Reviewerly/exHarmony huggingface dataset:
Genealogical Relationship Extraction from Unstructured Text Using Fine-Tuned Transformer Models

The paper tackles the task of extracting genealogical relationships, such as "sibling-of", "parent-of", "child-of", and "spouse-of", from unstructured, free-form text. In order to solve the problem, we propose a three-stage pipeline consisting of Named Entity Recognition (NER), Coreference Resolution (CR), and Relationship Classification (RC). NER identifies tokens in the text that refer to people, such as proper nouns or nicknames, using the SpaCy software. CR maps multiple tokens representing pronouns to their antecedents. For example, CR could map "She", "His sister", and "Maria" to the antecedent "Maria Johnson". CR allows us to transform a genealogical relationship between two tokens, such as the sibling relationship between "him" and "his sister", to a relationship between the corresponding antecedents, for example, "Bob Johnson" and "Maria Johnson". Our novel algorithm for coreference resolution is based on the AllenNLP software. The last step is the RC, which classifies the relationship between two sets of tokens given adjacent context. We use the LUKE transformer deep-learning model to extract the genealogical relationships. The end-to-end pipeline can extract and correctly classify genealogical relationships from our hand-labeled testing set of Wikipedia documents with macro precision, recall, and F1 scores of 0.794, 0.616, and 0.676, respectively.
A Neuro Symbolic Approach for Contradiction Detection in Persian Text

Detection of semantic contradictory sentences is a challenging and fundamental issue for some NLP applications, such as textual entailments recognition. In this study, contradiction means different types of semantic confrontation, such as negation, antonymy, and numerical. Due to the lack of sufficient data to apply precise machine learning and, specifically, deep learning methods to Persian and other low-resource languages, rule-based approaches are of great interest. Also, recently, the emergence of new methods such as transfer learning has opened up the possibility of deep learning for low-resource languages. This paper introduces a hybrid contradiction detection approach for detecting seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge. The proposed method consists of 1) a novel data mining method and 2) a transformer-based deep neural method for contradiction detection . Also, a simple baseline is presented for comparison. The data mining method uses frequent rule mining to extract appropriate contradiction detection rules employing a development set. Extracted rules are tested for different categories of contradictory sentences. In the first step, a classifier checks whether the rules work for an input sentence pair. Then, according to the result, rules are used for three categories of negation, numerical, and antonym. In this part, the highest F-measure is obtained for detecting the negation category (90%), the average F-measure for these three categories is 86%, and for the other four categories, in which the rules have a lower F-measure of 62%, the transformer-based method achieved 76%. The proposed hybrid approach has an overall f-measure of higher than 80%.&amp;nbsp;
Analyzing Optics' Pivotal Role in Augmented and Mixed Reality Displays

Abstract In designing a successful, engaging AR/MR display, optics knowledge is key.
Understanding the Impact of Digitalization on Buyer Supplier Relationship: A Qualitative Approach

This study analyses the impact of digitalization on the Buyer Supplier relationship (BSR) in the context of Industry 4.0.An empirical qualitative research methodology was employed, with semi-structured interviews conducted with 23 procurement specialists from service sector organizations in India to obtain empirical data.A qualitative content analysis was then conducted to arrive at common patterns and ideas.The study found that procurement specialists perceive that digitalization has an overall positive impact on buyer supplier relationship.However, it is important to exercise diligence during the implementation of any digital tools to restrict dissatisfaction, and human touch is essential in certain areas like negotiation and conflict management.Additionally, data analysis can provide strategic advantages, but the quality of input data must be considered.The study highlights the positive and negative impacts of digitalization on BSR and provides implications for research and corporate practice.It is recommended that future studies should qualify the results by analyzing the BSR in relation to supplier type, as well as by considering other digital technologies such as Machine learning and Artificial intelligence.
Entrepreneurship, Public Policy and Economic Development in Nigeria

