Reviewerly/exHarmony huggingface dataset:
Seeking of Agriculture Information through Mobile Phone among Paddy Farmers in Selangor

Introduction: Mobile phone is one of the communication tools that have always mattered in many fields included agriculture.The development of the agriculture sector in the rural area is significant to diminish poverty among farmers.Agricultural activities are known to provide opportunities for employment and generating income in the rural area.Poverty can be reduced through adequate investment and improvement in the agriculture sector.Enhancing the use of mobile phone will empower farmers with relevant agriculture information.Thus, agriculture production and profits as well as farmers' food security and livelihood will increase.Methodology: This study uses a quantitative approach, where an adopted and adapted questionnaire was employed to obtain the data needed.Through purposive sampling, 400 respondents were surveyed.The respondents selected were those who are involved in paddy farming in Selangor.Results: The finding of the study reveals that paddy farmers in Selangor are comfortable using mobile phones in seeking agricultural information.The result for communication apps indicates that WhatsApp is the best choice to get information using a mobile phone as 279 respondents responded they were comfortable using WhatsApp to get and share information through a mobile phone.Whereas, the highest agriculture information the farmers sought are information on pest control, farm safety information, and information on pesticide/weed.Conclusion/Recommendation: Due to the well-received mobile phones usage among farmers, it is recommended that organizations such as the Department of Agriculture and Barat Laut Selangor Integrated Agriculture Development Area (IADA) to improve strategies in distributing information on paddy farming using mobile phones.
Selection on Traditional Cryptographic Algorithm for Real-Time Video Transmission and Storage: A Study

Due to such high penetration of the internet and the popularity of social media, the transfer of multimedia contents has increased tremendously over the internet. Ascertaining the privacy and security of these multimedia has become a very important issue today. Earlier works were indeed done in the field of cryptography and steganography but it was meant to prevent text data and images only because their application on multimedia data was not feasible. Some works were done later on to secure the transfer of MMS, but mostly it was selective encryption which in few selective frames of video (like I, P and B frames) were chosen and encrypted, it had its limitations. To strengthen this selective encryption larger key-size and complex algorithms were used which made the computation even more inefficient. To overcome this we propose a new approach in which we would select a suitable video size using scheduling algorithm and break it into parts and process it in parallel. The approach is proposed using simple operations so it is computationally feasible and also prevents any loss of the data.
Trusted and Flexible Electronic Certificate Catalog Sharing System Based on Consortium Blockchain

The development of information and communication technology has influenced every dimension of society, including the way government interacts with citizens. Government bodies at all levels across the country issued a serial of guidelines to promote e-government services' quality and efficiency which include six national standards for electronic certificates. The legal validity of electronic certificates is equal to that of paper certificates issued by authorities at provincial, autonomous regional, municipal levels and related departments. This fact will accelerate the construction of e-government to further benefit people and motivate the market. In this paper, we propose a trusted and flexible electronic certificates catalog sharing (ECCS) system based on consortium blockchain to promote the accessibility and efficiency of e-government services which take advantage of immutability and time-stamping of transaction data in blockchain. Security analysis indicates that ECCS can achieve privacy-preservation of electronic certificates and catalog against unauthorized users, integrity of electronic data and authentication of data source. In comparison with other existing schemes which also achieve electronic certificates sharing services, ECCS has better efficiency in terms of traffic loads, and can fit for all scenarios related to electronic certificates sharing.
Highly linear sub-nanoradian tilt measurement based on dual-beam interferometry

