Reviewerly/exHarmony huggingface dataset:
A Blockchain based Fund Management Scheme for Financial Transactions in NGOs

Background: In the world of the latest technologies, the blockchain is one of the popular techniques for stopping fraudulent activities. Non-Government Organization (NGO) is increasingly being used to support all the needy people across the globe to shape the world’s responsibility towards society for sustainable development. The existing method of donating money and its monitoring is facing a major corruption problem in several world-renowned NGOs. A Blockchain can transform the way the current business of money transaction is being done in NGOs. The blockchain-based application works on the concept of a decentralized system. Methods: This article presents a blockchain-based transaction system to prevent corruption and money laundering in NGOs and government fundraising organizations. A smart contract has been designed to stop any illegitimate block changes during a financial transaction. Since every node has a copy of the ledger, so it is very difficult to perform malicious activity. Furthermore, the donator can watch how the money flows in the different transactions and everyone can browse the account history. An evaluative judgment, comparing with various consensus algorithms, has also been presented along with their complex nature. The decentralized approach has eliminated the chance of a single point of failure which in turn makes the system robust. Results: The developed framework for the financial transaction using blockchain has been tested using the Rinkeby Test Network. The generators and campaign contracts have been developed and deployed in the Rinkeby testing network. The results indicate that the computing is much more secure and free from the scam in comparison to the traditional client-server financial transaction system. Conclusion: Finally, the proposed approach suggests scenarios such as in NGOs where the introduced security approach should prove to be adequate.
Transitive Power Modeling for Improving Resource Efficiency in a Hyperscale Datacenter

Maintaining efficient utilization of allocated compute resources and controlling their capital and operating expenditure is important for running a hyperscale datacenter infrastructure. Power is one of the most constrained and difficult to manage resources in datacenters. Accurate accounting of power usage across clients of multi-tenant web services can improve budgeting, planning and provisioning of compute resources.
Self-Reflective and Introspective Feature Model for Hate Content Detection in Sinhala YouTube Videos

YouTube is considered one of the most popular social media platforms, which provides users with the ability to interact with each other by sharing videos, commenting or liking or disliking. Its free nature has enabled the spread of offensive and hateful content within this environment, resulting in violence and discrimination within society. Therefore, identifying hate content is crucial to mitigating the spread of hatred. This study describes a system to detect hate in Sinhala content associated in YouTube videos by natural language processing techniques. The categorizations are done based on user comments, thumbnail text, and Meta-data, which includes the title, description and tags. Here, the features were derived through self-reflective and introspective data associated with the YouTube video. This system is capable of detecting hate expressions in Sinhala language YouTube videos with nearly 90 percent accuracy.
Sentiment Analysis of Weak-RuleText Based on the Combination of Sentiment Lexicon and Neural Network

Aiming at social media texts with weak grammatical features, based on the weak-rule text and word vectors, this paper proposes a framework combining sentiment lexicon and neural network. Moreover, we study the influence of sentiment lexicon on improving the accuracy of text sentiment analysis. According to the structure of the word vector and the uniqueness of the sentiment score in the sentiment lexicon, our model first converts the text into a sentiment score vector. Secondly, the converted sentiment score vector is used to learn its hidden emotional representation through the neural network. At the same time, we use a combination of pre-training word vectors and the BiLSTM network to learn the hidden semantic representation of the original text. Finally, the hidden emotional representation and the hidden semantic representation are combined to analyze the weak-rule text sentiment. Experimental results on Weibo text with weak grammatical rules show that the performance of the method which use the sentiment score vector is better than traditional sentiment lexicon methods. The performance of combining hidden emotion representation and hidden semantic representation is also better than the BiLSTM that uses pre-trained word vectors and attention mechanism.
Interpretation of Food Crop Growth Progress Visualization and Prediction of Drone Based Production Estimates Based on Histogram Values in Government Areas-Case Study of Merauke Regency

Abstract Farming is one of the pioneers of national development that plays an important role, especially Merauke Regency, which is planned to be the area for national food self-reliance in agribusiness (Indonesian). Government is currently very difficult. Will see the latest developments in the growth of food crops in the region, starting at the village level / kelurahan to the national level. These kindergartens growth in the crop until harvest time, and several obstacles to failure of crops from pests and environmental conditions such as flooding. Production estimates are easy if this information is available. The research aims to be able to show the status of the land in the form of different colors by the histogram method, plant information and the type of pest disease, while the retrieval of data is carried out using the photographic images. The long-term proficiency based on the coordinates through the GPS found on the Dji-Phantom Phantom device can also be used to calculate the area of land as the main parameters and requirements of the seed, the condition of the land and restrictions b. The failure of crop cultivation as a supporting parameter to determine the assessment of plant production by means of liver methods.
Access Control Encryption from Group Encryption

