Reviewerly/exHarmony huggingface dataset:
Tourist Guide via Image Processing Techniques

To achieve the goal of identification, image processing and recognition is performed on the actual picture transformation. The amount of information included in an image is enormous because it is a two-dimensional space. Neural network image recognition is a new type of picture recognition technology developed by modern computer innovation. In this paper we have used neural network to implement image-based location identification. Using the image data, we have evaluated our proposed model’s predictive performance. By including more hidden layers in the convolution neural network, it is feasible to improve the network's training speed and reliability by having a large amount of data. However, 80% of the dataset is used for training and the remaining 20% is used for testing in our studies. The preliminary findings have shown that using a neural network to detect photos is both successful and practical. The proposed methods serve as a guide for travelers or tourists who are unfamiliar with a country. They simply need to point the camera at any historical or well-known location to learn more about it.
Explaining Predictive Model Performance: An Experimental Study of Data Preparation and Model Choice

Although confirmatory modeling has dominated much of applied research in medical, business, and behavioral sciences, modeling large data sets with the goal of accurate prediction has become more widely accepted. The current practice for fitting predictive models is guided by heuristic-based modeling frameworks that lead researchers to make a series of often isolated decisions regarding data preparation and cleaning that may result in substandard predictive performance. In this article, we use an experimental design to evaluate the impact of six factors related to data preparation and model selection (techniques for numerical imputation, categorical imputation, encoding, subsampling for unbalanced data, feature selection, and machine learning algorithm) and their interactions on the predictive accuracy of models applied to a large, publicly available heart transplantation database. Our factorial experiment includes 10,800 models evaluated on 5 independent test partitions of the data. Results confirm that some decisions made early in the modeling process interact with later decisions to affect predictive performance; therefore, the current practice of making these decisions independently can negatively affect predictive outcomes. A key result of this case study is to highlight the need for improved rigor in applied predictive research. By using the scientific method to inform predictive modeling, we can work toward a framework for applied predictive modeling and a standard for reproducibility in predictive research.
FGFlick: Augmenting Single-Finger Input Vocabulary for Smartphones with Simultaneous Finger and Gaze Flicks

FGFlick is an interactive technique featuring simultaneous single-finger operation and a gaze. The user flicks a smartphone and moves their gaze linearly. FGFlick thus augments the single-finger input vocabulary. As a result of the evaluation of the FGFlick gestures, we achieved success rates of 84.0%.
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues

We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community. Real-life scenario dialogue summarization has a wide potential application prospect in chat-bot and personal assistant. It contains unique challenges such as special discourse structure, coreference, pragmatics, and social common sense, which require specific representation learning technologies to deal with. We carefully annotate a large-scale dialogue summarization dataset based on multiple public dialogue corpus, opening the door to all kinds of summarization models.
Shared Task on Feedback Comment Generation for Language Learners

In this paper, we propose a generation challenge called Feedback comment generation for language learners. It is a task where given a text and a span, a system generates, for the span, an explanatory note that helps the writer (language learner) improve their writing skills. The motivations for this challenge are: (i) practically, it will be beneficial for both language learners and teachers if a computer-assisted language learning system can provide feedback comments just as human teachers do; (ii) theoretically, feedback comment generation for language learners has a mixed aspect of other generation tasks together with its unique features and it will be interesting to explore what kind of generation technique is effective against what kind of writing rule. To this end, we have created a dataset and developed baseline systems to estimate baseline performance. With these preparations, we propose a generation challenge of feedback comment generation.
A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance

Despite the recent advances in image-guided neurosurgery, reliable and accurate estimation of the brain shift still remains one of the key challenges. In this paper, we propose an automated multimodal deformable registration method using hybrid learning-based and classical approaches to improve neurosurgical procedures. Initially, the moving and fixed images are aligned using classical affine transformation (MINC toolkit), and then the result is provided to the convolutional neural network, which predicts the deformation field using backpropagation. Subsequently, the moving image is transformed using the resultant deformation into a moved image. Our model was evaluated on two publicly available datasets: the retrospective evaluation of cerebral tumors (RESECT) and brain images of tumors for evaluation (BITE). The mean target registration errors have been reduced from 5.35 ± 4.29 to 0.99 ± 0.22 mm in the RESECT and from 4.18 ± 1.91 to 1.68 ± 0.65 mm in the BITE. Experimental results showed that our method improved the state-of-the-art in terms of both accuracy and runtime speed (170 ms on average). Hence, the proposed method provides a fast runtime for 3D MRI to intra-operative US pair in a GPU-based implementation, which shows a promise for its applicability in assisting the neurosurgical procedures compensating for brain shift.
Video Object Detection through Traditional and Deep Learning Methods

