Reviewerly/exHarmony huggingface dataset:
Bayesian inference with finitely wide neural networks

The analytic inference, e.g., predictive distribution being in closed form, may be an appealing benefit for machine learning practitioners when they treat wide neural networks as Gaussian process in a Bayesian setting. The realistic widths, however, are finite and cause weak deviation from the Gaussianity under which partial marginalization of random variables in a model is straightforward. On the basis of multivariate Edgeworth expansion, we propose a non-Gaussian distribution in differential form to model a finite set of outputs from a random neural network, and derive the corresponding marginal and conditional properties. Thus, we are able to derive the non-Gaussian posterior distribution in Bayesian regression task. In addition, in the bottlenecked deep neural networks, a weight space representation of a deep Gaussian process, the non-Gaussianity is investigated through the marginal kernel and the accompanying small parameters.
IoT forensics: Exploiting unexplored log records from the HIKVISION file system

CCTV surveillance systems are IoT products that can be found almost everywhere. Their digital forensic analysis often plays a key role in solving crimes. However, it is common for these devices to use proprietary file systems, which frequently hinders a complete examination. HIKVISION is a well-known manufacturer of such devices that typically ships its products with its proprietary file system. The HIKVISION file system has been analyzed before but that research has focused on the recovery of video footage. In this paper, the HIKVISION file system is being revisited regarding the log records it stores. More specifically, these log records are thoroughly examined to uncover both their structure and meaning. These unexplored pieces of evidence remain unexploited by major commercial forensic software, yet they can contain critical information for an investigation. To further assist digital forensic examiners with their analysis, a Python utility, namely the Hikvision Log Analyzer, was developed as part of this study that can automate part of the process.
On the Organological Potential of Video Games

The various—and often unpredictable—musical applications given to new digital technologies have fundamentally altered our ways of creating, performing, representing, and understanding musical instruments, giving way to new sounding artifacts that pose unprecedented challenges to musical iconography and organology. The multifaceted nature of digital musical instruments has exposed the limitations of traditional organology, prompting a redefinition of the concept of instrumentality and an upsurge of new methodologies. This renovation, however, has yet to recognize the organological potential of video games, which often make use of visual depictions and reconfigurations of musical instruments in the creation and characterization of virtual universes and characters. Musical instruments play an essential role in regulating the tensions between fiction and reality that underlie videoludic experiences by constituting an active agent in the player’s interaction and emerging relationships with the gameworld and its music. Research on these artifacts has been scarce yet promising thus far, providing this article with a wide-ranging collection of references that have served to inform the proposal of a new cataloguing card model for the identification, examination, and classification of musical instruments represented in, and constituted by, video games and consoles. This model merges traditional organological knowledge inherited from hierarchical systems of classification—such as the Hornbostel-Sachs—with new paradigms and taxonomical categories. In so doing, this model aims to accommodate the dual nature of ludomusical instruments as both iconographical resources of undeniable sociocultural transcendence, and organological artifacts with a set of assigned roles within their virtual worlds. The ultimate goal is to provide a framework that fully recognizes the impact of ludomusical instruments in musical experiences and organological knowledge and expectations both inside and outside the medium.
An Analysis of Students’ Difficulties on Reading Text in Finding Main Idea at the Tenth Graders of SMK Darul A’mal Metro

Reading is about looking through the text for details. The context of this research is explained about the students’ difficulties on reading text in finding main idea. The objective of this research is to know the difficulties that faced by the students in reading text, the factors of the students’ difficulties in finding the main idea in text, the students overcome the difficulties in finding main idea in the text at the tenth graders of SMK Darul A’mal Metro. The research design of this research was qualitative research. The data collecting technique of this research was documentation, observation, and interview. The sample of this research was ten students of Computer and Network Engineering in e class at SMK Darul A’mal Metro. The result of this research shows that the students’ difficulties in reading text at the tenth graders of SMK Darul A’mal Metro is the students still difficult to find the main idea in English text. The factors that caused the students difficult to find the main idea in text are students need a long time in answering question of main idea question, students do not focus while answering question, and students also do not know the meaning of the sentence. Also most of students at the tenth graders of SMK Darul A’mal Metro are do not know what is main idea, and the students do not know how to find the main idea in the text. Another reason of the factor that make the students difficult to find the main idea in English text because of the long sentence, limit vocabulary of students, and the students’ lack of reading strategy. The students’ solving of the difficulties in finding main idea in the text are read again the text, ask the teacher, and ask friends for a help.&#x0D; Keywords: Students’ Difficulties Reading, Finding Main Idea, Reading Text
Study of Shared Heritage Through Frescoes on the Walls of Dera Baba Bir Singh

