Reviewerly/exHarmony huggingface dataset:
Wireless Sensor Network with Blockchain Technology using Elliptic Curve Discrete Logarithm Problem and its Applications

Wireless sensor network (WSN), a type of communication system, is normally deployed into the unattended environment where the intended user can get access to the network. The sensor nodes collect data from this environment. If the data are valuable and confidential, then security measures are needed to protect them from the unauthorized access. This situation requires an access control protocol (ACP) in the design of sensor network because of sensor nodes which are vulnerable to various malicious attacks during the authentication and key establishment and the new node addition phase. Nowadays, wireless sensor networks are being widely applied in many fields of human life such as civil and military applications. Although WSNs can bring a lot of benefits and conveniences. However, when applying the WSNs in the real world we have to face many challenges such as security, storage due to its centralized server/client model. Therefore, it is necessary to apply the distributed model in the WSNs system. One of the newest distributed systems today is Blockchain (BC). Blockchain is a decentralized technology that can help the computation and management processes as well as security in WSNs. In this paper, we propose a secured ACP for such WSN. This protocol is based on Elliptic Curve Discrete Log Problem (ECDLP) and double trapdoor chameleon hash function which secures the WSN from malicious attacks such as node masquerading attack, replay attack, man-in-the-middle attack, and forgery attacks. Proposed ACP has a special feature known as session key security. Also, the proposed ACP is more efficient as it requires only one modular multiplication during the initialization phase. This article provides an overview of Blockchain integration in WSN with highlighting the benefits and challenges of applying this technology to WSN. We can conclude that using Blockchain technology to solve the problem of security and distributed storage for WSN can be an effective approach. It could pave the way for new research directions and distributed applications.
ENGLISH STUDENTS’ STRATEGIES TO SPEAK ENGLISH (A CASE STUDY OF SIXTH SEMESTER STUDENTS OF ENGLISH EDUCATION DEPARTMENT) THE STATE INSTITUTE FOR ISLAMIC STUDIES SULTHAN THAHA SAIFUDDIN JAMBI

This study aimed to discuss about English students’ strategies to speak English of sixth semester English Education Department of The State Institute for Islamic Studies Sulthan Thaha Saifuddin Jambi. The design of the study is Qualitative Method with a case study approach. Subjects of the study are the Sixth Semester Students of English Education Department and Faculty of Education and Teacher Training of The State Institute for Islamic Studies Sulthan Thaha Saifuddin Jambi. In the collecting of data, the researcher used Observation and Interview. The analyzing technique is the data reduction, data display and conclusion drawing. The results of the research show that most of English students have strategies applied to increase their ability to speak English. They use learning strategies from watching movies, absorb the structure of English through Silent Period and Improving Fluency. Researchers did not find the learning strategies of Native Speakers applied by English students because very difficult to find native speakers in their areas. Researchers also find difficulties faced by English students to speak English such as Pronunciation, Vocabulary and Meaning and Grammar. But researchers have also found the other difficulties faced by English students which are fearful or nervous and the lack friends / partners who want to speak English with them to increase their speaking skills.
COVID-19 AND E-GOVERNMENT IN PAKISTAN: ANALYSIS OF MOBILE PHONE APPS

Application of information and communication technologies in the daily business of life alike by governments, civil society, the corporate sector and by the private individual is referred to as e-governance. E-governance is convenient and enhances efficiency and transparency but requires an enabling environment that demands heavy investments in terms of resources by governments, organizations and private individuals to benefit from. That is why transition to e-governance is not that fast as to harness its full potential. Particularly low and middle income countries are not ready for universal transition to e-governance regarding public service delivery. However, covid-19 pandemic has reinforced its demand and rejuvenated the enthusiasm for transition to e-governance as it best copes with the pandemic. Instead of physical visits to public offices or to the market, citizens can avail services online staying at home through technology mediated electronic devices and the internet that best complies the standard operating procedures of the covid-19. Pakistan government is also well on track for e-governance and offers certain public services through mobile phone apps and web apps along with other means but still needs a long way to go for a full scale transition. This paper studies Pakistan’s readiness for transition to e-governance and public perception on convenience, efficiency and transparency e-governance brings, by investigating the usage experience of mobile phone apps Pakistan government has launched before and during the pandemic. This study is based on mixed methods approach of primary and secondary data collection (both quantitative and qualitative) including the review of relevant sources available online.
Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering

