Reviewerly/exHarmony huggingface dataset:
The underlying causal network from global dyadic events: allies and rivals in international relations

The ever-increasing amount of web information usually leads to more discovery of implicit relations using data-driven methods. While country-country relations are not exactly new for human learning, we illustrate that one can extract the knowledge of such sophisticated relations solely from data. Using the global dyadic events data that record the involving parties and timestamp, we identify the underlying causal network with common initiators or reactors, as well as the nature of pairwise relations as a counterfactual reasoning problem, thereby deriving the significance of causal effects in the network. To formalize the discovery process, we first validate some country-country relations in a small-world network structure after exploitative runs over the entire data set, suggesting the traditional allies and rivals as we know in the real world. Further, we use sentimental analysis based on Wikipedia text data to match the significance of bilateral relations in the causal discovery. Our results show that using time series data of dyadic event counts alone, combined with lexical-based validation, can extract the knowledge of bilateral and multilateral relations and has potential to be a means to measuring and monitoring the climate of complex geopolitics.
Researching on the Cold Start Technology of New Broadcast Television Storage Resource Allocation

With the development of new media, the traditional broadcast television has encountered a tremendous impact. So, the broadcast television industry is actively seeking innovation to break through the dilemma. It is becoming more and more urgent to build a reasonable framework for new broadcast television resource storage and distribution. Facing the deployment of new broadcast television, the primary issue is to realize the cold start based on new programs. This paper uses the clustering algorithm, similarity algorithm, and information entropy algorithm to construct a new broadcast television user interest model, which includes two sub-models, and finally achieves the cold start of programs. Compared with the Item-CF method, as the experimental results show, our new model is better than the traditional collaborative filtering algorithm in some indicators.
Go Green: A Web-Based Approach for Counting Trees from Google Earth Images

In this century, every government in the world is more concern about forest degradation and deforestation. Nowadays, the world population is increasing at a rapid rate. There are many countries which are economically developed but suffering from adverse effects of pollution. So, it has become essential for growing a sufficient number of trees for feeding oxygen to such a huge population. But with the time passing by, due to deforestation, the oxygen level is depleting to a great extent. As trees are the most important source of oxygen, so there is a need to keep a track of the number of trees in a particular area. A manual survey of tracking trees is practically impossible and costly. In this proposed approach, the web-based software is developed to count the number of trees in a particular area of town. The detection and counting of trees are done using TensorFlow Object Detection API to train dataset of Google Earth Image using Faster Region Convolutional Neural Network (Faster-RCNN) and Single Shot Multi-Box Detector (SSD) with InceptionV2 technique. This technique will save a large amount of manual work for monitoring/counting number of trees.
PAL-Net: Predicate-Aware Learning Network for Visual Relationship Recognition

Visual relationship recognition is essential for deeper scene understanding. It poses to recognize 〈subject-predicate-object〉 triplets between object pairs. Previous methods usually treat vastly different predicates equally and neglect the subtle differences between predicates. In this paper, we propose a novel and concise perspective called "predicate-aware learning network (PAL-Net)" for visual relationship recognition. "Predicate-aware" means that we take predicates as a condition in a task-driven manner. Our PAL-Net consists of two key modules: i) a predicate-guided regularization module designed to learn more differentiated representations for various predicates; ii) a predicate-aware contextual modeling module to integrate the efficacy of contextual objects for different predicates respectively. Extensive experiments on VRD and Visual Genome dataset yield remarkable performance gains, verifying the effectiveness of PAL-Net. Besides, PAL-Net also shows good applicability and achieves substantial improvement for human-object interaction detection.
Reading Strategies Used in Reading Academic Texts by Students of High and Intermediate Reading Proficiency Levels

In this study, the reading strategies used by students of high and intermediate reading proficiency levels were qualitatively examined. Reading strategies are important factors that affect someone's reading proficiency. In the development of education, reading strategies are continuously carried out to facilitate the learning process and sharpen students' reading skills. The purpose of this study is to discover the reading strategies used by the English Department students with high and intermediate reading proficiency levels in reading academic texts. This study uses reading strategies theory by Grabe (2009) combined with the interview questions by Mokhtari and Reichard (2002) to examine the reading strategies of ten participants consisting of five people with high reading proficiency level and five other people with intermediate reading proficiency level. This study showed that students of high reading proficiency levels tend to use more reading strategies than students with intermediate reading proficiency level. The biggest difference is in the use of text-structure awareness and inferencing strategies. It indicates that the mastery of text structure is directly related to reading effectiveness. Without text structure awareness, other reading strategies such as summarizing, inferencing, and elaborative interrogation will be seriously disrupted. Keywords: Reading Strategies, Academic Text, High Reading Proficiency Level, Intermediate Reading Proficiency Level
Promoting Sustainable Collaborative Mobile Learning Approaches for Remote Work Practices

