Reviewerly/exHarmony huggingface dataset:
Towards identifying emotional human behavior in online classes: first steps : Human emotional behavior in a virtual class

Emotional management is very important in face-to-face classes, in fact, it is the very basis of learning. However, there is still no clear answer to what happens when classes are online or on Moocs, referring to emotional reactions. In this work we present an initial proposal that allows obtaining the emotional behavior of the student when observing a class. The proposed methodology allows identifying the emotions on the face, grouping them, and then sending them to the ontological reasoner. Few methodological tests have been developed, but we hope that the proposal is valid and conclusive
Joint Learning-Based Anomaly Detection on KPI Data

Unsupervised anomaly detection on KPI (Key Performance Indicator) is an important research problem that has broad industrial applications. The dynamic change and infinity of KPI data make it a challenging problem to estimate the outlierness of KPI data. Existing methods detect anomalies only from a single perspective, which fails to capture the dynamic change of KPI data, leading to unsatisfying performance. In this paper, we propose a Joint Learning-based Anomaly Detection algorithm (JLAD) which integrates a bias-based model and a similarity-based model from two perspectives at the same time. Specifically, the similarity-based model motivates a data driven abnormal ratio setting which automatically obtains abnormal ratio for bias-based model to avoid manual setting. In turn, based on the bias-based model, we develop an automatic anomaly templates adding method that adds anomaly templates for similarity-based model to timely improve the prior knowledge. Experiments on 13 public KPI datasets empirically confirm the superiority of our algorithm with a F1-Score improvement up to 20.5% on average.
PreFace: Faceted Retrieval of Prerequisites Using Domain-Specific Knowledge Bases

While learning new technical material, a user faces difficulty encountering new concepts for which she does not have the necessary prerequisite knowledge. Determining the right set of prerequisites is challenging because it involves multiple searches on the web. Although a number of techniques have been proposed to retrieve prerequisites, none of them consider grouping prerequisites into interesting facets. To address this issue, we have developed a system called PreFace that (i) automatically determines interesting facets for a given concept of interest, and, (ii) determines prerequisites for the concept and facet. The key component of PreFace is a retrieval model that balances the trade-off between the relevance of the facets and their diversity. We achieve this by representing each facet as a language model estimated using a domain-specific knowledge base and a large corpus of research papers, and ranking them using a risk-minimization framework. Our evaluation of the results over a benchmark set of queries shows that PreFace retrieves better facets and prerequisites than state-of-the-art facet extraction techniques.
Validation of Software Requirements Specifications by Students

Context: Requirements validation is an important activity in software development, but frequently neglected in computing courses. This paper describes a study where requirements specifications produced by students are validated by their peers.Objective: To evaluate how well students perform the validation of requirements specifications.Methods: A correlational study.Results: Group’ grades correlate with teachers’ marks for a level of significance of 5%.Conclusion: Students reviews can be used for peer assessment, but with some care.
Enabling Drones Collaboration in ITS Applications Using a BDI Architecture Based on a 5-Dimensional Social Model

Thanks to their capabilities to track objects and events of interest as well as their capabilities to provide unique real-time views of road sections, drones are being increasingly used in Intelligent Transportation System (ITS) applications. To deal with the highly complex ITS tasks, several approaches are allowing these drones to collaborate and ultimately achieve goals beyond their individual capabilities. Moreover, recent approaches are highlighting the need to empower drones with intelligence and autonomy, particularly to enable them react more efficiently to changes in the commonly open and highly dynamic ITS environments. Intelligence and autonomy will also enable drones optimize the use of their limited onboard resources. Within this perspective, we are proposing in this paper an intelligent drone collaboration approach using the Multi-Agent System paradigm. Based on a new generic Belief-Intension-Desire (BDI) architecture, our approach implements a five-dimension social model that mimics humans’ reasoning and interactions. In contrast with the existing approaches, our solution enables drones to switch between selfish and collaborative behaviors. To demonstrate some benefits of our BDI architecture, we run simulations of two types of collaboration approaches; namely Collaboration based on Centralized Planning and Collaboration based on Participatory Planning. Our simulations are run to manage road traffic in the advent of an emergency situation like the COVID-19 pandemic.
Type Inference of Simple Recursive Functions in Scala