Object detection in videos is gaining more attention recently as it is related to video analytics and facilitates image understanding and applicable to . The video object detection methods can be divided into traditional and deep learning based methods. Trajectory classification, low rank sparse matrix, background subtraction and object tracking are considered as traditional object detection methods as they primary focus is informative feature collection, region selection and classification. The deep learning methods are more popular now days as they facilitate high-level features and problem solving in object detection algorithms. We have discussed various object detection methods and challenges in this paper
Recent Advances in Robot Trajectory Planning in a Dynamic Environment

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multisensor information fusion, the combination of local planning and global planning, and multirobot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.
Analysis of Momentous Fragmentary Formants in Talaqi-like Neoteric Assessment of Quran Recitation using MFCC Miniature Features of Quranic Syllables

The use of technological speech recognition systems with a variety of approaches and techniques has grown rapidly in a variety of human-machine interaction applications.Further to this, a computerized assessment system to identify errors in reading the Qur'an can be developed to practice the advantages of technology that exist today.Based on Quranic syllable utterances, which contain Tajweed rules that generally consist of Makhraj (articulation process), Sifaat (letter features or pronunciation) and Harakat (pronunciation extension), this paper attempts to present the technological capabilities in realizing Quranic recitation assessment.The transformation of the digital signal of the Quranic voice with the identification of reading errors (based on the Law of Tajweed) is the main focus of this paper.This involves many stages in the process related to the representation of Quranic syllable-based Recitation Speech Signal (QRSS), feature extraction, non-phonetic transcription Quranic Recitation Acoustic Model (QRAM), and threshold classification processes.MFCC-Formants are used in a miniature state that are hybridized with three bands in representing QRSS combined vowels and consonants.A human-guided threshold classification approach is used to assess recitation based on Quranic syllables and threshold classification performance for the low, medium, and high band groups with performances of 87.27%, 86.86%and 86.33%, respectively.
Implementation of a competency-based approach in the training of future primary school teachers

Ensuring the reform of today's primary school provides for a certain removal of restrictions in pedagogical activity, providing teachers with freedom in interpreting educational programs, using forms and methods of working with younger students. This requires from elementary school teachers not only deep knowledge, possession of a set of relevant professional skills and abilities, but also an orientation towards pedagogical creativity, towards the needs of the student in the educational process, an understanding of their own responsibility for the results obtained, the ability to act effectively in conditions of academic freedom and decentralization of the school. In modern conditions of education reform, the relevance of the problem under study is explained by a number of factors. Therefore, we consider the solution of certain social and educational problems to be an important component of the activity of a primary school teacher.
Gradient-Based Versus Gradient-Free Algorithms for Reinforcement Learning

Despite ongoing improvements, gradient-based reinforcement learning (RL) algorithms involving Neural Networks (NN), remain deficient in reaching the expected behavior by the gradient estimates only. Evolution based approaches can serve as an alternative for training NNs at RL tasks. This paper reports on a comparison of gradient-based Deep Q-Network (DQN) and Double DQN algorithms, with gradient-free (population-based) Genetic Algorithms (GA), on learning to play the Flappy Bird game that involves complex sensory inputs. The results revealed superiority of the GA-based approach and its ability to handle high dimensional search space. This paper reports the time taken for training the agents that deploy such schemes. The observations obtained are the gradient-free GAs required only a few hours to learn to play the game, even without GPUs, whereas DQN and Double DQN entailed more than 100 h, in spite of GPU-assisted training. This demonstrates the inappropriate nature of gradient-based routines for optimization, with the coincidence, enhanced potential of GA-based perspective, augmented by parallelizing and training GPUs.
PENGEMBANGAN MODEL E-LEARNING BERBASIS LMS-MOODLE UNTUK MENINGKATKAN HASIL BELAJAR SISWA

