Reviewerly/exHarmony huggingface dataset:
Visible Image Recognition of Power Transformer Equipment Based on Mask R-CNN

Visible image recognition of power substation equipment is an important part of intelligent operation and maintenance, fault location and infrared defect diagnosis. The image recognition methods used in the past have some problems, such as less details of equipment, vulnerability to environmental interference, low recognition rate and so on. In this paper, a visible image recognition model of substation equipment based on Mask R-CNN is established. The image recognition model based on Faster R-CNN is used to compare the results. The two types of image recognition models are trained by using 7,000 labeled data sets of visible photographs of power substation equipment. Using the image recognition model trained by 2,000 visible light photographs of substation equipment, this paper mainly studies the recognition effect of 11 typical substation equipment, such as transformer, current transformer and bushing. The results show that the model based on Mask R-CNN can realize the pixel-level recognition of power substation equipment and can be used for accurate identification of power substation equipment. Although it cannot achieve pixel-level recognition, the model based on Faster R-CNN is faster and more accurate than that based on Mask R-CNN in target box detection. The model based on Faster R-CNN is faster and more accurate than the model based on Mask R-CNN. Both models need to be further improved for the scene of power substation equipment.
Deep Learning Poison Data Attack Detection

Deep neural networks are widely used in many walks of life. Techniques such as transfer learning enable neural networks pre-trained on certain tasks to be retrained for a new duty, often with much less data. Users have access to both pre-trained model parameters and model definitions along with testing data but have either limited access to training data or just a subset of it. This is risky for system-critical applications, where adversarial information can be maliciously included during the training phase to attack the system. Determining the existence and level of attack in a model is challenging. In this paper, we present evidence on how adversarially attacking training data increases the boundary of model parameters using as an example of a CNN model and the MNIST data set as a test. This expansion is due to new characteristics of the poisonous data that are added to the training data. Approaching the problem from the feature space learned by the network provides a relation between them and the possible parameters taken by the model on the training phase. An algorithm is proposed to determine if a given network was attacked in the training by comparing the boundaries of parameters distribution on intermediate layers of the model estimated by using the Maximum Entropy Principle and the Variational inference approach.
The Impact of Olfactory and Wind Stimuli on 360 Videos Using Head-mounted Displays

Consuming 360 audiovisual content using a Head-Mounted Display (HMD) has become a standard feature for Immersive Virtual Reality (IVR). However, most applications rely only on visual and auditory feedback whereas other senses are often disregarded. The main goal of this work was to study the effect of tactile and olfactory stimuli on participants’ sense of presence and cybersickness while watching a 360 video using an HMD-based IVR setup. An experiment with 48 participants and three experimental conditions (360 video, 360 video with olfactory stimulus, and 360 video with tactile stimulus) was performed. Presence and cybersickness were reported via post-test questionnaires. Statistical analysis showed a significant difference in presence between the control and the olfactory conditions. From the control to the tactile condition, mean values were higher but failed to show statistical significance. Thus, results suggest that adding an olfactory stimulus increases presence significantly while the addition of a tactile stimulus only shows a positive effect. Regarding cybersickness, no significant differences were found across conditions. We conclude that an olfactory stimulus contributes to higher presence and that a tactile stimulus, delivered in the form of cutaneous perception of wind, has no influence in presence. We further conclude that multisensory cues do not affect cybersickness.
Hearing the Voice of Software Practitioners on Causes, Effects, and Practices to Deal with Documentation Debt

[Context and Motivation] It is common for teams to take shortcuts during software development that, in the future, will lead to maintainability issues and affect productivity and development cost. Different types of technical debt may affect software projects, including those associated with software documentation. Although there are many studies on technical debt, few focus on documentation debt in an industrial environment. [Question/Problem] We aimed to identify how software practitioners perceive the occurrence of documentation debt in their projects. We present a combined analysis of the results from two complementary studies: a survey (InsighTD) and an interview-based case study. [Principal Ideas/Results] We provide a list of causes and effects of documentation debt, along with practices that can be used to deal with it during software development projects. [Contribution] We find that documentation debt is strongly related to requirements issues. Moreover, we propose a theoretical framework, which provides a comprehensive depiction of the documentation debt phenomenon.
Exploration Potential of Fine-Fraction Heavy Mineral Concentrates from Till Using Automated Mineralogy: A Case Study from the Izok Lake Cu–Zn–Pb–Ag VMS Deposit, Nunavut, Canada

