Reviewerly/exHarmony huggingface dataset:
Exploiting Semantic Term Relations in Text Summarization

The traditional frequency based approach to creating multi-document extractive summary ranks sentences based on scores computed by summing up TF*IDF weights of words contained in the sentences. In this approach, TF or term frequency is calculated based on how frequently a term (word) occurs in the input and TF calculated in this way does not take into account the semantic relations among terms. In this paper, we propose methods that exploits semantic term relations for improving sentence ranking and redundancy removal steps of a summarization system. Our proposed summarization system has been tested on DUC 2003 and DUC 2004 benchmark multi-document summarization datasets. The experimental results reveal that performance of our multi-document text summarizer is significantly improved when the distributional term similarity measure is used for finding semantic term relations. Our multi-document text summarizer also outperforms some well known summarization baselines to which it is compared.
Context-Awareness Based Battlefield Situation Information Recommendation

Based on the analysis of the key factors involved in the situation information recommendation, this paper integrates the situation information into the traditional “user-item” recommendation system, and constructs a multi-dimensional and multi-level battlefield situation information recommendation model. Furthermore, the two-dimensional recommendation model is extended to multi-dimensional recommendation model to improve the accuracy of the recommendation system. On the basis of the multi-dimensional recommendation model, the ontology modeling method is used to model the situation information, and the battlefield situation information recommendation method based on ontology similarity is designed.
Consensus Based Vertically Partitioned Multi-layer Perceptrons for Edge Computing

Storing large volumes of data on distributed devices has become commonplace in recent years. Applications involving sensors, for example, capture data in different modalities including image, video, audio, GPS and others. Novel distributed algorithms are required to learn from this rich, multi-modal data. In this paper, we present an algorithm for learning consensus based multi-layer perceptrons on resource-constrained devices. Assuming nodes (devices) in the distributed system are arranged in a graph and contain vertically partitioned data and labels, the goal is to learn a global function that minimizes the loss. Each node learns a feed-forward multi-layer perceptron and obtains a loss on data stored locally. It then gossips with a neighbor, chosen uniformly at random, and exchanges information about the loss. The updated loss is used to run a back propagation algorithm and adjust local weights appropriately. This method enables nodes to learn the global function without exchange of data in the network. Empirical results reveal that the consensus algorithm converges to the centralized model and has performance comparable to centralized multi-layer perceptrons and tree-based algorithms including random forests and gradient boosted decision trees. Since it is completely decentralized, scalable with network size, can be used for binary and multi-class problems, not affected by feature overlap, and has good empirical convergence properties, it can be used for on-device machine learning.
“Coordinates’ Line Subtitles”: a Spicy Feature in VR360 Videos for Events“ Locations

VR360 videos for events locations (venues) and their surrounding areas are capable to provide a sensory and cognitive foretaste of the experience of the events, and at the same time constitute an effective means of inspiration for the creation and implementation of novel cultural and other events to the place where the video was captured. The above have been evaluated via the design and implementation of an innovative “Events’ spatiotemporal map” service for the region of Crete, where VR360 videos of events’ locations have been proved to promote the touristic and cultural experience.The documentation of the VR360 videos has followed narration and subtitling based on three main techniques: a) fictional description of the place and its use, b) the “truthful cinema” approach, i.e. through scenarios about the history and the activities of the place that are spontaneous, active and without mediation, and c) indirectly, through the description of a certain event that is taking place in the filmed location. According to Todorov the typology of the stories in either approach is comprised of three types: omniscient, objective and with point of view (subjective). During our research for the “Events’ spatiotemporal map” we elaborated on another way of VR360 videos documentation, the “coordinates’ line subtitles” which will result in a new type of narration, the one we have defined as “google-tive”. The narration/subtitles will include information from the Google maps service, mainly toponyms and POIs, correlated with “hidden” spatial information to VR360 video scenes.In this paper we set the theoretical bases of the new type of VR360 subtitling and present the implementation of the dataflow assisting the creation of “coordinates’ line subtitles” by use of the Google Places’ API to retrieve Points of Interest around events’ locations.
Linear and non-linear effects of infrastructures on inclusive human development in Africa

