Reviewerly/exHarmony huggingface dataset:
Learning Deep CNN Denoiser Priors for Depth Image Inpainting

Due to the rapid development of RGB-D sensors, increasing attention is being paid to depth image applications. Depth images play an important role in computer vision research. In this paper, we address the problem of inpainting for single depth images without corresponding color images as a guide. Within the framework of model-based optimization methods for depth image inpainting, the split Bregman iteration algorithm was used to transform depth image inpainting into the corresponding denoising subproblem. Then, we trained a set of efficient convolutional neural network (CNN) denoisers to solve this subproblem. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with three traditional methods in terms of visual quality and objective metrics.
Retraction Note: Prediction of economic growth by extreme learning approach based on science and technology transfer

The Editor-in-Chief has retracted this article (Karanikić et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Marković et al. 2016; Mladenović et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

In this paper, we present the first open-source implementation of the robust random cut forest (RRCF) algorithm-an unsupervised ensemble method for anomaly detection on streaming data (Guha, Mishra, Roy, & Schrijvers, 2016).RRCF offers a number of features that many competing anomaly detection algorithms lack.Specifically, RRCF:• Is designed to handle large volumes of streaming data.• Is well-suited to data of high dimension.• Reduces the influence of irrelevant dimensions in the input data.• Gracefully handles duplicates and near-duplicates that could otherwise mask the presence of outliers.• Features an anomaly-scoring metric with a clear underlying statistical meaning.The RRCF algorithm is currently used for anomaly detection in the Amazon Kinesis real-time analytics engine.The goal of our repository is to provide an open-source implementation of the RRCF algorithm and its core data structures for the purposes of facilitating experimentation and enabling future extensions.
Image encryption using phase-encoded exclusive-OR operations with incoherent illumination

Exclusive-OR (XOR) operation is a popular technique in optical cryptography. The linear and inverse properties of XOR operation provide a simple way of encryption but in some cases fail to provide strong security. In XOR-based image encryption, each pixel is individually encrypted, therefore secret information can be traced with partial knowledge of the key. In this paper, we describe a simple and practical method to improve the strength and effectiveness of XOR-based encryption. A higher level of security is achieved by implementing a phase retrieval algorithm with XOR operation. Another advantage of the proposed scheme is that the optical XOR operation is demonstrated using incoherent light sources, which make the system compact, speckle free, and cost effective. Also, to overcome the problem of quality degradation while decrypting, quick response code-based ciphertext has been implemented. Both simulation and experimental results are demonstrated as a proof-of-concept.
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature

Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection techniques. However, road anomaly patterns from crowdsourcing data are often shifted in time and exhibit local distortions/noise, thus existing methods rely on the original sensor data greatly limit the accuracy of road anomaly detection. In this paper, we present a road anomaly detection model by learning scale-invariant features from the differences between small local segments of road anomaly samples. Specifically, the proposed model consists of two phases: 1) Road anomaly segmentation. The phase is designed to roughly extract road anomaly subsequence using piecewise aggregate approximation representation of sensor series data, and 2) Road anomaly detection. In this phase, we observe the differences among road anomaly classes are attributed to small local segments, then we learn scale-invariant features from these small local segments for road anomaly detection. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets and one large-scale simulation dataset. The experimental results show our proposed model outperforms all baselines significantly in terms of road anomaly detection.
A Survey on Software Cost Estimation Techniques

The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades.For a successful software project, accurate prediction of the cost, time and effort is a very much essential task.This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods.The models considered in this review include both the traditional and the recent approaches for software cost estimation.The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used.Our findings show, in general, neural network based models outperforms other cost estimation techniques.However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.
Computational parasites and hydropower: A political ecology of Bitcoin mining on the Columbia River

