Reviewerly/exHarmony huggingface dataset:
Evaluating GSD-Aware

Global Software Development (GSD) is currently a strong industry trend. This means that if computer science engineers are to be trained to deal with this model, it is very important to include the topic in software engineering courses, attempting to ensure that students learn about GSD and become familiar with its advantages and challenges. However, software engineering courses do not always consider including it in their curricula. It must also be recognized that it is difficult to find a suitable method to teach/develop the different skills needed for GSD. There is often a lot of content and not a great deal of time available to teach it. In this article, we propose the use of a serious game called GSD-Aware, with which students can “suffer” some of the typical challenges of GSD by interacting with avatars and by using several means of communication to solve a number of problems posed. The article focuses on the description of the game and on the empirical study conducted to analyze whether GSD-Aware helps students to be conscious of GSD challenges. It was discovered that after 50 minutes playing the game, the students were aware of the greater influence that the following factors can have: lack of coordination, lack of trust, cultural differences, lack of face-to-face and informal communication, time difference, and lack of team spirit. In their final analysis, students agreed that the serious game scenarios helped them to understand what GSD is and to grasp the importance of some GSD challenges.
Face Depth Estimation With Conditional Generative Adversarial Networks

Depth map estimation and 3-D reconstruction from a single or a few face images is an important research field in computer vision.Many approaches have been proposed and developed over the last decade.However, issues like robustness are still to be resolved through additional research.With the advent of the GPU computational methods, convolutional neural networks are being applied to many computer vision problems.Later, conditional generative adversarial networks (CGAN) have attracted attention for its easy adaptation for many picture-to-picture problems.CGANs have been applied for a wide variety of tasks, such as background masking, segmentation, medical image processing, and superresolution.In this work, we developed a GAN-based method for depth map estimation from any given single face image.Many variants of GANs have been tested for the depth estimation task for this work.We conclude that conditional Wasserstein GAN structure offers the most robust approach.We have also compared the method with other two state-of-the-art methods based on deep learning and traditional approaches and experimentally shown that the proposed method offers great opportunities for estimation of face depth maps from face images.
Learning Multiple Local Metrics: Global Consideration Helps

Learning distance metric between objects provides a better measurement for their relative comparisons. Due to the complex properties inside or between heterogeneous objects, multiple local metrics become an essential representation tool to depict various local characteristics of examples. Different from existing methods building more than one local metric directly, however in this paper, we emphasize the effect of the global metric when generating those local ones. Since local metrics can be considered as types of amendments which describe the biases towards localities based on some commonly shared characteristic, it is expected that the performance of every single local metric for a specified locality can be “lifted” when learning with the global jointly. Following this consideration, we propose the Local metrIcs Facilitated Transformation (Lift) framework, where an adaptive number of local transformations are constructed with the help of their global counterpart. Generalization analyses not only reveal the relationship between the global and local metrics but also indicate when and why the framework works theoretically. In the implementation of Lift, locality anchored centers assist the decomposition of multiple local views, and a diversity regularizer is proposed to reduce the redundancy among biases. Empirical classification comparisons reveal the superiority of the Lift idea. Numerical and visualization investigations on different domains validate its adaptability and comprehensibility as well.
Brush like a Dentist: Accurate Monitoring of Toothbrushing via Wrist-Worn Gesture Sensing

Oral health has significant impact on people’s over-all well-being. While many activity recognition systems exist in the literature, accurately sensing toothbrushing activities remains an unsolved challenging problem due to the diversity of tooth-brushing habits among different users and subtle distinctions between different brushing actions. In this work, we propose Hygiea, an energy-efficient and highly-accurate toothbrushing monitoring system which exploits IMU-based wrist-worn gesture sensing using unmodified toothbrushes. To address toothbrushing variety, Hygiea incorporates a number of novel signal preprocessing techniques to automatically transform the sensory input during arbitrary toothbrushing activities to the consistent user coordinate system. To distinguish different brushing actions, Hygiea leverages an emerging deep learning model (e.g., AT-LSTM) to achieve fine-grained activity recognitions. Moreover, a POMDP model is incorporated for sampling control to balance activity detection and energy efficiency. Extensive real-world experiments show that the Hygiea system achieves a 11.7% accuracy gain compared to the state-of-the-art while maintaining energy-efficiency and zero modification on the toothbrushes.
Reversible AMBTC-Based Data Hiding With Security Improvement by Chaotic Encryption