This study investigates the impact of public policy measures in entrepreneurship and economic development in Nigeria. Time series data covering the period from 1981 – 2021 was used. Data sourced from the World Development Indicators (WDI) provided by the World Bank 2021 and Central Bank of Nigeria Statistical Bulletin 2021 was use. The variables employed are Income per capita (IPC), Institutional (IQ) Quality, Research and Development (RD) and Capacity of SMEs (COS). Results showed that there is a positive relationship IPC and COS. IQ and RD are statistically insignificant, and IQ indicated a negative relationship. It was also observed that there is a positive relationship between the intercept and COS. This implies that the problem in Nigeria is not policy formulation but that of implementation. Thus, creating a gap or missing-link in the structure of public policy formulation and implementation in Nigeria. We recommend among others that all hands must be on deck at every level of government and amongst all social groups to achieve the goals of policy as it affects all. In addition, the government should create enabling environment for the local entrepreneurs to compete globally and create a level playing ground for entrepreneurship at the domestic level to thrive healthily. Additionally, entrepreneurs need to take advantage of the role of technology to improve their overall productivity
Design of Prototype Model Augmented Reality-Based Disaster Mitigation Learning Media as a Disaster Education Facility

Indonesia is one of the most disaster-prone countries in the world as evidenced by the World Risk Index 2019 data, Indonesia is ranked 37th out of 180 countries. The younger generation as agents of change must be able to have a positive influence on the community on the importance of preparedness and mitigation as an effort to reduce the impact of disaster risk. This research intends to design the development of Augmented Reality-based disaster mitigation learning media as a means of disaster education. The method used is the development of a prototype model consisting of listen to customer, build/revise mock-up, customer test-drive mock-up. This stage is carried out with the stages of needs analysis, design, testing, implementation, and evaluation. The results showed that the Blackbox Fire Disaster Mitigation-AR test was successfully displayed as expected (valid) with the target user being the community and students, especially early childhood children building creative, critical, and innovative thinking patterns in solving problems about disasters. Novelty given in developing ideas about disaster mitigation educational media in order to realize quality education and handling climate change. It can be concluded that the presence of Augmented Reality-based disaster mitigation learning media is considered very useful in supporting the learning process in the 21st century and making it easier for users to implement the knowledge gained into daily life by knowing the stages before and after disaster mitigation through disaster education.
LR-BA: Backdoor attack against vertical federated learning using local latent representations

In vertical federated learning (VFL), multiple participants can collaborate in training a model with distributed data features and labels managed by one of them. The cooperation provides opportunities for a malicious participant to conduct a backdoor attack. However, the attack is challenging when the adversary does not own labels with the mitigation of other participants. In this paper, we discover that an adversary can exploit local latent representations output in the inference stage to inject a backdoor in VFL, even without access to labels. With little auxiliary labeled data, the adversary fine-tunes its bottom model to make it output specific latent representation for backdoor input instances, which induces the federated model to predict the attacker-specified label regardless of benign participants. Our experiments show that the proposed attack can achieve a high attack success rate with little loss of main task accuracy and outperform existing backdoor attacks. We also explore possible defenses against the attack. Our research demonstrates the potential security threat to VFL.
A signal feature extraction method based on Hough transform

The image processing method is used to extract the target trajectory and signal features. This paper improves the traditional image processing method Hough transform, uses the Canny operator to detect the contour, and improves the traditional Hough transform by decomposing the signal features and time iteration. It can extract irregular trajectory targets and extract signal frequency domain features. At the end of this paper, experimental data proves that the improved Hough transformation method can meet the stable extraction of irregular curve trajectories and cross trajectories, and can also perform feature localization and feature extraction on feature signals.
AICAS—PAST, PRESENT, AND FUTURE

Artificial intelligence circuits and systems (AICAS) are electronic circuits and systems designed to solve artificial intelligence (AI) problems and perform tasks [...]
Assessment of Digital Games in Technology Education

The paper contributes to solving the issue of digital games in education. It explains concepts such as gamification and Digital Game-Based Learning; it focuses on the typology of games and the positives and negatives of playing digital games while discussing the aspects of their implementation in education. The case study presented in the paper examines in more detail digital games as a supplement to the learning environment in technically oriented education at the primary level of elementary schools in Slovakia. The main focus is put on an analysis of digital and education aspects of selected digital games. Specifically, based on nine game assessment criteria, such as game content in line with the objectives, application of cross-cutting themes, motivation, way of manipulation, active participation of the pupil, design/graphic processing, playing time, game model and its objective. Each of these criteria can influence the application of selected games in teaching; therefore, we assessed the games individually through these criteria.
Fat Pointers for Temporal Memory Safety of C

