Reviewerly/exHarmony huggingface dataset:
Intellectual Property Rights Protection in Nigeria: Issues and Perspectives

Intellectual property right protection is of growing importance in most countries of the world due to its role in the development of any nation. However, intellectual property rights protection in Nigeria has not yielded any positive results as expected due to the problem of weak enforcement and non-implementation of protection laws. In view of this, the main purpose of this paper is to unravel the challenges militating against the protection of intellectual property rights of creators and inventors in Nigeria. This paper further discussed the benefits of intellectual property rights protection. In conclusion, intellectual property rights protection in Nigeria should be given the highest priority by government to aid National development as well as promoting creativity and innovation on the part of creators and inventors.
Dynamic List Based Data Integrity Verification in Cloud Environment

Cloud repository gives a proficient way to fathom issues of management and capacity, driven by high-speed information emergence. Consequently, a developing number of governing bodies and people lean towards storing their information within the cloud premises. In any case, due to the partition of information ownership and administration, it becomes exceptionally troublesome for the users or the owners to verify the integrity of data in a routine way. Hence, numerous analysts center on creating various protocols, that remotely check the astuteness of the information saved within the cloud. In this respect, a conceivable solution is proposed for dynamic reviewing by making use of a dynamic list-based index table to verify the integrity of the data which is more efficient than the state of the arts. Besides, with such a verification structure, it is proven that communication cost and storage cost at the client side is diminished effectively. The statistical analysis based on comprehensive tests illustrates that the proposed convention accomplishes the specified properties in comparison with the state of the arts.
Enhancing the Announcement Text Writing Skills of Nautical Science Students’ by Employing Scaffolding Approach

The aim of the study is to improve the announcement text writing skills of Nautical Science students by employing scaffolding technique. Participants of the study were Diploma Nautical Science (DNS), B.Sc. Nautical science students, and General Purpose rating students from Southern Academy of Maritime Studies (SAMS), Panapakkam. Essay writing was given to collect data for a sample size of fifty (50) Nautical Science students. The problem statement of the study is that the nautical students are weak in marine vocabularies and unable to understand the announcement text. Announcement text is very important in ship. They are unable to frame an announcement text with complete meaning. The hypothesis of the study is to enhance the announcement text writing skills of nautical science students by employing scaffolding approach. The objectives of the study are i) To improve the announcement text writing skills of maritime students ii) To learn the importance of standard marine navigational vocabulary iii) To bring awareness on marine English. The research questions are i) Can marine students develop their announcement text writing skills through scaffolding technique? ii) Can students learn standard marine navigational vocabulary? iii) Will students improve their language proficiency through learning announcement text? The co-operative theory is utilized in this study. The findings of the study are that students improved the announcement text writing skills of maritime students by employing scaffolding approach.
3-Cs of Cyberspace and Pakistan: Cybercrime, Cyber-Terrorism, and Cyber Warfare

This qualitative exploratory and embedded case study deliberates the nature of 3-Cs – cybercrime, cyber-terrorism, cyber warfare – against Pakistan. What are the three Cs and how are they impacting the state and society in Pakistan. How Pakistan has been dealing with these threats related to its cyberspace. While taking cue from cyber realism – which provides the basic lens to conduct this research – this study notes that states and state-sponsored individuals, groups, and organisations remain the main actors in the cyberspace who are active against each other. The perception that the cyberspace has diminished the role of state is an exaggeration. States still are the most important actors in the cyber world order animated by the typical great power politics. Pakistan remained a prime target of the cybercrimes, cyber-terrorism, and cyber warfare launched by the regional and extra-regional states. Though, it has implemented Prevention of Electronic Crime Act (PECA) and passed National Cyber Policy, yet it still has to go a long way in order to protect itself against the 3-Cs of the cyberspace. Adopting a proactive strategy to counter these threats prematurely is still a far-fetched cry for Pakistan.
Application Case Based Reasoning Method In Diagnosing Oil Palm Plant Diseases At PT Rapala, Gebang Sub-District

In the last five years, the development of computers is very rapid. This can be seen from the number of computers owned by the community, with various sophistication and features and information systems that can spoil its users. One area of application that can be considered is the field of diagnosing oil palm plant diseases. Difficulties in diagnosing oil palm plant diseases at PT. RAPALA Kec. Gebang often occurs when you want to take preventive or curative action when an oil palm plant is attacked by a disease, because the old diagnostic system is due to limited experts so that the quality of operational performance is lacking. With the application of an expert system in diagnosing oil palm plant diseases, it is hoped that it will make it easier for field workers to diagnose oil palm plant diseases quickly without waiting for experts. This system is designed using PHP My Admin programming software and MySQL as database. The result of this study is the process of diagnosing diseases in oil palm plants using the Case Based Reasoning method where the design begins with entering symptom data, then the system calculates the diagnostic results according to the Case Based Reasoning method and displays it in the form of a report.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PELANGGAN TERBAIK MENGGUNAKAN METODE WEIGHTED PRODUCT