The tilt measurement method based on dual-beam interferometry is presented in this Letter. Due to symmetric property of the nonlinear errors of two displacement measurement arms, the composite nonlinearity of the tilt measurement is counterbalanced, so that a small nonlinearity over a large dynamic range can be obtained. According to the theoretical analysis with a Gaussian beam model, the second-order nonlinear error is dominant. The experimental results show that a measurement noise of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:mn>4</mml:mn> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">r</mml:mi> <mml:mi mathvariant="normal">a</mml:mi> <mml:mi mathvariant="normal">d</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> </mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">H</mml:mi> <mml:mi mathvariant="normal">z</mml:mi> </mml:msqrt> </mml:math> at 1 Hz with a nonlinearity of less than 60 nrad for a measurement range of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo>±<!-- ± --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>500</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">r</mml:mi> <mml:mi mathvariant="normal">a</mml:mi> <mml:mi mathvariant="normal">d</mml:mi> </mml:mrow> </mml:math> has been achieved.
SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of real-time, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m−2) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m−2. For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m−2 for all seven locations. These results, together with the low-latency of the method (∼1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.
Identifying and Categorizing Opinions Expressed in Bangla Sentences using Deep Learning Technique

Identifying and categorizing opinions in a sentence is the most prominent branch of natural language processing.It deals with the text classification to determine the intention of the author of the text.The intention can be for the presentation of happiness, sadness, patriotism, disgust, advice, etc.Most of the research work on opinion or sentiment analysis is in the English language.Bengali corpus is increasing day by day.A large number of online News portals publish their articles in Bengali language and a few News portals have the comment section that allows expressing the opinion of people.Here a research work has been done on Bengali Sports news comments published in different newspapers to train a deep learning model that will be able to categorize a comment according to its sentiment.Comments are collected and separated based on immanent sentiment.The deep learning algorithms that have been used are Convolutional Neural Network (CNN), Multilayer Perceptron, Long Short-Term Memory (LSTM).
Multiple two-step oscillation regimes produced by the alto saxophone

A saxophone mouthpiece fitted with sensors is used to observe the oscillation of a saxophone reed, as well as the internal acoustic pressure, allowing to identify qualitatively different oscillating regimes. In addition to the standard two-step regime, where the reed channel successively opens and closes once during an oscillation cycle, the experimental results show regimes featuring two closures of the reed channel per cycle, as well as inverted regimes, where the reed closure episode is longer than the open episode. These regimes are well-known on bowed string instruments and some were already described on the Uilleann pipes. A simple saxophone model using measured input impedance is studied with the harmonic balance method, and is shown to reproduce the same two-step regimes. The experiment shows qualitative agreement with the simulation: in both cases, the various regimes appear in the same order as the blowing pressure is increased. Similar results are obtained with other values of the reed opening control parameter, as well as another fingering.
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments. Despite this potential, most of the robot learning systems today are deployed as a fixed policy and they are not being adapted after their deployment. Can we efficiently adapt previously learned behaviors to new environments, objects and percepts in the real world? In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption. In particular, we demonstrate how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, including changes in background, object shape and appearance, lighting conditions, and robot morphology. Further, this adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning, and that pre-training via RL is essential: training from scratch or adapting from supervised ImageNet features are both unsuccessful with such small amounts of data. We also find that these positive results hold in a limited continual learning setting, in which we repeatedly fine-tune a single lineage of policies using data from a succession of new tasks. Our empirical conclusions are consistently supported by experiments on simulated manipulation tasks, and by 52 unique fine-tuning experiments on a real robotic grasping system pre-trained on 580,000 grasps.
The Law of Blockchain

Blockchain technology is a new general-purpose technology that poses significant challenges to the existing state of law, economy and society. Blockchain has one feature that makes it even more distinctive than other disruptive technologies: it is, by nature and design, global and transnational. Moreover, blockchain operates based on its own rules and principles that have a law-like quality. What we may call the lex cryptographia of blockchain has been designed based on a rational choice vision of human behavior. Blockchain adopts a framing derived from neoclassical economics, and instantiates it in a new machinery that implements rational choice paradigms using blockchain in a semi-automatic way, across all spheres of life, and without regard to borders. Accordingly, a global law and crypto-economics movement is now emerging owing to the spread of blockchain.This Article suggests that such a rational choice paradigm is an insufficient foundation for the future development of blockchain. It seeks to develop a new understanding of blockchain and its regulation through code according to the emerging “law and political economy” framework. Blockchain should be understood as much more than a machine that enables the automation of transactions according to a rational choice framework. Blockchain should instead be understood as a technological infrastructure. Acknowledging the infrastructural dimension of blockchain technology may help identify a new role for the law in its interaction with blockchain, as well as for government in its interaction with the new technology. More precisely, identifying blockchain as an “infrastructural commons” helps us recognize that law and regulation should not be relegated to the role of merely facilitating the operation of the invisible hand of the market by and within blockchain, but should rather acquire more active roles, such as safeguarding access on non-discriminatory terms to users, on a model with net neutrality and other public utility safeguards. The Article closes by proposing a “law and political economy” framework for blockchain that is based on principles of publicness, trust, and interoperability.
The trade-off between security and quality using permutation and substitution techniques in speech scrambling system

