Reviewerly/exHarmony huggingface dataset:
Meningkatkan Minat Membaca Pada Anak Usia Sekolah Dasar Melalui Program Tayangan Televisi Dalam Perspektif Sosiologi

The study aims to describe (1). The role of parents fostering interest in reading elementary school age children through television programs. (2). The positive impact of the TV program "Tau Gak Sih" on the development of reading interest in elementary school age children. The research is objecting to a comprehensive detailed study of the main content, the role of parents in assisting watching television programs to foster interest in reading. The scope of the study includes analysis-based analysis activities, focusing on the role of managing elementary school children's interest in reading by assisting learning. Analysis requires realistic critical thinking to apply conceptualization, so that it uses a historical approach characterized by realistic critical inquiry into the development of a thought. Primary information sources are used as primary data. The technique of collecting data utilizes library research, which is reading various kinds of literature. The analysis was conducted descriptively qualitative, sourced from theoretical reading material, research and not research. The validity of the research can be achieved by utilizing content analysis, in order to analyze the meaning, the role of parents, the function of the family in developing interest in reading elementary school-age children through TV programs. Observation is used as an element to support the construction of the theory. The results of the study include; (1) Parents play an important role in providing assistance in fostering elementary school-age children's interest in reading through television programs. (2) The positive impact of the TV program "Tau Gak Sih" on the development of reading interest in elementary school age children is as a medium of learning, expanding knowledge and motivating reading activities
LibGuides: Movies & More Guide: Literature

Need a break? The Movies & More Guide helps you find online movies, literature, art, museums, animal webcams, and puzzles from LUC, public libraries, and Internet sources.
Multi-object tracking with self-supervised associating network.

Multi-Object Tracking (MOT) is the task that has a lot of potential for development, and there are still many problems to be solved. In the traditional tracking by detection paradigm, There has been a lot of work on feature based object re-identification methods. However, this method has a lack of training data problem. For labeling multi-object tracking dataset, every detection in a video sequence need its location and IDs. Since assigning consecutive IDs to each detection in every sequence is a very labor-intensive task, current multi-object tracking dataset is not sufficient enough to train re-identification network. So in this paper, we propose a novel self-supervised learning method using a lot of short videos which has no human labeling, and improve the tracking performance through the re-identification network trained in the self-supervised manner to solve the lack of training data problem. Despite the re-identification network is trained in a self-supervised manner, it achieves the state-of-the-art performance of MOTA 62.0\% and IDF1 62.6\% on the MOT17 test benchmark. Furthermore, the performance is improved as much as learned with a large amount of data, it shows the potential of self-supervised method.
Similarity Measurement Based on Non-linear Hash Coding

We propose an algorithm named non-linear deep hash (NLDH) to encode the object in the image into a series of compact binary codes through a structure called a non-linear hash coding module. Based on this, then, we propose a retrieval algorithm for similar images based on these binary codes in the image. This algorithm adopts the strategy from coarse-to-fine. Then the image level similarity calculation is carried out to complete the search of the most similar images. Finally, experiments were carried out on the Oxford buildings dataset and ancient painting image dataset in this paper. The experimental results show that our proposed algorithm has a higher retrieval ability than the ordinary deep hash method, and the retrieval accuracy and recall rate are greatly improved.
Graph-Theoretic Models of Resource Distribution for Cyber-Physical Systems of Disaster-Affected Regions

We propose a tool-supported framework to reason about requirements constraining resource distributions and devise strategies for routing essential services in a disaster-affected region. At the core of our approach is the Route Advisor for Disaster-Affected Regions (RADAR) framework that operates on high-level algebraic representations of the region, modelled as a cyber-physical system (cps) where resource distribution is carried out over an infrastructure connecting physical geographical locations. The Satisfiable-Modulo Theories (SMT) and graph-theoretic algorithms used by the framework supports disaster management decision-making during response and preparedness phases. We demonstrate our approach on a case study in disaster management and describe scenarios to illustrate the usefulness of RADAR.
Nested Session Types