Over the past three years, the dams of Chelan County, Washington, its watershed and fish, the electrical grid and the laborers who maintain it, and cleared land with warehouses filled with computers, have all been enrolled as part of the decentralized digital infrastructure of Bitcoin. While popular accounts of the Bitcoin network correctly report the massive scale of energy it consumes and its potential environmental ramifications, in practice, the material geographies of Bitcoin are highly uneven and intertwined with specific infrastructural, ecological, and economic systems. In this article, we examine Bitcoin's impacts on Chelan County, untangling the processes that occur as the distributed, digital infrastructure consumes the very real material resources of one place to produce digital goods used in another. In so doing, we examine not only the material costs of networks like Bitcoin, but also their historical ties to older processes of accumulation.
Learning to Infer Entities, Properties and their Relations from Clinical Conversations

Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration).It tackles the challenge of large label space and limited training data using a hierarchical two-stage approach that identifies the span of interest in a tagging step and assigns labels to the span in a classification step.We extend the SAT model to jointly infer not only entities and their properties but also relations between them.Most relation extraction models restrict inferring relations between tokens within a few neighboring sentences, mainly to avoid high computational complexity.In contrast, our proposed Relation-SAT (R-SAT) model is computationally efficient and can infer relations over the entire conversation, spanning an average duration of 10 minutes.We evaluate our model on a corpus of clinical conversations.When the entities are given, the R-SAT outperforms baselines in identifying relations between symptoms and their properties by about 32% (0.82 vs 0.62 Fscore) and by about 50% (0.60 vs 0.41 Fscore) on medications and their properties.On the more difficult task of jointly inferring entities and relations, the R-SAT model achieves a performance of 0.34 and 0.45 for symptoms and medications respectively, which is significantly better than 0.18 and 0.35 for the baseline model.The contributions of different components of the model are quantified using ablation analysis.DR: How often do you have pain in your arms?PT: It hurts every morning.DR: Are you taking anything for it?PT: I've been taking Ibuprofen.
DMPSO: Diversity-Guided Multi-Mutation Particle Swarm Optimizer

Particle swarm optimization (PSO) is a population based meta-heuristic search technique that has been widely applied to deal with various optimization problems.However, like other stochastic methods, PSO also encounters the problems of entrapment into local optima and premature convergence in solving complex multimodal problems.To tackle these issues, a diversity-guided multi-mutation particle swarm optimizer (abbreviated as DMPSO) is presented in this paper.To start with, the chaos opposition-based learning (OBL) is employed to yield high-quality initial particles to accelerate the convergence speed of DMPSO.Followed by, the self-regulating inertia weight is leveraged to strike a balance between the exploration and exploitation in the search space.After that, three different kinds of mutation strategies (gaussian, cauchy and chaotic mutations) are used to maintain the potential diversity of the whole swarm based on an effective diversity-guided mechanism.In particular, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of the DMPSO.Finally, extensive experiments on a set of well-known unimodal and multimodal benchmark functions demonstrate that DMPSO outperforms most of the other tested PSO variants in terms of both the solution quality and its efficiency.
Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes Using a Convolutional Neural Network

In this manuscript, a new method for the determination of vehicle trajectories using an optimal bounding box for the vehicle is developed. The vehicle trajectory is extracted using images acquired from a camera installed at an intersection based on a convolutional neural network (CNN). First, real-time vehicle object detection is performed using the YOLOv2 model, which is one of the most representative object detection algorithms based on CNN. To overcome the inaccuracy of the vehicle location extracted by YOLOv2, the trajectory was calibrated using a vehicle tracking algorithm such as a Kalman filter and intersection-over-union (IOU) tracker. In particular, we attempted to correct the vehicle trajectory by extracting the center position based on the geometric characteristics of a moving vehicle according to the bounding box. The quantitative and qualitative evaluations indicate that the proposed algorithm can detect the trajectories of moving vehicles better than the conventional algorithm. Although the center points of the bounding boxes obtained using the existing conventional algorithm are often outside of the vehicle due to the geometric displacement of the camera, the proposed technique can minimize positional errors and extract the optimal bounding box to determine the vehicle location.
Evaluating Lottery Tickets Under Distributional Shifts

