Reviewerly/exHarmony huggingface dataset:
A Study on the Professors’ Experience on Application to Learner Centered Teaching-Learning Methods Through CTL Support at A University

Objectives The purpose of this study was to understand the motivation, the process of participation, and the perceptions of participation results of the professors who design and develop, implement the learner-centered method in their undergraduate classes with the support of CTL program. Based on the results, it also purposed to draw effective strategy for the CTL program.&#x0D; Methods Semi-structured interviews were conducted with six professors, the study participants. All interviews were recorded and transcribed. Qualitative data were analyzed with constant comparison method doing open coding, categorizing, reviewing categories. For trustworthiness of the study, triangulation using interview data with a CTL researcher and program related documents and reports, member checking, and peer review were applied.&#x0D; Results A total of 27 categories were derived in the areas of the participants’ motivation, and the participation process and result. The participants participated in accordance with the school policy, incentive, and willingness to improve classes. In order to understand and apply learner-centered teaching-learning methods, they thought and agonized a lot along with self-directed learning. They faced various problems and solved them, and paid a lot of attention to classes for the good results and reports in mind. They experienced the effects and problems of the CTL services. They felt development of as a professor, experienced sophisticated teaching-learning methods, and participated responsibly thanks to CTL support.&#x0D; Conclusions In light of the previous studies, the experiences and perceptions of the participants were discussed. Implications were presented for CTL to support the professors’ design, development and implementation of learner-centered method effectively.
Vikaspur Knowledge Centre: Micro-Politics in a Community-Based Organization

The knowledge centres (KC) established by Development Research Foundation (DRF) were part of an ambitious project to disseminate valuable information to the rural masses. The KCs purpose was to bridge the digital divide, which was supposed to be run by the local community. The Vikaspur KC initiative had a lot of potentials, but due to local politics and conflict among a few stakeholders, it faced many problems. The differences among the volunteers were the core issues that ultimately led to the lockout of the KC. There was a lack of proper managerial structure at the ground level. The local village dynamics also influenced this issue. The political structures and dynamics also influenced the conflict at the Vikaspur KC at the micro-level (village level) and the meso-level (provincial level).
IoT human needs inside compact house

Indonesia is entering Industry 4.0, but is still not using supportive technology, especially in residential areas. A compact house is one of the dwellings built on limited modern land, providing flexible use of space according to the activities carried out by residents. The current version of this type of residence, namely smart homes, is still far from what is expected. There is a need for the modern use of IoT in every room, which can adapt to the activities of residents to make it easier for residents to move. A grounded theory and descriptive approach was used to find the continuity of modern Industry 4.0 and IoT technology in smart homes with the compact house concept to find the novelty of design standards. The findings are in the form of lighting that can be adjusted via IoT to reduce the crime rates in abandoned homes or in homes that are not active because the residents are traveling.
Semantic Matchmaking for Argumentative Intelligence in Ubiquitous Computing

The Semantic Web of Things enables the exchange of knowledge fragments through machine-to-machine interactions, supporting collaborative decision-making among ubiquitous smart objects. Available methodologies, however, have often limited generality of applications and interpretability of results. This paper introduces early work on a novel structured argumentation approach, integrating Dung-style abstract argumentation with Description Logics reasoning. A semantic matchmaking scheme, exploiting non-standard, non-monotonic inferences, allows the appraisal of argument relations. This enables the automatic evaluation of an argumentative graph with a graded acceptability ranking of arguments and a formal explanation. The proposal is general-purpose, but oriented toward SWoT multi-agent systems, as illustrated in a vehicular network case study.
Visual analytics for diagnosing spatial interpolation models

Abstract The development and widespread use of statistical learning models have brought the need for tools that help analysts diagnose, build, and refine those models. In this work, in particular, we focus on interpolation models, which spatially predict the value of a variable based on the values of its neighborhood. Investigating these results spatially or comparing them with other models at different levels of granularity is still a challenge for the analysts trying to understand and refine their models. To deal with that, we propose a visual analytics model‐agnostic tool for facilitating the comparison and refinement of spatial models at different levels of granularity using interactive visualization techniques. The tool was built in collaboration with specialists who used it to diagnose and improve a spatial model for predicting residential real estate prices.
The Radical Strategy Concerning Treating Our Encountered World View