Session types statically describe communication protocols between concurrent message-passing processes. Unfortunately, parametric polymorphism even in its restricted prenex form is not fully understood in the context of session types. In this paper, we present the metatheory of session types extended with prenex polymorphism and, as a result, nested recursive datatypes. Remarkably, we prove that type equality is decidable by exhibiting a reduction to trace equivalence of deterministic first-order grammars. Recognizing the high theoretical complexity of the latter, we also propose a novel type equality algorithm and prove its soundness. We observe that the algorithm is surprisingly efficient and, despite its incompleteness, sufficient for all our examples. We have implemented our ideas by extending the Rast programming language with nested session types. We conclude with several examples illustrating the expressivity of our enhanced type system.
Data augmentation as stochastic optimization

We present a theoretical framework recasting data augmentation as stochastic optimization for a sequence of time-varying proxy losses. This provides a unified language for understanding techniques commonly thought of as data augmentation, including synthetic noise and label-preserving transformations, as well as more traditional ideas in stochastic optimization such as learning rate and batch size scheduling. We then specialize our framework to study arbitrary augmentations in the context of a simple model (overparameterized linear regression). We extend in this setting the classical Monro-Robbins theorem to include augmentation and obtain rates of convergence, giving conditions on the learning rate and augmentation schedule under which augmented gradient descent converges. Special cases give provably good schedules for augmentation with additive noise, minibatch SGD, and minibatch SGD with noise.
Detecting Aspect Categories by Using NLP for Online Review Summarization

Using on the web customer overviews as electronic verbal trade to help purchase fundamental authority has ended up being continuously common. The Web gives an expansive wellspring of customer reviews; anyway one can hardly peruse all overviews to get a sensible appraisal of a thing or organization. A substance planning framework that can gather reviews would henceforth be alluring. A subtask to be performed by such a structure is find the general point classes would in general in overview sentences, for which this paper presents two strategies. Rather than most existing procedures, the vital system displayed is an unsupervised strategy that applies association control mining on co-occasion repeat data obtained from a corpus to find these point arrangements. While not practically identical to top tier coordinated methodologies, the proposed unsupervised methodology performs better than a couple of direct baselines, a similar anyway regulated procedure, and an oversaw standard, with a F1-score of 67%. The second procedure is a managed variety that beats existing methodologies with a F1-score of 84%.
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime

We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g., the behavior of wide single hidden layer neural networks, when exploration is encouraged through entropy regularization. The dynamics of these models is established as a Wasserstein gradient flow of distributions in parameter space. We further prove global optimality of the fixed points of this dynamics under mild conditions on their initialization.
Accelerating Metropolis-Hastings with Lightweight Inference Compilation

In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-source framework we call Lightweight Inference Compilation (LIC). LIC implements amortized inference within an open-universe declarative probabilistic programming language (PPL). Graph neural networks are used to parameterize proposal distributions as functions of Markov blankets, which during "compilation" are optimized to approximate single-site Gibbs sampling distributions. Unlike prior work in inference compilation (IC), LIC forgoes importance sampling of linear execution traces in favor of operating directly on Bayesian networks. Through using a declarative PPL, the Markov blankets of nodes (which may be non-static) are queried at inference-time to produce proposers Experimental results show LIC can produce proposers which have less parameters, greater robustness to nuisance random variables, and improved posterior sampling in a Bayesian logistic regression and $n$-schools inference application.
Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means

Snort is one of open source IDS to detect intrusion or potentially malicious activity on network traffic. Snort will give alert for every detected intrusion and write the alerts in log. Log data in IDS Snort will help network administrator to analyze the vulnerability of network security system. Clustering algorithm such as FCM can be used to analyze the log data of IDS Snort. Implementation of the algorithm is based on Python 3 and aims to cluster alerts in log data into 4 risk categories, such as low, medium, high, and critical. The outcome of this analysis is to show cluster results of FCM and to visualize the types of attacks that IDS Snort has successfully detected. Evaluation process is done by using Modified Partition Coefficient (MPC) to determine the validity of FCM.
On Genealogy of Musical Figurativeness in V. Krivulin’s Poems of the 1970s

The article analyses musical figurativeness in V. Krivulin’s poems of the 1970s. The research objective includes identifying specificity of musical figurativeness in his poetical collections “Sunday Clouds” and “Musical Instruments in Sand and Snow”. Scientific originality of the study lies in the fact that the researcher traces literary genealogy of musical images in V. Krivulin’s poetical collections of the 1970s. The research findings are as follows: the author shows that the musical theme in V. Krivulin’s poems is associated with intellectual contexts of the Silver Age literature, in particular, with the mythologeme of Dionysian and Apollonian elements in art. Krivulin’s interpretation of A. Blok’s conception of music is analysed. The amateur poet reconsidered this conception in his literary manifesto.
Alain Lieury: A life dedicated to memory