This study aims to overcome the problem of selecting the best customer at Pematangsiantar Subur Graphic Printing. For the smooth running of the printing business, Subur Graphics maintains good relations with consumers by giving rewards to the best customers. In determining the best customer for graphic fertile printing, it is still done manually. To help overcome the problem of selecting the best customer, a decision support system is designed. Web-based decision support system built using the Weighted Product method. This decision support system uses criteria consisting of total shopping, payment method, length of subscription, payment status and total visits. The result of this research is a web-based decision support system with an output consisting of recommendations for the three best customers, namely the first best alternative Pd_Pphn with a vector value of V 0.085, the second best alternative GPIB Maranatha with a vector value of V 0.080, and the third best alternative SPTI_Ib_Mrni with a vector value of V 0.077. With a decision support system for determining the best customer using the weighted product method, Subur Graphic Printing can easily select the best customer.
Generic medical concept embedding and time decay for diverse patient outcome prediction tasks

Many fields, including Natural Language Processing (NLP), have recently witnessed the benefit of pre-training with large generic datasets to improve the accuracy of prediction tasks. However, there exist key differences between the longitudinal healthcare data (e.g., claims) and NLP tasks, which make the direct application of NLP pre-training methods to healthcare data inappropriate. In this article, we developed a pre-training scheme for longitudinal healthcare data that leverages the pairing of medical history and a future event. We then conducted systematic evaluations of various methods on ten patient-level prediction tasks encompassing adverse events, misdiagnosis, disease risks, and readmission. In addition to substantially reducing model size, our results show that a universal medical concept embedding pretrained with generic big data as well as carefully designed time decay modeling improves the accuracy of different downstream prediction tasks.
Employing Interacting Qubits for Distributed Microgrid Control

To empower flexible and scalable operations, distributed control of multi-inverter microgrids, based on classical communication networks among distributed energy resources, has attracted considerable attention as it can guarantee synchronization and provide suitable remedies to the problem of improper power sharing. Notwithstanding this, resilience of the current schemes on classical communication makes microgrids vulnerable to cyber attacks. Inspired by recent revolutionary breakthroughs in quantum communication, in this paper, we devise a novel synchronization mechanism. We extend the synchronization framework utilized in distributed control algorithms to networks of quantum systems by generating pinning terms and coupling mechanism for the new synchronization rule via exploiting proper quantum jump operators and observables, and show that the quantum system will converge to a time-variant target state. Our devised quantum distributed controller (QDC) gives rise to a novel quantum communication scheme for distributed control of microgrids and enables microgrids to exploit the state-of-the-art quantum communication frameworks as communication infrastructure. Test results on two representative AC and DC networked microgrids validate the efficacy and universality of the quantum distributed control.
Reversible Data Hiding Based on Multichannel Difference Value Ordering for Color Images

Considering the correlation existing among different color channels of the image, we propose a novel reversible data hiding (RDH) scheme based on multichannel difference value ordering. By applying the technique of pixel value ordering (PVO), we establish a certain relationship by sorting the differences among adjacent pixels of different color channels. Combined with the optimized pairwise prediction-error expansion by analyzing the distribution of pixel difference, our scheme can adaptively select pixel channel to achieve reversible embedding of information. Through simulation experiments, the feasibility of this RDH scheme in image distortion and computational complexity is verified. When the threshold T is set to 3, the average PSNR of the test images embedded with 5000 bits of additional information in each color channel can reach 63.41, 64.38, and 63.96, respectively. When the embedding capacity is 20000 bits, the PSNR of the color image can reach 58.90 dB. Considering the embedding capacity and image distortion comprehensively from the simulation data, our proposed scheme has a better performance than the previous PVO-RDH schemes which only consider a single channel.
Neuroscience Tools for Human Resource Management in Contemporary Organisations

This study aims to highlight the connection between Neuromanagement and human resource performance within the company, with an obvious impact on the overall performance of organizations.In this context, the answer to the general question must be identified: To what extent does neuromanagement influence organizational performance?The study aims to analyze the influence of Neuromanagement on human resource performance and overall performance of organizations in Romania, and the approach will consider building a complex research scenario, which will combine a quantitative empirical study with the use of research and neuroscientific methods, combining psychometric, biometric and neurometric techniques.The research topic addressed in this paper is relatively new, insufficiently explored by Romanian researchers; therefore the results of this study will most likely contribute to raising the level of understanding of the concepts presented and their practical applicability, in order to increase the performance of organizations in Romania.
XBLQPS: An Extended Bengali Language Query Processing System for e-Healthcare Domain

