Reviewerly/exHarmony huggingface dataset:
Genre-Based Approach: Can It Improve the Informatics Engineering Students’ Writing Skill?

Because instructional process in English for Specific Purpose (ESP) context is oriented to students’ academic and professional settings within discourse community, challenges appear. Learning language and learning language are directed to improving students’ language skills to survive the 21st century. On the other hand, score-documentations proved that the students performed low skills; especially writing skill. This study aimed at improving the students’ skill in writing explanation text by using Genre-Based Approach (GBA) to Informatics Engineering students of a private university in South, Indonesia. Classroom Action Research (CAR) was conducted to the third semester students of the university within two cycles. To validate the findings, writing test, observation and questionnaire were employed. The test result proved implementing GBA improved the classroom’s average score from 54 to 59.95 in cycle 1. This improvement was supported by the students’ engagement and enthusiasm to the instructional process as suggested by questionnaire and observation respectively. Further, the implementation of cycle 2 revealed the average score improved from 59.5 to 70.5. This score improvement is followed by the students’ agility and cooperativeness during the instructional process. This concludes that implementing GBA can improve the students’ skill in writing explanation text. This finding is expected to give insight for teachers to undoubtedly using GBA in English for specific purposes context.&#x0D; HIGHLIGHTS:&#x0D; &#x0D; The instructional practice of teaching English for informatics engineering students in the private university still focuses on form-based mastery.&#x0D; The informatics engineering students are eager and high-determinate to learn language skills especially writing in collaborative framework.&#x0D; &#x0D;
Characteristics and Challenges of Chinese E-Learning Platforms in Indonesia

Abstract The digital and internet era have made technology integrated with education, which has encouraged the renewal of learning models, including the availability of learning platforms that can be accessed free or paid. The availability of the Mandarin language platform as the largest foreign language after English has also become more diverse, starting with free modules available on the web/blog, as well as online course learning models. This paper collects various kinds of Mandarin language learning platforms in Indonesia, researchers qualify their types into several sections: web content based, mobile app based, video streaming based and e-learning courses-based learning platforms. This paper compares the characteristics, advantages and disadvantages of each type of Chinese e-learning platform. The results of this study indicate that the challenges of the Mandarin language learning platform in Indonesia will have to prioritize functional, structural, varied and content management aspects, with the expectation of creating a massive, open and integrated learning management ecosystem.
Activity Recognition in the City using Embedded Systems and Anonymous Sensors

This paper presents an embedded system that performs activity recognition in the city. Arduino Due boards with infrared, distance and sound sensors are used to collect data in the city and the activity, profile, and group size recognition performance of different machine learning algorithms (RF, SVM, MLP) are compared. The features were extracted based on fixed-size windows around the observations. We show that it is possible to achieve a high accuracy for binary activity recognition with simple features, and we discuss the optimization of different parameters such as the sensors collection frequency, and the storage buffer size. We highlight the challenges of activity recognition using anonymous sensors in the environment, its possible applications and advantages compared to classical smartphone and wearable based approaches, as well as the improvements that will be made in future versions of this system. This work is a first step towards real-time online activity recognition in smart cities, with the long-term goal of monitoring and offering extended assistance for semi-autonomous people.
Research on Plot Authenticity Identification Method Based on PSO-MLP

Abstract Aiming at the influence of the clutter plots produced by the complex and strong clutters such as mountains, sea and cities on radar tracking performance, a method based on PSO-MLP is proposed to identify the authenticity radar plots. The method further distinguishes the target plots from the clutter plots, filters the residual clutter plots, and effectively improves the radar processing capacity and tracking performance. Based on the study of some differences between clutter and target plots, this method extracts multi-dimensional characteristic parameters, including doppler velocity, original amplitude and so on. Firstly, the parameters of MLP algorithm are optimized by PSO algorithm, and then the authenticity identification of clutter plots is carried out by MLP algorithm after parameter optimization, and then the remaining clutter plots are filtered out. Finally, the overall accuracy rate of plot identification reaches 85.75%. The experimental results show that the proposed algorithm has a high and stable accuracy of plot identification.
Train No Evil: Selective Masking for Task-Guided Pre-Training