Speech is one of the means of communication between people throughout the universe, so people want to communicate with each other no matter how far away since wireless networking offers, this feature, the freedom to connect from anywhere and anytime. Because speech is used extensively in wireless networks, it takes much time to encrypt it using well-known cryptographic algorithms, so, speech scrambler techniques have been used in the modern communication systems to overcome this problem. It has proven its presence in protecting data transmitted over wireless networks in the past few years. The main objective of this paper is to compare the systems that use process permutation with the systems that use the substitution process in the speech scrambling technique to protect speech information. To find out which is better use the permutation process or substitution in the technique of speech scrambling in terms of security and quality, security and quality results we will compare between our proposed system, which uses permutation process, with another system, uses substitution process. Based on the values of the segmental signal-to-noise ratio measure (SSNR), Comparison of the above two systems will take place because it used in both systems to evaluate the performance of the system in terms of security and quality of the recovered speech.
KoTAB: Korean Template-Based Arithmetic Solver with BERT

Math word problems are a type of mathematical question expressed in the linguistic form which can be mathematically formulated from situations encountered in everyday life. In this study, we propose KoTAB, a Korean template-based arithmetic solver with BERT, which is modified from TAB (Template-based Arithmetic Solver with BERT) framework that is used to solve English math word problems. Using KoTAB, we solved Korean math word problems using two datasets, CC and IL, which were translated from English to Korean. Four different pre-trained BERT models were used to fine-tune KoTAB; BERT-base-cased, BERT-multilingual-cased, Korean_BERT_WordPiece, Korean_BERT_ Morphology. Among these four pre-trained BERT models, the highest performance in accuracy was achieved when the Korean_BERT_Morphology model was used to evaluate CC (99.3%) and IL (85.22%) datasets. This result is comparable to the performance of English models for CC (99.5%), but not for IL (89%).
Retracted: KSSD: single‐stage multi‐object detection algorithm with higher accuracy

Considering that the single shot multibox detector (SSD) algorithm will be missed or even false when is used to detect the small- and medium-sized objects, in this study, Kullback–Leibler single shot multibox detection (KSSD) object detection algorithm is proposed to improve the accuracy of small- and medium-sized objects detection. Firstly, the details in the detection process are visualised with gradient-weighted class activation mapping technology, and the details of each detection layer are shown in the form of class activation maps. Then it is noted that the phenomenon of the false or missed detection of the objects to be detected on small- and medium-sized objects in the SSD algorithm is related to the regression loss function. Accordingly, Kullback–Leibler border regression loss strategy is adopted and non-maximum suppression algorithm is used to output the final prediction boxes. Experimental results show that compared with the existed detection algorithms, the improved algorithm in this study has higher accuracy and stability, and can significantly improve the detection effect on small- and medium-sized objects.
On the Memory-Tightness of Hashed ElGamal

We study the memory-tightness of security reductions in public-key cryptography, focusing in particular on Hashed ElGamal. We prove that any straightline (i.e., without rewinding) black-box reduction needs memory which grows linearly with the number of queries of the adversary it has access to, as long as this reduction treats the underlying group generically. This makes progress towards proving a conjecture by Auerbach et al. (CRYPTO 2017), and is also the first lower bound on memory-tightness for a concrete cryptographic scheme (as opposed to generalized reductions across security notions). Our proof relies on compression arguments in the generic group model.
Artificial Intelligence Systems-Aided News and Copyright: Assessing Legal Implications for Journalism Practices

