Reviewerly/exHarmony huggingface dataset:
Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing specific background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. While recent Large Language Models (LLMs) like GPT-3 have demonstrated their effectiveness in zero-shot ODQA using direct prompting methods, these methods still fall short of fully harnessing the potential of LLMs when implicitly invoked. In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations entirely from scratch. These generated elements are then utilized for in-context learning. Experimental results show that our method significantly surpasses previous state-of-the-art zero-shot methods on three widely-used ODQA datasets and even achieves comparable performance with various customized fine-tuned models on full training data. Our code is available at https://github.com/lockon-n/self-prompting.
Windows Forensics Analysis

Computer forensics investigates crimes on digital devices hard disk and any other digital storage devices to determine the evidence of any unauthorized access. Essentially, forensic investigators conduct forensic analysis with the help of different approaches (Static and Dynamic) and also tools like ProDiscover, Encase, Accessdata FTK and Autopsy to guarantee the security of a system in an organization. The process requires knowledge of different registry, laws, and regulations related to computer crimes, the operating system (Windows, Linux, etc.) and the network in an organization (e.g., Win NT). In this paper we will use different forensic tools to extract forensic evidence from a windows environment.
Investigating Factors M-Learning Acceptance and Use for Distance Learning Students in Higher Education

Many research has been conducted to examine the acceptance factors to use mobile learning (m-learning) for regular students. During the COVID-19 most of the higher education institutions around the world were converted to m-learning especially for regular students, in order to continue supporting the educational stage for these students. This situation, allow researches to tested the use of m-learning for regular students while they are studying in distance learning environment. However, limited researches, especially in developing countries, have been tested the acceptance factors to use m-leaning for distance learning students. In this study the behavioral intention to use mobile learning (m-learning) were examined as well as the m-learning factors that affecting its acceptance amongst the distance learning students were outlined. The study framework was depended on the model of Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative approach was used to analyze the data that collected from a random sample of 154 male and female participants from Saudi universities. The results indicated that significant factors influencing distance learning students’ behavioral intention include quality of service, effort expectancy, facilitating conditions, gender, educational level, and type of device. The regulations governing distance learning programs and the implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher Education are having a good impact and encouraging widespread use of m-learning.
Students’ Acceptance of Mobile Augmented Reality Applications in Primary and Secondary Biology Education

Augmented reality is often indicated as a usable educational technology that can be integrated into biology classes to overcome the shortcomings of traditional teaching (such as lack of visualization of abstract teaching content, students’ low participation and interest in classes, and their insufficient understanding of complex topics). Mobile applications with augmented reality experience mode have the potential to be used in online, blended/hybrid, and in-person teaching, which is particularly important during emergencies. This study’s purpose was to determine primary and secondary school students’ acceptance of augmented reality content in commercial mobile applications that can be used as a supplement in biology teaching. A total of 188 students (from schools included in this research) completed the online questionnaire. The results showed that the majority of students perceived mobile augmented reality applications as useful and easy to use, had a positive attitude, and expressed intention to use this educational technology if given the opportunity. The importance of prior evaluation regarding educational usability and performance is highlighted since technical quality (of used mobile applications) had a strong positive effect on perceived usefulness and perceived ease of use. There were no statistically significant differences between female and male and primary and secondary students, but students with prior experience with augmented reality rated perceived usefulness higher. Despite positive results, we need to raise our concerns regarding the reliability of using mobile augmented reality in biology education due to the lack of usable free content and the frequent cancellation of authoring tools and applications.
Parents’ Levels of Involvement and Role Construction in Children’s Reading at Home

Using a descriptive research methodology, this study determined parents’ levels of involvement and role construction regarding involvement in children’s reading at home. This study surveyed 379 parents of Grade 4 pupils from the Schools Division of Ilocos Norte for school year 2019-2020. The Assessment Tool on Parents’ Levels of Involvement in Children’s Reading at Home and Levels of Role Construction Regarding Involvement in Children’s Reading at Home was used to gather data. Mean was used to describe their responses to the survey-questionnaire with respect to modeling, cognitive and behavioral dimensions of parental involvement; and their levels of role construction regarding involvement in children’s reading at home. Majority of the parents had low levels of involvement in the modeling, cognitive, and behavioral dimensions; their level of role construction was high.
Intellectual property and artificial intelligence: an introduction

