Reviewerly/exHarmony huggingface dataset:
Segmented Lung Boundary Correction in Chest Radiograph Using Context-Aware Adaptive Scan Algorithm

Chest X-ray (CXR) is the most popular imaging modality used for preliminary diagnosis of pulmonary diseases. In automatic computer-aided diagnosis (CAD), the number of false-positive cases can be reduced by segmenting out the normal anatomical structures. Demarcating lung parenchyma on CXR image is challenging due to complex anatomical structures of the human thoracic cavity. The extracted lungs boundary suffers from undesirable artifacts such as ridges and pits. This paper presents an algorithm to adaptively scan the inner lung boundary to correct the undesirable artifacts. Further, the algorithm is context-aware, it takes care of normal cavity due to the aortic knuckle and diaphragm borders. The algorithm is tested on 138 binary lung mask extracted from digital CXR images from Montgomery dataset. The quantitative, qualitative, statistical results reveal that the proposed algorithm outperforms the existing method. The average increase in segmentation accuracy is 2.561%.
Attracting User Attention to Visual Elements within Website with the use of Fitts’s Law and Flickering Effect

Design of applications requires proper management of content, interaction elements and space. In web systems flow of interactions is often based on attention catching components in a form of elements of navigation or advertising content. Distance from central part of interface is affecting user behavior, what can be overcome by scaling size of the objects within the interface according to Fitts’s law. Here we examine the effects of flickering elements with different frequency, direction and distance from central elements of webpage on user attention and habituation reduction. The aim of the study was to examine how to overcome users’ resistance to stimuli based on objects flickering with different frequencies located in different directions and distances. We wanted to check if the higher flicker frequency can compensate the larger distance and increase and user focus depending on whether the element is located nearest or farthest, similarly like objects sizes for Fitts’s law. Results showed that flicker increases the attention to objects with higher distance but has no effect on nearest objects. I was possible to improve total time attracted by distance objects by twenty percent, however the higher performance was observed to nearest objects, no matter what frequency was used for them.
Tight security for the generic construction of identity-based signature (in the multi-instance setting)

An identity-based signature (IBS) scheme can be generically constructed from any ordinary signature scheme by appending a chain of signatures. Until now, it has been known that a generic construction cannot lead to a tightly secure IBS scheme, although any tightly secure signature scheme can be used as a building block. In this study, we demonstrate that the generic construction of IBS can achieve tightness if the underlying signature scheme is tightly secure in the multi-user setting with corruption. In addition, we extend the tightness result of IBS to the multi-instance setting, where an adversary can corrupt multiple key generation centers and obtain multiple related master secret keys. As instantiations, we adopt the efficient and tightly secure signature scheme in the multi-user setting with corruption, recently proposed by Gjøsteen and Jager (CRYPTO 2018). As a result, we can obtain the first efficient and tightly secure IBS schemes (in the multi-instance setting) based on the Diffie–Hellman assumptions in the random oracle model.
Drawing Graphs on the Sphere

Graphs are most often visualized in the two dimensional Euclidean plane, but spherical space offers several advantages when visualizing graphs. First, some graphs such as skeletons of three dimensional polytopes (tetrahedron, cube, icosahedron) have spherical realizations that capture their 3D structure, which cannot be visualized as well in the Euclidean plane. Second, the sphere makes possible a natural "focus + context visualization with more detail in the center of the view and less details away from the center. Finally, whereas layouts in the Euclidean plane implicitly define notions of "central and "peripheral nodes, this issue is reduced on the sphere, where the layout can be centered at any node of interest.
Sparsity-Aware Deep Learning Accelerator Design Supporting CNN and LSTM Operations

Sparsity of data and weights appears in many convolution neural networks (CNN) and recurrent neural networks such as long-short term memory (LSTM). In this paper, we design a sparsity-aware deep learning hardware accelerator exploiting both data and weight sparsity in CNN and LSTM models. The proposed hardware accelerator significantly reduces memory accesses and computations, leading to much lower power consumption.
How to Treat the Use of Grey Literature in Software Engineering

