Reviewerly/exHarmony huggingface dataset:
A CRITICAL REVIEW ON DEVELOPING READINGSKILLS USING SMARTPHONES BY ESL LEARNERS

Learning of the English language has always been a high road for ESL learners, especially formany from Tamil Nadu. English is taught in forms of poems, prose, text etc. Though many of the students whoreach the university to undergo their graduate programmes come from an English medium background, manyfind it challenging to understand and respond in English. Blame it for their innate fear they have towards thelanguage or any other reason. Technology is something that these students embrace with so much ease. It ispretty evident the way they use their smartphones and masters the skills of using it in no time. Surviving a daywithout a smartphone is simply impossible for this generation. Hence, taking the aspect of their ease and loyaltyto the smartphone, this paper attempts to critically analyse how the device will be of potent help in developingthe reading skills in English.
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition

Current facial expression recognition methods fail to simultaneously cope with pose and subject variations. In this paper, we propose a novel unsupervised adversarial domain adaptation method which can alleviate both variations at the same time. Specially, our method consists of three learning strategies: adversarial domain adaptation learning, cross adversarial feature learning, and reconstruction learning. The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning. By using personalized adversarial domain adaptation, this learning strategy can alleviate subject variations and exploit information from the source domain to help learning in the target domain. The second serves to perform feature disentanglement between pose- and expression-related feature representations by impulsing pose-related feature representations expression-undistinguished and the expression-related feature representations pose-undistinguished. The last can further boost feature learning by applying face image reconstructions so that the learned expression-related feature representations are more pose- and identity-robust. Experimental results on four benchmark datasets demonstrate the effectiveness of the proposed method.
텍스트 마이닝 기법을 활용한 설문 문항 개선에 관한 연구

The Marine Safety Culture Index (MSCI) was developed in the year 2018 for objectively assessing the public safety culture levels and for incorporating it as data to spread knowledge regarding the marine safety culture. The method for calculating the safety culture index should include issues that may affect the safety culture and should consist of appropriate attributes for estimating the current status. In addition, continuous verification and supplementation are required for addressing social and economic changes. In this study, to determine whether the questionnaire designed by marine experts reflects the people’s interests and needs, we analyzed 915 marine safety proposals. Text mining was employed for analyzing the unstructured data of the marine safety proposals, and network analysis and topic modeling were subsequently performed. Analysis of the marine safety proposals was centered on attributes such as education, public relations, safety rules, awareness, skilled workers, and systems. Eighteen questions were modified and supplemented for reflecting the marine safety proposals, and reliability of the revised questions was analyzed. Furthermore, compared to the previous year, the questionnaire’s internal consistency was improved upon and was rated at a high value of 0.895. It is expected that by employing the derived marine safety culture index and incorporating the improved questionnaire that reflects the requirements of marine experts and the people, the improved questionnaire will contribute to the establishment of policies for spreading knowledge regarding the marine safety culture.
Thumbnails for Data Stories: A Survey of Current Practices

When people browse online news, small thumbnail images accompanying links to articles attract their attention and help them to decide which articles to read. As an increasing proportion of online news can be construed as data journalism, we have witnessed a corresponding increase in the incorporation of visualization in article thumbnails. However, there is little research to support alternative design choices for visualization thumbnails, which include resizing, cropping, simplifying, and embellishing charts appearing within the body of the associated article. We therefore sought to better understand these design choices and determine what makes a visualization thumbnail inviting and interpretable. This paper presents our findings from a survey of visualization thumbnails collected online and from conversations with data journalists and news graphics designers. Our study reveals that there exists an uncharted design space, one that is in need of further empirical study. Our work can thus be seen as a first step toward providing structured guidance on how to design thumbnails for data stories.
ROI Pooled Correlation Filters for Visual Tracking