The aims of this study to: (1) Creat e-learning based on LMS-Moodle to improve student learning outcomes. (2) Improve students learning outcomes of Digital Simulation Subject of X Grade using e-learning model based on LMS-Moodle. In this study uses a development design (Research and Development). The subjects of this study is students of Automotive Engineering Skills Competency of Vocational High School Number 02 Bengkulu City. The techniques of collecting the data uses the assessment sheets and student test results. Data analysis used mean and t-test. The instrument of this study uses a Linkert scale. The conclusions of this research are; (1) The development of an e-learning model based on the LSM-Moodle can be used to improve student learning outcomes in digital simulation subject (2) The development of an e-learning model based on the LSM-Moodle can be feasible to improve learning outcomes of X Automotive Engineering grade at Vocational High School Number 02 Bengkulu City in digital simulation subject
BERT for Russian news clustering

This paper provides results of participation in the Russian News Clustering task within Dialogue Evaluation 2021.News clustering is a common task in the industry, and its purpose is to group news by events.We propose two methods based on BERT for news clustering, one of them shows competitive results in Dialogue 2021 evaluation.The first method uses supervised representation learning.The second one reduces the problem to binary classification.
EGMM video surveillance for monitoring urban traffic scenario

Purpose In the traffic monitoring system, the detection of stirring vehicles is monitored by fitting static cameras in the traffic scenarios. Background subtraction a commonly used method detaches poignant objects in the foreground from the background. The method applies a Gaussian Mixture Model, which can effortlessly be contaminated through slow-moving or momentarily stopped vehicles. Design/methodology/approach This paper proposes the Enhanced Gaussian Mixture Model to overcome the addressed issue, efficiently detecting vehicles in complex traffic scenarios. Findings The model was evaluated with experiments conducted using real-world on-road travel videos. The evidence intimates that the proposed model excels with other approaches showing the accuracy of 0.9759 when compared with the existing Gaussian mixture model (GMM) model and avoids contamination of slow-moving or momentarily stopped vehicles. Originality/value The proposed method effectively combines, tracks and classifies the traffic vehicles, resolving the contamination problem that occurred by slow-moving or momentarily stopped vehicles.
Integrating Tree Decompositions into Decision Heuristics of Propositional Model Counters (Short Paper).

Propositional model counting (#SAT), the problem of determining the number of satisfying assignments of a propositional formula, is the archetypical #P-complete problem with a wide range of applications in AI. In this paper, we show that integrating tree decompositions of low width into the decision heuristics of a reference exact model counter (SharpSAT) significantly improves its runtime performance. In particular, our modifications to SharpSAT (and its derivant GANAK) lift the runtime efficiency of SharpSAT to the extent that it outperforms state-of-the-art exact model counters, including earlier-developed model counters that exploit tree decompositions.
Beyond Genre: Classifying Virtual Reality Experiences

Because virtual reality (VR) shares common features with video games, consumer content is usually classified according to traditional game genres and standards.However, VR offers different experiences based on the medium's unique affordances.To account for this disparity, the article presents a comparative analysis of titles from the Steam digital store across three platform types: VR only, VR supported, and non-VR.We analyzed data from a subset of the most popular applications within each category (N = 141, 93, and 1217, respectively).The three classification types we analyzed were academic game genres, developer defined categories, and user-denoted tags.Results identify the most common content classifications (e.g., Action and Shooter within VR only applications), the relative availability of each between platforms (e.g., Casual is more common in VR only than VR supported or non-VR), general platform popularity (e.g., VR only received less positive ratings than VR supported and non-VR), and which content types are associated with higher user ratings across platforms (e.g., Action and Music/Rhythm are most positively rated in VR only).Our findings ultimately provide a foundational framework for future theoretical constructions of classification systems based on content, market, interactivity, sociality, and service dependencies, which underlay how consumer VR is currently categorized.
Reconciling optimization with secure compilation