Alain Lieury devoted half a century of his life to experimental general psychology and cognitive psychology. A student of Flores, Reuchlin, and Fraisse, and a specialist in memory, he arrived at the University of Rennes 2 in 1982. He foresaw future developments in the field : the dissemination of scientific and technical culture, educational innovations, the preservation and valorization of the scientific heritage of experimental psychology, the link between training and research, the convergence of research sites and training sites, and so on. Alain Lieury consolidated psychology as a scientific discipline, cementing its place in society and its institutional functioning, as well as inspiring generations of students.
Botnet attack investigation on Geography of Things (GoT) using INSPECT approach

The breakneck speed of Internet of Things (IoT) is continually growing with 5G networks to add new connected devices. Hackers make use of this IoT explosion as a perfect chance to launch attacks especially by building botnet army. There had been lot of research over the decade in detecting and investigating the Distributed Denial of Service (DDoS) attacks. This paper was aimed at the presentation of a cloud based forensic investigation framework that can adaptively acquire attack evidences from IoT environment. The investigation model is called INSPECT that worked in cloud data storageto acquire corresponding evidences of the DDoS attack launched on IoT. The model optimally selected and exploited the forensic fields alone from the vast cloud data logs in order to find the source of attack and to report dynamic chain of custody. As a continuous effort, an experimental setup was built with IoT Geo-spatial devices to launch DDoS attack scenario and investigation performed with contextual initialization based evidence acquisition. Significant progress was observed by isolating the trustworthy evidence data to avert any deliberate modification by attackers and presenting the chain of custody. The work provided way for the law enforcement authority to explore and reconstruct the crime scene using virtual machine snapshots with corresponding timestamp data. Experimental results revealed the high level of accuracy in the investigation of IoT data secured in the multitenant cloud.
CSUSM Library Guides: GEO 102 Library Resources : Finding evidence - activity

This guide includes library resources and assignments for students enrolled in GEO 102: General Education - Oral Communications
The Effect of Students' Perception of NCS-Based Curriculum on Career Competency: Focused on the Engine Course of Maritime High School

This study aims to analyze maritime high school students’ perception about the NCS-based curriculum and investigate the impact on career preparation behaviors as career competencies. For this purpose, a survey was conducted with students in the engine course, who had taken NCS-based practice subjects at a Maritime High School. As a result, first, the possession of NCS achievement goals perceived by students positively predicted the levels of career search activity, job experience activity, and job preparation activity as career preparation behaviors while the understanding of NCS perceived by students positively predicted career search activity, information gathering activity, job experience activity, and job preparation activity. Second, in the relationship in which students
Eye Movements Support Processing Spatial Configurations in Visual Working Memory

[Background] Previous research has shown that object locations are less memorized individually but rather dependent on the global spatial configuration consisting of all objects. Therefore, visual working memory performance is enhanced when the global configuration is shown at retrieval compared to a single object only. It is assumed that eye movements moderate this process. However, there are contrary findings whether eye movements support or attenuate configurational effects and with the present set of experiments, we investigated to what extent the significance of spatial configurations in visual working memory depends on eye movements. [Methods] Participants encoded the locations of grey objects and performed a location change detection task for one object probed at retrieval. This object was displaced in half of the trials. Participants were always shown a global configuration in the beginning of the trial. After a delay period, either the spatial configuration or a single object was shown at retrieval. We manipulated whether participants were allowed to make eye movements. There were different conditions in which participants either were allowed to move their eyes normally or they had to fixate the center of the spatial configuration. [Results] The presentation of the spatial configuration supported the detection of location changes. Importantly, configuration effects were attenuated with enforced fixation, both with a whole trial fixation and with a respective phase fixation. [Conclusion] Our findings provide evidence for the memory advantage of spatial configurations and indicate that eye movements support processing spatial configurations in visual working memory. Future investigations should further evaluate these findings in relation to configurational effects in visual working memory and transsaccadic memory.
The barriers of accepting Virtual Reality in Healthcare by older generation