This paper studies a category of visual question answering tasks, in which accessing external knowledge is necessary for answering the questions. This category is called outside-knowledge visual question answering (OK-VQA). A major step in developing OK-VQA systems is to retrieve relevant documents for the given multi-modal query. Current state-of-the-art asymmetric dense retrieval model for this task uses an architecture with a multi-modal query encoder and a uni-modal document encoder. Such an architecture requires a large amount of training data for effective performance. We propose an automatic data generation pipeline for pre-training passage retrieval models for OK-VQA tasks. The proposed approach leads to 26.9% Precision@5 improvements compared to the current state-of-the-art asymmetric architecture. Additionally, the proposed pre-training approach exhibits a good ability in zero-shot retrieval scenarios.
A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback

Variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models. Existing methods for debiasing the learning process have not been applied in a generative setting. We address this gap by introducing an inverse propensity scoring (IPS) based method for training VAEs from implicit feedback data in an unbiased way. Our IPS-based estimator for the VAE training objective, VAE-IPS, is provably unbiased w.r.t. selection bias. Our experimental results show that the proposed VAE-IPS model reaches significantly higher performance than existing baselines. Our contributions enable practitioners to combine state-of-the-art VAE recommendation techniques with the advantages of bias mitigation for implicit feedback.
PENGARUH MEDIA PEMBELAJARAN FILM KARTUN UNTUK MENINGKATKAN KEMAMPUAN BERBICARA DALAM MENCERITAKAN KEGEMARAN SISWA DI KELAS II SDN 1 SUKAMULYA

The Influence Of Cartoon Learning Media To Improve The Ability To Tell Student' Hobbie In Grade II SDN 1 Sukamulya. The purpose of this study was to determine the influence of cartoon ffilm media to improve speaking skills in telling students' hobbies in grade II SDN 1 Sukamulya. This research uses the One Group Pretest Design method – Posstest Design. The unit of analysis in this study is the cartoon film "The Samson Family and the Fahriani Family". The sampling technique in this study is by Purposive Sampling. Data was collected using observation instruments, documentation and tests. The data was analyzed using the N-Gain Score (Normailized Gain) formula. It can be seen that the use of cartoon film media and the speaking ability of students in grade II SDN 1 Sukamulya has an effect in accordance with expectations. Through this cartoon film media, students are able to improve their speaking skills in telling students' hobbies. &#x0D; Keywords: cartoon film media, speaking ability
Research on target recognition technology based on improved YOLOv5

Aiming at the problem of low detection accuracy in traditional UAV target recognition, an improved YOLOv5 target recognition method is proposed. The loss function of YOLOv5 is improved, and the CIoU loss function is used instead of the GIoU loss function used by YOLOv5 to optimize the training model. The accuracy of the algorithm is improved, and a more accurate identification of the target is realized. The experimental results show that the mAP value of the model trained on the aviation dataset NWPU VHR-10 by the improved YOLOv5 algorithm reaches 93.33%, which is 4% higher than the original YOLOv5 algorithm.
Retracted: Deep Learning Target Detection System for Sewage Treatment

[This retracts the article DOI: 10.1155/2022/2743781.].
A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications

Taking as input natural images and videos, augmented reality (AR) applications aim to enhance the real world with superimposed digital contents, enabling interaction between the user and the environment. One important step in this process is automatic scene analysis and understanding, which should be performed both in real time and with a good level of object recognition accuracy. In this work, an end-to-end framework based on the combination of a Super Resolution network with a detection and recognition deep network has been proposed to increase performance and lower processing time. This novel approach has been evaluated on two different datasets: the popular COCO dataset, whose real images are used for benchmarking many different computer vision tasks, and a generated dataset with synthetic images recreating a variety of environmental, lighting, and acquisition conditions. The evaluation analysis is focused on small objects, which are more challenging to correctly detect and recognise. The results show that the Average Precision is higher for small and low-resolution objects for the proposed end-to-end approach in most of the selected conditions.
A Smart Contract for Coffee Supply Chain