Software protections against side-channel and physical attacks are essential to the development of secure applications. Such protections are meaningful at machine code or micro-architectural level, but they typically do not carry observable semantics at source level. This renders them susceptible to miscompilation, and security engineers embed input/output side-effects to prevent optimizing compilers from altering them. Yet these side-effects are error-prone and compiler-dependent. The current practice involves analyzing the generated machine code to make sure security or privacy properties are still enforced. These side-effects may also be too expensive in fine-grained protections such as control-flow integrity. We introduce observations of the program state that are intrinsic to the correct execution of security protections, along with means to specify and preserve observations across the compilation flow. Such observations complement the input/output semantics-preservation contract of compilers. We introduce an opacification mechanism to preserve and enforce a partial ordering of observations. This approach is compatible with a production compiler and does not incur any modification to its optimization passes. We validate the effectiveness and performance of our approach on a range of benchmarks, expressing the secure compilation of these applications in terms of observations to be made at specific program points.
An Investigation of Covid-19 Papers for a Content-Based Recommendation System

The proliferation of scientific publications is a well-known phenomenon that was recently emphasized by the publications related to the Covid-19. The number of publications Covid-19 related that PubMed added in the period between January 17 and April 18, 2020 kept rising until it reached a number of 300 publications added in a single day. There are obvious issues related to this phenomenon, such as the difficulty for researchers to find papers strongly related to their applications. When searching for related papers, there could be issues with how a paper is preferred with respect to another. A paper could be recommended based on the greater number of citations, or on the connections between authors, that is as well related to the number of citations. For such reasons, the aim of this study is to build a recommendation system based exclusively on the abstracts of these publications. We provide a comparison between classical approaches—NLP-based such as TF-IDF and n-grams—and Deep Learning approaches for content-based recommendation systems, such as Transformers. We also provide an application to graphs that shows the relationships among related papers on the basis of the results obtained from the recommendation system developed.
SpecSafe: detecting cache side channels in a speculative world

The high-profile Spectre attack and its variants have revealed that speculative execution may leave secret-dependent footprints in the cache, allowing an attacker to learn confidential data. However, existing static side-channel detectors either ignore speculative execution, leading to false negatives, or lack a precise cache model, leading to false positives. In this paper, somewhat surprisingly, we show that it is challenging to develop a speculation-aware static analysis with precise cache models: a combination of existing works does not necessarily catch all cache side channels. Motivated by this observation, we present a new semantic definition of security against cache-based side-channel attacks, called Speculative-Aware noninterference (SANI), which is applicable to a variety of attacks and cache models. We also develop SpecSafe to detect the violations of SANI. Unlike other speculation-aware symbolic executors, SpecSafe employs a novel program transformation so that SANI can be soundly checked by speculation-unaware side-channel detectors. SpecSafe is shown to be both scalable and accurate on a set of moderately sized benchmarks, including commonly used cryptography libraries.
A K-means Text Clustering Algorithm Based on Subject Feature Vector

As one of the most popular clustering algorithms, k-means is easily influenced by initial points and the number of clusters, besides, the iterative class center calculated by the mean of all points in a cluster is one of the reasons influencing clustering performance. Representational initial points are selected in this paper according to the decision graph composed by local density and distance of each point. Then we propose an improved k-means text clustering algorithm, the iterative class center of the improved algorithm is composed by subject feature vector which can avoid the influence caused by noises. Experiments show that the initial points are selected successfully and the clustering results improve 3%, 5%, 2% and 7% respectively than traditional k-means clustering algorithm on four experimental corpuses of Fudan and Sougou.
Some new constructions of MDS self-dual codes over finite fields

In this paper, for some fixed q, we construct some new classes of maximum distance separable (MDS) self-dual codes over Fq by using (extended) generalized Reed-Solomon codes. Our constructions utilize the additive and/or multiplicative structures of the finite field Fq. It reveals that by choosing suitable subsets of Fq, more MDS self-dual codes with new parameters can be found.
Improving the BERT Model with Proposed Named Entity Recognition Method for Question Answering

Recently, the analysis of textual data has gained importance due to the increase in comments made on web platforms and the need for ready-made answering systems. Therefore, there are many studies in the fields of natural language processing such as text summarization and question answering. In this paper, the accuracy of the BERT language model is analyzed for the question answering domain, which allows to automatically answer a question asked. Using SQuAD, one of the reading comprehension datasets, the answers to the questions that the BERT model cannot answer are researched with the proposed Named Entity Recognition method in natural language processing. The accuracy of BERT models used with the proposed Named Entity Recognition method increases between 1.7% and 2.7%. As a result of the analysis, it is shown that the BERT model doesn’t use Named Entity Recognition technique sufficiently.