Total nonuniversality" means the nonuniversal nature of sensible content and the institute of conceptuality, taken together.It can bravely be stated that this is the message most difficult for humans.The message of total nonuniversity is opposed to our basic attitude.There is a need for a "place", to which the main elements of our encountered world view can be attached.I call this place the original environment.The original environment may be described as "large space, short time".One of my main goals is to establish that notion.
Revisiting the Canon of Sound Theory (Michel Chion, Audio-Vision: Sound on Screen)

Audio-Vision, Sound on Screen was fi rst published in French in 1990 and translated to English in 1994.For being one of the fi rst attempts to address sound in fi lm beyond the technological perspective, the book became widely referenced among scholars and practitioners.Twenty-fi ve years later, it has been republished by Columbia University Press.Th e author Michel Chion (1947), a French fi lm critic and composer, began his career as an assistant to Pierre Schaeff er in 1970, at the Groupe de Recherches Musicales (GRM). 1) Th is led him to collaborate with the French journal Cahiers du cinéma between 1982-87 which, together with the arrival of videotape, triggered a book trilogy about sound in fi lm: La voix au cinéma (1982; Th e Voice in Cinema in 1999) 2) in which he analyzes the hierarchy of voice in the soundtrack ("vococentrism" -p.6); Le son au cinema (1985; Film, a Sound Art in 2003), 3) where he continues to access the language in fi lmsound; and La toile trouée.La parole au cinéma (1988), 4) in which he accesses the development of sound-fi lm through its dialogues. 5) Audio-Vision: Sound on Screen is a compilation of this trilogy.It describes the relationship between sound and image and establishes the core terminology of sound for fi lm, which has been frequently cited since then.At the time, there was no antecedent, which is partly due to a general diffi culty in speaking objectively about sound.Th us, as formulated in the foreword by Walter Murch, the terminology very oft en recurs to ordinary language and therefore is self-explanatory.For example, "points of synchronization" (p.36), which are the moments in which image and sound meet in time; or "extension" (p.84), which is sound's capacity to extend the perception of the space outside the visual frame.Very oft en, Chion extends his explanations to other subjects, grasping diff erent fi elds without going beyond the surface.For example, when he attempts to analyze a certain psychology of hearing in fi lm, by discussing "infl uences of sound on perceptions of movement and speed" (p.11), or "on perception of time in the image" (p.12).Or, when he states that "textual speech has been considerably dis-Revisiting the Canon of Sound Theory
Statistical Mixture of Kaleidoscope States Interacting with a Two-Level Atom: Entropy and Purification

We investigate some of the fundamental features of the interaction of a mixture of coherent states, namely, a Kaleidoscope states mixture, with two-level atoms in the Jaynes–Cummings model framework. We begin our analysis by calculating the von Neumann entropy of the field, which is determined with the help of the virtual atom method. The oscillations appearing in the entropy indicate a state of purity greater than the initial state, i.e., a purification of the initial state due to a transfer of coherence from the atom to the field. In this oscillatory region, we obtain a negative Wigner function that hints at a (noisy) multiple Schrödinger cat.
Soiling Detection Investigation in Solar Irradiance Sensors Systems

Dirt deposition on solar irradiance measurement instruments can lead to errors in radiation measurements and, as a result, in incorrect energy production management issues. Climate and meteorological conditions may affect in different way the deposition process and thus, soiling severity and type are geographical and time-dependent. To investigate the soiling impact, here we describe a methodology that allows estimating the effect of dirt deposition on solar irradiance measurement systems, e.g., pyrheliometer. The methodology relies on the evaluation of a soiling index, based on pyranometer data measurements and it has been in field validated, using a solar irradiance measurement system deployed at ENEA Research Center of Portici. The results show that, under suitable assumptions, the computed index may be used to obtain a reasonable estimation of soiling effect.
A Nonlocal Model for Reconstructing Images Corrupted by Cauchy Noise

Abstract The aim of this paper is to present the mathematical and numerical study of a nonlocal nonlinear model based on the variable exponent p ( x )-Laplacian for removing Cauchy noise, which is a type of impulsive and non-Gaussian degradation. The proposed model benefits from the performance of the nonlocal approach to preserve small details and textures, and the efficiency of the variable exponent to reduce the execution time. To demonstrate the reliability of our proposed model, we provide some experimental denoising results and illustrate the comparison with some models from the literature.
Analysis of Deep Convolutional Neural Network Models for the Fine-Grained Classification of Vehicles