Access control encryption (ACE) enforces both read and write permissions. It kills off any unpermitted subliminal message channel via the help of a sanitizer who knows neither of the plaintext, its sender and receivers, nor the access control policy. This work aims to solve the open problem left by the seminal work of Damgård et al. (TCC 2016), namely, "to construct practically interesting ACE from noisy, post-quantum assumptions such as LWE." We start with revisiting group encryption (GE), which allows anyone to encrypt to a certified group member, whom remains anonymous unless the opening authority decided to reveal him/her. We propose: 1) the notion of sanitizable GE (SGE), with specific changes for non-interactive proof, 2) the notion of traceable ACE (tACE), which helps damage control by tracing after-the-fact if some secret were leaked unluckily, 3) a generic construction of (t)ACE for equality policy (ACE-EP) from SGE, 4) a generic construction of ACE for general policy from ACE-EP, 5) a lattice-based instantiation of SGE, which comes with 6) a simple mechanism for checking that the randomness of ciphertexts can span the randomness space.
Dual Contrastive Universal Adaptation Network

We study Universal Domain Adaptation (UniDA) problem, which is recently proposed. Different from existing domain adaptation (DA) methods, e.g., Closed set, Open set and Partial DA, UniDA does not need any prior knowledge about the overlap across the source and target label sets. We have two challenges in UniDA problem: i) Domain shift. ii) Category shift. Towards tackling above challenges, we formulate a universal adaptation network called Dual Contrastive Network (DCN), where a contrastive module and a transferability rule are included. The experimental results reveal that DCN can work stably on different UniDA settings and exceeds the state-of-the-art performance across five benchmarks against existing DA methods.
Student Result Prediction in Covid-19 Lockdown using Machine Learning Techniques

Abstract Covid-19 virus has moved the world to a static state. This has confused the education field in conducting the end examinations. The school education has declared the results of a few classes as pass without conducting the end examinations. But, this may not be possible for all the education system, especially for final year college student results. For this reason, the prediction of result based on their previous performance is essential. Prediction can be effectively achieved by using machine learning algorithms. Machine learning automatically learns and improves from example and experience. Supervised machine learning algorithms Logistic regression and SVM are used for this work. The data set of 1460 students result of a college is considered for the study. Finally, the trained machine predicts accurately whether the student is eligible to acquire the degree or not and the same is viewed in the college portal.
Gated Convolutional Sequence to Sequence Based Learning for English-Hingilsh Code-Switched Machine Translation.

Code-Switching is the embedding of linguistic units or phrases from two or more languages in a single sentence.This phenomenon is practiced in all multilingual communities and is prominent in social media.Consequently, there is a growing need to understand codeswitched translations by translating the codeswitched text into one of the standard languages or vice versa.Neural Machine translation is a well-studied research problem in the monolingual text.In this paper, we have used the gated convolutional sequences to sequence networks for English-Hinglish translation.The convolutions in the model help to identify the compositional structure in the sequences more easily.The model relies on gating and performs multiple attention steps at encoder and decoder layers.
FPGA Acceleration of Number Theoretic Transform

Fully Homomorphic Encryption (FHE) is a technique that enables arbitrary computations on encrypted data directly. Number Theoretic Transform (NTT) is a fundamental component in FHE computations as it allows faster polynomial multiplication. However, it is computationally intensive and requires acceleration for practical deployment of FHE. The latency and throughput of existing NTT hardware designs are limited by the complex data communication pattern between adjacent NTT stages and the modular arithmetic operations. In this paper, we propose a parameterized architecture for NTT on FPGA. The architecture can be configured for a given polynomial degree, modulus and target hardware in order to optimize the latency and/or throughput. We develop a novel low latency fully pipelined modular arithmetic logic to implement the NTT core, the key computational unit of NTT. Streaming permutation network is used to reduce the data communication complexity between NTT stages. We implement the proposed architecture for various polynomial degrees, moduli, and data parallelism on state-of-the-art FPGAs. Experimental results show that our architecture configured to perform 4096 polynomial degree NTT achieves up to $$1.29\times $$ and $$4.32\times $$ improvement in latency and throughput respectively over state-of-the-art designs on FPGA.
A Security Model for Controller-Pilot Data Communication Link