Coffee is a highly popular and widely traded beverage across the globe. Its consumption is steadily increasing at an annual rate above 2%, as per the 'Coffee development report 2019' by ICO. It has been observed that the coffee market has grown significantly by over 60% since the year 1990. In India, there are around 250,000 coffee growers, out of which 98% are small growers with farms of 10 acres or fewer. Due to limited access to official financial support, middlemen play a crucial role in providing credit, technical support, and trading services to smallholder coffee growers. However, there are risks associated with adulteration, poor storage, and transportation facilities. Moreover, climate changes have also adversely affected coffee production. Conventionally, coffee trade is conducted through middlemen via phone calls and paper documentation, which leaves room for potential fraud and data manipulation. The objective is to resolve these challenges and ensure full transparency in trading through the utilization of advanced technologies. We suggest employing a smart contract for the coffee supply chain by incorporating Blockchain and IoT devices. Blockchain is a tamper-proof digital ledger distributed across the network, containing immutable digital records. This will guarantee complete transparency of data and avoid any manipulation. Throughout this project, we gained expertise in various aspects of the supply chain and acquired technical knowledge of IoT devices and Blockchain. We designed a proposal that could be utilized for industrial purposes and promote societal development. Additionally, this smart contract can be utilized for other products besides coffee, thereby enhancing the complete supply chain of different agri-products throughout the nation.
Addressing the Elephant in the Middle: Implications of the Midscale Disagreement Problem Through the Lens of Body-Object Interaction Ratings

When participants disagree about their judgments on a Likert-type scale, the average rating will be naturally drawn towards its middle. The present work’s goal is to explore the implications of this midscale disagreement problem for psycholinguistic norms by using the literature on Body-Object Interaction (BOI) ratings as a case study. Through a series of graphical analyses, we argue that (i) the average rating of most midscale items cannot be interpreted as their true position on the variable’s continuum; (ii) other variables driving the disagreement in judgements can introduce an independent midscale effect in word processing performances; (iii) the typical sample sizes used by norming studies are likely insufficient to reliably detect disagreements and can lead to significant measurement error. A methodological review of the studies on BOI’s effect in word processing reveals that most of them suffer from the midscale disagreement problem, either because of inadequate word sampling or statistical modelling. Whereas these observations provide initial clues for the interpretation and use of the ratings, it remains difficult to determine the full scope of the disagreement problem based only on the summary statistics reported by rating studies. To address this point, we present new BOI ratings for a set of 1019 French words which we use to perform item-level descriptive and exploratory analyses. Overall, the results confirm that unipolar Likert-type scale ratings such as BOI capture the dimension of interest mainly at the two ends of the scale, while they represent increasing disagreement among participants as they approach the middle. These observations provide initial best-practice recommendations for the use and interpretation of subjective variables. Our analyses can additionally serve as general guidelines to interpret similar ratings and to assess the validity of previous findings in the literature based on standard summary statics.
Improving Stochastic Gradient Descent Initializing with Data Summarization

Linear Regression (LR) is the prototypical statistical model, which can be applied on a wide range of predictive problems. Ordinary Least Squares (OLS), is the standard technique for estimating the parameters of the LR model. However, such computation can be slow and resource-hungry for large data sets with high dimensionality, due to heavy matrix operations. More importantly, OLS may be impractical for large data sets as the entire data set is required to be loaded into main memory, often exceeding RAM capacity. These limitations emphasize the need for optimization techniques to compute LR. Two state of the art algorithms used to compute LR are: Stochastic Gradient Descent (SGD) and Data Summarization (DS), combined with a matrix factorization. A few decades ago DS was the main technique to accelerate data mining computations, followed by SGD. Nowadays, SGD has become the workhorse behind most ML algorithms and deep neural networks. Merging both techniques, we propose to initialize SGD with a solution computed via DS on the initial batch of points, leaving SGD computation on the remaining points “as is”. An experimental evaluation with several data sets shows our improved SGD algorithm reaches higher quality solutions (lower MSE error, higher $$R^2$$ ) and it converges faster (less iterations, reduced data usage, less computation time). We believe our simple SGD change can benefit many more ML models beyond LR.
Prevention of Fake Comments using web3

The volume of information on the internet is currently rising dramatically. Social media platforms/e-commerce market place is producing a lot of data, including reviews, comments, and opinions, every day. As there are a number of fake reviews should incorporate Spam detection to produce a genuine opinion. Fake reviews are growing problem in online shopping, and they have a significant impact on consumer’s decision-making. Many people today base their decisions when choosing a product or service on social media opinions. Because so many false or phoney evaluations have been written by businesses or individuals for a variety of reasons, detecting opinion spam is a difficult and time-consuming task. They produce fictitious reviews to deceive users or automated detection systems by elevating or degrading the reputations of their target products in order to elevate or lower them. In this article, we’ll regulate it by leveraging blockchain technology to make the review system more authentic by allowing only legitimate product purchasers to submit evaluations using their account credentials. We use the Ethereum blockchain to authenticate user credentials, and we only permit verified users to purchase things. Also, we only permit customers to leave reviews or comments on products, ensuring that the reviews are accurate.
Demonstration Method: An Effective Alternative in Improving Student Mathematics Learning Outcomes in Elementary School