The main goal of this chapter is to learn more about the sustainability of collaborative mobile learning approaches. As for utilising the available resources on mobile devices for developing didactic programmes for pre-service teachers involved in remote or distance learning, the role of sustainable collaborative learning is also explored. Additionally, the chapter illustrates a new and effective pedagogical technique as a means to increase their usage and usage among instructional developers. A teaching model that can be utilised in a variety of environments is proposed, like in the current global pandemic in the form of remote work and distance learning. Such teaching models can therefore help to optimise online learning materials to help students follow the teaching and learning process in the absence of traditional classroom activities.
The effect of nonequilibrium entropy production on the quantum Fisher information and correlations

In this study, we apply quantum master equations beyond secular approximation and investigate the nonequilibrium thermodynamic cost of enhanced quantum metrology and quantum correlations. We find that the nonequilibrium conditions enhance quantum Fisher information (QFI) and quantum correlations predominantly for weak tunneling scenarios. The enhancement is assisted by a corresponding increase in the thermodynamic cost characterized by the entropy production rate (EPR). For the strong tunneling regimes, the QFI and quantum correlations cannot be unceasingly boosted by higher thermodynamic costs and decay once the system is overburdened with extremely large energy currents. The result indicates that for open systems with weak tunneling rates, thermodynamic cost can be potentially exploited to improve the quantum metrology and quantum correlations.
Feature Fusion Human Object Detection Algorithm

Due to the strong changes in the illumination intensity and the serious mutual occlusion of outdoor substation environment, it is difficult to detect human objects. In this paper, a Feature Fusion Faster SSD (Triple F-SSD) personnel object detection algorithm based on adaptive difference threshold for substation scene is proposed. The algorithm includes two parts: (1) Background Adaptive Difference (BAD) method based on the fixed background reference. This method utilizes the image difference method based on the fixed background reference to effectively remove the background information in the image. (2) Triple F-SSD personnel object detection algorithm. The algorithm adopts the feature fusion of the original image and the pre-processed image to retain the image context information while enhancing the features of the target area. According to different object scales, different feature maps are selected for processing, so as to improve the computational efficiency of the algorithm while enhancing the object features. Experimental results show that the object recognition accuracy of the proposed method can reach 92\% when it comes to person object detection in complex background of substation.
Modeling of Looping Behavior in a Flexible Instrument Using a Bézier Curve

Abstract Flexible medical instruments undergo looping during insertion and navigation inside the human body. It makes the control of the distal end difficult and raises safety concerns. This paper proposes the minimum strain energy concept to get the deformed shape of a flexible instrument in three-dimensional space. A Bézier curve is used to define the trajectory of the deformed shape under different loading conditions and constraints. Looping behavior is studied for different end shortening conditions. The effect of end twist on looping behavior is studied. It is observed that end twist leads to early onset of out-of-plane deformation leading to looping. The strain energy plot gives an insight into the behavior of these instruments with respect to end shortening and twist. The strain energy plot shows the minimum value for 2π end twist. Therefore, the instrument tends to go for looping if the end twist is present. Force and torque characteristics are obtained which will lead to the design and control of these instruments. Force and torque plots show negative stiffness when the instrument is going for looping. The unlooping phenomenon is also discussed and a strategy is proposed to mitigate looping. The proposed modeling approach can be utilized to address the complex behavior of a flexible instrument in medical as well as in other industrial applications. The insight developed will help in designing and developing control for safe and reliable usage of flexible instruments in various domains.
Software Reuse Technologies

Different technical, managerial, and organizational methods are available for developing reusable software components. The best-known ones are Component-Based Software Engineering (CBSE), Commercial-Off-the-Shelf-Software (COTS)–Based Development, and Product-Line Engineering (PLE). However, these methods are not orthogonal, that is, they have a lot of issues in common. The common terminology and techniques they share have already been introduced in the previous chapters. Therefore, this chapter will briefly review these methods, while pointing out their differences.
Unsupervised Feature Representation Learning using Sequence-to-sequence Autoencoder Architecture for Low-resource Language