Exploration under thick glacial sediment cover is an important facet of modern mineral exploration in Canada and northern Europe. Till heavy mineral concentrate (HMC) indicator mineral methods are well established in exploration for diamonds, gold, and base metals in glaciated terrain. Traditional methods rely on visual examination of &gt;250 µm HMC material, however this study applies modern automated mineralogical methods (mineral liberation analysis (MLA)) to investigate the finer (&lt;250 µm) fraction of till HMC. Automated mineralogy of finer material allows for rapid collection of precise compositional and morphological data from a large number (10,000–100,000) of heavy mineral grains in a single sample. The Izok Lake volcanogenic massive sulfide (VMS) deposit, one of the largest undeveloped Zn–Cu resources in North America, has a well-documented fan-shaped indicator mineral dispersal train and was used as a test site for this study. Axinite, a VMS indicator mineral difficult to identify optically in HMC, is identified in till samples up to 8 km down ice. Epidote and Fe-oxide minerals are identified, with concentrations peaking proximal to mineralization. Corundum and gahnite are intergrown in till samples immediately down ice of mineralization. Till samples also contain chalcopyrite and galena up to 8 km down ice of mineralization, an increase from 1.3 km for sulfide minerals in till previously reported for coarse HMC fractions. Some of these sulfide grains occur as inclusions within chemically and physically robust mineral grains and would not be identified visually in the coarse HMC visual counts. Best practices for epoxy mineral grain mounting and abundance reporting are presented along with the automated mineralogy of till samples down ice of the deposit.
Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering

Graph-based recommendation algorithms treat user-item interactions as bipartite graphs, based on which low-dimensional vector representations of users and items seek to preserve the relationships among them. Previous methods usually capture users' preferences by directly learning first-order neighborhood patterns for each node, which limits their ability to exploit the similarity between two distant users/items as well as a user's preferences toward distant items. To address this potential weakness, in this paper, we propose SMOG-CF (Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering), a GCN-based framework that can directly capture high-order connectivity among nodes. Instead of implicitly capturing high-order connectivity through embedding propagation, SMOG-CF facilitates ‘path-level’ information propagation between neighboring nodes at any order. The matrix form of our embedding propagation formulas yields a model that is easy to deploy and can be extended to a general framework by adopting various information construction and aggregation equations. Experiments on several datasets of varying scale demonstrate the efficacy of our model.MSC codesRecommendation SystemsNetwork EmbeddingGraph Convolutional NetworksHigh-order Connectivity
Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K-singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.
DeciTrustNET: A graph based trust and reputation framework for social networks

The world wide success of large scale social information systems with diverse purposes, such as e-commerce platforms, facilities sharing communities and social networks, make them a very promising paradigm for large scale information sharing and management. However the anonymity, distributed and open nature of these frameworks, that, on the one hand, foster the communication capabilities of their users, may contribute, on the other hand, to the propagation of low quality information, attacks and manipulations from users with malicious intentions. All of these risks could end up decreasing users’ confidence in these systems and in a reduction of their utilisation. With these issues in mind, the objective of this contribution is to create DeciTrustNET, a trust and reputation based framework for social networks that takes into consideration the users relationships, the historic evolution of their reputations and their profile similarity to develop a tamper resilient network that guarantees trustworthy communications and transactions. An extensive experimental analysis of the developed framework has been carried out confirming that the proposed approach supports robust trust and reputation establishment among the users, even in social network under the presence of malicious users.
A Novel Implicit Trust Recommendation Approach for Rating Prediction