Recently, pre-trained language models mostly follow the pre-train-then-fine-tuning paradigm and have achieved great performance on various downstream tasks. However, since the pre-training stage is typically task-agnostic and the fine-tuning stage usually suffers from insufficient supervised data, the models cannot always well capture the domain-specific and task-specific patterns. In this paper, we propose a three-stage framework by adding a task-guided pre-training stage with selective masking between general pre-training and fine-tuning. In this stage, the model is trained by masked language modeling on in-domain unsupervised data to learn domain-specific patterns and we propose a novel selective masking strategy to learn task-specific patterns. Specifically, we design a method to measure the importance of each token in sequences and selectively mask the important tokens. Experimental results on two sentiment analysis tasks show that our method can achieve comparable or even better performance with less than 50% of computation cost, which indicates our method is both effective and efficient. The source code of this paper can be obtained from https://github.com/thunlp/SelectiveMasking.
Application of Quantum Key in Secure Communication for Power Distribution and Utilization

The power distribution and utilization network is an important link between the grid and the user.The data privacy protection is related to the secure and stable communication of the whole network.Based on quantum secure communication and combined with existing power security protection measures, this paper proposed a quantum key application solutions.The system architecture contained the primary station layer, access layer and terminal layer.Through the quantum key distribution network, the key generation devices of both sides of the communication negotiated to generate the quantum key, which was used to encrypt the transmitted data.In the aspect of quantum key distribution, the paper proposed the distribution mode of online distribution and offline distribution, which promoted the flexible distribution of the terminal side quantum key, and realized the low-cost application.The feasibility of the scheme was verified through the pilot application.
Attribute Selection Techniques to Clustering the Entrepreneurial Potential of Student based on Academic Behavior

A key factor in the process of knowledge discovery in databases is the quality of data that consists of a set of attributes that explain the characteristics of the data. For that, we need the right attribute selection method for optimal data mining performance. In this case, the attributes tested with machine learning are the result of mapping factors is affecting entrepreneurship of students based on behavioral science theory on the attributes of Indonesia Higher Education Database. Testing dataset attributes using four different methods, namely Correlation, Information Gain, OneR, and Relief F. The results of clustering experiments with the Simple K-Means algorithm show that OneR method decrease in the largest drop of Sum of Squared Errors (17%) compared to the other three methods. With the most important attribute differences in each attribute selection method, the instances cluster profile generated is also different.
Q-NTRU Cryptosystem for IoT Applications

Interesting in the Internet of things (IoT) has begun to grow rapidly since it deals with the everyday needs of humans and becomes dealing with a huge amount of personal information. This expansion is accompanied by a number of challenges; one of them is the need for solving the problem of security challenges by using algorithms with high security and the adversaries unable to attack them. But such algorithms need high computation power. On the other hand, the Internet of things has limited resources. Therefore, high security cryptosystem with low computation power is needed. NTRU (Nth-degree TRUncated polynomial ring) is one of lattice-based cryptosystems that meets these requirements. However, this system has weak points, including the ability to attack it under certain condition using Lenstra–Lenstra–Lovász lattice basis reduction algorithm (LLL algorithm) to discover either the original secret key, or an alternative secret key which is useful to decrypt the cipher texts. In this paper, modifications are made on the NTRU cryptosystem algorithm to ensure that the attack by using Lenstra–Lenstra–Lovász algorithm can be thwarted by adding a new parameter with a variable value. The implementation results showed that this modification gives NTRU resistance against this attack.
The Impact of Transformational Leadership on Entrepreneurial Orientation via the Mediating Role of Organizational Learning Capability