Scala is a well-established multi-paradigm programming language known for its terseness that includes advanced type inference features. Unfortunately this type inferring algorithm does not support typing of recursive functions. This is both against the original design philosophies of Scala and puts an unnecessary burden on the programmer. In this paper we propose a method to compute the return types for simple recursive functions in Scala. We make a heuristic assumption on the return type based on the non-recursive execution branches and provide a proof of this method's correctness. The algorithm does not have a significant effect on the compilation speed. We implemented our method as an extension prototype in the Scala compiler and used it to successfully test our method on various examples. The compiler extension prototype is available for further tests.
Degraded image restoration based on quadtree decomposition in scattering media

Image restoration has attracted the attention of many scientists due to the image is degraded by the bad weather (such as haze, smog, fog). Clear images provide a means for security surveillance, remote sensing and various military application to understand objective facts. Many dehaze methods have been proposed by the experts for image restoration, especially for image dehaze. The dark channel prior dehaze method is a typical image restoration method based on atmospheric physical model. This method is a kind of statistics of outdoor haze-free images, and it is a simple but effective remove haze from a single input image. However, this method fails to restore the sky region of degraded image, and it has a high computational cost associated with soft matting algorithm. To overcome these problems, we propose an image restoration method based on quadtree decomposition to restore the images degraded by scattering media. The proposed method uses the quadtree decomposition to find the sky region for the atmospheric light estimation. The transmission of sky region is improved by the proposed method to obtain an accurate global transmission of degraded image. The degraded image can be quickly restored by our proposed method without halo effect or color distortion. The proposed method will be helpful to the security surveillance, remote sensing and various military application et al.
Single Fog Image Dehazing via Truncated Total Variation Method

Existing dehazing methods are usually to appear visual problems. In the paper, we put forward a truncated total variation method (TTV) to eliminate haze. A histogram analysis is firstly developed to obtain global atmospheric light. Then, using an adaptive boundary constraint TTV to optimize the transmission properly. Finally, a new DCP is presented to remove haze. Shown in experimental results, our method can outperform existent methods on the visual effect.
Depth-Adaptive Discriminant Projection with Optimal Transport

Discriminant projection is a key technique for dimensionality reduction, especially in many classification tasks of high-dimensional, small-sample datasets. In this paper, we propose a Depth-Adaptive Discriminant Projection (DADP) method to improve the discriminability of the projection subspace. First, we propose a novel single-layer discriminant projection by adjusting projected points with nonlinear transformation. Second, the single-layer structure is extended to multiple-layer ones according to the proposed Adaptive Depth Determination Criterion (ADDC), naturally constructing a deep projection model with adaptive depth, which makes DADP allow for the avoidance of over-fitting problem for small-scale datasets. Furthermore, the regularized optimal transport (OT) is introduced to learn the optimal weights for pairwise data, which balances the local and global information incorporated in scatter estimation. Experimental results on several high-dimensional, small-scale datasets show the effectiveness of our algorithm both in terms of visualization and classification prediction.
Effective Smart Sensors Based Cognitive Infocommunications Weight Loss System

Weight loss is one of the most common health problems nowadays. It can affect any personal health and lifestyle. It often happens that we meet people who try many diet and workout programs to lose their overweight and be in shape, but they keep failing to reach their goal. One of the causes for that failure is that most of that diet and exercise programs are general and not designed for that specific person. Although some of these programs are built to fit everybody, we need to create a training program and diet for each person, depending on his body type and his genetic information. This paper uses cognitive infocomunication to introduce an effective weight loss system. The system tries to increase the human brain's ability to understand a person's body characteristic, and develop a typical weight loss plan for that person. It uses the inter-cognitive communication mode of communication where data will be transferred from a set of sensors connected to the human body to a software program that runs on a smartphone. The data will give a better understanding of the weight loss factors and result the best mixture fit of diet and exercise program, which can provide the best result for that body.
The Research of the control system of the intelligent vacuum robot

Abstract In this paper, a household intelligent vacuum robot based on STC89C52 is researched. The hardware composition and working principle of the system are introduced, and the program and flow chart are given. After continuous study and discussion, the general research, circuit research, physical welding and system debugging of the whole system are completed. In this paper, the home intelligent dust collection robot is researched to detect the obstacles along the way through e18.d80nk infrared obstacle avoidance sensor, and transmit the detected signals to the single-chip microcomputer for discrimination. The single-chip microcomputer then controls the motor steering through the L298N driving module to achieve the obstacle avoidance function, and carries out the dust collection through the bd6081 fan circuit when traveling. After the completion of the research, it can basically achieve the functions of quick response, obstacle avoidance and dust collection of household intelligent cleaner.
RGB-topography and X-rays image registration for idiopathic scoliosis children patient follow-up

Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient’s health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of such systems is a set of augmented images and scoliosis related angles. However, these angles, confuse the physicians due to their large number. Moreover, the lack of X-rays scans makes it impossible for the physician to compare RGB and X-rays images, and decide whether to reduce X-rays exposure or not. In this work, we exploit both RGB images of scoliosis captured during clinical diagnosis, and X-rays hard copies provided by patients in order to register both images and give a rich comparison of diagnoses. The work consists in, first, establishing the monomodal (RGB topography of the back) and multimodal (RGB and Xrays) image database, then registering images based on patient landmarks, and finally blending registered images for a visual analysis and follow up by the physician. Proposed registration is based on a rigid transformation that preserves the topology of the patient’s back. Parameters of the rigid transformation are estimated using a proposed angle minimization of Cervical vertebra 7, and Posterior Superior Iliac Spine landmarks of a source and target diagnoses. Experiments conducted on the constructed database show a better monomodal and multimodal registration using our proposed method compared to registration using an Equation System Solving based registration.
Optimization of Low Illumination Image Enhancement Algorithm Based on Dark Channel Prior

The dark channel prior algorithm has achieved a certain effect in the enhancement of low-illuminance images, but this method has a large calculation amount and high memory requirements, and the dark channel calculation is not accurate enough. In this paper, based on the dark channel prior theory, by performing edge detection on the brightness channel of the image to generate a mask, the edge area of the object in the image and the large area flat area are processed separately. Using this method, it is possible to restore the basic method that is basically consistent with the original method or even more accurate. As a result, it can greatly reduce consumption and increase the calculation speed.
Abrupt Shot Change Detection using Midhinge Local Binary Pattern

Video shot change detection is an extremely important task for video analysis and is considered as the preliminary step for effective video summarization, browsing and retrieval. In this work, a new texture based descriptor has been proposed using Midhinge Local Binary Pattern (MHLBP) concept for identification of abrupt transition in videos. The discrimination capability of MHLBP is better than basic LBP and its versions (mean LBP and midrange LBP). MHLBP histogram is constructed by applying midhinge statistics on each mask of video frame. The distance between histogram features of the adjacent frames are evaluated using Euclidean distance. The obtained distance values are subjected to adaptive thresh-old mechanism for identifying of abrupt shot changes in a video. The planned framework is tested on a subset of TRECVID 2001 dataset. The result shows that the proposed method outperforms with other existing shot change detection algorithms in terms of precision, recall and F-measures.
Monitoring distributed computing beyond the traditional time-series histogram

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.
Texture feature analysis for the liver cancer diseases using statistical based feature extraction technique

Feature extraction is one of the important steps in image processing because the accuracy of the system would completely depend on this step. Human soft tissues are diagnosed by a diverse set of image scanning techniques namely Sonographer, Tomography and Magnetic resonance imaging (MRI). All these Imaging techniques are applied depending on the nature of the disease and type of organ. Texture analysis of the liver can be used for classifying the liver into normal and diseased. The texture is a fusion of repeated patterns that have regular or irregular frequency [5]. Texture visualization feature helps in the classification of the disease. Different textural analysis techniques have been developed for extracting texture features of the liver for classification such as structure base, statistical-based, model-based, transform-based and further classification is done by using single classifier or combination of classifiers. Here, the Microscopic images of the liver are used to classify it into the normal liver and diseased liver. The texture features are extracted using the 1st order and 2nd order feature extraction techniques. The texture analysis helps in classifying the liver cancer into benign or malignant.
Multi-scale Object Detection in Optical Remote Sensing Images Using Atrous Feature Pyramid Network