Social networking and cloud computing are being extensively used, and in this era, the frequency of sending information or images to each other is increasing. The prevention of private information leakage during communication over the Internet has become a concern in the past decades. Several data protection methods, such as cryptographic, watermarking, and steganography techniques, have been proposed to protect private data. In this paper, an embedding method is proposed based on an absolute moment block truncation coding (AMBTC)-compressed image. High and low mean tables are extracted from a compressed image and are divided into non-overlapping blocks. An adaptive variable N-bit bit plane truncation image embedding method is proposed to embed the secret data in each block. In this method, at the receiver end, the secret data are extracted, and the original AMBTC image could be recovered by recalling the stored peak and zero points. In addition, a chaotic encryption scheme is integrated into the proposed system to improve robustness against security vulnerability. The results show that the proposed method has superior performance and higher payload compared with the reference methods.
A Framework for Modeling Cloud Infrastructures and User Interactions

Nowadays, cloud computing technologies are commonly used both by the public and private institutions, so as to save money and computing efforts, by using a network of remote servers hosted on the Internet to manage their services. Thus, cloud clients must interact with cloud providers in order to make use of the services deployed in the cloud, and the cloud infrastructure must be designed taking into account both the physical devices and equipment required (racks, switches, and so on) and also the user demands, expressed in terms of the services they need. This paper focuses on this latter aspect, in which we analyze how cloud clients interact with a cloud provider, providing a Unified Modeling Language-based framework to model a cloud system, including the underlying infrastructure, the user resource requirements, and their interactions with the cloud provider.
Hyperspectral Image Denoising Using Group Low-Rank and Spatial-Spectral Total Variation

Hyperspectral images (HSIs) are frequently corrupted by various types of noise, such as Gaussian noise, impulse noise, stripes, and deadlines due to the atmospheric conditions or imperfect hyperspectral imaging sensors. These types of noise, which are also called mixed noise, severely degrade the HSI and limit the performance of post-processing operations, such as classification, unmixing, target recognition, and so on. The patch-based low-rank and sparse based approaches have shown their ability to remove these types of noise to some extent. In order to remove the mixed noise further, total variation (TV)-based methods are utilized to denoise HSI. In this paper, we propose a group low-rank and spatial-spectral TV (GLSSTV) to denoise HSI. Here, the advantage is twofold. First, group low-rank exploits the local similarity inside patches and non-local similarity between patches which brings extra structural information. Second, SSTV helps in removing Gaussian and sparse noise using the spatial and spectral smoothness of HSI. The extensive simulations show that GLSSTV is effective in removing mixed noise both quantitatively and qualitatively and it outperforms the state-of-the-art low-rank and TV-based methods.
Quantifying Exposure Bias for Neural Language Generation

The exposure bias problem refers to the training-generation discrepancy, caused by teacher forcing, in maximum likelihood estimation (MLE) training for auto-regressive neural network language models (LM). It has been regarded as a central problem for neural language generation (NLG) model training. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore` to alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we first identify the self-recovery ability of MLE-trained LM, which casts doubt on the seriousness of exposure bias. We then propose sequence-level (EB-bleu) and word-level (EB-C) metrics as reasonable proxies to quantify the impact of exposure bias. We conduct experiments for the LSTM/transformer model, in both real and synthetic settings. In addition to the unconditional NLG task, we also include results for a seq2seq machine translation task. Surprisingly, all our measurements suggest that removing the training-generation discrepancy only brings very little performance gain. In our analysis, we hypothesise that although there exist a mismatch between the model distribution and the data distribution, the mismatch is still in the model's comfortable zone, and is not big enough to induce significant performance loss.
Face hallucination from low quality images using definition-scalable inference