Acute pain causes great anxiety in patients and is a significant challenge for the NHS staff. Chronic pain is a burden to the UK economy and causes substantial disability. Pain management is predicted to increase within the aging population in the UK. At the moment Pharmaceutical pain management techniques are commonly used, which are not only costly, but also resulting in significant side effects. Virtual Reality is an acceptable complement with minimal side effect. However there have been resistance in using VR amongst elderly population. This study explores the existing attitude and challenges expressed by elderly population in using VR technology for pain management. The study followed an experimental design and the finding indicated that pre-existing knowledge and experience of pain and VR, alongside the perceived ease of use and perceived usefulness of the technology has an impact on elderly’s attitude and adoption of VR technology.
Real Time Three-Dimensional image processing with Application to Automated Cell Surgery Visual Servoing

The locations of the target cell, its internal structures and surgical tools are essential to achieve visual servoing control for automated cell surgery. Real time image feedback allows the controller to adjust promptly for location variation due to motion of the cell when in contact with surgery tools. This paper uses Z-stack images of a 2-blastomere mouse embryo cell to develop a novel real time three-dimensional (3D) image processing algorithm. The proposed algorithm computes the centroid of each embryo blastomere and surgical tool tip in 3D image-plane coordinate. 3D Canny edge detector processes the embryo to produce a segmented 3D model. By converting the 3D model into a point cloud, the centroid of each blastomere is then estimated using an unsupervised machine learning technique, k-means clustering. For the surgical tool, 2D Canny edge detector is used in this case to compress computation time. The surgical tool tip location is selected as the furthest point away from the image edge where surgical tool appears. With 6.1μm computation variation and 2.4Hz update frequency, the proposed algorithm is suitable to perform automated cell surgery using visual servoing, especially Image-Based Visual Servoing (IBVS), with the obtained image-plane locations in the 3D image. The proposed algorithm has also shown theoretical potential to be implemented into other embryo development stages.
Pidgins and Creoles

Like virtually any form of linguistic practice, the use of pidgin and creole resources has been impacted by the rise of mass media, networked communication, and increased mobility in people’s interactions. Globalised societies provide new and rich conditions for the use of pidgins and creoles. Indeed, communication technologies are now widely viewed as a powerful catalyst of language contact and hybridisation, and the rise of mobile communication has been recognised as one of the central factors in current globalised linguistic diversity. This chapter provides an overview on pidgins and creoles under the conditions of mobile and mediated communication, and reviews the major contributions of linguistic research in this field from a theoretical background, to current approaches to globalisation and networked communication, and an overview of the different technologies and domains involved. Specific topics to be outlined include the notion of the multilingual internet (and conflicting scenarios of homogenisation vs. linguistic diversity); digital public space as a potential linguistic contact zone; patterns and practice of pidgin and creole use in digital environments (such as code-switching or orthographic norm-building), and finally the discourses and language ideologies that are prevalent in online communities of pidgin and creole-speaking users.
Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization

We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark. The core is a two-level recurrence hierarchy that operates on the word and character levels separately---enabling faster training and inference than comparable traditional models. A cross-level attention module further connects the two, and opens the door for network interpretability. The task module is a softmax classifier that enumerates valid combinations of diacritics. This architecture can be extended with a recurrent decoder that optionally accepts priors from partially diacritized text, which improves results. We employ extra tricks such as sentence dropout and majority voting to further boost the final result. Our best model achieves a WER of 5.34%, outperforming the previous state-of-the-art with a 30.56% relative error reduction.
SITUATION TASKS FOR STUDENTS OF GRADES 5–6 ON THE ETHICAL FOUNDATIONS OF INTERNET COMMUNICATION

The article discusses the problem of violations of ethical norms of communication on the Internet by students of grades 5–6. Young adolescent children are found on the Internet in violation of ethical standards and often violate them themselves. The actual task of teachers is to form students' skills to protect themselves from threats to the information environment that are unethical in nature. The article presents examples of situational tasks of three types: tasks that reveal the key concepts of the ethical foundations of communication on the Internet; situations of students' search activity in the network; situations of interaction of younger adolescents in the information environment. The developed set of tasks allows organizing the practical activities of students in grades 5–6 on the development of ethical norms of behavior in the global network both at informatics lessons and during extracurricular activities.