The digital India program encourages Indian citizens to become conversant with e-services which are primarily English language-based services.However, the vast majority of the Indian population is comfortable with vernacular languages like Bengali, Assamese, Hindi, etc.The rural villagers are not able to interact with the Relational Database Management system in their native language.Therefore, create a system that produces SQL queries from natural language queries in Bengali, containing ambiguous words.This paper proposes a Bengali Query Processor named Extended Bengali language Query Processing System (XBLQPS) to handle queries containing ambiguous words posted to a Healthcare Information database in the electronic domain.The Healthcare Information database contains doctor, hospital and department details in the Bengali language.The proposed system provides support for the Bengalispeaking Indian rural population to efficiently fetch required information from the database.The proposed system extracts the Bengali root word by removing the inflectional part and categorizing them to a specific part of speech (POS) using modified Bengali WordNet.The proposed system uses manually annotated parts of speech detection of a word based on Bengali WordNet.Patterns of noun phrases are generated to detect the correct noun phrase as well as entity and attribute(s).Entity and attributes are used to prepare the semantic table which is utilized to create the Structured Query Language (SQL).The simplified LESK method is utilized to resolve ambiguous Bengali phrases in this query processing system.The accuracy, precision, recall and F1 score of the system is measured as 70%, 74%, 73%, and 73% respectively.
Structural Vetting of Academic Proposals

Increasing postgraduate enrollments gives rise to many proposal documents required for vetting and human supervision.Reading and comprehension of large documents is a boring and somewhat difficult task for humans which can be delegated to machines.One way of assisting supervisors with this routine screening of academic proposals is to provide an artificial intelligent (AI) tool for initial structural vetting -checking if sections of proposals are complete and appear where they are supposed to.Natural Language Processing (NLP) techniques in AI for document vetting has been applied in legal and financial domains.However, in academia, available tools only perform tasks such as checking proposals for plagiarism, spellings or grammar, word editing, and not structural vetting of academic proposal.This paper presents a tool named Autoproofreader that attempts to perform the task of structural document review of proposals on behalf of the human expert using formal techniques and document structure understanding hinged on context free grammar rules (CFGs).The experimental results on a corpus of 20 academic proposals using confusion matrix technique for evaluation gives an overall of 87% accuracy.This tool is expected to be a useful aid in postgraduate supervision for vetting students' academic proposals.
Development of a Low-Cost Teleoperated Explorer Robot (TXRob)

Natural disasters such as earthquakes or mudslides destroy everything in their path, causing buildings to collapse, which can cause people to lose their lives or suffer permanent injuries.Rescuers and firefighters are responsible for entering these ruined buildings, this work being very dangerous for them because they can get trapped in the rubble or suffocate due to the harmful gases found inside these buildings.Taking into consideration the risk in this type of operations, technological innovations can be used to help in the exploration of ruined buildings and the rescue of people.Therefore, this article describes the development of TXRob, a low-cost teleoperated robot used in the exploration of post-disaster scenarios.TXRob has artificial vision, environmental gas recognition sensors, a real-time data display panel, is sized to enter buildings, and is capable of moving over uneven surfaces, such as debris or cracks, thanks to its track system.A human operator can remotely monitor and control the robot.The TXRob's versatility as well as sensors performance has been tested on uneven and harsh surfaces in a simulated disaster environment.These tests suggest that the designed robot is suitable for use in rescue situations.
SSD-based Lightweight Recyclable Garbage Target Detection Algorithm

Reducing garbage pollution to the environment will improve the quality of people’s living environment and achieve a green cycle of life. In response to the existing traditional garbage recycling and classification, which requires a lot of wasted human and material resources, this paper proposes a garbage detection network with higher accuracy for garbage identification and localization by improving the SSD network. Firstly, the backbone network is improved to generate multiple scales of feature maps through the Resnet network as the basis for subsequent detection. In addition, the SE module is introduced to optimize the features needed for the model and weaken the environment’s interference in the detection model. At the input image size of 320×320, the map of the improved model in terms of accuracy is 90.18%, which is an improvement of 1.82% compared to the original network. It can also meet the demand for real-time detection in terms of detection rate. Finally, compared with the common model, the improved model has better results in terms of detection accuracy and detection rate.
Using Visualization Strategy to Improve Students’ Reading Skill in Comprehending Detailed Information