The demonstration method is a very effective method, because it helps students to find answers with their own efforts based on facts or correct data by demonstrating both directly and through the use of teaching media that are in accordance with the material presented. Talking about the effectiveness of the demonstration method, the importance of this method can be seen from the way it is applied in learning, one of which is in mathematics learning. Learning mathematics is one of the learning that is often a scary thing for every student. Therefore, to overcome this, a method that suits students during learning is needed. Take this into account. So, the purpose of this study is to see the effectiveness of the demonstration method on student learning outcomes in mathematics subjects. This research is a classroom action study, where this research was carried out on grade II students at SD Negeri 2 Merauke for the 2022/2023 school year. In this study, 24 students were the subjects of the study. By applying two cycles with two meetings each in each cycle, the study also used tests and observations as research data. As a result, there was a very significant improvement in student learning outcomes starting from cycle I to cycle II. Therefore, it is expected that the role of teachers is to advance in the achievements achieved by students, so teachers must be more creative and innovative to create learning that is fun, easy to understand and able to increase student interest so that learning objectives can be achieved.
Understanding Consensus Mechanisms in Blockchain: A Comprehensive Overview

Consensus mechanisms are a critical element of blockchain technology, enabling decentralized networks to achieve agreement at the validity and ordering of transactions across more than one nodes without counting on a central authority.Blockchain's consensus protocols ensure that each one participants in the network reach a common, immutable state of the disbursed ledger, fostering accept as true with and safety in an otherwise trustless environment.various consensus algorithms have been developed, each with its unique characteristics and trade-offs.The most wellknown and widely used consensus mechanisms include Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Practical Byzantine Fault Tolerance (PBFT), among others.In PoW-based blockchains, participants (miners) compete to solve complex cryptographic puzzles to validate transactions and add blocks to the chain.PoS, however, selects validators based totally on the number of tokens they hold and their willingness to "stake" the ones tokens as collateral.DPoS extends PoS by way of introducing a small group of elected delegates who validate transactions on behalf of the larger network, improving scalability.Consensus in blockchain is a crucial thing, directly impacting the network's safety, scalability, and decentralization.choosing the right consensus mechanism relies upon at the particular use case, the goals of the blockchain project, and the desired level of trust amongst members.
Implementation of Administration in MIN 1 Medan

This study aims to (1) Describe how the implementation (implementation) of administration and (2) the constraints and solutions that exist in the implementation of administration in MIN 1 Medan. This research was conducted with a descriptive qualitative research approach. The informants of this study were madrasa heads, teachers, administrative staff and students. Data collection techniques used are interviews, observation, and documentation. Then the data were analyzed by interactive analysis which flowed with activities including data reduction, data display, and conclusion drawing/verification. Testing the validity of the data is done by triangulation of methods and sources. From the results of the study it can be concluded that: (1) The implementation of administration in MIN 1 Medan has been carried out properly and is organized as a function of the administration itself (2) Obstacles in the implementation of administration even if there are solutions can be found by first reflecting and evaluating.
DR-SISM: A dual reversible secret image sharing mechanism

Secret image sharing (SIS) technology has received wide attention from researchers due to its multi-party management of secret information. Traditional SIS algorithms usually convert secret information into the shares of noise-like images, which may attract the attention of hackers and cannot be effectively managed. Moreover, for communication scenario with multiple user participation, participants hope to embed additional data in their shares, while the secret image and the embedded additional information to be reversibly recovered. However, the embedding capacity, security, and authentication capability are limited in the existing SIS algorithms. Therefore, this paper proposes a dual reversible secret image sharing mechanism, called DR-SISM, which includes reversible hiding stage and reversible sharing stage. This proposed DR-SISM can simultaneously achieve the reversible recovery of secret image and additional data embedded. In the first stage, a reversible data hiding method is constructed that leverages the predictive technique of median edge detection to obtain the layered label map for capturing a higher embedding load and utilizes the bit-plane replacement technique to embed additional information. In the second stage, a novel (k,n) threshold reversible SIS strategy is presented by employing the Chinese remainder theorem (CRT), which can generate visually meaningful shares. The authentication bits produced by employing the hash function are embedded into the share, which are utilized to compare with the authentication bits owned by users for verifying the identities of the participants before the secret image reconstruction. Through the comparison of advanced SIS algorithms, our proposed DR-SISM in this article is preferred over the recent algorithms in respect of embedding capacity, security, and authentication capability.