The objectives of this paper are to verify the linear and the non-linear effects of infrastructural development on inclusive human development in Africa. The results of the system GMM estimations show a positive effect of infrastructural development on inclusive development across all the infrastructural development indexes employed, except the ICT infrastructural composite index which presents an insignificant negative effect. Besides, a non-linear effect of infrastructures on inclusive development was established across all the infrastructure indicators except for the ICT indicator. Negative thresholds for complementary policies are established for the African Infrastructure Development Index (AIDI) and the transport index while positive thresholds are apparent for the electricity index and the water and sanitation infrastructure index (WSS). Accordingly, in order to sustain the positive incidence of the AIDI and transport index on human development, complementary policies should be engaged to avoid an overall negative effect on human development when the indexes are respectively, 31.12% and 25.56%. In the same vein, the electricity index and WSSI should exceed critical levels of respectively 49.79% and 41.92%, to engender an overall positive effect on inclusive human development.
Extension of Annotation Function in Collaborative Composition System

Due to the development of social media networks, there are now increasing opportunities for multiple people to collaboratively compose music using desktop music and digital audio workstation systems. To facilitate such efforts, we have been developing a system that supports creative, collaborative compositions, have analyzed the conditions required to support such efforts, and have proposed and implemented a collaborative composition system that satisfies these conditions. This system is an asynchronous groupware application with which communications among users are performed by exchanging character-based text comments. In this paper, we describe how we extended the comment function of this previous system to support handwritten annotations.
Let’s Create! Automated Certificate Management for End-users

The automatic creation of X.509 certificates has helped the Web to make end-to-end encrypted connections the default. Email, however, lacks such a protection as can be seen by most users not encrypting their messages. While multiple standards for encrypted emails exist, none has clearly been established as the standard way of securing messages. One reason for the lack of adoption is arguably the absence of automatic key creation and management. This paper fills this gap by evaluating the automatic provisioning of S/MIME certificates for end-users through an extension of the popular ACME protocol. We automate the key management and implement a full ACME client in a popular cross-platform Email client (Thunderbird). With our method, users can have an S/MIME certificate created from within their mail client and do not need to depend on the Web sites provided by the certificate vendors. We evaluate our method with a study on certificate vendors and measure the time and number of interactions required to obtain and install an S/MIME certificate. With our method, users reduce the time needed to acquire and install a certificate from 165 seconds to 3 seconds. Similarly, the number of interactions required is reduced from 15 to 3. We conclude that our method provides a faster and less error-prone way which may positively impact the uptake on encrypted email communication.
Ghost-Free Fusion of Multi-Exposure Images in the Global Gradient Region Under Patch Alignment

High dynamic range (HDR) technology is one of the most widely used ways to improve image quality, and fusion of a series of low dynamic range (LDR) images is the main measure to obtain a HDR image. However, because moving objects are often found in a series of LDR images, the fused HDR images produce ghostly shapes. In order to eliminate ghosts, this paper proposes a ghost-free multi-exposure fusion method. Firstly, aligning the moving object in the input multi-exposure sequence images with the moving object in the reference image, and the aligned sequence images are obtained. In order to consider assigning more weight to pixels in the better exposure area, two weighting functions are defined. One is to measure pixel values relative to the overall brightness and adjacent exposure images, and the other one is to reflect pixel values within a range that has a larger global gradient relative to other exposures. Based on these two weighting functions, the low exposure sequence images aligned in the Laplacian pyramid are finally fused. Through experimental comparison, the obtained image has no ghost, good visual effect, and rich details.
Schema Complexity in Propositional-Based Logics

The essential structure of derivations is used as a tool for measuring the complexity of schema consequences in propositional-based logics. Our schema derivations allow the use of schema lemmas and this is reflected on the schema complexity. In particular, the number of times a schema lemma is used in a derivation is not relevant. We also address the application of metatheorems and compare the complexity of a schema derivation after eliminating the metatheorem and before doing so. As illustrations, we consider a propositional modal logic presented by a Hilbert calculus and an intuitionist propositional logic presented by a Gentzen calculus. For the former, we discuss the use of the metatheorem of deduction and its elimination, and for the latter, we analyze the cut and its elimination. Furthermore, we capitalize on the result for the cut elimination for intuitionistic logic, to obtain a similar result for Nelson’s logic via a language translation.
Deep-learning aided consensus problem constrained by network-centrality

