Reviewerly/exHarmony huggingface dataset:
PRINSIP PENGEMBANGAN BUKU AJAR BAHASA INGGRIS PARWISATA PADA PERGURUAN TINGGI

The presence of a textbook in the English learning process is very important, especially if the English learning process aims to prepare university students to be able to communicate actively in the context of the tourism industry. However, many English lecturers do not prepare and develop English textbook optimally, especially textbook that is used in teaching English for Tourism. This is mainly because the English lecturers are developing English textbook without paying much attention to basic principles underlying the process of developing a good English textbook. Therefore, from this occurrence then present study was aiming at describing principles of developing a good English text book to be used as a tool in teaching English for tourism. To fulfill this purpose of study, library research method was used to have deeper understanding toward the issue.  The approach of literature research is to read, evaluate, and categorize literatures in order to determine the key characteristics of the issue being studied. From the literature study, it was found that there are several experts giving key principles underlying the process of developing a good English textbook for tourism program. Those principles were seen very beneficial as a guidance for the lecturers in developing their tourism English textbook for university students.
Review on Carbon-Graphene Nanocomposite based conductive-ink in printed electronics

Energy is the divesting need of human being to make the life easy going and comfortable. But according to the recent scenario, there will be insufficient source of generating energy. Printed electronics is a new concept of printing technology. PE is safest printing ink technology for securing documentation, for making circuits on different subtracts, less time consuming for making and easily detect if counterfeit. Nanoprint technology with semiconductor materials is in improvising and it has been future trend in energy sector with enhancing various properties of nanomaterials. It has been developed in the area of the digital printing technology by using and combining different nanomaterials. With ever-increasing demand for light-weight, small, and transportable devices, the speed of production of electronic and optoelectronic devices is consistently increasing, and alternatives to the current vital, voluminous, fragile, semi semiconducting and clear materials will inevitably be needed at intervals the long run. This review article explains about the carbon-Graphene nanocomposite based conductive ink, which can be a source in digital printing technology to draw various types of circuits and to make different surface to conductive surfaces. It also describes brief information about carbon and Graphene material individually. It also concludes carbon-graphene nanocomposite based conductive ink in printed electronics technology with several literature surveys.
Announcing The New Bavard NLU Intent Service

We at Bavard AI recently overhauled significant parts of our natural language understanding AI engine to make it faster and more accurate, allowing you to now update your chatbot's NLU capabilities in near-real time. In this article, we discuss the business value of this new service, and announce the open-sourcing of a large meta-dataset we have curated for training few-shot intent classification systems. Finally we detail the technical implementation of the intent classification and out-of-domain detection models which power this new service.
x Unit like TT4V Text-based Testing Environment for Voice based Services

Like traditional software products, voice-based services should be tested. However, during testing, the service is activated and executed by a spoken command. Speaking the command for every test case would be very time consuming. TT4V saves testing time by enabling text-form test cases instead of voice-form test cases. It implements an xUnit-style interface. xUnit has been a de facto standard framework for testing tools for many years. Without TT4V, we roughly measured the time to speak 55 commands, listen to 55 responses, and then verify the correctness of the responses. First, we ran five of the 55 test cases without TT4V, and it took 28.03 seconds. For 55 test cases, it would be multiplied by 11 roughly. We ran 55 test cases using TT4V. It took only 0.61 seconds from executing the test cases to showing the analysis of the testing results.
How to Write Scientific Research Article? A General Guide

A general guide for writing scientific research article, present all methods and strategies regarding article structure, common research sections, IMRAD techniques and so on.
La nueva normalidad y el uso del método hermenéutico en las investigaciones en el período pospandémico desde una perspectiva teórica. The new normality and the use of the hermeneutic method in investigations in the postpandemic period from a theoretical perspective

Nowadays, researchers must perceive the new reality with methods of perception that feed the path of analysis and interpretation. The objective is focused on the identification of concepts and evolution of hermeneutics as an alternative method to face, understand and interpret the different situations that the educational field goes through at a post-pandemic level. The methodology used was under a qualitative approach of bibliographic order, with a universe of 30 documents, which were processed in the Atlas.Ti8 software, allowing the discovery of relationship patterns. Within the results after the analysis, a total of 68 codes were identified, which were grouped into three general patterns (new reality, hermeneutic method, and post-pandemic research) allowing the identification of new communicational approaches and systems in the field of education. Finally, it is clear that hermeneutics is one of the oldest methods within the interpretation of texts and that is where its importance lies in the academic field since the researcher sees the need to investigate information using his hermeneutical ability to be able to perceive from an objective and interpretive way the information available leading to a very responsible and technical analysis process.
Siamese network text similarity calculation with multi-head self-attention mechanism