Artificial intelligence is improving rapidly in terms of its capabilities and utility, which is going to have disruptive effects socially, economically and legally. Among other things, advances in AI are challenging fundamental tenets of IP law by generating creative and inventive output, by using IP in new ways such as for training machine learning systems and for generating insights, by facilitating administrative processes and decision making, and even by deceiving consumers. These technological advances require us to give careful thought to how legal rules should be applied in the future if IP systems are to continue to achieve their goals. This chapter considers some of the key questions in the emerging field of AI and IP and discusses the contributions of a group of world-leading authors to how to think about AI and IP. It concludes with perspectives on AI functionally generating historically protectable sorts of works in the absence of traditional authors or inventors.
Applications and Prospects of Fourth Industrial Revolution Technology in Environmental Areas - Focusing on Environmental Policy based Public Technology Development Projects -

Objectives : Recently, the Fourth Industrial Revolution has been actively discussed in all fields around the world. And the related R&amp;D(Research and Development) has been widely conducted in the environmental field. The core of the Fourth Industrial Revolution is hyperconnectivity, superintelligence, and convergence. Major technologies related with it are AI(Artificial Intelligence), IoT(Internet of Things), 5G(Fifth Generation communication technology), robots, blockchain, drones, 3D(Three Dimension) printers, big data, unmanned transportation, biotechnology, new materials, sharing economy, and VR/AR(Virtual Reality/Augmented Reality), etc. It is intended to seek development plans through the examples of the 4th industrial revolution technology’s environmental application.Methods : In concentration of the public technology development project, based on environmental policy, conducted from 2011 to 2020, some cases of the 4th industrial revolution technology’s environmental application have been analyzed and the future prospects have been derived.Results and Discussion : The 4th Industrial Revolution technology has been applied in various fields such as design, operation, maintenance, investigation, monitoring, and service provision in the environmental field. Therefore, in the future, it is expected that there will be working environment improvement, the progress of service quality and operational efficiency.Conclusion : With the transition to smart environmental technology, it is expected that it will be possible to advance the industry and create high value-added things. To do so, government policy support and technology development should be continuously executed.
Behaviour Coding Approach for Assessing Pitfalls in a Questionnaire Instrument towards assessing healthcare security Practice

Questionnaires are useful instruments for gathering responses to specific factual questions. However, the problems of questionnaire responses impede the effective use of the questionnaire. Some of these problems including non-responses, non-completion, issues of judgement, and social desirability such as information that the respondent is not willing to disclose need to be dealt with. The situation is more compounded in a scenario where an information security practice study in a typical hospital consists of broad categories of respondents and the survey findings are to be relied on to actually address information security compliance issues. This study, therefore, shares "pitfalls" to watch when preparing a questionnaire to measure the information security practice level in a hospital which is characterized by different respondents with varying domain knowledge such as knowledge in information security, information communication technology, and that of the domain knowledge of healthcare. A synergy of a conventional pretesting method and behaviour coding were therefore used to pretest the questionnaire. Questionnaire problems including a lack of understanding of the healthcare information systems&amp;rsquo; structure of all hospitals, unclear questions, the insignificant difference between questions, problematic questions, inadequate questions, and complex terms were among the identified pitfalls to watch. Out of a total of 118 questionnaire items that were used in the pretesting, a total of 50 questionnaire items (representing 42%) were identified to have problems after the pretesting was conducted with a total of 36 respondents in behavior coding and 21 respondents in conventional pretesting.
Implementasi Augmented Reality (AR) Sebagai Media Pengenalan Aksara Ka Ga Nga Rejang Lebong Berbasis Android