Intelligent transportation systems (ITS) is a broad area that encompasses vehicle identification, classification, monitoring, surveillance, prediction, management, reduction of traffic jams, license plate recognition, etc. Machine learning has practical and significant applications in ITS. Intelligent transportation systems rely heavily on vehicle classification for traffic management and monitoring. This research uses convolutional neural networks to classify cars at fine-grained classifications (make and model). Numerous obstacles must be overcome in order to complete the task, the greatest of which are intra- and inter-class similarities between the manufacturer and model of vehicles, different lighting effects, the shape and size of the vehicle, shadows, camera view angle, background, vehicle speed, colour occlusion and environmental conditions. This paper studies various machine learning algorithms used for the fine-grained classification of vehicles and presents a comparative analysis in terms of accuracy and the size of the implemented deep convolutional neural network (DCNN). Specifically, four DCNN models, mobilenet-v2, inception-v3, vgg-19 and resnet-50, are evaluated with three datasets, BMW-10, Stanford Cars and PAKCars. The evaluation results show that mobileNet-v2 is the smallest model as it is not computationally intensive due to depthwise separable convolution. However, resnet-50 and vgg-19 outperform inception-v3 and mobilenet-v2 in terms of accuracy due to their complex structure.
Introduction to a New Open Access Journal Published by MDPI: Blockchains

Blockchain, one of the representative technologies of Web 3 [...]
A proof system for graph (non)-isomorphism verification

In order to apply canonical labelling of graphs and isomorphism checking in interactive theorem provers, these checking algorithms must either be mechanically verified or their results must be verifiable by independent checkers. We analyze a state-of-the-art algorithm for canonical labelling of graphs (described by McKay and Piperno) and formulate it in terms of a formal proof system. We provide an implementation that can export a proof that the obtained graph is the canonical form of a given graph. Such proofs are then verified by our independent checker and can be used to confirm that two given graphs are not isomorphic.
Cascading auto-regressive exponential smoothing of image sequences for reducing turbulence induced motion

Atmospheric turbulence can significantly degrade images taken over a long horizontal path near the ground. This can hinder the identification of objects in a scene. We consequently introduce the Cascading Auto-Regressive Exponential Smoothing (CARES) algorithm, which is a fast real-time algorithm that suppresses the effects of atmospheric turbulence in image sequences. CARES is a spatial/temporal filtering algorithm that decomposes the image into a Laplacian Image Pyramid (LIP). Each component of the LIP represents the image smoothed to a specific length scale, which is then temporally filtered using an Auto-Regressive Exponential Smoothing (ARES) filter. The ARES filters have a cut-off frequency that are adjusted in such a way for each LIP component to define a critical velocity. Objects in the scene moving below the critical velocity pass through the CARES filter with little distortion or delay. We assess the performance of CARES using turbulent imaging data. We find that CARES improves image quality using a variety of image quality metrics. We use a simple CARES simulation to show that the salient features of a moving object lag behind by one pixel or less.
Cross-domain remote sensing image retrieval with Gabor-based CNN

Domain adaptation is the ability to improve the learning efficiency of the target domain by using the prior knowledge of the source domain. When applied to new tasks in the target domain, the performance of domain adaptation trained in the source domain declines sharply. The purpose is to realize a new retrieval task in the target domain using only a small number of labelled data samples, with the aid of the prior knowledge learned in the source domain. This paper focuses on semi-supervised domain adaptation in remote sensing image retrieval. The contributions of this paper are threefold. First, we construct Gabor-based CNNs to facilitate the networks to effectively capture the texture information of images. Second, we propose a cross-domain knowledge transfer strategy based on dual Gabor neural network learning. Third, we propose an unsupervised random feature mapping method based on probability distance. A large number of experiments have been conducted on UCM, WHU-RS, RSSCN7, AID, and PatternNet datasets. The results show that this method greatly improves the retrieval accuracy on the target domain and obtains state-of-the-art retrieval accuracy. The source code is available at http://nave.vr3i.com.
Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier's performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses.
Blockchain Adoption for Autonomous Train: Opportunities and Challenges

