Reviewerly/exHarmony huggingface dataset:
Path-Following by a UAV-UGV Formation Using Null Space-Based Control

This work addresses the control of a formation composed by an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV), using the concept of control based on null space. The formation navigates in a coordinated manner, with the UAV following the UGV, maintaining a set pose relative to it, while the UGV follows a given path. The goal is to guide the UAV to land on the UGV at any desired time, a situation quite similar to returning the UAV to a mobile base after delivering a package. The null space-based control techniques is used to obtain more precise control of the relative posture between the robots, increasing the reliability of the landing operation. Experimental results are presented and discussed, which validate the proposed approach to accomplish the desired task.
STRUCTURAL &amp; DYNAMIC ANALYSES AND SIMULATION OF MOBILE TRANSPORTATION ROBOT

In this study, structural and dynamic analyses were applied to the mobile transportation robot to find the structural strength and to calculate the power and torque requirements. The mobile transportation robot was designed using CAD software as two modules; mecanum wheeled locomotion module and robotic arm module, respectively. The analyses were carried on the CAE Software using the static structural and rigid body dynamics tools. In the structural analysis, the weight of the robot and the additional load (98.1 N) were applied to the body of the robot. The material of the robot body was selected as Aluminum 6061 O sheet metal. In the dynamic analysis, the operation environment and conditions were simulated in the CAE software. The structural analysis results showed that the factor of safety value was obtained as 10.282 under applied loads and this value indicates that the chassis of mobile transportation robot is supposed to carry loads reaching up to 30 kg without any functional and mechanical problem. Furthermore, from the dynamic analysis, the total torque requirement of the mobile robot was about 16.56 Nm.
Synthetic Generation of Passive Infrared Motion Sensor Data Using a Game Engine

Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor's output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count.
Toward unbounded open-set recognition to say "I don't know" for glomerular multi-lesion classification

Glomeruli are histological structures located at the beginning of the nephrons in the kidney, having primary importance in diagnosing many renal diseases. Classifying glomerular lesions is time-consuming and requires experienced pathologists. Hence automatic classification methods can support pathologists in the diagnosis and decision-making scenarios. Recently most of state-of-the-art medical imaging classification methods have been based on deep-learning approaches, which are prone to return overconfident scores, even for out-of-distribution (OOD) inputs. Determining whether inputs are OOD samples is of underlying importance so as to ensure the safety and robustness of critical machine learning applications. Bare this in mind, we propose a unified framework comprised of unbounded open-set recognition and multi-lesion glomerular classification (membranous nephropathy, glomerular hypercellularity, and glomerular sclerosis). Our proposed framework classifies the input into in- or OOD data: If the sample is an OOD image, the input is disregarded, indicating that the model “doesn’t know” the class; otherwise, if the sample is classified as in-distribution, an uncertainty method based on Monte-Carlo dropout is used for multi-lesion classification. We explored an energy-based approach that allows open-set recognition without fine-tuning the in-distribution weights to specific OOD data. Ultimately, our results suggest that uncertainty estimation methods (Monte-Carlo dropout, test-time data augmentation, and ensemble) combined with energy scores slightly improved our open-set recognition for in-out classification. Our results also showed that this improvement was achieved without decreasing the 4-lesion classification performance, with an F1-score of 0.923. Toward an unbounded open-set glomerular multi-lesion recognition, the proposed method also kept a competitive performance.
Cycle-Consistent Adversarial Networks for Smoke Detection and Removal in Endoscopic Images

During endoscopic surgery, smoke removal is important and meaningful for increasing the visual quality of endoscopic images. However, unlike natural image dehaze, it is practical impossible to build a large paired endoscopic image training dataset with/without smoke. Therefore, in this paper, we propose a new approach, called Desmoke-CycleGAN, which combined detection and removal of smoke together, to improve the CycleGAN model for endoscopic image smoke removal. The detector can provide information about smoke locations and densities, which helps the GAN model to be more stable and efficient for smoke removal. Although some imperfections still exist, the experimental results have demonstrated that this method outperforms other state-of-the-art smoke removal approaches with unpaired real endoscopic images.Clinical Relevance— This can help improve the visibility in endoscopic surgery and to get smoke-free endoscopic images with better quality.
Hypergraph-Supervised Deep Subspace Clustering

Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays an essential role in regularizing the feature learning. However, the self-reconstruction adversely affects the discriminative feature learning of AE, thereby hampering the downstream subspace clustering. To address this issue, we propose a hypergraph-supervised reconstruction to replace the self-reconstruction. Specifically, instead of enforcing the decoder in the AE to merely reconstruct samples themselves, the hypergraph-supervised reconstruction encourages reconstructing samples according to their high-order neighborhood relations. By the back-propagation training, the hypergraph-supervised reconstruction cost enables the deep AE to capture the high-order structure information among samples, facilitating the discriminative feature learning and, thus, alleviating the adverse effect of the self-reconstruction cost. Compared to current DSC methods, relying on the self-reconstruction, our method has achieved consistent performance improvement on benchmark high-dimensional datasets.
Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users.
Gradient-Based Compact Binary Coding for Facial Expression Recognition

Facial Expressions are one of the most essential and general means for human beings to express their feelings and communicate their emotions; thus, Facial Expression Recognition (FER) has gained a significant research interest and an attractive topic in Human-Computer Interactions (HCI).Towards such, this paper proposes a novel face descriptor, Gradient Direction Pattern (GDP), for facial expression recognition.With the help of gradients, GDP encodes the structure of the facial image in a more compact way such that the FER is robust to pose variations.Furthermore, the GDPs are measured for edge feature maps, extracted from Gaussian filtered and Gabor filtered facial images at different orientations.These edge feature maps make the recognition system robust to noise, illumination, scaling, and orientational variations.Initially, the facial image is divided into small regions, and GDPs are extracted from them.Then these patterns are concatenated, and Histograms are measured, called GDP Histograms (GH).Simulation experiments were conducted over two standard datasets, such as CK+ and JAFFE, and the average accuracy is observed as 95% and 91% approximately.
Research on the Best Route Strategy Model of Rescue Patrol Based on Floyd Algorithm

The best route strategy is actually graph theory, and its core is the shortest path problem. Floyd algorithm is the classic algorithm for finding the shortest path. It has the advantages of simple, effective, compact structure, high planning efficiency, and is widely used. In this paper, a practical rescue patrol case is presented. The best rescue route is discussed from two cases, which are all known and partially known, and the Floyd algorithm is used to build the model to solve the problem. Finally, the corresponding best rescue route strategy is given. The results show that the best route strategy model of rescue patrol based on Floyd algorithm solves the practical problems and is an effective shortest path algorithm. At the same time, case analysis also provides theoretical reference and reference for fire safety relief, intelligent disaster prevention and mitigation, marine biological engineering and other fields, and has wide application value.
Plataforma digital para difusão dos acervos de arquitetura e design da FAUUSP

This article presents the collections of the Faculty of Architecture and Urbanism of the University of São Paulo and describes the results obtained from the implementation of an online interface for FAUUSP collections, developed with the Omeka S platform for sharing and collaborating with organized and structured data and information. As part of the discussion, it summarizes the historical moment in which the importance of the design and architecture archives, and collections is discussed, raising the history of the institution’s catalogue and covering issues involving their online dissemination. A special topic of metadata, specifically Dublin Core, the standard adopted by FAUUSP, is also presented. Results are commented and perspectives for the continuity of the platform are also discussed.
TasKar: A Research and Education Tool for Calculation and Representation of Binary Classification Performance Instruments

This study covers almost the ultimate set of binary-classification performance instruments derived from four dimensions of a confusion matrix, namely true positives/negatives and false positives/negatives and enhances their representation by establishing a meaningful interpretation of the dimensions. A common textual formatting scheme is provided to improve the readability and comprehensibility of performance instruments&#x2019; representation. A compact dashboard (named &#x2018;TasKar&#x2019;, the abbreviation of &#x2018;Tasnif Karnesi&#x2019;, &#x2018;Classification Report&#x2019; in Turkish) is developed and provided online to calculate and visualize a total of 52 performance instruments (27 measures, 23 metrics, and 2 indicators) by entering confusion matrix elements only. Taking parametric, variant, and recently proposed instruments the number covered becomes 65. Despite the limited approaches in confusion matrix visualization in the literature, three new graphics were devised to visualize true/false positive/negative rates (TPR, FPR, TNR, FNR), positive/negative predictive values (PPV, NPV), and false discovery/omission rates (FDR, FOR) performance metrics. It is expected that the proposed method and tool will be used by researchers in computation, interpretation, and standardized representation of classification performance as well as by teachers and students in machine learning education.
A Novel Track Association Method Based on Overlapping Clustering and Evidence Combination