The deep-learning aided parameter optimization method for the average consensus problem with a complex network has been proposed by Kishida et al., which can significantly accelerate its convergence performance. However, the optimized parameters cannot be applied to the different network topology from the one used in the training. This work proposes a new optimization method constrained by the restriction caused by the network centrality, in which the optimized weighting factors can apply to the different network topology used in the training. Specifically, this work considers five types of network centralities and discusses which centrality is most suitable for the constraint of the proposed method.
Appraising Research Data for <scp>Long‐Term</scp> Preservation: An Analysis of Research Data Collection Policies

Abstract Traditionally, research data has been managed independently by individual researchers and only shared at the discretion of the researcher upon request. Appraisal and selection of research data has become a major issue in recent years as the number of data types and volume of data have grown greatly, but little empirical study has been done regarding how academic libraries document their research data appraisal practices. This study surveyed academic libraries' research data collection policies, which govern the identification, appraisal, and selection of research data. A total of 17 academic libraries' research data collection policies were examined in this study.
A Critical Discourse Analysis of Negative Propaganda in Selected American and Chinese Channels: (COVID- 19) as a Case Study

Propaganda has important power as a product of modern media (social media, satellite television, the internet). Those means enhance the fast spread of information, news, and events to the public. The thought of a propaganda phenomenon, methodically funded by doctrine, persuades individuals. Propaganda generally involves untrue things that are regarded as aggressive. Essentially, propaganda can be an aware communication act with effective people. For instance, leaders and politicians depend on specific strategies to create many elements of excitement. The obvious example of conversion of people to causing harm to others is the negative propaganda of World War II. As a part of language communication, the study of negative propaganda in visual media is one of the most motivating topics to find out, because of the ability of this matter to manufacture people to understand the insight of propaganda in an altered way. The researcher uses the seventh edition of the (APA) style to introduce this paper. The present study makes a distinctive effort to survey the 'ideological discourse structures' as one of CDA's fundamental concepts .The study goals to analyze and survey the forms and types of propaganda techniques which are employed by the CGTN Chinese and CNN American channels under study. It also goals to investigate the use of illocutionary types in these chosen channels of COVID-19. To achieve the aims of the current study, a proportion of hypotheses are proposed, containing "negative propaganda" that is the main type of content in both the Chinese and American channels within the study. The study covered CGTN and CNN news reports related to the coronavirus. The eclectic model consists of: Van Dijk (2000), Yourman (1939), Shabo (2008), and Ellul (1965). Three of those models (Ellul, Yourman, and Shabo) engaged in propaganda. The rest have certain frames to deal with. According to the analysis of the data, the central conclusions of this investigation have clearly demonst
A Comparative Analysis of Agile Teamwork Quality Models

[Background] The literature reports multiple models (or instruments) for measuring Teamwork Quality (TWQ) for Agile Software Development However, these models have different constructs and measures, and there is a lack of empirical evidence comparing them. [Goal] This study fills this gap by analyzing if two agile TWQ models yields equivalent results. [Method] First, we mapped the models’ variables given their definitions. Then, we collected data using both a BN model (Baysean Network model), TWQ-BN, and Structural Equation Modeling, TWQ-SEM, by interviewing 162 team members from two software development companies. We analyzed the collected data by applying the Bland-Altman method. [Results] We obtained enough evidence to conclude that the results for Communication, Coordination, Cohesion and Mutual Support are not equivalent. Conversely, we did not have enough evidence to claim that the models do not agree for measuring Effort and Balance of member contribution. [Conclusions] The results of this study detail how two state-of-the-art agile TWQ compare in terms of their measures as well as potential research areas for further investigation.
A Hybrid Recommender System for Cultural Heritage Promotion

Assisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularity-, knowledge-, and content-based, according to different hybridization strategies. Experimental evaluation shows the versatility of the hybrid recommender with respect to the other paradigms adopted individually.
The Emergent Dimension of Play