In this paper, we aim to improve the traditional bottleneck feature extraction under the low-resource scenario. We employ the factorized hierarchical variational autoencoder (FHVAE) to learn an unsupervised feature representation by encoding the linguistic-relevant information into latent variables. In order to obtain more significant latent variables, the attention mechanism is introduced into the encoders of FHVAE. In addition to the reconstruction decoder of FHVAE, the phonetic-aware decoder is introduced to backward transmit the phonemic information into the latent variables, enhancing the performance of feature representation learning. The idea of multi-task learning is used to organize the encoders of FHVAE, the reconstruction decoder of FHVAE and the phonetic-aware decoder into the training process. To demonstrate the effectiveness of the proposed method, the ABX discriminability and the language identification are evaluated on the ZeroSpeech 2017 and the LRE 2017 respectively. These experimental results shown that the learned feature representation outperforms traditional acoustic feature.
Formation of socio-communicative competence of the future teacher

The article is devoted to the problem of the formation of a teacher's social and communicative readiness to solve professional problems as one of the basic pedagogical competencies in the context of the digitalization of education. The problem of creating conditions for the formation and development of a digital learning culture is considered from the point of view of the updated conceptual apparatus of modern pedagogy, the introduction of such concepts as digital learning, mixed classes, network interaction. Potential educational opportunities and risks of organizing the pedagogical process using digital means and technologies are identified. Consideration of the essential characteristics and structure of social and communicative competence made it possible to reveal the practice-oriented features of the teacher's professional readiness, focused on the active use of distance learning technologies. The analysis of the structures and content of models for the formation of social and communicative competence in the system of training pedagogical personnel allows us to variably present the algorithms and mechanisms for mastering the «new culture of learning» in the realities of the digital society.
The (Greatest) Fragment of Classical Logic that Respects the Variable-Sharing Principle (in the FMLA-FMLA Framework)

We examine the set of formula-to-formula valid inferences of Classical Logic, where the premise and the conclusion share at least a propositional variable in common. We review the fact, already proved in the literature, that such a system is identical to the first-degree entailment fragment of R. Epstein's Relatedness Logic, and that it is a non-transitive logic of the sort investigated by S. Frankowski and others. Furthermore, we provide a semantics and a calculus for this logic. The semantics is defined in terms of a \(p\)-matrix built on top of a 5-valued extension of the 3-element weak Kleene algebra, whereas the calculus is defined in terms of a Gentzen-style sequent system where the left and right negation rules are subject to linguistic constraints.
Global-Affine and Local-Specific Generative Adversarial Network for semantic-guided image generation

&lt;p style='text-indent:20px;'&gt;The recent progress in learning image feature representations has opened the way for tasks such as label-to-image or text-to-image synthesis. However, one particular challenge widely observed in existing methods is the difficulty of synthesizing fine-grained textures and small-scale instances. In this paper, we propose a novel Global-Affine and Local-Specific Generative Adversarial Network (GALS-GAN) to explicitly construct global semantic layouts and learn distinct instance-level features. To achieve this, we adopt the graph convolutional network to calculate the instance locations and spatial relationships from scene graphs, which allows our model to obtain the high-fidelity semantic layouts. Also, a local-specific generator, where we introduce the feature filtering mechanism to separately learn semantic maps for different categories, is utilized to disentangle and generate specific visual features. Moreover, we especially apply a weight map predictor to better combine the global and local pathways considering the highly complementary between these two generation sub-networks. Extensive experiments on the COCO-Stuff and Visual Genome datasets demonstrate the superior generation performance of our model against previous methods, our approach is more capable of capturing photo-realistic local characteristics and rendering small-sized entities with more details.&lt;/p&gt;
Preparing the guide robot to operation

This article is dedicated to the issue of developing social robots based on the example of Marry robot guide developed by MAI and APZ. The methodology of preparing the robot to operation at an exhibition or in a museum is considered. The authors present the performance scenario structure and define the requirements. Based on the methodology, an algorithm for building a working tour route is shown. The algorithm receives inputs in the form of time limitations and various options of presenting various exhibits. As a result, the algorithm produces a finished tour scenario (a sequence of presentation fragments). The stories of individual exhibits are prepared by the operator in a special interface. The look of the interface and its software structure are presented. The tests of Marry guide robot have confirmed its ability to perform the key functions of a guide robot.
Intelligent Automated Operation and Operation Management of Large Enterprise Cloud Data Center Based on Artificial Intelligence