Under the condition of dense tracks and unknown number of targets, how to achieve track association and improve the accuracy of association is still a topic worth studying in the field of information fusion. The traditional track association methods are basically to make pairwise association judgment on the track information. This process is complicated, and the judgment criteria are greatly influenced by human factors, which will interfere with the association results. Clustering algorithm has high efficiency when dealing with large data sets which can be used to solve the above problem. In this paper, a novel track association method based on overlapping clustering and evidence combination is proposed. The distance threshold is introduced to improve the non-exhaustive, overlapping k - means algorithm, and the algorithm is used to cluster the tracks, which realizes the adaptive change of the number of overlapping tracks. The different clustering results gained by adjusting the distance thresholds are converted into evidence, and the final track association result is obtained by using evidence combination. Some experiments demonstrate the effectiveness of the method proposed in this paper.
Psychosocial Features for Identifying Hate Speech in Social Media Text

This study uses natural language processing to identify hate speech in social media codeswitched text. It trains nine models and tests their predictiveness in recognizing hate speech in a 50k human-annotated dataset. The article proposes a novel hierarchical approach that leverages Latent Dirichlet Analysis to develop topic models that assist build a high-level Psychosocial feature set we call PDC. PDC organizes words into word families, which helps capture codeswitching during preprocessing for supervised learning models. Informed by the duplex theory of hate, the PDC features are based on a hate speech annotation framework. Frequency-based models employing the PDC feature on tweets from the 2012 and 2017 Kenyan presidential elections yielded an f-score of 83 percent (precision: 81 percent, recall: 85 percent) in recognizing hate speech. The study is notable because it publicly exposes a rich codeswitched dataset for comparative studies. Second, it describes how to create a novel PDC feature set to detect subtle types of hate speech hidden in codeswitched data that previous approaches could not detect.
Pengembangan e-Modul Interaktif Berbasis Android pada Mata Pelajaran Penerapan Rangkaian Elektronika Kelas XI Teknik Audio Vidio

Penelitian dan pengembangan ini bertujuan untuk menghasilkan produk yaitu e-modul interaktif berbasis android pada mata pelajaran Penerapan Rangkaian Elektronika dengan format APK yang dapat dijalankan pada smartphone android. E-modul interaktif berbasis android dirancang menggunakan software Smart Apps creator . Metode penelitian dan pengembangan e-modul interaktif berbasis android ini adalah Desigh and Development (D&amp;D) metode Richey and Klein yang terdiri dari tiga tahapan yaitu Planning (perancangan), Production (memproduksi), dan Evaluation (evaluasi). Hasil dari penelitian ini adalah (1) Validasi ahli materi I diperoleh nilai persentase 92% dan validasi ahli materi II diperoleh persentase 84% sehingga dikategorikan “Sangat Valid” untuk digunakan sebagai media pembelajaran. (2) Validasi ahli media I diperoleh hasil persentase 83,07% dan validasi ahli media II diperoleh 93,84% sehingga dikategorikan “Sangat Valid” untuk digunakan sebagai media pembelajaran. Kata kunci : Pengembangan, Media Pembelajaran, E-Modul, Android This research and development aims to produce the product, the namely is an android-based interactive e-module in the subject of aplication of electronics circuits with APK format that can be run on android smartphones. Android-based interactive e-module are designed using with Smart Apps Creator software. The research and development method of this android based interactive e-module is the Desigh and Development (D&amp;D) Richet and Klein method which consists from three stages Planning, Production, and Evaluation. The results from this research are : (1) The percentage value of validation from the first matery expert is 92% and the percentage value of validation from the secont matery expert is 84% thet could be categorized as “Highly Validation” to be used a learning media. (2) The percentage value of validation from the first media expert is 83,07% and the percentage value of validation from the secont media expert is 93,84% thet could be categorized as “Highly Validation” to be used a learning media. Keyword s : Development, Learning Media, E-modul, Android
Duygu Algılamaya Dayalı Evrişimli Sinir Ağı ile Küçük Resmi Seçimi