&lt;p class="abstrak"&gt;&lt;em&gt;The difficulty of students in understanding reading, especially finding detailed information still becomes the main problems in teaching reading. One of the solutions to break the problem is practicing visualization strategy in teaching reading. Visualization strategy is recognized as one of the strategies that are effective and easy to apply in the teaching and learning process. The strategy is used to create their own images from the text in their minds. The image created in the minds of students is strongly influenced by their background knowledge. Strategies also help readers to find detailed information. This study aims to determine the application of visualization strategies to improve reading comprehension. This study applied a library research approach. Content analysis was used to analyze the data. This study found that visualization strategies can improve students' reading comprehension, especially in understanding detailed information.&lt;/em&gt;&lt;/p&gt;&lt;p class="abstrak"&gt;&lt;em&gt;Kesulitan siswa dalam memahami bacaan, terutama menemukan informasi rinci masih menjadi masalah utama dalam pengajaran membaca. Salah satu solusi untuk memecahkan masalah tersebut adalah mempraktekkan strategi visualisasi dalam pengajaran membaca. Strategi visualisasi diakui sebagai salah satu strategi yang efektif dan mudah diterapkan dalam proses belajar mengajar. Strategi tersebut digunakan untuk membuat gambar sendiri dari teks dalam pikiran mereka. Citra yang tercipta di benak siswa sangat dipengaruhi oleh latar belakang pengetahuannya. Strategi juga membantu pembaca untuk menemukan informasi rinci. Penelitian ini bertujuan untuk mengetahui penerapan strategi visualisasi untuk meningkatkan pemahaman bacaan. Penelitian ini menggunakan pendekatan penelitian kepustakaan. Analisis isi digunakan untuk menganalisis data. Studi ini menemukan bahwa strategi visualisasi dapat meningkatkan pemahaman membaca siswa, terutama dalam memahami informasi rinci.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Variability testing of software product line: A preference-based dimensionality reduction approach

Multi- and many-evolutionary algorithms have been applied to derive products for the variability testing of Software Product Lines (SPLs). This problem refers to the selection of an adequate product set to test a SPL by optimizing some objectives related to the number of products to be tested, testing criteria to be satisfied, and revealed faults. However, some problems emerge when the number of objectives to be optimized increases, for example: the solutions generated by the optimization algorithms become incomparable, designing a Pareto-front in this context requires a large number of solutions, and the visualization of such solutions requires special techniques. Several techniques are proposed to tackle this problem, such as decomposition and algorithms based on indicators. Among them, algorithms based on dimensionality reduction and user preferences are widely used, but there are no studies in the literature investigating the usage of both in a combined way. In light of this, we introduce COR-NSGA-II (Confidence-based Objective Reduction NSGA-II). COR-NSGA-II defines for each objective a confidence-level calculated with the user preferences provided interactively. The objectives with higher values of confidence are removed from the next algorithm execution. For assessing the feasibility of COR-NSGA-II, experiments were conducted by using six different SPLs, seven objectives, two types of reference points representing the user preferences, and two scenarios to simulate different user profiles. COR-NSGA-II is evaluated against four algorithms explored in the literature for the problem, and outperforms most of them according to R-HV and R-IGD. It takes less time to execute and generates a reduced number of solutions, all of them satisfying the user preferences. A qualitative analysis performed with 12 potential users shows that the task of selecting a solution generated by COR-NSGA-II is easier than selecting a solution generated by the other algorithms.
Learning Low-Precision Structured Subnetworks Using Joint Layerwise Channel Pruning and Uniform Quantization

Pruning and quantization are core techniques used to reduce the inference costs of deep neural networks. Among the state-of-the-art pruning techniques, magnitude-based pruning algorithms have demonstrated consistent success in the reduction of both weight and feature map complexity. However, we find that existing measures of neuron (or channel) importance estimation used for such pruning procedures have at least one of two limitations: (1) failure to consider the interdependence between successive layers; and/or (2) performing the estimation in a parametric setting or by using distributional assumptions on the feature maps. In this work, we demonstrate that the importance rankings of the output neurons of a given layer strongly depend on the sparsity level of the preceding layer, and therefore, naïvely estimating neuron importance to drive magnitude-based pruning will lead to suboptimal performance. Informed by this observation, we propose a purely data-driven nonparametric, magnitude-based channel pruning strategy that works in a greedy manner based on the activations of the previous sparsified layer. We demonstrate that our proposed method works effectively in combination with statistics-based quantization techniques to generate low precision structured subnetworks that can be efficiently accelerated by hardware platforms such as GPUs and FPGAs. Using our proposed algorithms, we demonstrate increased performance per memory footprint over existing solutions across a range of discriminative and generative networks.