Temporal memory safety bugs, especially use-after-free and double free bugs, pose a major security threat to C programs. Real-world exploits utilizing these bugs enable attackers to read and write arbitrary memory locations, causing disastrous violations of confidentiality, integrity, and availability. Many previous solutions retrofit temporal memory safety to C, but they all either incur high performance overhead and/or miss detecting certain types of temporal memory safety bugs. In this paper, we propose a temporal memory safety solution that is both efficient and comprehensive. Specifically, we extend Checked C, a spatially-safe extension to C, with temporally-safe pointers. These are implemented by combining two techniques: fat pointers and dynamic key-lock checks. We show that the fat-pointer solution significantly improves running time and memory overhead compared to the disjoint-metadata approach that provides the same level of protection. With empirical program data and hands-on experience porting real-world applications, we also show that our solution is practical in terms of backward compatibility---one of the major complaints about fat pointers.
Competencies and Values of Barangay Nutrition Scholars in Eastern Visayas: Basis for Program Development

The study determined the competencies and values of Barangay Nutrition Scholars (BNS) in Eastern Visayas and the relationship between the said variables along the socio demographic variables, which formed as bases in proposing for a capacity training development program for BNS in the local community. The study utilized the quantitative research design in determining the values and competencies of the BNS. Descriptive and inferential statistics were employed to measure and analyze the variables. It was affirmed that there were significant differences in mean ratings for competence in implementing, monitoring and evaluating, and documenting and record keeping, while there are no significant differences in mean ratings for competence in planning, coordinating, and mobilizing resource. In terms of the core values practiced, it was affirmed that the BNS “practiced” the values of integrity, transparency, excellence in work, respect for human rights, and accountability while they only “moderately practiced” values of efficiency and equity on the other hand, MNAOs indicates that the BNSs “practiced” values of integrity, transparency, respect for human rights, and accountability BNSs only “moderately practiced” values of efficiency, equity, and excellence in work. Further, there is significant difference between the BNS and MNAO mean ratings. As a recommendation, it was put forward that the selection criteria for BNS be reviewed and consider factors that could improve nutrition program management including gender mainstreaming criteria. It is imperative to formulate a simplified version and an extensive practical approach to nutrition education training needs to be looked into when designing and modifying the existing programs.
The Importance of Digital Literacy to Enhance Students' ability in English Language

The pandemic in Indonesia views that Indonesian people are still experiencing “technology stuttering”, especially in the education field, where technology is very rare due to the availability of technology devices. The phenomenon of pandemic situations makes it hard for teachers and students to carry online learning because they do not have adequate skills to implement online learning. It affects the quality teaching process and students outcomes in school, especially in English Language subjects. This research aims to observe the ability of senior high school student’s digital literacy skills in Singaraja, Bali. This research informed by descriptive qualitative employed semi-structured interviews to investigate 21 students. These interviews included the use of digital literacy skills in English Language Learning, and the results of interviews will be discussed in the paper. Digital literacy skills are the student’s ability to search information on the Internet. Moreover, with digital literacy skills, students can select the information carefully. This research shows the student’s ability in low level, and students cannot find information to support their learning process. Also, this research explains the importance of digital literacy skills, especially for ESL students and how to utilize digital literacy for the English language in the post-pandemic situation in Indonesia
M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate localization and blurred edges of lesions. To address this challenge, we propose a general multi-scale in multi-scale subtraction network (M$^{2}$SNet) to finish diverse segmentation from medical image. Specifically, we first design a basic subtraction unit (SU) to produce the difference features between adjacent levels in encoder. Next, we expand the single-scale SU to the intra-layer multi-scale SU, which can provide the decoder with both pixel-level and structure-level difference information. Then, we pyramidally equip the multi-scale SUs at different levels with varying receptive fields, thereby achieving the inter-layer multi-scale feature aggregation and obtaining rich multi-scale difference information. In addition, we build a training-free network ``LossNet'' to comprehensively supervise the task-aware features from bottom layer to top layer, which drives our multi-scale subtraction network to capture the detailed and structural cues simultaneously. Without bells and whistles, our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks of diverse image modalities, including color colonoscopy imaging, ultrasound imaging, computed tomography (CT), and optical coherence tomography (OCT). The source code can be available at \url{https://github.com/Xiaoqi-Zhao-DLUT/MSNet}.
An Image Compression Method Based on Subspace and Downsampling