This paper discusses the shared heritage related to the traditions of Hinduism and Sikhism in the state of the Punjab, India. The coexistence or the intermingling of cultures has been preserved through the beautiful murals which adorn the walls of a shrine known as Gurudwara of Baba Bir Singh. The Gurudwara is located in the
AI-Driven Produce Management and Self-Checkout System for Supermarkets

With the increasing demand for fresh and healthy food options, more and more customers are turning to purchase fruits and vegetables in supermarkets. However, the current system for purchase can be time-consuming and cumbersome, involving long queues and delays, which can be frustrating for customers. In addition to this stocking fruits, and vegetables can be a challenging task as they are prone to spoilage, requiring constant manual monitoring to keep track of the remaining quantity. This paper proposes an innovative approach using deep learning to develop a self-checkout system and also streamline the stocking process by implementing an automated system that tracks the purchases and inventory in real-time. This not only improves the overall shopping experience but also benefits the supermarkets in several ways. This helps enhance the supermarkets’ efficiency, optimizes inventory management, minimizes waste, provides data-driven insights, and improves customer satisfaction. The proposed method involves training a deep learning model to recognize and classify fruits and vegetables and automate the billing process. The produce to be purchased is automatically scanned, weighed and billed thus significantly saving not only time but also manpower involved in the traditional manual process. By utilizing the current stock details as input, the system employs deep learning algorithms to provide real-time notifications, ensuring timely restocking and minimizing stock shortages in an automated and efficient manner. The quality and variety of the dataset used to train the deep learning model is a crucial step in ensuring its accuracy, precision, and recall. The model’s performance is evaluated on set metrics to determine its effectiveness and work on its improvement. Overall, the proposed use of deep learning to improve the purchase of fruits and vegetables in supermarkets has the potential to revolutionize the way customers shop for fresh produce. This innovative approach has the potential to transform the shopping experience by reducing checkout times and meeting customer needs, giving supermarkets a competitive edge. The potential limitations of the proposed method, such as the potential for errors in recognition and classification are to be factored in for the further development of this idea.
Chinese Learner Challenges in English Subject-Verb Agreement

The second language learners are experiencing manifold challenges when acquiring a new language. English is one of the most prevalent languages nowadays. The English speakers hail from around the world. Therefore, English has become a compulsory course in China since the late 1980s. As Chinese is my first language and English is the second, I can intermittently perceive the learner challenges especially the English grammar. Grammar is the set of rules in fields of phonology, morphology, and syntax, often complemented by phonetics, semantics, and pragmatics. Chinese grammar differs from English to a great extent, including subject-verb (S-V) agreement. It is sometimes really hard to compose grammatically correct sentences due to the influence of Chinese grammar patterns as well as the sociocultural context. Consequently, the aim of the paper is to define the challenges that the learners face whose first language is Chinese and seek for answers to this problem in second language acquisition.
Fast clustering algorithm based on MST of representative points

Minimum spanning tree (MST)-based clustering algorithms are widely used to detect clusters with diverse densities and irregular shapes. However, most algorithms require the entire dataset to construct an MST, which leads to significant computational overhead. To alleviate this issue, our proposed algorithm R-MST utilizes representative points instead of all sample points for constructing MST. Additionally, based on the density and nearest neighbor distance, we improved the representative point selection strategy to enhance the uniform distribution of representative points in sparse areas, enabling the algorithm to perform well on datasets with varying densities. Furthermore, traditional methods for eliminating inconsistent edges generally require prior knowledge about the number of clusters, which is not always readily available in practical applications. Therefore, we propose an adaptive method that employs mutual neighbors to identify inconsistent edges and determine the optimal number of clusters automatically. The experimental results indicate that the R-MST algorithm not only improves the efficiency of clustering but also enhances its accuracy.
INOVASI PEMBELAJARAN ABAD 21 DENGAN PENDEKATAN SAINTIFIK DAN MODEL KOOPERARIF TIPE STAD

Learning to know, learning to do, learning to be, and learning to live together are all connected to the four pillars of education. In order to achieve the pillars of education, it is necessary to adapt to the current learning trend of cooperative learning and innovate learning. Using a scientific method and a cooperative learning model similar to STAD, the goal of this study is to make it more likely that students will be able to develop skills relevant to the modern world. With a total sample of 25 students, a quasi-experimental design was used for the research. Tests, questionnaires, observation sheets, and other methods of data collection are utilized. This study's findings demonstrate how STAD-style cooperative learning and a scientific approach support 21st-century students' skills and influence learning achievement. so that students' problem-solving, critical thinking, mathematical communication, and collaboration abilities will all benefit from the STAD learning model's implementation.
A Text-To-SL Synthesis System Using 3D Avatar Technology