Communication systems in aviation tend to focus on safety rather than security. Protocols such as ADS-B are known to use plain-text, unauthenticated messages and thus are open to various attacks. Controller-Pilot Data Communication Link is no exception and was shown vulnerable also in practice. In this paper, we propose a cryptographic mechanism to provide secure mobility for CPDLC that can enable data encryption and authentication. The protocol is formally verified with the Proverif tool. We also estimate the byte overhead in CPDLC use.
A secure and highly efficient first-order masking scheme for AES linear operations

Abstract Due to its provable security and remarkable device-independence, masking has been widely accepted as a noteworthy algorithmic-level countermeasure against side-channel attacks. However, relatively high cost of masking severely limits its applicability. Considering the high tackling complexity of non-linear operations, most masked AES implementations focus on the security and cost reduction of masked S-boxes. In this paper, we focus on linear operations, which seems to be underestimated, on the contrary. Specifically, we discover some security flaws and redundant processes in popular first-order masked AES linear operations, and pinpoint the underlying root causes. Then we propose a provably secure and highly efficient masking scheme for AES linear operations. In order to show its practical implications, we replace the linear operations of state-of-the-art first-order AES masking schemes with our proposal, while keeping their original non-linear operations unchanged. We implement four newly combined masking schemes on an Intel Core i7-4790 CPU, and the results show they are roughly 20% faster than those original ones. Then we select one masked implementation named RSMv2 due to its popularity, and investigate its security and efficiency on an AVR ATMega163 processor and four different FPGA devices. The results show that no exploitable first-order side-channel leakages are detected. Moreover, compared with original masked AES implementations, our combined approach is nearly 25% faster on the AVR processor, and at least 70% more efficient on four FPGA devices.
A High Speed Montgomery Multiplier used in Security Applications

Security plays a major role in data transmission and reception. Providing high security is indispensable in communication systems. The RSA (Rivest-Shamir-Adleman) cryptosystem is used widely in cryptographic applications as it offers highly secured transmission. RSA cryptosystem uses Montgomery multipliers and it involves modular exponentiation process which is attained by performing repeated modular-multiplications. This leads to high latency and owing to improve the speed of multiplier, highly efficient modular multiplication methodology needs to be applied. In the conventional methodology, Carry Save Adder (CSA) is used in the multiplication and it consumes more area and it has larger delay, but in the suggested methodology, the Reverse Carry Propagate (RCP) adder is used in the place of CSA adder and the obtained output shows promising results in terms of area and latency. The simulation is done with Xilinx ISE design suite. The proposed multiplier can be used effectively in signal processing, image processing and security based applications.
Sentiment analysis of GO-JEK services quality using Multi-Label Classification

Abstract One of the most popular online transportation providers in Indonesia is GO-JEK. At the first establishment, GO-JEK is only an online taxi motorbike service (in Bahasa Indonesia: ojek) that transforms from a conventional taxi motorbike. After several years GO-JEK began to develop more services, like GO-FOOD, GO-SEND, GO-CAR, GO-MART, GO-RIDE, GO-PAY, GO-TIX, GO-BOX, and GO-MED. As the GO-JEK services develop into more categories, it is more challenging to automatically analyze the sentiment polarity for each category of services category. An ordinary classification algorithm or single-label classification is concerned with learning from a set of examples associated with a single label classification. However, in this case, we want to classify GO-JEK services based on two-class targets, which are GO-JEK service categories and polarity sentiment classification. This research methodology contains Dataset Preparation, Feature Selection, Basic text mining process, train, and split dataset, and Classification. We implemented two classification methods: Multi-Label Classification and a simultaneous classification using the Random Forest Algorithm as a comparison. Based on this dataset, the most mentioned GO-JEK service is GO-FOOD followed by GO-SEND, GO-RIDE, GO-CAR, and GO-MART. Based on the service category, GO-FOOD gets the most positive reviews following by GO-RIDE and GOJEK 90. Some service categories like GO-SEND and GO-MART get more negative reviews than positive reviews. The accuracy of the Multi-label classification method raised 76%. Simultaneous classification accuracy using the Random Forest algorithm produce for service category classification yields 97% and only 78% for sentiment polarity classification. We can see that both algorithm, multi-label classification, and Random Forest algorithm, yields almost the same classification accuracy for polarity sentiment classification. We can conclude that the imperfect accuracy in polarity sentiment classification is related to the difficulty of identifying the most suitable polarity for each tweet. Many sarcasm tweets and slang words that cannot be identified.
Knowledge System Supporting ITS Deployment