The ROI (region-of-interest) based pooling method performs pooling operations on the cropped ROI regions for various samples and has shown great success in the object detection methods. It compresses the model size while preserving the localization accuracy, thus it is useful in the visual tracking field. Though being effective, the ROI-based pooling operation is not yet considered in the correlation filter formula. In this paper, we propose a novel ROI pooled correlation filter (RPCF) algorithm for robust visual tracking. Through mathematical derivations, we show that the ROI-based pooling can be equivalently achieved by enforcing additional constraints on the learned filter weights, which makes the ROI-based pooling feasible on the virtual circular samples. Besides, we develop an efficient joint training formula for the proposed correlation filter algorithm, and derive the Fourier solvers for efficient model training. Finally, we evaluate our RPCF tracker on OTB-2013, OTB-2015 and VOT-2017 benchmark datasets. Experimental results show that our tracker performs favourably against other state-of-the-art trackers.
Comparative Study Design of Multiple Coordinated Views for 2D Large High-Resolution Display with 3D Visualization Using Mixed Reality Technology

We present the design of our qualitative assessment of user interaction and data exploration using our hybrid 2D and 3D visual analytic application with 2D visual analytics application running on Large High-Resolution Display (LHRD) and 3D visual analytics application running on mixed reality immersive displays. The application used for the study visualizes our Monte Carlo simulation over time showing topological, geospatial, and temporal aspects of the data in multiple views. We assessed attitudinal responses on the usefulness of visual analytics using 2D visualization on LHRD, and compare that with visual analytics using 2D visualization on LHRD with 3D visualization using mixed reality display. We first perform a usability test, where the participants complete a couple of exploratory tasks: one, identifying corresponding assets in a visualization, and two, identifying patterns/relationships between particular assets. Participants perform the same tasks using two different system configurations: using 2D visualization on LHRD, using 2D and 3D visualization together but as separate application, and using 2D visualization on LHRD and 3D visualization on Microsoft HoloLens with multiple coordinated views across the two systems. A pilot study were conducted on the experimental design on the relative effectiveness of the different setups towards accomplishing the given tasks. We further discuss how the results of the pilot study confirm current system design decisions, and also discuss additional user-centric characteristics that must be considered to inform future design decisions.
Detection of Elliptical Traffic Signs

Detection of elliptical features is a challenging and important task in computer vision. In fact, ellipses can describe many objects in real images like manufactured objects, cells, ball, or traffic signs. Furthermore, an ellipse is defined by five parameters: the center coordinates, the semi major axe, the semi minor axe and the orientation, which require more computational power to estimate them. Some non-ideal ellipses cause also difficulties for detection such as occlusion, appearance of multiple ellipses at same time and non-parallel ellipse. In this paper, we are interested in detecting elliptical traffic signs. We present a method for detecting different cases of ellipses: simple, partially occluded, non-parallel, multiple ellipses in images. The method selects three lines to find the ellipse center, and then it calculates the value of the semi minor and major axes of the ellipse. Experiments show that the proposed method performs well on real images in the presence or not of noises.
THE DEVELOPMENT OF AUDIO VISUAL AS LEARNING MEDIA WITH THE THEME OF MY RESIDENCE AREA ON THE 4TH GRADE STUDENTS AT SDN KEPATIHAN 01 JEMBER

AbstractThis research was conducted in class IV of Kepatihan Elementary School 01 Jember with 38 research subjects. The purpose of this study is to describe the process and find out the results of the development of audio visual as learning media with the theme of my residence area on the 4th grade students at SDN Kepatihan 01 Jember. This type of research is research and development. This development research uses ADDIE model type (Analyze, Design, Development, Implementation, Evaluation). The data analysis technique in this study used a validation sheet of audio visual learning media by the media expert validator and the material expert validator, and student learning outcomes assessment sheets and student responses. The results of the research data analysis showed the effectiveness of the results of the validation carried out by the two validators obtained results of 81,7 while the results obtained from the student learning outcomes test were 94,7% and student responses amounted to 73,7% so that it can be concluded that the audio visual learning media has very fasible and effective criteria try out. Keywords: audio visual media, effectiveness of learning outcomes and responses
THE ROLE OF THE TEACHER IN TEACHING THE BIBLE

The role of the teacher is crucial in the education process. The emphasis in the teaching situation should be on the learning process as facilitated by the teacher and not the ‘conveying of knowledge’ by the teacher. Competencies rather than content have become an imperative to enable students to comprehend a continuously changing world. To realise this aim teaching methods and communication of the teacher play an important role. Knowledge of the communication process on the one hand and the receiver on the other hand has become an imperative.
PENGARUH PROFESIONALISME GURU TERHADAP PRESTASI BELAJAR SISWA PADA SEKOLAH MENENGAH PERTAMA (SMP) YAPIS MERAUKE