Automated news, or artificial intelligence systems (AIS)-aided production of news items, has been developed from 2010 onwards. It comprises a variety of practices in which the use of data, software and human intervention is involved in diverse degrees. This can affect the application of intellectual property and copyright law in many ways. Using comparative legal methods, we examine the implications of them for some legal categories, such as authorship (and hence required originality) and types of works, namely collaborative, derivative and, most especially, collective works. Sui generis and neighboring rights are also considered for examination as being appliable to AIS-aided news outputs. Our main conclusion is that the economics intellectual property rights are guaranteed in any case through collective works. We propose a shorter term of duration before entering public domain. Still, there is a place for more authorial, personal rights. It shows, however, more difficulty when coming to moral rights, especially in Common Law countries.
Radar Emitter Identification Based on Feedforward Neural Networks

The aim of the article is implement radar emitter Identification. This paper applies neural network classifier in identification, and proposes a approach based on combination of multiple classifiers to improve the training efficiency. In the study, feedforward neural networks are utilized for identifying radar radiation sources, and the parameters of radar signals (direction of arrival, pulse width, pulse repetition frequency and radar frequency) are selected as features for training classifier. In order to reduce the time for training classifier, the outputs are divided into different groups and different classifiers are generated for each group. The experimental results reveal that feedforward neural networks show outstanding performance in radar emitters recognition, and the scheme presented can effectively save time for training.
Measurement Models of Information Security Based on the Principles and Practices for Risk-Based Approach

Elements of good practice and principles of a risk-based approach are often used in measurement models to assess the level of security of the information resource. The practical problem of measuring the level of information security is the selection of an appropriate model, followed by measures and a method that will be adequate to the specific organization. This puts in light second problem related to set a proper method of binding them together to generate consistent measure for determining the total information security level within organization. Paper presents two models proposed by authors for this use case. The proposed models can become the starting point for creating an information security evaluation system for each type of organization.
Fast Gradient Boosting Decision Trees with Bit-Level Data Structures

A gradient boosting decision tree model is a powerful machine learning method that iteratively constructs decision trees to form an additive ensemble model. The method uses the gradient of the loss function to improve the model at each iteration step. Inspired by the database literature, we exploit bitset and bitslice data structures in order to improve the run time efficiency of learning the trees. We can use these structures in two ways. First, they can represent the input data itself. Second, they can store the discretized gradient values used by the learning algorithm to construct the trees in the boosting model. Using these bit-level data structures reduces the problem of finding the best split, which involves counting of instances and summing gradient values, to counting one-bits in bit strings. Modern CPUs can efficiently count one-bits using AVX2 SIMD instructions. Empirically, our proposed improvements can result in speed-ups of 2 to up to 10 times on datasets with a large number of categorical features without sacrificing predictive performance.
A New Multiple-Distribution GAN Model to Solve Complexity in End-to-End Chromosome Karyotyping

With significant development of Internet of medical things (IoMT) and cloud-fog-edge computing, medical industry is now involving medical big data to improve quality of service in patient care. Karyotyping refers classifying human chromosomes. However, performing karyotyping task generally requires domain expertise in cytogenetics, long-period experience for high accuracy, and considerable manual efforts. An end-to-end chromosome karyotype analysis system is proposed over medical big data to automatically and accurately perform chromosome related tasks of detection, segmentation, and classification. Facing image data generated and collected by means of edge computing, we firstly utilize visual feature to generate chromosome candidates with Extremal Regions (ER) technology. Due to severe occlusion and cross overlapping, we utilize ring radius transform to cluster pixels with same property to approximate chromosome shapes. To solve the problem of unbalanced and small dataset by covering diverse data patterns, we proposed multidistributed generated advertising network (MD-GAN) to perform data enhancement by generating additional training samples. Afterwards, we fine-tune CNN for chromosome classification task by involving generated and sufficient training images. Through experiments in self-collected datasets, the proposed method achieves high accuracy in tasks of chromosome detection, segmentation, and classification. Moreover, experimental results prove that MD-GAN-based data enhancement contributes to classification results of CNN to a certain extent.