Intelligent transportation systems are one of the most rapidly evolving areas, requiring an appropriate response from standardization bodies and adequate support from EU regulations. This results in a high and ever-expanding volume of technical standards, which makes their practical use difficult and the harmonization process unsustainable. Therefore, standardization bodies are officially required to supply public information, based on which potential users can decide whether they need to buy or use a particular standard or not. The authors demonstrate how to solve this problem and achieve sustainability in terms of operating in a more intelligent and efficient manner. The proposed solution relies on the creation of standard extracts using a hybrid method that combines syntactic and semantic analysis and assumes human expert involvement. The paper presents the practical experience and results obtained from a long-term national project. A practical example is included so that the reader can comprehend a basic idea of the achieved results. The authors believe the proposed method can be adopted across other professional domains and other European countries.
Developing Students’ Cognitive Abilities in E-Learning Environment

Big challenges of providing continuous educational process under the COVID-19 pandemic are boosting the creation of a new E-learning environment in modern educational establishments. On the one hand, it requires much cognitive effort to cope with new tasks of working in virtual environment, and on the other, it promotes further development of cognitive abilities of all participants in the educational process. Reforms include changes in learning environment (for students), and new approaches to teaching methods (for teaching staff). It is worth mentioning that lately most of the teaching staff have also become ‘students’ while acquiring new skills of working with information technologies in E-learning environment. The current research was held on the basis of two universities, the Moscow State Institute of International Relations (MGIMO University) and Moscow Aviation Institute (MAI University). The aim of the study is to analyze students’ and educators’ attitude (expectations, readiness, experience obtained) towards the e-learning environment and to identify the essence of cognitive abilities development for students in the format of forced distance learning. The research included 234 respondents (134 from MAI University and 100 from MGIMO University) who were to take part in two questionnaires (one for educators, and the other for students of foreign languages) to get their attitude to the forced substitution of the traditional, in-person, form of education; benefits remote telecommuting classes can offer; virtual learning and educational environment; new skills and abilities e-learning environment require and develop. The research has also proved an increase in digital ‘literacy’ of both educators and students. It presumes that educators have acquired new digital skills and have developed new forms of distance learning and virtual collaboration. As for the students, e-learning has promoted development of their cognitive abilities (information comprehension, procession, content structuring) and new skills of telecommuting, telecommunicating and telepresenting the results of their work. All respondents highlighted that on-line education and collaboration has required psychological adaption and self-discipline. Nevertheless, current situation caused by the pandemic proves that developing cognitive abilities has become a prerequisite for sustainable education. It is necessary to work out scientific tools and procedures to assess the cognitive abilities of students and teaching staff in order to elaborate effective mechanisms of collaboration in virtual environment. The results may assist students, educators, regulators and employers in efficient and fruitful cooperation with IT experts in order to address the current challenges in the sphere of education.
Algorithms Creating Paradoxes of Power: Explore, Exploit, Embed, Embalm

Algorithms play an increasing role in food retailing and distribution. Through a longitudinal study supported by qualitative interviews, we explore how such technologies have turned the sourcing of food into a highly automated transaction, moving from explore and exploit to embed and embalm. We demonstrate the impact that embedding algorithms can have on organizational processes and structures and that this may shift the balance of power within the value chain without being visible to management.
Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

One of the main problems faced by Data Warehouse (DW) designers is fragmentation. Several studies have proposed data mining-based horizontal fragmentation methods, which focus on optimizing query response time and execution cost to make the DW more efficient. However, to the best of our knowledge, it does not exist a horizontal fragmentation technique that uses a decision tree to carry out fragmentation. Given the importance of decision trees in classification, since they allow obtaining pure partitions (subsets of tuples) in a data set using measures such as Information Gain, Gain Ratio and the Gini Index, the aim of this work is to use decision trees in the DW fragmentation. This chapter presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka considering four evaluation metrics (Precision, ROC Area, Recall, and F-measure) for different selected data sets using the SSB (Star Schema Benchmark). Several experiments were carried out using two attribute selection methods: Best First and Greedy Stepwise, the data sets were pre-processed using the Class Conditional Probabilities filter and it was included the analysis of two data sets (24 and 50 queries) with this filter, to know the behavior of the decision tree algorithms for each data set. Once the analysis was concluded, we can determine that for 24 queries data set the best algorithm was RandomTree since it won in two methods. On the other hand, in the data set of 50 queries, the best decision tree algorithms were LMT and RandomForest because they obtained the best performance for all methods tested. Finally, J48 was the selected algorithm when neither an attribute selection method nor the Class Probabilities filter are used. But, if only the latter is applied to the data set, the best performance is given by the LMT algorithm.