This study is entitled The Effect of Teacher Professionalism on Student Learning Achievement in Yapis Merauke Middle School (SMP). This study aims to find out how much the influence of teacher professionalism on student achievement both simultaneously and partially.The study population is the total number of teachers in the Yapis Merauke junior high school as many as 35 people.The results of the multiple linear regression equation are: Y = 15.147 + 0.076X₁ + 0.096X₂ + 0.088X₃. While the results of the study showed that the variables of teacher professionalism, namely education, experience and attitude simultaneously had a positive and significant effect on student achievement in Yapis Merauke junior high school with a Fcount value of 7.583, a significant level of 0.000, while a Ftable value with an alpha of 0.05 was obtained value of 2,679. Education has an influence on student achievement in junior high school (SMP) Yapis Merauke with a tcount of 2,563 and a sig of 0,000. Experience has an influence on student achievement in junior high school (SMP) Yapis Merauke with a tcount of 1.742 and a sig of 0.001. Attitude has an influence on student achievement in junior high school (SMP) Yapis Merauke with a tcount of 1.742 and a sig of 0.001. Attitude variable h&#x0D; , ,, .
Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin

Rainfall warning Based on indexs teleconnection, Synoptic Patterns of Atmospheric Upper Levels and Climatic elements a case study of Karoun basin
Clustered Hierarchical Entropy-Scaling Search of Astronomical and Biological Data

Both astronomy and biology are experiencing explosive growth of data, resulting in a "big data" problem that stands in the way of a "big data" opportunity for discovery. One common question asked of such data is that of approximate search ($\rho-$nearest neighbors search). We present a hierarchical search algorithm for such data sets that takes advantage of particular geometric properties apparent in both astronomical and biological data sets, namely the metric entropy and fractal dimensionality of the data. We present CHESS (Clustered Hierarchical Entropy-Scaling Search), a search tool with virtually no loss in specificity or sensitivity, demonstrating a $13.6\times$ speedup over linear search on the Sloan Digital Sky Survey's APOGEE data set and a $68\times$ speedup on the GreenGenes 16S metagenomic data set, as well as asymptotically fewer distance comparisons on APOGEE when compared to the FALCONN locality-sensitive hashing library. CHESS demonstrates an asymptotic complexity not directly dependent on data set size, and is in practice at least an order of magnitude faster than linear search by performing fewer distance comparisons. Unlike locality-sensitive hashing approaches, CHESS can work with any user-defined distance function. CHESS also allows for implicit data compression, which we demonstrate on the APOGEE data set. We also discuss an extension allowing for efficient k-nearest neighbors search.
Challenges and Opportunities for Sharing Threat Information with Radioactive Materials Operators

Operators are required to implement security measures to address requirements set by the regulatory body or competent authority.These security requirements are generally based on the national threat level and information provided by the relevant law enforcement authority, intelligence agencies, and other relevant stakeholders.However, not all States can share this information with those who hold radioactive materials (e.g., operators), especially if they take a more prescriptive approach to regulation on security.The same situation often exists when a performance-based approach is used because there are multiple barriers that restrict the competent authority from sharing threat information.For example, competent authorities need to protect confidentiality and comply with national laws, regulations, and other information security considerations.In this paper, the author presents some challenges and opportunities relevant to exchanging threat information.The objective is to reflect on current practices, including good practices at the state and operator levels, to facilitate cooperation 1 Duguay: Challenges and Opportunities for Sharing Threat Information with Radioactive Materials Operators
On the Splitting Property for Epistemic Logic Programs (Extended Abstract)

Epistemic logic programs constitute an extension of the stable model semantics to deal with new constructs called "subjective literals." Informally speaking, a subjective literal allows checking whether some objective literal is true in all or some stable models. However, its associated semantics has proved to be non-trivial, since the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. In this paper, we review an extension of the well-known splitting property for logic programs to the epistemic case. This "epistemic splitting property" is defined as a general condition that can be checked on any arbitrary epistemic semantics. Its satisfaction has desirable consequences both in the representation of conformant planning problems and in the encoding of the so-called subjective constraints.
Dung's semantics satisfy attack removal monotonicity

