Reviewerly/exHarmony huggingface dataset:
Image semantic segmentation method based on improved ERFNet model

In order to solve the problems in the existing image semantic segmentation methods, such as the poor segmentation accuracy of small target object and the difficulty in segmentation of small target area, an image semantic segmentation method based on improved ERFNet model is proposed. Firstly, combining the asymmetric residual module and the weak bottleneck module, the ERFNet network model is improved to improve the running speed and reduce the loss of precision. Then, global pooling is used to fuse the feature channels after pyramid pooling to preserve more important feature information. Finally, the network model is implemented based on PyTorch deep learning framework, and the proposed method is demonstrated by experiments, in which the model retraining method is adopted to learn and train it. The experimental results show that the proposed method improves the segmentation ability of small-scale objects and reduces the possibility of misclassification. The average pixel accuracy (MPA) and average intersection merge ratio (MIOU) of the proposed method are higher than those of other contrast methods.
Conditions of localization and patterns of distribution of gold-poly-metal mineralization of the Dagkesaman deposits (Lesser Caucasus, Azerbaijan)

&#x0D; &#x0D; &#x0D; We studied the structural-morphological types and mineral composition of ore bodies, stages of ore development and conditions of development of the deposits. The research revealed significant elements of the corresponding conditions of localization of gold-polymetallic mineralization, the most characteristic being albitophyre subvolcanic bodies and steeply-dipping damages related to them. According to the structural-morphologic peculiarities, the ore bodies should be united under three categories: 1) simple vein; 2) complex vein; 3) veinlet-impregnation types. The positions of gold ore bodies and their morphological peculiarities were found to be determined by structural factors. Presence of small ruptures, differently-oriented systems of fractures and faults, which run across the area, gentle curves of these systems and other structural elements –all of this promotes gold ore development. There research determined aureoles of ore-containing altered rocks (serialization, chloritization, kaolization and calcinations, and limonitization from hypergenical zones) and presence of zonal structure. Gold-productive areas were identified as the quartz-pyrite association with disperse gold, and especially, the second mineral association where there is a notable close paragenetic connection between noticeable gold and chalcopyrite-galena parageneis of polymetallic stage. In the process of ore development, the physical-chemical conditions have presumably changed from average deep (quartz-pyrite-sericite) to shallow (quartz-galena- sphalerite-gold ore) and close-to-the-surface (quartz-chalcocite-hematite). Gold that occurs with all the mentioned minerals of this stage has developed in the temperature interval of 220–160 °C. The study found the ores to be complex, containing the following dditional elements: Fe, Cu, S, Bi, Zn, Pb, As, Se, Te. The main fossil component is gold. Industrially valuable are also cadmium, zink, copper and silver, found in the ores. We found the following morphological types of grains of native gold: homogenous, zonal, mono-granular, spotted, which are of great significance for concluding on genesis of the deposits, prognoses, explorations and assessments.&#x0D; &#x0D; &#x0D;
Validation of Meteonorm 8 for energy estimation of Solar Power Plants in Sri Lanka, Using PVsyst Software

The study presents a validation of using MT 8 (Meteonorm 8) international weather data provider's data for energy forecasting of photovoltaic energy. The study has used PVsyst software for a 1 MW DC photovoltaic solar power station in the Anuradhapura region, Sri Lanka, as a case study. A custom-built weather station situated at the solar power station premises has gathered weather data such as global horizontal irradiance (GHI), ambient temperature, module temperature, wind speed, wind direction, and rain data for six months. These weather station data are used as the inputs in PV syst software for the PV energy forecasting and validating the model. Also, the study has used weather data of six months of MT8. According to the validated model, two different energy yield forecasting was done for both types of weather data, and a comparison is made with the actual PV energy generation of the solar power station with the simulation done using MT8 weather data. The simulation-based on MT8 shows a monthly generation error from −6.0% to 14.2% compared to the actual energy generation of the power station.
A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.
STellaR – A Stationary Telepresence Counselling System for Collaborative Work on Paper Documents

