, openEyeTrack, a low-cost, high-speed, low-latency, open-source video-based eye tracker.Video-based eye trackers can perform nearly as well as classical scleral search coil methods and are suitable for most applications (Kimmel, Mammo, & Newsome, 2012).High-speed eye trackers improve ability to detect saccades and microsaccades for real-time behavioral and physiological experiments and also improve eye position estimation.
Faster R-CNN based Robust Circulating Tumor Cells Detection with Improved Sensitivity

Automatic detection and numeration of circulating tumor cells (CTCs) in scanned microscopic images of peripheral blood provides effective auxiliary means for clinical diagnosis of cancer, individualized treatment, prognosis judgement evaluation and so on. There have been some work aims at reducing the subjectivity and labor intensity of cytologists with machine learning methods. In this paper, a robust CTCs detection algorithm based on improved Faster R-CNN network is proposed. Special efforts have been devoted to detection of single CTC for improved sensitivity, which is more favorable in real applications. Firstly, a whole slide image preprocessing algorithm is proposed based on statistical analysis of the self-constructed CTCs database. Then, an anchor adjustment and extension method is proposed to alleviate the imbalance between limited number of positive CTCs examples and huge amount of negative normal cells. Experimental results carried out on the self-established CTCs database show that the proposed algorithm demonstrates improved performance in CTCs detection.
SVS-JOIN: Efficient Spatial Visual Similarity Join for Geo-Multimedia

In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently.
Towards depth estimation in a single aerial image

Nowadays, depth estimation from a single image is a task that has been successfully addressed by Convolutional Neural Network (CNN) architectures. In this regard, several authors have taken advantage of depth datasets publicly available to the scientific community to train their CNN-based methods. From a project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago has emerged KITTI (acronym derived from the institutions' names) as one of the most popular public datasets providing depth estimates associated to RGB (Red, Green, Blue) images. Regarding the depth data in KITTI and typically in many other datasets, these include monocular or stereo RGB images associated with depth images obtained via laser, stereo cameras or a combination of both. These images and depth data have been collected by driving around outdoor urban environments with cameras looking forward to the horizon. In contrast, in this work, we are interested in CNN-based depth estimation in a single aerial image for which depth datasets are not available. In addition, popular CNN architectures for depth estimation in a single-image struggle to estimate depth in aerial scenes due to the fact that the camera angle and object appearance in aerial imagery are significantly different. Nevertheless, we propose to harvest the depth information available in KITTI in order to tackle the problem of depth estimation in a single aerial image. To this end, our approach is a two-step methodology based on patch processing that is later used as input for a set of proposed CNN architectures. Our results indicate that this approach is promising, and those datasets such as KITTI may indeed be exploited in other domains, especially where the data acquisition may be expensive or difficult to be carried out such as for aerial scenes.
Collaborative Preference Embedding against Sparse Labels

Living in the era of the internet, we are now facing with a big bang of online information. As a consequence, we often find ourselves troubling with hundreds and thousands of options before making a decision. As a way to improve the quality of users' online experience, Recommendation System aims to facilitate personalized online decision making processes via predicting users' responses toward different options. However, the vast majority of the literature in the field merely focus on datasets with sufficient amount of samples. Different from the traditional methods, we propose a novel method named as Collaborative Preference Embedding (CPE) which directly deals with sparse and insufficient user preference information. Specifically, we represent the intrinsic pattern of users/items with a high dimensional embedding space. On top of this embedding space, we design two schemes specifically against the limited generalization ability in terms of sparse labels. On one hand, we construct a margin function which could indicate the consistency between the embedding space and the true user preference. From the margin theory point-of-view, we then propose a generalization enhancement scheme for sparse and insufficient labels via optimizing the margin distribution. On the other hand, regarding the embedding as a code for a user/item, we then improve the generalization ability from the coding point-of-view. Specifically, we leverage a compact embedding space by reducing the dependency across different dimensions of a code (embedding). Finally, extensive experiments on a number of real-world datasets demonstrate the superior generalization performance of the proposed algorithm.