Rating predictions, as an application that is widely used in recommender systems, have gradually become a valuable way which can help user narrow down their choices quickly and make wise decisions from the vast amount of information.However, most existing collaborative recommendation models suffer from poor accuracy due to data sparsity and cold start problems that recommender systems contain only a few explicit data.To solve this problem, a new implicit trust recommendation approach (ITRA) is proposed to generate item rating prediction by mining and utilizing user implicit information in recommender systems.Specifically, user trust neighbor set that has similar preference and taste with a target user is first obtained by trust expansion strategy via user trust diffusion features in a trust network.Then, the trust ratings mined from user trust neighbors are used to compute trust similarity among users based on user collaborative filtering model.Finally, using the above filtered trust ratings and user trust similarity, the prediction results are generated by a trust weighting method.In addition, the empirical experiments are conducted on three real-world datasets, and the results demonstrate that our rating prediction model has obvious advantages over the state-of-the-art comparison methods in terms of the accuracy of recommendations.
Current Challenges in Teaching English in Least-developed Region in Indonesia

Teaching English as a Foreign Language is challenging. The body of literature has addressed the issue on the current challenges in teaching English as Foreign Language. However, only few studies have investigated the issue in remote areas of Indonesia. This qualitative study investigates the current challenges in teaching English in least-developed region of Indonesia based on the following criteria: language comprehension, method and approach, practical implementation, and socio-cultural environment. The data were collected from four English lecturers in one of teaching universities in Indonesia. The participants were selected using purposive sampling technique. The data were collected using in-depth interview and field observation which were then analyzed descriptively. The results of the study reveal that the participants have to deal with several obstacles in teaching English. Those obstacles include the students’ low proficiency of language comprehension, limited resources of materials, communication gap between lecturers and students, students’ lack of enthusiasm and participation in learning and lack of environmental support from society. From the observation, the participants show that the challenges are mitigated by adjusting their choice of pedagogical instruction and learning materials according to students’ level of language comprehension. The study suggests that the infrastructure development is strongly essential to establish a good teaching environment. Besides, creating positive understanding and paradigm about the sanctity of learning English for students might be able to help the students’ to study English better.
Adversarial-learning-based image-to-image transformation: A survey

Recently, the generative adversarial network (GAN) has attracted wide attention for various computer vision tasks. GAN provides a novel concept for image-to-image transformation by means of adversarial learning. In recent years, numerous adversarial-learning-based methods have been proposed, and impressive results have been achieved. Related reviews have mainly focused on the basic GAN model and its general variants; in contrast, this survey aims to provide an overview of adversarial-learning-based methods by focusing on the image-to-image transformation scenario. First, a brief review of basic GAN is presented; next, the related approaches are roughly divided into adversarial style transfer and adversarial image restoration, e.g., super-resolution, image inpainting, and de-raining. The network architectures of generative models and loss functions are introduced and discussed in detail. Finally, we conclude the survey with an analysis of the trends and challenges.
An Efficient Bar Code Image Recognition Algorithm for Sorting System

In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images.It results in bar code recognition rate being low and real time being poor.Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed.First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image.Failure indicates that the compression ratio is improper or the image is skewed.Then, the source image is enhanced to identify the source image directly.Finally, the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image.The results of multitype image experiments show that the proposed method is improved by 5+ times computational efficiency compared with the former methods, and can recognize fuzzy images better.
SentiVec: Learning Sentiment-Context Vector via Kernel Optimization Function for Sentiment Analysis

Deep learning-based sentiment analysis (SA) methods have drawn more attention in recent years, which calls for more precise word embedding methods. This article proposes SentiVec, a kernel optimization function system for sentiment word embedding, which is based on two phases. The first phase is a supervised learning method, and the second phase consists of two unsupervised updating models, object-word-to-surrounding-words reward model (O2SR) and context-to-object-word reward model (C2OR). SentiVec is aimed at: 1) integrating the statistical information and sentiment orientation into sentiment word vectors and 2) propagating and updating the semantic information to all the word representations in a corpus. Extensive experimental results show that the optimal sentiment vectors successfully extract the features in terms of semantic and sentiment information, which makes it outperform the baseline methods on word similarity, word analogy, and SA tasks.
MetalGAN: Multi-domain label-less image synthesis using cGANs and meta-learning

Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain label-less problem and it is validated on facial attribute transfer, using CelebA dataset.
RF-Dial: Rigid Motion Tracking and Touch Gesture Detection for Interaction via RFID Tags

With the rising of demands for novel human-computer interaction approaches in the 2D plane, a number of intelligent devices come into being. For example, Microsoft Surface Dial supports simple clicks and rotations for the interaction with computer. However, these approaches are dedicated devices, and they might require batteries or have limited functions. In this paper, we propose RF-Dial to realize a light-weight, battery-free and functional 2D human-computer interaction solution via commercial off-the-shelf (COTS) passive RFID tags. What RF-Dial shines is that it can easily turn an ordinary object, e.g., a board eraser, into an intelligent interaction device. By deploying a tag array on the side face of the object together with a dipole tag on the top face, RF-Dial cannot only track the rigid motion of the object but also detect the touch gesture of a user on the surface of the object, including translation, rotation, click, press and hold, and swipe. To do the motion tracking, RF-Dial builds a phase-based model that captures the translation and the rotation of the tagged object simultaneously, by jointly exploiting the information of phase variations and the topology of the tag array. To detect the touch gesture, RF-Dial builds an RSSI-based model that uses the impact of the touching finger on the tag antenna’s impedance to estimate the touch position in real time, which is robust to environmental factors like position or orientation. We implemented a prototype of RF-Dial with commodity RFID devices. Extensive experiments show that RF-Dial achieves an accurate rigid motion tracking, with a small error of 0.6cm for the translation tracking, and a small error of 1.9 degrees for the rotation estimation. Besides, RF-Dial can also detect the touch gesture accurately, as the 90 percent of touch position errors are less than 2.09mm.
Constrained Oversampling: An Oversampling Approach to Reduce Noise Generation in Imbalanced Datasets With Class Overlapping

Imbalanced datasets are pervasive in classification tasks and would cause degradation of the performance of classifiers in predicting minority samples. Oversampling is effective in resolving the class imbalance problem. However, existing oversampling methods generally introduce noise examples into original datasets, especially when the datasets contain class overlapping regions. In this study, a new oversampling method named Constrained Oversampling is proposed to reduce noise generation in oversampling. This algorithm first extracts overlapping regions in the dataset. Then Ant Colony Optimization is applied to define the boundaries of minority regions. Third, oversampling under constraints is employed to synthesize new samples to get a balanced dataset. Our proposal distinguishes itself from other techniques by incorporating constraints in the oversampling process to inhibit noise generation. Experiments show that it outperforms various benchmark oversampling approaches. The explanation for the effectiveness of our method is given by studying the impact of class overlapping on imbalanced learning.
No boundaries: data exfiltration by third parties embedded on web pages

Abstract We investigate data exfiltration by third-party scripts directly embedded on web pages. Specifically, we study three attacks: misuse of browsers’ internal login managers, social data exfiltration, and whole-DOM exfiltration. Although the possibility of these attacks was well known, we provide the first empirical evidence based on measurements of 300,000 distinct web pages from 50,000 sites. We extend OpenWPM’s instrumentation to detect and precisely attribute these attacks to specific third-party scripts. Our analysis reveals invasive practices such as inserting invisible login forms to trigger autofilling of the saved user credentials, and reading and exfiltrating social network data when the user logs in via Facebook login. Further, we uncovered password, credit card, and health data leaks to third parties due to wholesale collection of the DOM. We discuss the lessons learned from the responses to the initial disclosure of our findings and fixes that were deployed by the websites, browser vendors, third-party libraries and privacy protection tools.