To hallucinate super-resolution (super-res) face from a real low-quality face, a super-resolution technique based on definition-scalable inference (SRDSI) is proposed in this paper. In the proposed strategy, all high-res labeled faces are first decomposed into basic faces and enhanced faces to train a basic face and an enhanced face inferring model, and then two inferring models are used to hallucinate super-res basic face with low-definition and enhanced faces with high-frequency information from a single low-res face. Finally, the basic face is merged with its enhanced face into a super-res face with high-definition. In addition, this paper employs SIFT key-points to evaluate the similarity between the super-res face and its high-res labeled face. Experimental results show that SRDSI can effectively recover more structural information as well as SIFT key-points from real low-res faces and achieves better performance than state-of-the-art super-resolution techniques in terms of both visual and objective quality.
Two-Level Attentions and Grouping Attention Convolutional Network for Fine-Grained Image Classification

The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional network, which characterizes the object-level attention and the pixel-level attention. Then, we combine the two kinds of attention through a second-order response transform algorithm. Furthermore, we propose a clustering-based grouping attention model, which implies the part-level attention. The grouping attention method is to stretch all the semantic features, in a deeper convolution layer of the network, into vectors. These vectors are clustered by a vector dot product, and each category represents a special semantic. The grouping attention algorithm implements the functions of group convolution and feature clustering, which can greatly reduce the network parameters and improve the recognition rate and interpretability of the network. Finally, the low-level visual features and high-level semantic information are merged by a multi-level feature fusion method to accurately classify fine-grained images. We have achieved good results without using pre-training networks and fine-tuning techniques.
Cryptocurrency-Related Crimes: Key Criminological Trends

The authors have analyzed crimes connected with the use of virtual currency in the regional and international aspects. They introduce a new category of «cryptocrime» understood as the aggregate of publically dangerous acts, united by their common systemic characteristics, committed against or using the products of distributed registries (cryptocurrency, tokens and other forms of digital financial assets). They analyze each of the cryptocrime segments separately: illegal trade in psychoactive substances (narcotics, psychoactive substances, precursors), pornography and other prohibited content (including illegal services); laundering of criminal proceeds; theft of cryptocurrency and tokens. Using the scientific research methods (comparative, sociological, statistical analysis and extrapolation of data, building a trend line, etc.) the authors identify regularities in the dynamics of each type of cryptocrime as well as key factors facilitating them. The goal of the authors is to conduct a systemic examination of crimes committed against and using cryptocurrency and to determine the prospects of developing different segments of cryptocrime. To achieve this goal, they analyze qualitative and quantitative characteristics of illegal trade in narcotics and pornography, legalization of criminal proceeds and theft of digital assets. They name the anonymity of cryptocurrency as a factor facilitating illegal trade in drugs, while the growing scope of the legalization of criminal proceeds and theft is facilitated by the fact that cryptocurrency and tokens do not have a legal status as objects of civil law and objects of encroachments on property. The analysis allows the authors to conclude that without effective criminological measures the level of such crimes will continue to grow and may double by the end of 2019. According to the authors, the priority directions of international criminal policy in the sphere of cryptocrime prevention include determining cryptocurrencies’ legal status, licensing cryptocurrency trade (stock exchange services, exchange platforms, companies issuing tokens), setting international standards of counteracting the legalization of criminal proceeds and the financing of terrorism, creating a cryptocrime database.
Detecting Examinees With Pre-knowledge in Experimental Data Using Conditional Scaling of Response Times