The third and final examination of data focuses on the use of sound as data. I use audio, image and text to address the soundscape of the club, considering the affordances of taking such an approach to investigate lived experience. I provide a link to a short audio version of a compilation of sounds recorded during the fieldwork. I also provide a visual ‘map’ of the soundscape, inspired by the composer John Cage’s work around non-representational musical notation. This ‘map’ is deconstructed in the form of a non-chronological comic strip that demonstrates one possible reading of the club’s soundscape. This helps to reveal the noisy, lively and animated nature of the club, as sound is used as a means of marking territory and shaping space by imposing order, stability and exercising power though exuberant play.
Calibration of intrinsic camera parameters with a conic and its asymptotes

This study proposes a method for camera calibration using the properties of conic asymptotes, which are combined with the projective invariance, images of the asymptotes, and image of the absolute points to obtain the vanishing line. Constraints for the image of the absolute conic are obtained from a set of orthogonal vanishing points, the homography, and images of the circular points. The proposed calibration algorithm is compared with several other research methods, and its effectiveness and robustness are verified within a certain error range from the results of simulated and real experiments for different eccentricities without loss of generality.
Machine Learning-based Human-Following System: Following the Predicted Position of a Walking Human

Human&#x2013;robot interaction (HRI) has been widely researched in diverse applications. A robot following a person is one such scenario investigated in the HRI field. However, human movements and actions are complex and can change dramatically. We herein demonstrate a machine learning-based system that allows a person-following robot to track in real-time the predicted future motion of a walking human, from a first-person perspective. We assume that a depth sensor that can detect the human skeleton is loaded on a mobile robot to provide data on the user&#x2019;s motion from a first-person perspective. The system calculates the coordinates of the center of gravity (COG) and 25 body joints of the user. These coordinates of COG and 25 body joints are relative to the robot based on the position of the person tracked, and these are used for the input dataset of a neural network (NN) that predicts human motion. A five-layered NN estimates the relative vectors in real-time between the current person&#x2019;s COG and the future position of the 25 body joints. Using a proportional&#x2013;integral&#x2013;derivative (PID) controller, the person-following robot can track the predicted position of a walking human 0.5 s in advance to increase the robustness of following and to avoid delays.
Learning Non-Parametric Surrogate Losses With Correlated Gradients

Training models by minimizing surrogate loss functions with gradient-based algorithms is a standard approach in various vision tasks. This strategy often leads to suboptimal solutions due to the gap between the target evaluation metrics and surrogate loss functions. In this paper, we propose a framework to learn a surrogate loss function that approximates the evaluation metric with correlated gradients.We observe that the correlated gradients significantly benefit the gradient-based algorithms to improve the quality of solutions. We verify the effectiveness of our method in various tasks such as multi-class classification, ordinal regression, and pose estimation with three evaluation metrics and five datasets. Our extensive experiments showed that our method outperforms conventional loss functions and surrogate loss learning methods.
DEMO: Extracting Multiple Promotional Media Elements from Urban Spaces

In this study, we construct an object detection model that targets billboard advertisements in images and detects them regardless of their content and models to extract multiple elements from billboards. In addition, we try multiple methods for each task to clarify an appropriate method. As a demonstration, we prepare a web application that extracts elements from images of billboards by using the model constructed in this study.
FTAFace

In face detection, it is a common strategy to treat samples differently according to their difficulty for balancing training data distribution. However, we observe that widely used sampling strategies, such as OHEM and Focal loss, can lead to the performance imbalance between different tasks (e.g., classification and localization). Through analysis, we point out that, due to the driving of classification information, these sample-based strategies are difficult to coordinate the attention of different tasks during the training, thus leading to the above imbalance. Accordingly, we first confirm this by shifting the attention from the sample level to the task level. Then, we propose a fine-grained task attention method, a.k.a FTA, including inter-task importance and intra-task importance, which adaptively adjusts the attention of each item in the task from both global and local perspectives, so as to achieve finer optimization. In addition, we introduce transformer as a feature enhancer to assist our convolution network, and propose a context enhancement transformer, a.k.a CET, to mine the spatial relationship in the features towards more robust feature representation. Extensive experiments on WiderFace and FDDB benchmarks demonstrate that our method significantly boosts the baseline performance by 2.7%, 2.3% and 4.9% on easy, medium and hard validation sets respectively. Furthermore, the proposed FTAFace-light achieves higher accuracy than the state-of-the-art and reduces the amount of computation by 28.9%.