Computers are widely used to assist in completing human work, jobs that previously could only be done manually, but with the rapid development of technology the work can be done with the help of computers which is certainly very easy and can save time. And one of the computer technologies that are currently being developed is Augmented Reality. The script in the ulu or kaganga script is inscribed on bamboo slats commonly called gelumpai or written on bark called kahas, gelumpai or kahas, for example in the form of letters that are tilted to the right and broken. The Kaganga script is the name of a collection of several related scripts used by ethnic groups and cultures in southern Sumatra. The scripts belonging to this group include the Rejang, Kerinci, Lampung, Rencong scripts and others. Batak script or Batak letters are also related to this group. Augmented Reality technology can help students in establishing information connections between the real world and the virtual world more easily, as a form of perspective in increasing learning motivation.
Emergent Analogical Reasoning in Large Language Models

The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here, we performed a direct comparison between human reasoners and a large language model (the text-davinci-003 variant of GPT-3) on a range of analogical tasks, including a non-visual matrix reasoning task based on the rule structure of Raven's Standard Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings; preliminary tests of GPT-4 indicated even better performance. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.
DSI++: Updating Transformer Memory with New Documents

Differentiable Search Indices (DSIs) encode a corpus of documents in model parameters and use the same model to answer user queries directly. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents ($+12\%$). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting significantly. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.
LENS: A Learnable Evaluation Metric for Text Simplification

Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation. However, existing human evaluation datasets for text simplification have limited annotations that are based on unitary or outdated models, making them unsuitable for this approach. To address these issues, we introduce the SimpEval corpus that contains: SimpEval_past, comprising 12K human ratings on 2.4K simplifications of 24 past systems, and SimpEval_2022, a challenging simplification benchmark consisting of over 1K human ratings of 360 simplifications including GPT-3.5 generated text. Training on SimpEval, we present LENS, a Learnable Evaluation Metric for Text Simplification. Extensive empirical results show that LENS correlates much better with human judgment than existing metrics, paving the way for future progress in the evaluation of text simplification. We also introduce Rank and Rate, a human evaluation framework that rates simplifications from several models in a list-wise manner using an interactive interface, which ensures both consistency and accuracy in the evaluation process and is used to create the SimpEval datasets.
Mineral deposit models in Bulgaria – basic principles, scope and classification scheme

The necessity of development of mineral deposit models in Bulgaria, their basic principles and classification schemes are presented in the paper. Three types of models (descriptive, genetic, and quantitative) for each main deposit type are being developed by a uniform classification scheme of numerous geological, mineralogical, geochemical, industrial, and environmental characteristics and factors. These models will be used as a basis for metallogenic analysis of the ore productivity on the territory of Bulgaria by geological epochs, geodynamic settings, ore-forming systems and regions.
Remote Sensing Extraction of Photovoltaic Panels in Desert Areas Based on Feature Optimization

Aiming at the problem of low efficiency of remote sensing imagery for PV (Photovoltaic) panel extraction in desert areas, this paper proposes a remote sensing identification method for PV panels based on the optimization of multi-feature combinations, taking Qinghai province as an example. The research uses the GEE cloud platform to construct a feature set containing topographic features, spectral features and index features, filters the feature set according to the feature importance and recursive elimination idea, and introduces feature correlation analysis to filter the feature set to get the optimal feature combination, and uses random forest RF to achieve PV panel extraction, and designs four experiments to verify the effectiveness of the preferred features. The results show that: the best effect of PV panel extraction is achieved by the random forest algorithm with feature selection, the overall accuracy of classification reaches 95.86%, and the Kappa coefficient reaches 0.9197; and the accuracy of PV panel area extraction for Qinghai province can reach 95.68%; the feature optimization method proposed in this paper can effectively improve the extraction accuracy of PV panels in desert areas.
Emergence of sense of body ownership but not agency during virtual tool-use training is associated with an altered body schema