The Lottery Ticket Hypothesis (Frankle and Carbin, 2019) suggests large, overparameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy.However, the initialization and generalizability of the obtained sparse subnetworks have been recently called into question.Our work focuses on evaluating the initialization of sparse subnetworks under distributional shifts.Specifically, we investigate the extent to which a sparse subnetwork obtained in a source domain can be re-trained in isolation in a dissimilar, target domain.In addition, we examine the effects of different initialization strategies at transfer-time.Our experiments show that sparse subnetworks obtained through lottery ticket training do not simply overfit to particular domains, but rather reflect an inductive bias of deep neural networks that can be exploited in multiple domains.
Inductive general game playing

Abstract General game playing (GGP) is a framework for evaluating an agent’s general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all the games. In this paper, we invert this task: a learner is given game traces and the task is to learn the rules that could produce the traces. This problem is central to inductive general game playing (IGGP). We introduce a technique that automatically generates IGGP tasks from GGP games. We introduce an IGGP dataset which contains traces from 50 diverse games, such as Sudoku , Sokoban , and Checkers . We claim that IGGP is difficult for existing inductive logic programming (ILP) approaches. To support this claim, we evaluate existing ILP systems on our dataset. Our empirical results show that most of the games cannot be correctly learned by existing systems. The best performing system solves only 40% of the tasks perfectly. Our results suggest that IGGP poses many challenges to existing approaches. Furthermore, because we can automatically generate IGGP tasks from GGP games, our dataset will continue to grow with the GGP competition, as new games are added every year. We therefore think that the IGGP problem and dataset will be valuable for motivating and evaluating future research.
Social media markets for prescription drugs: platforms as virtual mortars for drug types and dealers

Purpose Internet use has changed the mechanics of drug dealing. Although this has spurred some initial academic interest in how markets and their users have been changing, the issue is still under-researched. The purpose of this paper is to understand how the organisation of the distribution of prescription drugs and other illegal drugs overlap in these online markets by analysing data gathered from observation of the Swedish Facebook drug market and its participants. Design/methodology/approach Data were gathered during three months of digital ethnography conducted among Swedish Facebook posters supplemented by 25 interviews with sellers (20) and buyers (5). Screenshots and interview data were coded by carrying out an NVivo-based content analysis. The analysis is based on descriptive statistics of drug types, co-occurrence with other drugs, group size and the demographic characteristics of sellers. Additionally, the interviewees’ descriptions of the marketplace and their drug dealing or buying activities were included in the analysis. Findings In total, 57 Swedish Facebook groups that sold illegal substances were located. The groups rarely specialised in specific drug types, but were convened around demographic factors, such as specific cities and locales. The sales of prescription drugs were part of the overall activity of groups selling other illegal drugs, but they were more often sold in separate Facebook posts, possibly by specialist sellers. Swedish Facebook sales primarily concerned alprazolam, tramadol, pregabalin and clonazepam, and were sold by both professional and amateur sellers. Originality/value This study reports findings from a Nordic comparative study on social media drug dealing, representing the first in-depth study of digitally mediated prescription drug dealing outside of cryptomarkets.
Generation of hybrid maximally entangled states in a one-dimensional quantum walk

We study the generation of hybrid entanglement in a one-dimensional quantum walk. In particular, we explore the preparation of maximally entangled states between position and spin degrees of freedom. We address it as an optimization problem, where the cost function is the Schmidt norm. We then benchmark the algorithm and compare the generation of entanglement between the Hadamard quantum walk, the random quantum walk and the optimal quantum walk. Finally, we discuss an experimental scheme with a photonic quantum walk in the orbital angular momentum of light. The experimental measurement of entanglement can be achieved with quantum state tomography.
NURBSforms