The use of video broadcasting platforms is increasing day by day. The competition for developing platforms for the broadcasting and sharing of movies and TV series is increasing. The purpose of reproducing these platforms is to increase the quality and to trace them on a single platform. Film and TV series platforms use artificial intelligence algorithms for these shares. The aim of this study is to create more attractive cover photos for users by finding suitable frames from a movie or TV series. First, the frames that were transformed into covers/small pictures on the platform were obtained. Unnecessary frames which consist of closed eyes, blurry frames, or faceless images have been removed. Also, deep learning is used to label images with objects and emotions based on the identity of the face. The thumbnails with the most repeating faces were selected by developing a face recognition model at each step. The experimental results showed that the emotion model was successful.
The Online Teaching Mode of College English in the Context of Gaussian Variant Genetic Algorithm

The current college English online teaching mode is mainly based on the traditional online MOOC teaching, which has some problems such as poor interaction. Under the mixed background, this paper studies the online college English teaching model based on the Gaussian mutation genetic algorithm and neural network algorithm. Firstly, it briefly introduces the general situation of network English teaching and the hybrid application of the Gaussian mutation genetic algorithm. Through the investigation and test analysis of students before and after class, the experiment evaluates students’ network teaching quality in many aspects. On this basis, a better teaching quality evaluation model is proposed. Finally, the practical application shows that the model in this paper is very feasible. In the end, students have higher enthusiasm and seriousness in the hybrid context of college English online teaching based on the dual algorithm. English teaching quality can make use of each student’s test scores in English classroom. This paper realizes the overall teaching through real-time dynamic tracking. Quantitative indicators are used to sort the influence degree of English classroom teaching indicators, which can effectively evaluate the quality of English classroom teaching.
Does gamification in virtual reality improve second language learning?

Previous works have shown the great potential of Virtual Reality (VR) in the area of Education. This paper studies if users can learn a second language when using a gamified VR application through an English learning test and how learning influences user satisfaction, sense of presence, cybersickness, and quality of experience through questionnaires. For this purpose, the VirtualeaRn game was developed. 20 Portuguese participants were exposed to the application, and the learning test was used before and after using the application. Result analysis shows an increase in learning results after using the VR gamified application, indicating the technology's efficacy in learning a second language. A positive user satisfaction, sense of presence, and quality of experience were also found. Some cases of cybersickness were reported. The outcomes are promising and provide enough information to show the potential of the gamification of VR technology for the area of learning a second language.
The Newspaper “Vestnik Leib-Gvardii” (1992–1997) as a Historical Source

This article examines the monthly newspaper "Vestnik Leib-Gvardii" as a historical source for the period 1992-1997 in the south of Russia.Issues of the monthly newspaper "Vestnik Leib-Gvardii," which was published in Sochi, Russian Federation, in the years 1992-1993 and again after a hiatus in 1996-1997, were this study's sources.A total of fourteen issues were published (five issues in 1992, from August to December; four in 1993, from January to April; one in 1996
Removal of Multiplicative Gamma Noise from Images via SRAD Model Amelioration

In this paper, an improved Speckle Reducing Anisotropic Diffusion (SRAD), destined to remove multiplicative gamma noise applied to different images is proposed. The basic idea is to divide the image into several riddled areas and then calculate the Equivalent Number of Look (ENL) of each region. The largest value of the ENL is the best optimal homogeneous region of the image. This optimal choice allows us to solve the major problem of the SRAD algorithm articulated around a visual choice of the homogeneous region which is not satisfactory and causes non-uniformity in this area. To give more validity to the proposed method, several experimentations were conducted using different kinds of images and were approved by some quantitative metrics like PSNR, SNR, VSNR, and SSIM. The computer simulation results confirm the efficiency of the proposed method which outperformances the classical SRAD method.