With the advent of the era of big data and information, people pay more and more attention to information security and data protection. The way you use the cloud to process your data is no longer new. As early as 2010, the government listed the cloud computing industry as a strategic emerging industry that the state focuses on cultivating and developing. The purpose of this paper is to combine artificial intelligence to study intelligent automation operations management of large enterprise cloud data centers. Based on artificial intelligence security and data security protection, this paper aims at improving the computing speed of the algorithm while taking into account data management, and then collects, organizes and analyzes the relevant information, adopts the domestic and foreign literature analysis method, sandbox simulation operation, and a variety of intelligent algorithms finally get the results. Experimental results show that the use of artificial intelligence technology can safely protect cloud data and realize automated operation and maintenance management at the same time.
Tuning idiophone bar torsional modes with three-dimensional cutaway geometries

The bars of marimbas, vibraphones and similar idiophones are tuned by shaping their “cutaway” or “undercut” geometries. Makers commonly shape bar cutaways to tune three flexural modes of vibration. The first flexural mode is tuned to the fundamental frequency of the bar’s musical note. Two other flexural modes are tuned such that their frequencies become specific multiples of the fundamental. The remaining flexural modes and all other modes are left untuned. Makers have complained of untuned torsional modes polluting bar timbre over specific sections of the keyboard. In wooden marimba bars this problem has proven difficult to predict, filling reject bins with valuable tonewood. This work investigates tuning these torsional modes by varying bar cutaway geometry in three dimensions. No additional mass or heterogeneous materials are employed. Modal frequencies are determined via finite element analysis. Mode shapes are identified algorithmically, enabling analyses to explore the parameter space unsupervised. Geometries are tuned using a gradient-based search method. The approach, designed to efficiently solve underdetermined systems with multiple objectives, is readily applicable to other problems. A selection of tuned example models will be showcased, including rosewood and aluminum bars with common and uncommon tuning ratios.
Structure-Resonant Discriminator for Image Super-Resolution

Convolutional neural networks are data models. Their design should embrace the structural properties of the data being modeled, e.g., the natural images. We argue that this also holds for discriminators of adversarial training frameworks for photo-realistic image restoration. We develop this idea to highlight three essential structural features of natural images: translation equivariance, rotation invariance, and hierarchy of scale. The analysis leads to a new discriminator, Structure-Resonant Discriminator (SRD), which can capture image structures in need. The proposed SRD is demonstrated in the perceptual single image super-resolution task. By replacing only the discriminator, our method restores more visually pleasing high-resolution images than the previous state-of-the-art techniques, while exhibits the least distortions.
Open-source project recommendation model

Open-source has become a very important topic in this era. the number of open-source projects on github Shows a huge growth trend. Facing so many open-source projects, it’s not easy to find the projects and topics that the developers interested in. so, it is necessary to model the user's behavior data,So as to automatically recommend projects to developers. to explore this problem, we constructed a dataset of 90w users and 461w projects based on github log and did a lot of cleaning work on the data. finally, we model the data through the improvement of the Light-GCN model to recommend relevant open-source projects to users. The experimental results show that the accuracy of our model is more than 15%.
Importance of Forensic Science in Crime Investigation

Forensic science has been used for justice delivery system since ages. However it started gaining groundin early fifties as an independent discipline and since then it has derived applications from every field ofscience and technology. Today it is well settled and much sought after and ever growing technology forensicsand forensic investigations are fast changing their face as every other day new types of crimes are beingcommitted. To cope up with the demanding nature of this profession, forensic services need up-gradation atevery stage. This paper focuses on the applicability of Forensic science in criminal investigation and the roleit plays in keeping law and order in a society, also throws light on how forensic science is acting as a bridgebetween the medical scientists and legal scientists.
TEACHING SCHOOLCHILDREN IN PROGRAMMING USING VIRTUAL WORLDS

The article reveals the concept of "virtual world", gives a classification of virtual worlds, describes the use of these worlds in human life. The article considers the importance of using virtual worlds in modern education, the possibility of using gamification with the use of didactic computer games in teaching basic school students, methods of teaching basic school students programming using virtual worlds and robotic constructors. The requirements for interactive learning virtual worlds are described. The use for teaching algorithms of such environments as Minecraft: Education Edition; RobotC programming environment used for programming VEX robotic constructors; LEGO Digital Designer is considered. In face-to-face learning, virtual worlds complement traditional teaching technologies and improve the perception of theoretical material. In distance learning, virtual worlds can replace an entire laboratory and provide learners with a hands-on experience without actually doing an experiment. In addition, students can study on their own, which also facilitates the learning process.