This paper gives an overview of the ongoing project STellaR – A stationary telepresence counselling system for collaborative work on paper documents. The system consists of dedicated rooms for video counselling, which clients can use to connect to a remotely located counselor without requiring any knowledge about computers or the internet. STellaR rooms are equipped with a large monitor, high quality microphone, camera, and sound system, which represent the counselor in life size.
An Early Rico Retrospective: Three Years of Uses for a Mobile App Dataset

The Rico dataset, containing design data from more than 9.7 k Android apps spanning \(27\) categories, was released in 2017. It exposes visual, textual, structural, and interactive design properties of more than 72 k unique UI screens. Over the years since its release, the original paper has been cited nearly 100 times according to Google Scholar and the dataset has been used as the basis for numerous research projects. In this chapter, we describe the creation of Rico using a system that combined crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. We then describe two projects that we conducted using the dataset: the training of an autoencoder to identify similarity between UI designs, and an exploration of the use of Google’s Material Design within the dataset using machine learned models. We conclude with an overview of other work that has used Rico to understand our mobile UI world and build data-driven models that assist users, designers, and developers.
A Generative Adversarial Network Structure for Learning with Small Numerical Data Sets

In recent years, generative adversarial networks (GANs) have been proposed to generate simulated images, and some works of literature have applied GAN to the analysis of numerical data in many fields, such as the prediction of building energy consumption and the prediction and identification of liver cancer stages. However, these studies are based on sufficient data volume. In the current era of globalization, the demand for rapid decision-making is increasing, but the data available in a short period of time is scarce. As a result, machine learning may not provide precise results. Obtaining more information from a small number of samples has become an important issue. Therefore, this study aimed to modify the generative adversarial network structure for learning with small numerical datasets, starting with the Wasserstein GAN (WGAN) as the GAN architecture, and using mega-trend-diffusion (MTD) to limit the bound of virtual samples that the GAN generates. The model verification of our proposed structure was conducted with two datasets in the UC Irvine Machine Learning Repository, and the performance was evaluated using three criteria: accuracy, standard deviation, and p-value. The experiment result shows that, using this improved GAN architecture (WGAN_MTD), small sample data can also be used to generate virtual samples that are similar to real samples through GAN.
Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access

Representing a sparse histogram, or more generally a sparse vector, is a fundamental task in differential privacy. An ideal solution would use space close to information-theoretical lower bounds, have an error distribution that depends optimally on the desired privacy level, and allow fast random access to entries in the vector. However, existing approaches have only achieved two of these three goals. In this paper we introduce the Approximate Laplace Projection (ALP) mechanism for approximating k-sparse vectors. This mechanism is shown to simultaneously have information-theoretically optimal space (up to constant factors), fast access to vector entries, and error of the same magnitude as the Laplace-mechanism applied to dense vectors. A key new technique is a unary representation of small integers, which we show to be robust against "randomized response'' noise. This representation is combined with hashing, in the spirit of Bloom filters, to obtain a space-efficient, differentially private representation. Our theoretical performance bounds are complemented by simulations which show that the constant factors on the main performance parameters are quite small, suggesting practicality of the technique.
Span Labeling Approach for Vietnamese and Chinese Word Segmentation

In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SpanSeg. We compare the span labeling approach with the conditional random field by using encoders with the same architecture. Since Vietnamese and Chinese have similar linguistic phenomena, we evaluated the proposed method on the Vietnamese treebank benchmark dataset and five Chinese benchmark datasets. Through our experimental results, the proposed approach SpanSeg achieves higher performance than the sequence tagging approach with the state-of-the-art F-score of 98.31% on the Vietnamese treebank benchmark, when they both apply the contextual pre-trained language model XLM-RoBERTa and the predicted word boundary information. Besides, we do fine-tuning experiments for the span labeling approach on BERT and ZEN pre-trained language model for Chinese with fewer parameters, faster inference time, and competitive or higher F-scores than the previous state-of-the-art approach, word segmentation with word-hood memory networks, on five Chinese benchmarks.
Vicinal Counting Networks