Multi-scale feature representations are widely exploited by both the one-stage object detectors and the two-stage object detectors in recent years. Object instances in optical remote sensing images change greatly in scale, orientation, and shape. Therefore, the detection of multi-scale objects, especially densely small objects, is a challenging problem in optical remote sensing images. To alleviate the problem arising from scale variation across object instances, we propose an Atrous Feature Pyramid Network (AFPN) to construct more effective feature pyramids network. The proposed network is an enhanced Faster R-CNN with a novel feature fusing module called Atrous Feature Fusing Module (AFFM). The multi-level features generated by the feature fusion module will be fed to detectors respectively to predict the detection results. The proposed network can not only detect objects more accurately in optical remote sensing images, but also improve the detection accuracy of natural images. Our network can achieve 96.89% mAP on NWPU VHR-10 dataset, 96.75% on RSOD dataset, which ensures the generalization ability of the method on larger and harder datasets.
Non-contextual Long-Term Person Re-ID Under Sudden Illumination Conditions

Person Re-ID (Re-ID) has been an emerging topic and there is a need for non-contextual, long term person Re-ID algorithm since most of the crimes occur in public places such as airports, railway stations where the video is recorded for a long duration across arbitrary camera views. The proposed work comprises of two fusion frameworks. First, the people detection is accomplished by fusing Histogram of Oriented Gradients (HOG) and extended Center Symmetric Local Binary Pattern (XCS-LBP) features which overcomes several disadvantages like missing detection and false detection due to sudden illumination, and near/far field of view changes. Next, each body part of the tracked person is learned using Deformable Part Model (DPM) which is robust to different viewpoints. Secondly, the feature level fusion of Gabor (appearance feature) and Skeleton Recurrent Motion Image (SRMI Gait biometric feature) is proposed to overcome the homogeneity issue of distinguishing people when both the texture and color of the people attire are similar. Finally, person Re-ID is achieved by relevance metric learning method with list wise constraints (RMLLCs). The performance measure, Cumulative Matching Curve (CMC) Rate shows the improved matching accuracy compared to other state-of-the-art algorithms with benchmark datasets.
Occupation-related Information Seeking Behaviour Models: A Comparative Study

The paper shows how different factors influence the information needs of professionals. The study investigates information needs and occupational-related information seeking behaviour models. Various occupational-related information behaviour models are studied and relevant qualitative research method is adopted. Five occupational seeking behaviour models have been analysed and the testing groups identified. The study summarises that each model characterizes a different but also similar approach to occupational information-seeking behaviour research.
An automatic learning for re-ranking in social information retrieval

A Social Information Retrieval (SIR) result re-ranking system offers several techniques and methods that meet the need of the user to express through a given query and return a re-ranked list of returned results. This list should contains the information relevant to the top ranking since the majority of these users are only interested in the first results displayed. However, optimizing the returned results ranking becomes a fundamental problem when searching for tweets. In this regard, this paper presents a re-ranking model of social retrieval results based on automatic learning. Indeed, this model can re-rank the list of results obtained by the SIR system based on adaptive features given query category. Also, our model devises queries into categories and learn the representative features of each. More precisely, in order to reorder the result obtained from a given query, our model allows recalculating a new relevance score for each corresponding tweet. To validate this proposed approach, the experiments were carried out based on queries from different categories. The results obtained from his experiments show the effectiveness of our model compared to classic ranking approaches.
Characterizing Human Behaviours Using Statistical Motion Descriptor

Identifying human behaviors is a challenging research problem due to the complexity and variation of appearances and postures, the variation of camera settings, and view angles.In this paper, we try to address the problem of human behavior identification by introducing a novel motion descriptor based on statistical features.The method first divide the video into N number of temporal segments.Then for each segment, we compute dense optical flow, which provides instantaneous velocity information for all the pixels.We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32 bins.We then compute statistical features from the obtained HOOF forming a descriptor vector of 192dimensions.We then train a non-linear multi-class SVM that classify different human behaviors with the accuracy of 72.1%.We evaluate our method by using publicly available human action data set.Experimental results shows that our proposed method out performs state of the art methods.
The Use of the Cloud Platform to Register and Perform Intelligent Analysis of Energy Consumption Parameters in the Service and Industrial Sectors

This communication describes the developed web system by which companies of the industrial and service sectors can access their energy consumption data (electricity, gas, and water) and perform energy efficiency analysis. Data is collected using measuring devices installed in the clients’ premises and transmitted to the analysis platform. Each client of this service, after being authenticated, can access and consult his historical consumption data, displayed in the form of graphs and personalized reports. The user can obtain consumption performance patterns through advanced data analysis.