In this study we examined if training with a virtual tool in augmented reality (AR) affects the emergence of ownership and agency over the tool and whether this relates to changes in body schema (BS). 34 young adults learned controlling a virtual gripper to grasp a virtual object. In the visuo-tactile (VT) but not the vision-only (V) condition, vibro-tactile feedback was applied to the palm, thumb and index fingers through a CyberTouch II glove when the tool touched the object. Changes in the forearm BS were assessed with a tactile distance judgement task (TDJ) where participants judged distances between two tactile stimuli applied to their right forearm either in proximodistal or mediolateral orientation. Participants further rated their perceived ownership and agency after training. TDJ estimation errors were reduced after training for proximodistal orientations, suggesting that stimuli oriented along the arm axis were perceived as closer together. Higher ratings for ownership were associated with increasing performance level and more BS plasticity, i.e., stronger reduction in TDJ estimation error, and after training in the VT as compared to the V feedback condition, respectively. Agency over the tool was achieved independent of BS plasticity. We conclude that the emergence of a sense of ownership but not agency depends on performance level and the integration of the virtual tool into the arm representation.
Approach for Cryptography Digital Ecosystem Deployment

The paper introduces the Cryptography Digital Ecosystem concept and describes the rationale for its implementation. A checklists approach is offered to ensure efficient and high-quality ecosystem deployment and helps to ensure consistency and completeness of necessary tasks. A collection of checklists are indicated, which have been analytically developed based on extensive analysis of public literature regarding the development of quantum encryption solutions and adequate compliance with the solution requirements.
Half-trek criterion for identifiability of latent variable models

We consider linear structural equation models with latent variables and develop a criterion to certify whether the direct causal effects between the observable variables are identifiable based on the observed covariance matrix. Linear structural equation models assume that both observed and latent variables solve a linear equation system featuring stochastic noise terms. Each model corresponds to a directed graph whose edges represent the direct effects that appear as coefficients in the equation system. Prior research has developed a variety of methods to decide identifiability of direct effects in a latent projection framework, in which the confounding effects of the latent variables are represented by correlation among noise terms. This approach is effective when the confounding is sparse and effects only small subsets of the observed variables. In contrast, the new latent-factor half-trek criterion (LF-HTC) we develop in this paper operates on the original unprojected latent variable model and is able to certify identifiability in settings, where some latent variables may also have dense effects on many or even all of the observables. Our LF-HTC is an effective sufficient criterion for rational identifiability, under which the direct effects can be uniquely recovered as rational functions of the joint covariance matrix of the observed random variables. When restricting the search steps in LF-HTC to consider subsets of latent variables of bounded size, the criterion can be verified in time that is polynomial in the size of the graph.
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors

Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, the detection methods are generally validated by assuming a single implicitly known attack strategy, which does not necessarily account for real-life threats. Indeed, this can lead to an overoptimistic assessment of the detectors' performance and may induce some bias in the comparison between competing detection schemes. We propose a novel multi-armed framework, called MEAD, for evaluating detectors based on several attack strategies to overcome this limitation. Among them, we make use of three new objectives to generate attacks. The proposed performance metric is based on the worst-case scenario: detection is successful if and only if all different attacks are correctly recognized. Empirically, we show the effectiveness of our approach. Moreover, the poor performance obtained for state-of-the-art detectors opens a new exciting line of research.
EFFECTIVE PUBLIC SPEAKING TRAINING FOR YOUTH ORGANIZATIONS IN ARJASARI VILLAGE

An organization is a place or place to collect ideas, thoughts, and even ideas to achieve a goal that is designed together. In this case, it is necessary to have internal communication between members of the organization itself. It is also necessary to disseminate information to audiences in an effective way so that the message conveyed can be received easily. In a village, a community organization becomes a beacon for residents in fostering and developing to realize organizational goals. Here the ability to speak in public (public speaking) is needed so that the message conveyed can be well received. Therefore, organizing public speaking training (public speaking) so that members of the organization are accustomed to speaking in public properly so that the intended message can be easily understood without any misunderstandings.