We tackle the task of Few-Shot Counting. Given an image containing multiple objects of a novel visual category and few exemplar bounding boxes depicting the visual category of interest, we want to count all of the instances of the desired visual category in the image. A key challenge in building an accurate few-shot visual counter is the scarcity of annotated training data due to the laborious effort needed for collecting and annotating the data. To address this challenge, we propose Vicinal Counting Networks, which learn to augment the existing training data along with learning to count. A Vicinal Counting Network consists of a generator and a counting network. The generator takes as input an image along with a random noise vector and generates an augmented version of the input image. The counting network learns to count the objects in the original and augmented images. The training signal for the generator comes from the counting loss of the counting network, and the generator aims to synthesize images which result in a small counting loss. Unlike GANs which are trained in an adversarial setting, Vicinal Counting Networks are trained in a cooperative setting where the generator aims to help the counting network in achieving accurate predictions on the synthesized images. We also show that our proposed data augmentation framework can be extended to other counting tasks like crowd counting.
Effect of body-gender transfer in virtual reality on the perception of sexual harassment

While sexual harassments are inappropriate behaviors in the society, the interpretation of and sensitivity toward sexual harassment can vary by individual. Differences across individuals, such as gender, may influence whether one interprets an action to be sexually harassing or not. Virtual reality technology enables human behavior assessment without interfacing with physical danger. The present work examined whether gender and body-gender transfer in VR influenced the perception of sexually harassing behaviors, and explored the utility of emerging technology in increasing one’s awareness of behaviors that may be considered sexually harassing. Participants (n=12) embodied in virtual characters of different genders and experienced seven sexually harassing scenarios in an immersive virtual environment in random order. In general, participants provided higher rating to the sensitivity toward sexual harassment in the VR harassment scenarios than those scenarios described on paper. There was an increase in participants’ sensitivity toward sexual harassment after experiencing sexual harassment scenarios from the perspective of the victim in VR. Participants perceived higher level of sexual harassment when they embodied in female avatars, which suggested there was an effect of VR with body-gender transfer on perception of sexual harassment. There were gender differences in awareness of harassing behaviors in VR environment, and VR may be a training method to narrow gender gap and increase awareness toward sexual harassment.
Adaptive Denoising and Alignment Agents for Infrared Imaging

Clinical infrared imagers, such as those used to visualize subcutaneous vasculature, rely on the image-projection feedback loops. Specifically, these instruments embed model-based feedback algorithms to process the acquired ’invisible’ infrared data streams and then to project a visible copy of them back to the imaged region (e.g., the skin of the patient’s forearm). Being inherently noisy, the infrared frames and their projections are prone to misalignment, demanding frequent instrument tuning and recalibration. To address this challenge, we propose to reconsider the feedback loop entailed in such imagers from the standpoint of multi-agent deep learning. Both proposed agents – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Denoiser</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aligner</i> – are adaptive in that they continuously optimize the corresponding target value functions. Namely, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Denoiser</i> learns the proper frequency decomposition of the acquired infrared data until the target segmentation metric is maximized; whereas, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aligner</i> learns the intensity fluctuations within the segmentation mask tuned by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Denoiser</i> until its maximal overlap with the source infrared image. The idea is validated synthetically on a benchmark dataset and experimentally on a bench-top vein scanner in the lab, with the duet of agents proving efficient in handling the distortions and the misalignment.
Tracking CS graduates and non-retained students to the following year's academic programs

research-article Share on Tracking CS graduates and non-retained students to the following year's academic programs Authors: Cara Tang Portland Community College Portland Community CollegeView Profile , Cindy S. Tucker Bluegrass Community and Technical Collage Bluegrass Community and Technical CollageView Profile , Mark Allen Weiss Florida International University Florida International UniversityView Profile , Stuart H. Zweben The Ohio State University The Ohio State UniversityView Profile Authors Info & Claims ACM InroadsVolume 12Issue 4December 2021 pp 45–57https://doi.org/10.1145/3494575Published:15 November 2021Publication History 0citation70DownloadsMetricsTotal Citations0Total Downloads70Last 12 Months70Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Development of a Bahraini job seeking based web portal for UOB-IS graduates

