Reviewerly/exHarmony huggingface dataset:
Unsupervised Person Image Generation with Semantic Parsing Transformation

In this paper, we address unsupervised pose-guided person image generation, which is known challenging due to non-rigid deformation. Unlike previous methods learning a rock-hard direct mapping between human bodies, we propose a new pathway to decompose the hard mapping into two more accessible subtasks, namely, semantic parsing transformation and appearance generation. Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning. Secondly, an appearance generative network learns to synthesize semantic-aware textures. Thirdly, we demonstrate that training our framework in an end-to-end manner further refines the semantic maps and final results accordingly. Our method is generalizable to other semantic-aware person image generation tasks, eg, clothing texture transfer and controlled image manipulation. Experimental results demonstrate the superiority of our method on DeepFashion and Market-1501 datasets, especially in keeping the clothing attributes and better body shapes.
Graph Spectral Domain Blind Watermarking

This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model. The distortion minimisation model is proposed to reduce the watermarking distortion by establishing the relationship between the error distortion using mean square error and the selected Graph Fourier coefficients to embed the watermark. The robustness model is proposed to improve the watermarking robustness against the attacks by establishing the relationship between the watermark extraction and the effect of the attacks, namely, additive noise and nodes data deletion. The proposed models were verified by the experimental results.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees. We show three concrete realizations of this label representation space including : (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees. By combining the effect of shallow trees and generalized label representation, Bonsai achieves the best of both worlds - fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, \bonsai outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for Bonsai is available at \url{this https URL}
$$L_0$$-ARM: Network Sparsification via Stochastic Binary Optimization

We consider network sparsification as an $L_0$-norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attached with a stochastic binary gate, whose parameters are jointly optimized with original network parameters. The Augment-Reinforce-Merge (ARM), a recently proposed unbiased gradient estimator, is investigated for this binary optimization problem. Compared to the hard concrete gradient estimator from Louizos et al., ARM demonstrates superior performance of pruning network architectures while retaining almost the same accuracies of baseline methods. Similar to the hard concrete estimator, ARM also enables conditional computation during model training but with improved effectiveness due to the exact binary stochasticity. Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training. Extensive experiments on multiple public datasets demonstrate state-of-the-art pruning rates with almost the same accuracies of baseline methods. The resulting algorithm $L_0$-ARM sparsifies the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete estimator cannot. The code is public available at https://github.com/leo-yangli/l0-arm.
Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews Helpfulness

Despite the popularity of the Massive Open Online Courses, small-scale research has been done to understand the factors that influence the teaching-learning process through the massive online platform. Using topic modeling approach, our results show terms with prior knowledge to understand e.g.: Chuck as the instructor name. So, we proposed the topic modeling approach on helpful subjective reviews. The results show five influential factors: “learn easy excellent class program”, “python learn class easy lot”, “Program learn easy python time game”, and “learn class python time game”. Also, research results showed that the proposed method improved the perplexity score on the LDA model.
Designing LED lights for a robot to communicate gaze

Eye gaze is considered to be a particularly important non-verbal communication cue. Gaze research is also becoming a hot topic in human–robot interaction (HRI). However, research on social eye gaze for HRI focuses mainly on human-like robots. There remains a lack of methods for functional robots, which are constrained in appearance, to show gaze-like behavior. In this work, we investigate how we can implement gaze behavior in functional robots to assist humans in reading their intent. We explore design implications based on LED lights as we consider LEDs to be easily installed in most robots while not introducing features that are too human-like (to prevent users from having high expectations towards the robots). In this paper, we first developed a design interface that allows designers to freely test different parameter settings for an LED-based gaze display for a Roomba robot. We summarized design principles for well simulating LED-based gazes. Our suggested design is further evaluated by a large group of participants with regard to their perception and interpretation of the robot's behaviors. On the basis of the findings, we finally offer a set of design implications that can be beneficial to HRI and HCI researchers.
Socio-Psychological Adaptation of International Students to Learning and Professional Activities