Autonomous Train is increasingly attracting the interest of researchers and industrials due to its numerous advantages regarding security and safety, service improvement, and costs reduction. Nonetheless, the Autonomous Train still faces many challenges including safety and security, fleet coordination, interoperability, and traceability. In this paper, the disruptive technology Blockchain is explored for a potential adoption for Autonomous Train deployment in order to overcome some of the Autonomous Train challenges. Finally, an illustrative example of a potential Blockchain application in the Autonomous Train domain is presented.
Evaluation and Characterization of the Influence of Solar Position Algorithm on the Performance of Parabolic Trough Solar System

For the open loop control system based on the solar position algorithm (SPA), without sensor correction, the error of SPA will bring the tracking error directly to decrease the efficiency of the solar system. By comparing with SPA proposed by NREL, this paper first evaluates main SPAs with different accuracy and presents the error of SPA on tracking error. Based on the annual average efficiency of the solar trough system, this paper evaluates the impact of the tracking error caused by SPA on the solar trough system, and proposes that the average SPA calculation error can be applied to characterize the impact of SPA on the trough solar system. By making a comparison of solar trough system efficiency calculated with fixed annual average tracking error and normal constantly changing tracking error within a year, respectively, the evaluation results show that for most SPAs, the introduced error is less than 0.05%, and only a few empirical algorithms with large tracking errors introduce larger errors, but they are not suitable for the trough solar system. Therefore, the SPA evaluation method proposed in this paper is applicable to the solar trough system.
An adaptive feature region-based line segment matching method for viewpoint-changed images with discontinuous parallax and poor textures

Many methods have been proposed to extract line segment (LS) correspondences for images with viewpoint variations. However, the matching performance on images with viewpoint variations is still limited due to the influence of discontinuous parallax and poor textures on images. Existing line segment matching (LSM) methods rarely consider the coexistence of these two situations. This study attempts to address this problem by proposing an adaptive feature region (FR)-based LSM method based on the observation that different FRs are suitable for different image scenes under image viewpoint change. In the proposed method, LSs are paired, and two types of FRs, symmetric FR centered on the line-pair intersection and asymmetric FR with the intersection as a vertex, are constructed for every line pair. The asymmetric FR of a line pair is a subregion of its symmetric FR. And the complement region of asymmetric FR in symmetric FR is called complement FR. To choose a suitable FR for the matching of each line pair adaptively, the asymmetric FR-based feature descriptor similarity and epipolar line-based constraints are combined to determine candidate line pair matches firstly. Then, the candidate matches with similar complement FR-based feature descriptors are considered initial matches and used to construct a topological constraint to check other candidates. Finally, we estimate an adaptive influence region-based local homography for each matched line pair to constrain the matching of unmatched individual LSs. The experimental results on the test images with viewpoint changes show that our approach outperforms the state-of-the-art methods in terms of Recall, matching precision (MP), and F-Measure (reflecting the overall matching performance of Recall and MP). Especially, it improves the average Recall and average F-Measure of the best method among the comparison methods by 45.20% and 25.83%, respectively.
Enhancing Conversational Troubleshooting with Multi-modality: Design and Implementation

Conversational troubleshooting is an increasingly popular technology that consists in utilising dialogue agents to support users of a system through a conversation-based question-answering process, typically through a chatbot. Despite their widespread use, current troubleshooting technologies lack a thorough integration with the applications on which they are overlaid, as they rely only on text to help the users. In this paper, we present TINI, an open-source conversational troubleshooting tool that is multi-modal and multilingual, relies on simple configuration files, and is ready to be deployed in web applications. Users can ask questions to the conversational agent explaining the issue faced; the system analyses it together with the interaction’s context to locate the root problem. Finally, it proposes a solution which engages the user multi-modally: with text in the chat and hints in the graphical interface. A table-based configuration improves system maintainability and enables dialogue designers and field experts to work on the conversation without any coding experience required.
Enabling Learning Through Play: Inclusive Gaze-Controlled Human-Robot Interface for Joystick-Based Toys

A significant proportion of the world population is living with some form of disability. The most isolated section of disability spectrum are persons with severe speech and motor impairment (SSMI). They face challenges not only in everyday activities but in accessing education and proper employment in later stages of life. Persons with SSMI must rely on manual gaze-based interactions for everyday communication. This research aims to enable and support the learning and education of persons with SSMI through engaging eye-gaze-controlled playful activities. This work proposes and evaluates gaze-based interaction to drive a toy car using augmented and mixed reality interfaces. The results show improvements in otherwise dull, gaze-based generic pointing and selection tasks after performing an unrelated yet more engaging and playful car driving activity.