Calculating text similarity is the key to natural language processing. Existing text similarity calculation methods can not fully extract deep semantic information. And the ability to calculate the similarity of long texts is limited. Aiming at the shortcoming of existing text similarity calculation methods, a Siamese network based on multi-head self-attention mechanism is proposed. This method uses the Siamese model based on bidirectional GRU to accurately extract the semantic information of the context. Then learning deep semantic information of long text by adding multi head self-attention mechanism. Experimental results show that Siamese network with multi-head self-attention mechanism can learn the deep semantic information of long text in the SICK dataset. Compared with other text similarity calculation methods, the correlation coefficient of the proposed method is significantly improved. The effect of processing long text is improved.
An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services

With sensors becoming increasingly ubiquitous, there is tremendous potential for Internet of Things (IoT) services that can take advantage of the data collected by these sensors. Although there are a growing number of technologies focused on IoT services, there is relatively limited foundational work on them. This is partly because of the lack of precise understanding, specification, and analysis of such services, and, consequently, there is limited platform support for programming them. In this paper, we present a formal model for understanding and enabling reasoning about distributed IoT services. The paper first studies the key properties of the IoT services profoundly, and then develops an approach for fine-grained resource coordination and control for such services. The resource model identifies the core mechanisms underlying IoT services, informing design and implementation decisions about them if implemented over a middleware or a platform. We took a multi-agent systems approach to represent IoT services, broadly founded in the actors model of concurrency. Actor-based services can be built by composing simpler services. Furthermore, we created a proximity model to represent an appropriate notion of IoT proximity. This model represents the dynamically evolving relationship between the service’s sensing and acting capabilities and the environments in which these capabilities are exercised. The paper also presents the design of a runtime environment to support the implementation of IoT services. Key mechanisms required by such services will be implemented in a distributed middleware.
(Retracted) Inspection of unmanned aerial vehicles in oil and gas industry: critical analysis of platforms, sensors, networking architecture, and path planning

The rapid growth of unmanned aerial vehicles (UAVs) has huge potential in the oil and gas industry (OGI). These are especially helpful in situations in which human lives are at risk. OGI pipelines are threatened by natural and man-made disasters, which are harmful to both people and assets. Usually, oil and gas pipelines pass through extreme environments for which standard inspection, maintenance, and repairing approaches such as rope access, scaffolds, telescopic elevation platforms supported by cranes, and manned helicopters are not secure and difficult and expensive to implement. However, technological advancements like device miniaturization have boosted the performance of UAVs, offering cost-effective, efficient, and high mission flexibility. UAVs are capable of carrying sensors and cameras to perform monitoring. As pipelines span thousands of kilometers, multi-UAV systems, commonly referred to as flying ad-hoc networks (FANETs), can collaboratively complete monitoring missions more effectively and economically as compared with single UAV systems. Moreover, many issues must be resolved before the effective use of UAVs can provide stable and reliable context-specific networks. Several OGI-specific issues of UAVs such as architecture design, platform, sensors, networking architectures, and path planning models for different OGI pipeline surveillance scenarios must be resolved to use FANETs effectively for robust and sustainable networks. The prime objective of this research is a state-of-the-art review of UAVs in OGI. We first present OGI midstream challenges and parameters to give a brief overview of the challenges faced by the OGI for pipeline surveillance. Then we discuss OGI-specific scenarios for sensor readings, visual leak detection, and detection of any unusual activity that happens in the pipeline while monitoring through UAVs. We also present different OGI-specific UAV platforms, UAV networking architectures, and path planning models of FANET for efficient communication and collaboration. Finally, the challenges of UAVs and future research prospects in OGI-specific UAVs are highlighted.
Optimizing continuous dynamical decoupling with machine learning