The detection of examinees who have previously accessed proprietary test content is a primary concern in the context of test security. Researchers have proposed using item response times to detect examinee pre-knowledge, but progress in this area has been limited by a lack of real data containing credible information about pre-knowledge and by strict statistical assumptions. In this work, an innovative, but simple, method is proposed for detecting examinees with pre-knowledge. The proposed method represents a conditional scaling that assesses an examinee’s response time to a particular item, compared to a group of examinees who did not have pre-knowledge, conditioned on whether or not the item was answered correctly. The proposed method was investigated in empirical data from 93 undergraduate students, who were randomly assigned to have pre-knowledge or not. Participants took a computerized GRE Quantitative Reasoning test and were given no items, half the items, or half the items with correct answers to study before the test, depending on their condition. Exploratory analysis techniques were used to investigate the resulting values at both the item and person-level, including factor analyses and cluster analyses. The proposed method achieved impressive accuracy of separation between disclosed and undisclosed items and examinees with and without pre-knowledge (96% and 97% accuracy for cluster analyses, respectively), demonstrating detection power for item disclosure and examinee pre-knowledge. The methodology requires minimal assumptions about the data and can be used for a variety of modern test designs that preclude other types of data forensic analyses.
Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.
Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification

A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography), based on the fusion of Riesz and deep learning features, is presented. First, discriminative parametric lung tissue texture signatures are learned from Riesz representations using a one–versus–one approach. The signatures are generated for four diseased tissue types and a healthy tissue class, all of which frequently appear in the publicly available Interstitial Lung Diseases (ILD) dataset used in this article. Because the Riesz wavelets are steerable, they can easily be made invariant to local image rotations, a property that is desirable when analyzing lung tissue micro–architectures in CT images. Second, features from deep Convolutional Neural Networks (CNN) are computed by fine–tuning the Inception V3 architecture using an augmented version of the same ILD dataset. Because CNN features are both deep and non–parametric, they can accurately model virtually any pattern that is useful for tissue discrimination, and they are the de facto standard for many medical imaging tasks. However, invariance to local image rotations is not explicitly implemented and can only be approximated with rotation–based data augmentation. This motivates the fusion of Riesz and deep CNN features, as the two techniques are very complementary. The two learned representations are combined in a joint softmax model for final classification, where early and late feature fusion schemes are compared. The experimental results show that a late fusion of the independent probabilities leads to significant improvements in classification performance when compared to each of the separate feature representations and also compared to an ensemble of deep learning approaches.
Enhancing Unsupervised Generative Dependency Parser with Contextual Information

Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse.Probabilistic generative models usually explicit decompose the desired dependency tree into factorized grammar rules, which lack the global features of the entire sentence.In this paper, we propose a novel probabilistic model called discriminative neural dependency model with valence (D-NDMV) that generates a sentence and its parse from a continuous latent representation, which encodes global contextual information of the generated sentence.We propose two approaches to model the latent representation: the first deterministically summarizes the representation from the sentence and the second probabilistically models the representation conditioned on the sentence.Our approach can be regarded as a new type of autoencoder model to unsupervised dependency parsing that combines the benefits of both generative and discriminative techniques.In particular, our approach breaks the context-free independence assumption in previous generative approaches and therefore becomes more expressive.Our extensive experimental results on seventeen datasets from various sources show that our approach achieves competitive accuracy compared with both generative and discriminative state-of-the-art unsupervised dependency parsers.
Unified Graph-Based Multicue Feature Fusion for Robust Visual Tracking

Visual tracking is a complex problem due to unconstrained appearance variations and a dynamic environment. The extraction of complementary information from the object environment via multiple features and adaption to the target’s appearance variations are the key problems of this paper. To this end, we propose a robust object tracking framework based on the unified graph fusion (UGF) of multicue to adapt to the object’s appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multicue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric. The adaptation strategy updates the appearance model to accommodate variations in scale, illumination, and rotation. Both qualitative and quantitative analyses on benchmark video sequences from OTB-50, OTB-100, VOT2017/18, and UAV123 show that the proposed UGF tracker performs favorably against 18 other state-of-the-art trackers under various object tracking challenges.