The discipline of information systems (IS) at the university of Bahrain (UOB) is characterized by its dynamicity and diversity in terms of the employment of diverse and up to date information technologies to achieve the strategic goals and dynamic operational objectives of organizations. However, the UOB-IS graduates and IS job employers alongside face difficulties in job seeking/provision. Seekers hardly find the best matching job offerings to their degree qualifications and skills, while employers hardly find the best candidates for the required job positions. This project is aimed at developing a Bahraini web-based job offering portal (J4IS) that acts as an automated recruiter for the job seekers and employers to facilitate an efficient and professional medium of communication and time and cost reduction tool to seek/provide suitable jobs for both sides. The theoretical framework is based on a systematic literature review (SLR) and web content analysis (CA) techniques. For the portal's application development, a six phased system development life cycle (SDLC) methodology was employed at which three questionnaires, two of which were conducted for collecting user and system requirements, while the third was for the web portal's evaluation. The research outcomes recognized the nature of the Bahraini workforce and the qualifications and skills of the UOB-IS graduates, the shortcomings of the commercial online job portals and the related reasons, and subsequently informed the user (seekers and employers) requirements of the developed “J4IS” which revealed a 94% of usability acceptance.
Model-based configuration of access protection units for multicore processors in embedded systems

Multiprocessor system-on-chip platforms are becoming increasingly common in embedded systems. To facilitate a logical isolation of physically connected on-chip components, internal communication links of such platforms are often equipped with dedicated access protection units. When performed manually, the configuration of these units is both time-consuming and error-prone. Therefore, we present a model-based design methodology that allows designers to describe envisaged communication link transactions as well as the desired information flow policy in a platform-independent manner. After automatically verifying the consistency between specified transactions and the declared policy, the proposed toolchain delegates model instances to an extensible generation framework. This framework translates the envisaged transactions into configuration code for access protection units of particular platforms. We give a formal description of both the model and the verification procedure, describe the platform-specific generation framework, and evaluate the approach by applying it to an example scenario from the field of autonomous driving.
A Transfer Learning-Based Multi-cues Multi-scale Spatial–Temporal Modeling for Effective Video-Based Crowd Counting and Density Estimation Using a Single-Column 2D-Atrous Net

Crowd count and density estimation (CCDE) is an emerging research area and is a useful tool for crowd analysis and behavior modeling. The existing video-based CCDE approaches utilize spatial–temporal modeling for the CCDE. However, these approaches fail to address some of the major issues, such as scale variation because of perspective distortion in the frame and volume of frames and minimization of background influence during spatial–temporal modeling. To attain these issues, we are motivated to design a transfer learning-based multi-cues multi-scale spatial–temporal modeling for video-based CCDE. The proposed model utilizes a pre-trained Inception-V3 to extract multi-scale features for four different video frames cues, such as color frame, the foreground map of the frame, volume of the frame, and volume of foreground maps. The foreground maps are obtained by the Gaussian mixture model. The extracted multi-cue multi-scale features are then concatenated and fed into a single-column 2D-Atrous-Net. The 2D-Atrous-Net estimates the crowd density by regression on the ground-truth density maps. The experiments are conducted on two datasets, namely the Mall and Venice. The model outperforms the state-of-the-art techniques and yields an effective CCDE model by achieving better MAE and RMSE.
MiXiM

In this paper we present MiXiM, a simulation framework for mixnets that allows researchers to evaluate different design options and their tradeoffs. This framework is flexible and allows to quickly run experiments to assess combinations of mixnet building blocks, such as mixing strategies and network topologies, as well as study the effect of different parameters related to each component. The framework provides results for a number of metrics including anonymity, end-to-end latency and traffic overhead.