Decoherence of a quantum system is one of the main difficulties for quantum information processing. Continuous dynamical decoupling has achieved great success in the improvement of the coherence of a quantum state but is difficult to optimize. Here we exploit the black-box optimization process in a machine learning algorithm to optimize the dynamical decoupling scheme. By applying the discrete optimization process to continuous driving fields, we achieve a longer coherence time in comparison to representative schemes of dynamical decoupling. Our black-box-optimization-based machine learning algorithm provides a general routine to tackle the challenging task of improving the coherence of a quantum state for which the dephasing of the system is crucial for operation.
Contrastive Proposal Extension With LSTM Network for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has attracted more and more attention since it only uses image-level labels and can save huge annotation costs. Most of the WSOD methods use Multiple Instance Learning (MIL) as their basic framework, which regard it as an instance classification problem. However, these methods based on MIL tend to only converge on the most discriminative regions of different instances, rather than their corresponding complete regions, that is, insufficient integrity. Inspired by the human habit of observing things, we propose a new method by comparing the initial proposals and the extended ones to optimize those initial proposals. Specifically, we propose one new strategy for WSOD by involving contrastive proposal extension (CPE), which consists of multiple directional contrastive proposal extensions (D-CPEs), and each D-CPE contains LSTM-based encoders and dual-stream decoders. Firstly, the boundary of initial proposals in MIL is extended to different positions according to well-designed sequential order. Then, the CPE compares the extended proposal and the initial one by extracting the feature semantics of them using the encoders, and calculates the integrity of the initial proposal to optimize its score. These contrastive contextual semantics will guide the basic WSOD to suppress bad proposals and improve the scores of good ones. In addition, a simple dual-stream network is designed as the decoder to constrain the temporal coding of LSTM and improve the performance of WSOD further. Experiments on PASCAL VOC 2007, VOC 2012 and MS-COCO datasets show that our method has achieved the state-of-the-art results.
SILA: a system for scientific image analysis

A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset.
Skeleton-based motion prediction: A survey

Human motion prediction based on 3D skeleton data is an active research topic in computer vision and multimedia analysis, which involves many disciplines, such as image processing, pattern recognition, and artificial intelligence. As an effective representation of human motion, human 3D skeleton data is favored by researchers because it provide resistant to light effects, scene changes, etc. earlier studies on human motion prediction focuses mainly on RBG data-based techniques. In recent years, researchers have proposed the fusion of human skeleton data and depth learning methods for human motion prediction and achieved good results. We first introduced human motion prediction research background and significance in this survey. We then summarized the latest deep learning-based techniques for predicting human motion in recent years. Finally, a detailed paper review and future development discussion are provided.
On Bernoulli trials with unequal harmonic success probabilities

A Bernoulli scheme with unequal harmonic success probabilities is investigated, together with some of its natural extensions. The study includes the number of successes over some time window, the times to (between) successive successes and the time to the first success. Large sample asymptotics, statistical parameter estimation, and relations to Sibuya distributions and Yule–Simon distributions are discussed. This toy model is relevant in several applications including reliability, species sampling problems, record values breaking and random walks with disasters.
Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

To identify which Machine Learning (ML) algorithms are the most successful in predicting and diagnosing breast cancer according to accuracy rates.The "College of Wisconsin Breast Cancer Dataset", which consists of 569 data and 30 features, was classified using Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA), XgBoost (XGB), Ada-Boost (ABC) and Gradient Boosting (GBC) ML algorithms. Before the classification process, the dataset was preprocessed. Sensitivity, accuracy, and definiteness metrics were used to measure the success of the methods.Compared to other ML algorithms used in the study, the GBC ML algorithm was found to be the most successful method in the classification of tumors with an accuracy of 99.12%. The XGB ML algorithm was found to be the lowest method with an accuracy rate of 88.10%. In addition, it was determined that the general accuracy rates of the 11 ML algorithms used in the study varied between 88-95%.When the results obtained from the ML classifiers used in the study are evaluated, the efficiency of the GBC algorithm in the classification of tumors is obvious. It can be said that the success rates obtained from 11 different ML algorithms used in the study are valuable in terms of being used to predict different cancer types.
PROBONITE