We present NURBSforms: a modular shape-changing interface for prototyping curved surfaces. Each NURBSform module represents an edge of variable curvature that, when joined together with other modules, enables designers to construct surfaces and adjust their curvature interactively. NURBSform modules vary their curvature using active and passive shape memory materials: an embedded shape memory alloy (SMA) wire increases the curvature when heated, while an elastic material recovers the flat shape when the SMA wire cools down. A hall effect sensor on each module allows users to vary the curvature by adjusting the distance of their hand. In addition, NURBSforms provides functions across multiple modules, such as 'save', 'reset', and 'load', to facilitate design exploration. Since each module is self-contained and individually controllable, NURBSform modules scale well and can be connected into large networks of curves representing various geometries. By giving examples of different NURBSforms assemblies, we demonstrate how the modularity of NURBSforms, together with its integrated computational support, enables designers to quickly explore different versions of a shape in a single integrated design process.
A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping

Smart building control, managing queues for instant points of service, security systems, and customer support can benefit from the number of occupants information known as occupancy. Due to interrupted real-time continuous monitoring capabilities of state-of-the-art cameras, a vision-based system can be easily deployed for occupancy monitoring. However, processing of images or videos over insecure channels can raise several privacy concerns due to constant recording of an image or video footage. In this context, occupancy monitoring along with privacy protection is a challenging task. This paper presents a novel chaos-based lightweight privacy preserved occupancy monitoring scheme. Persons’ movements were detected using a Gaussian mixture model and Kalman filtering. A specific region of interest, i.e., persons’ faces and bodies, was encrypted using multi-chaos mapping. For pixel encryption, Intertwining and Chebyshev maps were employed in confusion and diffusion processes, respectively. The number of people was counted and the occupancy information was sent to the ThingSpeak cloud platform. The proposed chaos-based lightweight occupancy monitoring system is tested against numerous security metrics such as correlation, entropy, Number of Pixel Changing Rate (NPCR), Normalized Cross Correlation (NCC), Structural Content (SC), Mean Absolute Error (MAE), Mean Square Error (MSE), Peak to Signal Noise Ratio (PSNR), and Time Complexity (TC). All security metrics confirm the strength of the proposed scheme.
A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&amp;E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&amp;E staining on Ki67-immunostained/H&amp;E-decolored tissue samples. Finally, we tested our digital framework on a case series of oral squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA (tissue microarray) block, respectively stained with H&amp;E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&amp;E slides and generated a “false color map” (FCM) based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features on H&amp;E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears to be a promising tool to quickly forecast the tumor’s proliferation fraction directly on routinely H&amp;E-stained digital sections.
Layered Clause Selection for Theory Reasoning

Explicit theory axioms are added by a saturation-based theorem prover as one of the techniques for supporting theory reasoning. While simple and effective, adding theory axioms can also pollute the search space with many irrelevant consequences. As a result, the prover often gets lost in parts of the search space where the chance to find a proof is low. In this paper, we describe a new strategy for controlling the amount of reasoning with explicit theory axioms. The strategy refines a recently proposed two-layer-queue clause selection and combines it with a heuristic measure of the amount of theory reasoning in the derivation of a clause. We implemented the new strategy in the automatic theorem prover Vampire and present an evaluation showing that our work dramatically improves the state-of-the-art clause-selection strategy in the presence of theory axioms.
Online technologies in dance education (China and worldwide experience)

The article analyses the current prospects for the use of Internet technologies in the area of study ‘Choreography and Dance’ in the field of higher education and professional e-Learning. The work aims to determine how students studying ‘Choreography and Dance’ perceive the impact of online education on the formation of subject-specific competencies and to identify those competencies that the ideal online training program instills. The study shows that the format of online dance training is a promising direction, but the quality control system of such training needs to be improved. The main result of the study is the development of a competence system of Master of Arts (Dance education). The system substantiates the necessity and expediency of implementing a competence-based approach in developing educational programs for students in the area ‘Choreography and Dance’. The system defines groups of professional competences, qualities, knowledge, and skills that make up the profile of teachers working in the field of choreography and dance. The developments of the study are universal in nature and can be applied in both national (regional) and global educational management practices.