The current research aims to investigate the effect of transformational leadership on entrepreneurial orientation in Jordanian commercial banks, and whether organizational learning capability mediates the effect of transformational leadership on entrepreneurial orientation. Adopting a quantitative research design, data were collected by means of a questionnaire-based survey of employees in Jordanian commercial banks. Based on 330 usable responses, the results revealed the significant effect of transformational leadership and two of its dimensions (inspirational motivation and intellectual stimulation) on entrepreneurial orientation. Two other dimensions (idealized influence and individualized consideration) did not contribute to entrepreneurial orientation. Additionally, the results showed that transformational leadership has a significant effect on organizational learning capability, and that organizational learning capability in turn affects entrepreneurial orientation. The findings confirm that organizational learning capability fully mediates the effect of transformational leadership on entrepreneurial orientation. A number of recommendations are advanced, the most important of which is that banks should improve and develop managers’ transformational attributes by training them on how to deal with employees in order to increase their entrepreneurial orientation. Banks should also consider improving their learning capability, as this plays a significant role in enhancing and supporting the effect of transformational leadership attributes on entrepreneurial orientation.
Evaluating deep road segmentation techniques for low-altitude UAS imagery

Semantic segmentation, the task of assigning a class label to each pixel within a given image, has applications in a wide variety of domains, ranging from medicine to self-driving vehicles. One successful deep neural network model that has been developed for semantic segmentation tasks is the U-Net architecture, a "U"-shaped neural network initially applied to segmentation of cell membranes in biomedical images. Additional variants of the U- Net have been developed within the research literature that incorporate new features such as residual layers and attention mechanisms. In this research, we evaluate various U-Net-based architectures on the task of segmenting the road and non-road in low-altitude UAS visible spectrum imagery. We show that these models can successfully extract the roads, detail a variety of performance metrics of the respective networks' segmentations, and show examples of successes and pending challenges using U.S. Army ERDC imagery collected from a variety of ight routes and altitudes in a complex environment.
Hyperintensional Reasoning Based on Natural Language Knowledge Base

The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy’s Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.
Efficient elliptic curve Diffie‐Hellman computation at the 256‐bit security level

In this study, the authors introduce new Montgomery and Edwards form elliptic curves targeted at the 256-bit security level. To this end, they work with three primes, namely , and . While has been considered earlier in the literature, and are new. They define a pair of birationally equivalent Montgomery and Edwards form curves over all the three primes. Efficient 64-bit assembly implementations targeted at Skylake and later generation Intel processors have been made for the shared secret computation phase of the Diffie-Hellman key agreement protocol for the new Montgomery curves. Curve448 of the Transport Layer Security, Version 1.3 is a Montgomery curve which provides security at the 224-bit security level. Compared to the best publicly available 64-bit implementation of Curve448, the new Montgomery curve over leads to a 3–4% slowdown and the new Montgomery curve over leads to a 4.5–5% slowdown; on the other hand, 29 and 30.5 extra bits of security, respectively, are gained. For designers aiming for the 256-bit security level, the new curves over and provide an acceptable trade-off between security and efficiency.
Prediction of Students' Academic Performance by K-Means Clustering

Schooling system must provide high quality learning opportunities to meet the educational needs and ensuring achievement for every student. All teachers monitor their students’ progress throughout the year, includes formative assessment, questioning, providing feedback, etc. This practice helps teachers continually assess students’ academic performance and evaluate the effectiveness of their teaching. In this paper, k-means clustering method with deterministic model is used to analyze the student's overall performance. The results is important for educators to identify students who are at risk academically and areas where teaching strategies may need adjustment to better meet these students' needs.
Step height standards based on self-assembly for 3D metrology of biological samples

Abstract Modern microscopes and profilometers such as the coherence scanning interferometer (CSI) approach sub-nm precision in height measurements. Transfer standards at all measured size scales are needed to guarantee traceability at any scale and utilize the full potential of these instruments, but transfer standards with similar characteristics upon reflection to those of the measured samples are preferred. This is currently not the case for samples featuring dimensions of less than 10 nm and for biosamples with different optical charasteristics to silicon, silica or metals. To address the need for 3D images of biosamples with traceable dimensions, we introduce a transfer standard with dimensions guaranteed by natural self-assembly and a material that is optically similar to that in typical biosamples. We test the functionality of these transfer standards by first calibrating them using an atomic force microscope (AFM) and then using them to calibrate a CSI. We investigate whether a good enough calibration accuracy can be reached to enable a useful calibration of the CSI system. The result is that the calibration uncertainty is only marginally increased due to the sample.
The Usefulness of Cell Phones for Crop Farmers in Selected Regions of Bangladesh