We show that preferred, stable, complete, and grounded semantics satisfy attack removal monotonicity. This means that if an attack from b to a is removed, the status of a cannot worsen, e.g. if a was skeptically accepted, it cannot become rejected.
A New Method for Mapping Active Joint Locations of Skeletons to Pre-Shape Space for Action Recognition

Being a class of effective feature descriptors for action recognition, action representations based on skeleton sequences have yielded excellent recognition results. Most methods used to construct these action representations are based on the information from all the joint positions in actions. Unfortunately, some joints in the actions do not improve the accuracy of action recognition, and may even cause unnecessary inter-class errors. In this study, the authors propose a new method for action recognition by selecting active joints which are closely related to the movement of the body as the first step. Further, a skeleton is characterized as a set of its active-joint positions, and the set can be mapped to a point on pre-shape space to filter out the scale and translation variability. Then, a skeleton sequence (an action) can be regarded as points on the space. Because the timing-sequence relationship between skeletons is very valuable for action recognition, a tensor-based linear dynamical system (tLDS) is employed to model the temporal information of the action. To avoid using a finite-order sequence to estimate the infinite-order feature descriptor of a tLDS, the descriptor is mapped to a point on an infinite Grassmannian composed of the extended observability subspaces. The action is classified using sparse coding and dictionary learning (SCDL) on the infinite Grassmannian. Experimental results demonstrate that the recognition accuracies of the proposed method outperform state-of-the-art ones on four different action datasets.
Optimizing GPU Cache Policies for MI Workloads

In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. Optimizing these workloads is important but complicated. As memory demands grow and data movement overheads increasingly limit performance, determining the best GPU caching policy to use for a diverse range of MI workloads represents one important challenge. To study this, we evaluate 17 MI applications and characterize their behaviors using a range of GPU caching strategies. In our evaluations, we find that the choice of caching policy in GPU caches involves multiple performance trade-offs and interactions, and there is no one-size-fits-all GPU caching policy for MI workloads. Based on detailed simulation results, we motivate and evaluate a set of cache optimizations that consistently match the performance of the best static GPU caching policies.
Commonsense Reasoning to Guide Deep Learning for Scene Understanding (Extended Abstract)

Our architecture uses non-monotonic logical reasoning with incomplete commonsense domain knowledge, and incremental inductive learning, to guide the construction of deep network models from a small number of training examples. Experimental results in the context of a robot reasoning about the partial occlusion of objects and the stability of object configurations in simulated images indicate an improvement in reliability and a reduction in computational effort in comparison with an architecture based just on deep networks.
LibGuides: English 101: Composition I: Annotated Bibliography & Paper

Resources to support research in English. Examples, templates and help on annotations and MLA style
Model of organization of summer camp day stay of children in event and activity format.

The article is devoted to the problem of increasing the educational potential of summer camps. The proposed approach to its solution is based on ideas about the children's and adult collective as an event community of participants in joint creative activity. The theoretical foundations of the design of the educational process in the conditions of a summer camp are described, its results are described as new competencies not achieved in the traditional class-lesson teaching system. The idea of individualization of the educational process is disclosed as the leading methodological basis of the summer camp project in the event-activity format. The educational event is presented as a design unit of the educational process. Sociocultural practices, creatively mastered by the participants of the camp shift, are characterized. The principles of their development by the example of the art of theater and cinema are disclosed. The logic of building sociocultural practice as a sequence of certain phases and immersions is described.
On the Likelihood of Local Projection Models

A local projection model is defined by a set of linear regressions that account for the associations between exogenous variables and an endogenous variable observed at different time points. While it is standard practice to separately estimate individual regressions using the ordinary least squares estimator, some recent studies treat a local projection model as a multivariate regression with correlated errors, i.e., seemingly unrelated regressions, and propose Bayesian and non-Bayesian methods to improve the estimation accuracy. However, it is not clear how and when this way of treatment of local projection models is justified. The primary purpose of this paper is to fill this gap by showing that the likelihood of local projection models can be analytically derived from a stationary vector moving average process. By means of numerical experiments, we confirm that this treatment of local projections is tenable for finite samples.