Context: Following on other scientific disciplines, such as health sciences, the use of grey literature (GL) is becoming widespread in Software Engineering (SE) research. Whilst the number of papers incorporating GL on SE is increasing, there is little empirically known about different aspects of the use of GL in SE research. In particular, there is a lack of excellent evaluation standard for the quality of GL. Aim: Our research is aimed at systematically reviewing the use of GL in SE, empirically exploring SE researchers' views on GL and providing a guide for using GL in SE and for quality assessment of the GL to be included. Method: We used a mixed-methods approach for this research. We carried out a Systematic Literature Review (SLR) of the use of GL in SE. Then we surveyed the authors of the papers included in the SLR (as GL users) and the invited experts in the SE community on the use of GL in SE research. Results: We systematically selected and reviewed 102 SE secondary studies that incorporate GL in SE research, from which we identified two groups based on their reporting: 1) 76 reviews only claim their use of GL; 2) 26 reviews report the results by including GL. We also obtained 20 replies from the GL users and 24 replies from the invited SE experts. Conclusion: There is no common understanding of the meaning of GL in SE. Researchers define the scopes and the definitions of GL in a variety of ways. We found five main reasons of using GL in SE research. The findings have enabled us to propose a conceptual model for how GL works in SE research lifecycle. In the next workThere is a need for research to develop guidelines for using GL in SE and for assessing quality of GL.
Automated Destructive Behavior State Detection on the 1D CNN-Based Voice Analysis

The article considers one of the approaches to the solution of the problem of finding the effect of a user on the Internet through media files including acoustic materials. The solution to the given problem is a part of the project on the development of a uniform socio-cyberphysical control system of the Internet content to identify the destructive materials and protect Internet users. Authors developed an algorithm including a classification model based on an artificial neural network (1D CNN-based) with the application of deep learning and the further application of the statistical probability approach to the solution of the problem of identification of the destructive type of emotion with the accuracy of 74%. The article considers one of the approaches to solving the problem of identifying the impact on the Internet of a user through media files, including acoustic materials. The solution to this problem is an integral part of the project to create a unified socio-cyberphysical system for managing Internet content in order to identify destructive materials and protect network users.
Building a theory of software teams organization in a continuous delivery context

Based on Grounded Theory guidelines, we interviewed 27 IT professionals to investigate how organizations pursuing continuous delivery should organize their development and operations teams. In this paper, we present the discovered organizational structures: (1) siloed departments, (2) classical DevOps, (3) cross-functional teams, and (4) platform teams.
Flying object detection system using an omnidirectional camera

While the effective utilization of drones is expected to grow in a wide range of fields, there are concerns over new threats related to the exploitation of drones that have not been considered thus far. From the perspective of surveillance, the remarkable ability of drones to fly in any arbitrary direction imposes the need for wider range sensing than ever. Cameras are commonly used as surveillance devices; however, traditional cameras capture a limited field of view and, although the output can have a spatially dense resolution, there are significant blind spots. Omnidirectional cameras are emerging as devices that can capture almost all directions simultaneously with one device. In this paper, we present a drone detection system that uses an omnidirectional camera. We use system modularization to improve the detection accuracy for small objects through a combination of image dividing and state-of-the-art object detection algorithms. Three-dimensional direction of detected objects is visualized by our system using the well-calibrated intrinsic/extrinsic parameters of the omnidirectional camera. Our system was evaluated using a dataset for actual flying drones recorded using an omnidirectional camera. Our quantitative evaluation indicates that our system achieved an at-best average precision and average recall of over 0.8 for small objects represented as 10–20 pixel squares in the original image.
On the Road With 16 Neurons: Towards Interpretable and Manipulable Latent Representations for Visual Predictions in Driving Scenarios

This paper proposes a strategy for visual perception in the context of autonomous driving. Humans, when not distracted or drunk, are still the best drivers you can currently find. For this reason, we take inspiration from two theoretical ideas about the human mind and its neural organization. The first idea concerns how the brain uses structures of neuron ensembles that expand and compress information to extract abstract concepts from visual experience and code them into compact representations. The second idea suggests that these neural perceptual representations are not neutral but functional to predicting the future state of affairs in the environment. Similarly, the prediction mechanism is not neutral but oriented to the planning of future action. We identify within the deep learning framework two artificial counterparts of the aforementioned neurocognitive theories. We find a correspondence between the first theoretical idea and the architecture of convolutional autoencoders, while we translate the second theory into a training procedure that learns compact representations which are not neutral but oriented to driving tasks, from two distinct perspectives. From a static perspective, we force separate groups of neural units in the compact representations to represent specific concepts crucial to the driving task distinctly. From a dynamic perspective, we bias the compact representations to predict how the current road scenario will change in the future. We successfully learn compact representations that use as few as 16 neural units for each of the two basic driving concepts we consider: cars and lanes. We maintain the two concepts separated in the latent space to facilitate the interpretation and manipulation of the perceptual representations. The source code for this paper is available at https://github.com/3lis/rnn_vae.
Real-Time Semantic Scene Completion Via Feature Aggregation And Conditioned Prediction

Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. In this paper, we propose a real-time semantic scene completion method with a feature aggregation strategy and conditioned prediction module. Feature aggregation fuses feature with different receptive fields and gathers context to improve scene completion performance. And the conditioned prediction module adopts a two-step prediction scheme that takes volumetric occupancy as a condition to enhance semantic completion prediction. We conduct experiments on three recognized benchmarks NYU, NYUCAD, and SUNCG. Our method achieves competitive performance at a speed of 110 FPS on one GTX 1080 Ti GPU.
Navigation Command Matching for Vision-based Autonomous Driving

Learning an optimal policy for autonomous driving task to confront with complex environment is a long- studied challenge. Imitative reinforcement learning is accepted as a promising approach to learn a robust driving policy through expert demonstrations and interactions with environments. However, this model utilizes non-smooth rewards, which have a negative impact on matching between navigation commands and trajectory (state-action pairs), and degrade the generalizability of an agent. Smooth rewards are crucial to discriminate actions generated from sub-optimal policy. In this paper, we propose a navigation command matching (NCM) model to address this issue. There are two key components in NCM, 1) a matching measurer produces smooth navigation rewards that measure matching between navigation commands and trajectory; 2) attention-guided agent performs actions given states where salient regions in RGB images (i.e. roadsides, lane markings and dynamic obstacles) are highlighted to amplify their influence on the final model. We obtain navigation rewards and store transitions to replay buffer after an episode, so NCM is able to discriminate actions generated from suboptimal policy. Experiments on CARLA driving benchmark show our proposed NCM outperforms previous state-of-the- art models on various tasks in terms of the percentage of successfully completed episodes. Moreover, our model improves generalizability of the agent and obtains good performance even in unseen scenarios.
Semi-Supervised Graph Convolutional Hashing Network For Large-Scale Cross-Modal Retrieval

Cross-modal retrieval aims to provide flexible retrieval results across different types of multimedia data. To confront with scalability issue, binary codes learning (a.k.a. hash technique) is advocated since it permits exact top-K retrieval with sub-linear time complexity. In this paper, we propose a new method called Semi-supervised Graph Convolutional Hashing network (SGCH), which tries to learn a common hamming space by preserving both intra-modality and intermodality similarities via an end-to-end neural network. On one hand, graph convolutional network is utilized to explore high-order intra-modality similarity, and simultaneously propagate the semantic information from labeled samples to unlabeled data. On the other hand, a siamese network is connected to project the learnt features into a common hamming space. To bridge the inter-modality gap, adversarial loss which aims to learn modality-independent features by confusing a modality classifier is incorporated into the overall loss function. Experimental evaluations on cross-media retrieval tasks demonstrate that SGCH performs competitively against the state-of-the-art methods.
The Interplay Between Artificial Intelligence and Users’ Personalities: A New Scenario for Human-Computer Interaction in Gaming

The latest business reports showed that Augmented Reality (AR) and Artificial Intelligence (AI) are ranked among the top 10 strategic trends for 2018. For these reasons, in this paper, we provide an interdisciplinary focus on design and personality issues, trying to discuss the interplay between games with personality and Artificial Intelligence. First, we describe taxonomy models on personality in games and empirical studies aimed at exploring personality traits of Pokémon GO users. Second, we explore virtual humans employed in investigating chess personalities via simulating human chess players. In this research article, the term virtual human is used to describe a computer program that simulates a human in some aspects such as playing chess. The results of personality and gaming are sparse and mixed. It remains unclear whether personality traits would similarly predict adoption and usage in the context of AR mobile game. On the contrary, results about AI showed that virtual humans have characteristics similar to those of humans and helped researchers explore existing patterns in societies. In our discussion, we try to evidence the importance of personality in gaming also considering that an increasing sale for these technologies is forecasted in 2020 to be 21 times higher than in 2016 (from US$2.9 billion to US$61.3 billion; Superdata Research, 2017).
VLSI Design of Tree-Based Inference for Low-Power Learning Applications

The use of Machine Learning techniques in battery-powered devices has increased in recent years. Therefore, evaluating power-accuracy trade-offs of inference models has become important. This work explores decision trees architectures, analyzing the effects of model complexity and approximation in power and accuracy. By quantizing the inputs to limited widths, we increased the accuracy of the models in up to 8.7% compared to precise cases. In terms of power, the variation in model complexity was more significant than in input width, as we obtained reductions of up to 97% by decreasing the tree depth, compared to 88% when decreasing the width.
Research on Electricity Consumption Behavior of Users Based on Deep Learning