It remains well established that international students face a number of challenges when they arrive at a foreign institute to pursue higher education. This research aims to examine the socio-psychological adaptation of international students to learning and professional activities of Elabuga Institute (branch) of Kazan Federal University, Russia. Using a number of socio-psychological adaptation methods of assessment (such as the sense of social security satisfaction, social frustration and psychological adaptation to new life conditions), the research aims to make remedial arrangements to boost the psychosocial adaptation of international students to educational and professional activities.&#x0D; The findings from the assessment methods were deployed to create an intervention programme,which in turn created the effective adaptation environment for overcoming the psychological inconveniences of language and culture barriers, integrating students into personal and business relations with the native speakers, corporate training of Russian language in conversation clubs, and invitation for participation in mass cultural institute events.
The art of phishing: past, present and future

What may seem like one of the oldest tricks in the cyber criminal handbook is on the rise – phishing. These scams are becoming so common and advanced that it's often difficult to discern between genuine and fake emails, and the vast majority of cyber attacks begin with a phishing email. 1 Phishing scams are becoming so common and advanced that it's often difficult to discern between genuine and fake emails.
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for unsupervised and semi-supervised training in such models. This work builds upon recent results showing notable improvements in semi-supervised training using cycle-consistency and related techniques. Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models. In particular, this work proposes a new semi-supervised loss combining an end-to-end differentiable ASR$\rightarrow$TTS loss with TTS$\rightarrow$ASR loss. The method is able to leverage both unpaired speech and text data to outperform recently proposed related techniques in terms of \%WER. We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora. Our code is provided in ESPnet to reproduce the experiments.
EFL learners’ peer negotiations and attitudes in mobile-assisted collaborative writing

Based on the advantages of collaborative writing and the advanced development of mobile technology, this study investigated the communication process and attitudes of a group of college students toward collaborative writing using a Google Docs app on an English writing course. Online collaborative writing assignments were assigned by the instructor in class, while the English as a foreign language (EFL) students were required to work on the writing assignments online. The researchers examined participantsâ€™ peer negotiations in this mobile-enhanced, collaborative writing project, focusing on these EFL learnersâ€™ negotiation discourse patterns and strategies. The research questions were: 1) What kinds of discourse functions do students produce in online collaborative writing tasks? 2) What kinds of peer negotiation strategies do students use in online collaborative writing tasks? and 3) What are studentsâ€™ attitudes toward online collaborative writing tasks? A mixed methodology was applied. The findings revealed that participants had a generally positive attitude toward the collaborative writing experience. The results are also discussed in terms of learnersâ€™ peer negotiation strategies and types of discourse functions.
Teaching ways of compiling tasks as the fundamentals of the development of professional competence of future mathematics teachers

П. Ю. Романов, Л. В. Смирнова, О
Virtual reality and augmented reality applications and simulation in vascular access management with three-dimensional visualization

Obtaining adequate and precise anatomical information is mandatory to prevent vascular access-related complications in dialysis patients. For this purpose, we underwent Doppler ultrasound, vascular access angiogram, and plain computer-assisted tomography scan of the arm with vascular access. With the use of computer graphics software, the anatomical structure of the vascular access can be visualized three dimensionally which is shared among the staffs for precise and better recognition. Furthermore, created object is applicable for virtual reality and/or augmented reality presentation that provides useful means for education and practical procedures in the management of vascular access.
Application of Blockchain in Document Certification, Asset Trading and Payment Reconciliation

In economic activities, there are massive contracts, transactions, and payments. The consistency, integrity, and security of these important transaction data are crucial. The application of blockchain technology in document certification, asset trading and payment reconciliation, solves the security vulnerabilities existing in traditional technical solutions, optimizes business models, improves efficiency, ensures security, and enhances core competitiveness.
Discovering and Controlling for Latent Confounds in Text Classification Using Adversarial Domain Adaptation