This research endeavours the usefulness of cell phones for crop farmers in selected region of Bangladesh. For adequate findings and to achieve its purpose, structured interview schedule was adopted to collect data from 281 randomly selected farmers and it was revealed that a little over 60% of them found cell phones very useful, while only 5.3% respondents found the cell phone as less useful. Based on average talk time hours spend in the last six months, top three sources of agricultural information were friends and relatives, distributors and middlemen, and farmers in advanced categories. The results of the ordered logit model showed that their usefulness was significantly determined by age, farm size, per month call charges, and experience in using cell phones. Higher call rates, lack of awareness and paucity of mobile-based information sources were major bottlenecks in using cell phones for agricultural information. The recommendations suggested therein lead to connecting farmers with reliable and rich information sources, use of MMS and SMS, voice call activities, providing subsidized SIM cards, and ultimately undertake widespread campaigns for training of aged farmers to persuade their interest towards the use of cell phones and mobile-based information sources.
DeblurGAN-C: image restoration using GAN and a correntropy based loss function in degraded visual environments

While machine learning-based image restoration techniques have been the focus in recent years, these algorithms are not adequate to address the effects of a degraded visual environment. An algorithm that successfully mitigates these issues is proposed. The algorithm is built upon the state-of-the-art DeblurGAN algorithm but overcomes several of its deficiencies. The key contributions of the proposed techniques include: 1)Development of an effective framework to generate training datasets typical of a degraded visual environment; 2) Adopting a correntropy based loss function to integrate with the original VGG16 based perceptual loss function and an L1 loss function; 3) Conducting substantial experiments against images from the artificial training datasets and demonstrate the effectiveness of the proposed algorithm.
The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic

This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining questions similarity are explored in this work. The proposed models achieved high F1-scores, which range from (88% to 96%). Our official best result is produced from the ensemble model of using a pre-trained multilingual BERT model with different random seeds with 95.924% F1-Score, which ranks the first among nine participants teams.
Sequential Cluster Estimation: A Generalized Model for Finding Large Numbers of Clusters in Data

Clustering is an unsupervised learning method that partitions a set of objects into groups (clusters) of similar objects, where similarity is often computed from numerical object feature vectors (also called data points). An early (and still very popular) clustering algorithm, called k-means, finds clusters by minimizing the sum of the squared distances between data points and associated cluster centers. The k-means algorithm (like many other so-called hard clustering algorithms) assigns each object to one and only one of the considered clusters. In practice, however, cluster assignments may often be ambiguous. Objects may partially belong to several clusters or fit to none of these clusters. Such kinds of ambiguity can be mathematically handled by what is termed fuzzy set theory. A fuzzy variant of k-means called fuzzy c-means (FCM) has emerged to become one of the most popular fuzzy clustering methods, with hundreds of thousands of scientific publications. For a survey. Fuzzy clustering is often used to generate membership functions for fuzzy rule-based systems. Alternating cluster estimation (ACE) is an extension of FCM for arbitrary membership function shapes. This article introduces sequential cluster estimation (SCE), a variant of ACE that finds clusters sequentially and outperforms nonsequential clustering for data with many clusters.
Optimized registration based on an ant colony for markerless augmented reality systems

Accurate registration in augmented reality systems is essential to guarantee the visual consistency of the augmented environment. Although error in the virtual-real alignment is almost unavoidable, different approaches have been proposed to quantify and reduce such errors. However, many of the existing solutions require a lot of a priori information, or they only focus on camera calibration to guarantee good results in the registration. This article presents a heuristic method that aims to reduce registration errors in markerless augmented reality systems. The proposed solution sees error reduction as a mono-objective optimization problem, which is addressed by means of the Ant Colony Optimization (ACO) algorithm. Experimental results reveal the validity of the proposed method, reaching an average error of 1.49 pixels for long video sequences.