In order to better realize the classified management of the massive user electricity data and do a better job for peak shifting and averting of user side, this paper presents a hybrid model of deep belief network and extreme learning machine (DBN-ELM) for the analysis of electricity consumption behavior of users. Firstly, the total load curve is calculated. Then, the load curve of resident users is compared with the total load curve to make the classification rules of user electricity consumption behavior. Furthermore, the DBN-ELM hierarchy is optimized, and by traversing the number of hidden nodes, a reasonable number of hidden layer nodes are selected considering the simulation accuracy of different nodes. Finally, load data from Irish users are used as data sources, through comparative experiments with ELM and DBN networks, the results show that the proposed DBN-ELM algorithm for electricity consumption behavior analysis can extract high-level features better from the underlying features, and can better analyze the user's electrical consumption behavior, to improve the effect of peak shifting and averting work.
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In this paper, we have proposed a higher order non-linear PDE model containing diffusion terms of TV−L2 and TV−H−1 model for effective image denoising. Fourier spectral method for space and convexity splitting method for time have been used to solve the proposed PDE model. Stability analysis for the time discretized equation has been derived. Numerical experiment has been done on some test images and the results have been compared with the results of some existing models.
Security of Facial Forensics Models Against Adversarial Attacks

Deep neural networks (DNNs) have been used in digital forensics to identify fake facial images. We investigated several DNN-based forgery forensics models (FFMs) to examine whether they are secure against adversarial attacks. We experimentally demonstrated the existence of individual adversarial perturbations (IAPs) and universal adversarial perturbations (UAPs) that can lead a well-performed FFM to misbehave. Based on iterative procedure, gradient information is used to generate two kinds of IAPs that can be used to fabricate classification and segmentation outputs. In contrast, UAPs are generated on the basis of over-firing. We designed a new objective function that encourages neurons to over-fire, which makes UAP generation feasible even without using training data. Experiments demonstrated the transferability of UAPs across unseen datasets and unseen FFMs. Moreover, we conducted subjective assessment for imperceptibility of the adversarial perturbations, revealing that the crafted UAPs are visually negligible. These findings provide a baseline for evaluating the adversarial security of FFMs.
Keyphrase Extraction in Scholarly Digital Library Search Engines

Scholarly digital libraries provide access to scientific publications and comprise useful resources for researchers who search for literature on specific subject areas. CiteSeerX is an example of such a digital library search engine that provides access to more than 10 million academic documents and has nearly one million users and three million hits per day. Artificial Intelligence (AI) technologies are used in many components of CiteSeerX including Web crawling, document ingestion, and metadata extraction. CiteSeerX also uses an unsupervised algorithm called noun phrase chunking (NP-Chunking) to extract keyphrases out of documents. However, often NP-Chunking extracts many unimportant noun phrases. In this paper, we investigate and contrast three supervised keyphrase extraction models to explore their deployment in CiteSeerX for extracting high quality keyphrases. To perform user evaluations on the keyphrases predicted by different models, we integrate a voting interface into CiteSeerX. We show the development and deployment of the keyphrase extraction models and the maintenance requirements.
An Overview of the Frameworks for Measuring the Digital Competencies of College Students

The purpose of this chapter is to analyze the options for measuring the digital entrepreneurial competencies the college students acquire during the course of their formal education. The chapter examines the key aspects of various digital competence-related frameworks and proposes the development of a new methodology that will be focused on the digital entrepreneurial competencies of the students. This chapter proposes the development of the corresponding online assessment tool which could serve to measure the level of the acquired competencies by the students. The findings presented in this chapter may apply to other areas as well. The goal is to develop a set of competence assessment tools that could effectively determine the level of competencies the students acquire during the course of their formal studies and enable the formulation of adequate corrective measures in the curriculum plan.
Building Reusable Repertoires for Stochastic Self-* Planners

Plan reuse is a promising approach for enabling self-* systems to effectively adapt to unexpected changes, such as evolving existing adaptation strategies after an unexpected change using stochastic search. An ideal self-* planner should be able to reuse repertoires of adaptation strategies, but this is challenging due to the evaluation overhead. For effective reuse, a repertoire should be both (a) likely to generalize to future situations, and (b) cost effective to evaluate. In this work, we present an approach inspired by chaos engineering for generating a diverse set of adaptation strategies to reuse, and we explore two analysis approaches based on clone detection and syntactic transformation for constructing repertoires of adaptation strategies that are likely to be amenable to reuse in stochastic search self-*planners. An evaluation of the proposed approaches on a simulated system inspired by Amazon Web Services shows planning effectiveness improved by up to 20% and reveals tradeoffs in planning timeliness and optimality.