Decision trees are among the most widespread machine learning models used for data classification, in particular due to their interpretability that makes it easy to explain their prediction. In this paper, we propose a novel protocol for the private classification of a client request in a non-interactive manner. In contrast to existing solutions to this problem, which are either interactive or require evaluating all the branches of the decision tree, our approach only evaluates a single branch of the tree. Our protocol is based on two primitives that we also introduce in this paper and that may be of independent interest : Blind Node Selection and Blind Array Access. Those contributions are based on recent advances in homomorphic cryptography, such as the functional bootstrapping mechanism recently proposed for the Fully Homomorphic Encryption over the Torus scheme TFHE. Our private decision tree evaluation algorithm is highly efficient as it requires only one round of communication and d comparisons, with d being the depth of the tree, while other state-of-the-art non-interactive protocols need 2^d comparisons.
The EFL Learning Process: An Examination of the Potential of Social Media

Social media as a technological tool has recently come to support learning in both academic and public use. Students typically use social networks to enhance their education by discussing and exchanging academic content. However, its impact that needs carefully study to the vast inroads that social media has made into the academic sphere. Therefore, the purpose of this study is to examine its impact on the process of learning English language in English as a Foreign Language (EFL) contexts. It also aims to determine the impact of employing a social media platform in the Saudi classroom on the learning of EFL students. This study explores the role of social media by giving a panoramic view of the types of social media and social networking sites, the use of social medias in education, social media in learner engagement, social media and students' achievement, social media application in the EFL classroom and finally, social media research in the Saudi higher education scene, and the challenges of each of these. This study concludes that students can benefit the most from these media when they are encouraged to use their mobile devices as learning tools. This conclusion echoes earlier findings in the Saudi context that showed the positive impact of social media applications in boosting students' English language learning. Based on a review of the literature gathered from diverse sources, it is recommended to investigate the inclusion of social media applications, platforms and sites in the English language course descriptions at Saudi universities.
GBCL: Reduce Concurrency Conflicts in Hyperledger Fabric

Hyperledger Fabric is a well-known permissioned blockchain platform with a high throughput that is frequently used in a variety of applications. However, while Hyperledger Fabric achieves higher throughput, it also generates more concurrency conflicts, which reduces its efficiency. In this paper, we propose a solution to the concurrency conflict problem in Hyperledger Fabric. We create a simulation system based on gPRC communication and test it to ensure that our solution is correct. On this basis, a number of experiments are conducted with the GBCL solution, which is built on Hyperledger Fabric V2.2. The results of the experiments reveal that GBCL effectively handles the Hyperledger Fabric concurrency conflict problem.
Leaky Way: A Conflict-Based Cache Covert Channel Bypassing Set Associativity

Modern $\times$86 processors feature many prefetch instructions that developers can use to enhance performance. However, with some prefetch instructions, users can more directly manipulate cache states which may result in powerful cache covert channel and side channel attacks. In this work, we reverse-engineer the detailed cache behavior of PREFETCHNTA on various Intel processors. Based on the results, we first propose a new conflict-based cache covert channel named NTP+NTP. Prior conflict-based channels often require priming the cache set in order to cause cache conflicts. In contrast, in NTP+NTP, the data of the sender and receiver can compete for one specific way in the cache set, achieving cache conflicts without cache set priming for the first time. As a result, NTP+NTP has higher bandwidth than prior conflict-based channels such as Prime+Probe. The channel capacity of NTP+NTP is 302 KB/s. Second, we found that PREFETCHNTA can also be used to boost the performance of existing side channel attacks that utilize cache replacement states, making those attacks much more efficient than before.
Tracking individuals’ health using mobile applications and Machine Learning

The widespread availability of smartphones and their high processing power have made them powerful mobile tools able to host and run various apps. In addition, wearable devices with low cost and accurate sensors gathering various physiological data and information are now available. Meanwhile, automated activity recognition is a rapidly evolving research area directly related to the mobile Health (mHealth) field. Rapid advancements in the Human Activity Recognition (HAR) field are mainly based on combining smartphones and wearable devices to succeed in advancing health tracking. This paper presents a mobile app designed and developed for monitoring changes in variables related to the physiological health status of an individual when he is moving around. The app tracks the physiological status of a human along with machine learning algorithms able to recognize and identify human activity and produce automatic alerts warning of dangerous health situations.