In text classification, the testing data often systematically differ from the training data, a problem called dataset shift. In this paper, we investigate a type of dataset shift we call confounding shift. Such a setting exists when two conditions are met: (a) there is a confound variable Z that influences both text features X and class label Y; (b) the relationship between Z and Y changes from training to testing. While recent work in this area has required confounds to be known ahead of time, this is unrealistic for many settings. To address this shortcoming, we propose a method both to discover and to control for potential confounds. The approach first uses neural network-based topic modeling to discover potential confounds that differ between training and testing data, then uses adversarial training to fit a classification model that is invariant to these discovered confounds. We find the resulting method to improve over state-of-the-art domain adaptation method, while also producing results that are competitive with those obtained when confounds are known ahead of time.
Determination of an appropriate mother wavelet for de-noising of weak GPS correlation signals based on similarity measurements

The Wavelet Transform (WT) is one of the most widely used tools for de-noising of Global Positioning System (GPS) signals which is a method for enhancing the sensitivity of GPS receivers in the acquisition of weak signals. The appropriate selection of mother wavelet which results in concentration of the major part of the power of a signal on a few numbers of wavelet coefficients is one of the most determinative factors in the efficiency of the de-noising process. Considering the importance of this issue, for the first time, in this paper, we introduce a quantitative method for selecting an appropriate mother wavelet for decomposing weak GPS correlation signals. Our proposed method uses the similarity measurements to choose the fittest mother wavelet. We use P, Q, and Magnitude-Shape (MS) criteria which are three individual remodeling factors, to evaluate the capability of different mother wavelets in the proper decomposition of weak GPS correlation signals. Eventually, the mother wavelet is selected based on our introduced Figure of Merit (FOM) which increases the reliability of the method. Our suggested method improves the sensitivity of GPS receivers in the acquisition of weak signals by enhancing the quality of the de-noising process using WT.
An Ontology-driven ECHONET Lite Adaptation Layer for Smart Homes

ECHONET Lite is a leading protocol for controlling devices in Japan smart homes. However, it lacks interoperability with service platforms that provide ambient assisted living (AAL) services to residents which are actively researched in order to deal with the population aging. This research proposes an adaptation layer for ECHONET Lite protocol which provides the semantic interoperability based on ontology. In order to verify the proposed solution, a service gateway based on the proposed architecture was implemented to integrate ECHONET Lite protocol into the universAAL platform, a leading AAL platform in Europe.
Automatic trimap generation and artifact reduction in alpha matte using unknown region detection

Image Matting is an actively researched topic due to its massive impact on the filmmaking industry. It refers to the accurate extraction of the foreground from an image and is a severely under-constrained problem. To constrain this issue, user input in the form of a trimap is required. However, users must generate this trimap manually, which is an exhausting and time-consuming process. To tackle this issue, we develop an expert system which is capable of generating trimaps automatically without any user interaction. We utilize three different techniques which use machine learning at the core. Our expert system receives knowledge from these three techniques which are processed by our system to generate trimaps automatically. We strongly believe that our study will have a strong impact on the filmmaking industry. Our study lays the groundwork for automatic trimap generation. Furthermore, our study can be applied to expert and intelligent systems related to image retrieval and automatic foreground/primary object segmentation in images or videos. We propose a simple yet effective approach to generate optimal trimaps automatically by combining image saliency, graph cut segmentation (lazy snapping), and fuzzy c-means clustering (FCM). Lazy snapping is an interactive segmentation technique that requires foreground and background scribbles as input. Instead of using user-provided foreground scribbles, we utilize a saliency map as foreground scribbles and input it to the lazy snapping. This results in a coarsely segmented foreground object. We use the corners as the background scribbles based on the assumption that most foreground objects are located in the center of the image. To generate an optimal trimap, we locally cluster the boundary region of the foreground segmentation using FCM. We tested our algorithm on alpha matting evaluation and salient object datasets. In addition to generating accurate trimaps automatically, the alpha mattes generated by our optimal trimaps contain fewer artifacts as compared to trimaps generated by previous works. Moreover, the alpha mattes computed using our optimal trimaps were computed faster as compared to computing alpha mattes with trimaps from previous works. We showed in our experiments that optimal trimaps can improve alpha matte quality by reducing artifacts. Finally, our approach does not rely on depth data like the previous methods. Our experiments show the effectiveness of our method.
Binarized Depthwise Separable Neural Network for Object Tracking in FPGA