In this study, a new Karhunen-Loeve transform based algorithm with acceptable computational complexity is developed for lossy image compression. This method is based on obtaining an autocorrelation matrix by clustering the highly correlated image rows obtained by applying downsampling to the image. The KLT is applied to the blocks created from the downsampled image using the eigenvector (or transform) matrix obtained from the autocorrelation matrix; thus, the transform coefficient matrices are obtained. Then these coefficients were compressed by the lossless coding method. One of the proposed method’s essential features is sufficient for a test image to have one transform matrix, which has low dimensional. While most image compression studies using PCA (or KLT) in the literature are used in hybrid methods, the proposed study presents a simple algorithm that only downsamples images and applies KLT. The proposed method is compared with JPEG, BPG, and JPEG2000 compression methods for the PSNR-HVS and the SSIM metrics. In the results found for the test images, the average PSNR-HVS and SSIM results of the proposed method are higher than JPEG, very close to JPEG2000, and lower than BPG. It has been observed that the proposed method generally gives better results than other methods in images containing low-frequency components with high compression ratios.
Organised crime - the cyber dimension

This chapter reviews a few critical technological developments and reflects both on their trajectory as well as on the opportunities that they create for novel forms of cyber-enabled criminality.While the future of organised crime remains forever out of grasp, primarily due to the uncertainties created by the introduction of any new technology and the radical innovation that criminals exhibit, there are sensible aspects that we can consider and learn from.By taking examples from the dark web, blockchain technology and artificial intelligence, this chapter puts forward several new research directions that reflect on the intersection between technology and organised crime.These must prompt researchers to design new studies, new scholarly explorations, and consider the ways in which a deeper understanding of organised crime can emerge from how it adopts new technologies.
The Association of Knowledge Management and Academic Performance in Academia

The usage and practices of knowledge management in higher educational institutions are growing after the development of information and communication technology. Knowledge management in educational institutions has a strong link with academic activities aligned to innovative change in the research as well as teaching and learning practices. Knowledge management in academia has also a direct impact on the academic performance of faculty members where they are responsible for conducting academic activities and promoting academic discourses. In this background, this research adopts a quantitative methodology to collect data from the 445 academic staff employed at four universities in Nepal. The factor analysis was used to explore the variables and items associated with knowledge management and academic performance. This process has identified seven dimensions of knowledge management and four dimensions of academic performance. The relationship was carried out by using correlation analysis while the multiple regression analysis was used to measure the association of knowledge management and academic performance. The research shows the association between the dimensions of knowledge management and academic performance. The association and relationship of knowledge management and academic performance matter highly in academia. Furthermore, the knowledge creation process enhances the intellectual capital of every individual which has an impact on the knowledge economy of a society.
Strategic Planning Using the Balanced Scorecard and its Impact on Marketing Creativity within the Organization in the Kingdom of Saudi Arabia (a Field Study on a Group of Private Sector Companies in the Dammam Industrial Area)

The study aimed to clarify the role of strategic planning using the balanced scorecard and its impact on marketing creativity within the organization in the Kingdom of Saudi Arabia, and to find out the extent to which the organization adopted the concept of marketing creativity, and to measure the impact of using the value-added perspective and the internal process perspective on it. The research problem is that some companies in the Kingdom of Saudi Arabia face severe regional competition, which requires them to increase work by increasing their marketing capacity in line with the importance of the role they play in attracting customers by developing the various operations they carry out to meet their needs and desires and develop work methods according to Modern management concepts, the most important of which is the balanced scorecard, which leads to increased marketing creativity within these organizations. Organizations are well aware of the importance of evaluating the performance of their activities in general, and marketing creativity in particular. Although the balanced scorecard enjoys a privileged position among performance appraisal tools, and has witnessed wide application in European and American organizations, the application of this tool still witnesses clear shortcomings in developing countries, especially the Kingdom of Saudi Arabia. The study population included a group of private sector companies in the industrial area of Dammam, Saudi Arabia. The descriptive analytical method was used to analyze the data. The results indicated that there is a statistically significant effect of at least one of the dimensions of the balanced scorecard in promoting marketing creativity in the organizations under study, and that there is a statistically significant effect of the customer dimension as one of the dimensions of using the balanced scorecard in promoting marketing innovation, and that there is a statistically significant effect of the value dimension Additive for stakeholders in promoting marketing innovation.