Object tracking has achieved great advances in the past few years and has been widely applied in vision-based application. Nowadays, deep convolutional neural network has taken an important role in object tracking tasks. However, its enormous model size and massive computation cost have became the main obstacle for deployment of such powerful algorithm in low power and resource limited embedded system, such as FPGA. Due to the popularization of the power-sensitive mobile platform, low power real-time tracking solution is strongly required. In order to address these challenges, we propose a low power and energy-efficient object tracking FPGA implementation based on a newly proposed binarized depthwise separable deep convolutional neural network. It can significantly reduce the model size and computation complexity simultaneously utilizing binarized (i.e., +1 and -1) depthwise separable convolution kernel and our proposed trainable threshold group binarization activation function. It can completely converts the dot product and accumulation based convolution operations into bit-wise XNOR and bit-count operations, while achieving state-of-the-art accuracy. Our proposed binarized depthwise separable model achieves ~57% Intersection over Union (IOU) on DJI object tracking dataset with only ~143.9Kb model parameter size. We then deploy our proposed model into the Xilinx PYNQ Z1 board with only 4.9Mb on-chip RAM. The experiment results show that our FPGA implementation achieves 11.1 frames per second for object tracking with only 2.61W.
An Information Theoretic Interpretation to Deep Neural Networks

With the unprecedented performance achieved by deep learning, it is commonly believed that deep neural networks (DNNs) attempt to extract informative features for learning tasks. To formalize this intuition, we apply the local information geometric analysis and establish an information-theoretic framework for feature selection, which demonstrates the information-theoretic optimality of DNN features. Moreover, we conduct a quantitative analysis to characterize the impact of network structure on the feature extraction process of DNNs. Our investigation naturally leads to a performance metric for evaluating the effectiveness of extracted features, called the H-score, which illustrates the connection between the practical training process of DNNs and the information-theoretic framework. Finally, we validate our theoretical results by experimental designs on synthesized data and the ImageNet dataset.
Advances in Sensor Monitoring Effectiveness and Applicability: A Systematic Review and Update

To provide an updated review article studying the applicability and effectiveness of sensor networks in measuring and supporting activities of daily living (ADLs) among non-demented older adults.Systematic review following PRISMA guidelines.Systematic search of PubMed, Embase, PsycINFO, INSPEC, and the Cochrane Library, from October 26, 2012 to January 3, 2018 for empirical studies, measuring and supporting ADLs among independently living, non-demented older adults, investigating wireless sensor monitoring networks.The search queries yielded 10,782 hits of which 162 articles were manually reviewed. Following exclusion criteria, 13 relevant articles were retained. Although various types of sensor networks with different analyzing algorithms were proposed, from simple video monitoring to complex sensor networks distributed throughout a house, all articles supported the use of wireless sensors for identifying changes in activity patterns.Wireless sensor networks appear to be developing into an effective solution for measuring ADLs and for identifying changes in their patterns. They offer a promising solution to support older adults living independently at home. However, there is too much focus on technology, and practical usefulness still needs to be further elaborated. Sensors should focus on ADLs that are sensitive to the earliest signs of cognitive decline, as well as quantitative markers, such as errors in the execution of ADLs.
Additive Noise Annealing and Approximation Properties of Quantized Neural Networks

We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural networks and analyze their approximation capabilities, showing in particular that any Lipschitz-continuous map defined on a hypercube can be uniformly approximated by a quantized neural network. We then focus on the regularization effect of additive noise on the arguments of multi-step functions inherent to the quantization of continuous variables. In particular, when the expectation operator is applied to a non-differentiable multi-step random function, and if the underlying probability density is differentiable (in either classical or weak sense), then a differentiable function is retrieved, with explicit bounds on its Lipschitz constant. Based on these results, we propose a novel gradient-based training algorithm for quantized neural networks that generalizes the straight-through estimator, acting on noise applied to the network's parameters. We evaluate our algorithm on the CIFAR-10 and ImageNet image classification benchmarks, showing state-of-the-art performance on AlexNet and MobileNetV2 for ternary networks.