In this paper, we present our Text-to-SL system that synthesizes sign language (SL) from a textual specification by means of a 3D virtual signer. This system is based on a multi-channel composition mechanism, which spatially and temporally arranges linguistic elements to generate SL utterances. New hybrid synthesis modules are proposed that enrich our initial SignCom system, combining procedural and data-driven synthesis. They implement grammatical dynamics specific to SL, in particular grammatical inflection mechanisms based on the fundamental concepts of spatiality and iconicity. We also present a methodology and perspectives for automatically translating written text in a spoken language into sign language.
Secure Codes with List Decoding

In this paper we consider combinatorial secure codes in traitor tracing for protecting copyright of multimedia content. First, we introduce a new notion of secure codes with list decoding (SCLDs) for collusion-resistant multimedia fingerprinting, which includes many existing types of fingerprinting codes as special cases. Next, we build efficient identifying algorithms for SCLDs with complete traceability and establish bounds on its largest possible code rate. In comparison with the existing fingerprinting codes, it is shown that SCLDs have not only much more efficient traceability than separable codes but also a much larger code rate than frameproof codes. As a byproduct, new bounds on the largest code rate of binary separable codes are established as well. Furthermore, a two-stage dynamic traitor tracing framework is proposed for multimedia fingerprinting in the dynamic scenario, which could not only efficiently achieve the complete traceability but also provide a much larger capacity than the static scenario.
End-to-End Open Vocabulary Keyword Search With Multilingual Neural Representations

Conventional keyword search systems operate on automatic speech recognition (ASR) outputs, which causes them to have a complex indexing and search pipeline. This has led to interest in ASR-free approaches to simplify the search procedure. We recently proposed a neural ASR-free keyword search model which achieves competitive performance while maintaining an efficient and simplified pipeline, where queries and documents are encoded with a pair of recurrent neural network encoders and the encodings are combined with a dot-product. In this article, we extend this work with multilingual pretraining and detailed analysis of the model. Our experiments show that the proposed multilingual training significantly improves the model performance and that despite not matching a strong ASR-based conventional keyword search system for short queries and queries comprising in-vocabulary words, the proposed model outperforms the ASR-based system for long queries and queries that do not appear in the training data.
Analytical reentry guidance framework based on swarm intelligence optimization and altitude-energy profile

Aimed at improving the real-time performance of guidance instruction generation, an analytical hypersonic reentry guidance framework is presented. The key steps of the novel guidance framework are the parameterization of reentry guidance problems and the optimization of parameters. First, a quintic polynomial function of energy was designed to describe the altitude profile. Then, according to the altitude-energy profile, the altitude, velocity, flight path angle, and bank angle were obtained analytically, which naturally met the terminal constraints. In addition, the angle of the attack profile was determined using the velocity parameter. The swarm intelligent optimization algorithms were used to optimize the parameters. The path constraints were enforced by the penalty function method. Finally, extensive simulations were carried out in both nominal and dispersed cases, and the simulation results showed that the proposed guidance framework was effective, high-precision, and robust in different scenarios.
Pendampingan Peningkatkan Kemampuan Literasi dan Numerasi Siswa SD Negeri Fatupisa

The development and changes in the global world are so fast, human resources play an important role in dealing with the changes that occur. If human resources are less qualified and do not have global competitiveness, then of course we will be far behind other nations. Quality human resources and good competitiveness are of course a must for everyone. The results of the PISA survey show that the ranking of Indonesian students' numeracy literacy skills is still at the bottom. In line with the findings in the field and the results of interviews with the head of SDN Fatupisa regarding the numeracy literacy skills of students there are still lacking. This activity aims to improve students' ability in terms of numeracy literacy. The method used is lectures using PowerPoint and question and answer discussions. It was concluded that the response and enthusiasm of the students and teachers at SDN Fatupisa was very good. The results of discussions and questions and answers conducted by resource persons and school members found that most students did not fully understand literacy and numeracy. Then the teachers also become more knowledgeable and have solutions to solving problems at SDN Fatupisa about numeracy literacy.
Child with Developmental Difficulties in Kindergarten

The paper deals with the issue of developmental difficulties in children in the context of mainstream education in kindergarten. It is based on the WHO definition of developmental difficulties. These are conditions that put a child at risk of suboptimal development, which may be social, medical or other disadvantages. From this perspective, it is a diverse range of issues in children that educators may encounter in kindergarten. Such children are often a challenge for educators. This paper presents qualitative research in which six case studies of children with diverse developmental difficulties in kindergarten were processed. It outlines the various challenges these children face. As well as need to focus on the child with developmental difficulties in kindergarten from different aspects: biological, developmental, psychological, pedagogical and social (family, school, community), which requires a non-categorical, multi-disciplinary and coordinated approach.
LEARNING ASSISTANCE WITH THE SILABA METHOD ON CHILDREN'S READING ABILITY

Reading is an activity that is beneficial for everyone. The low interest in reading students greatly affects the quality and quality of education in Indonesia. Learning programs at the elementary school level, especially in the lower grades, are directed at building reading skills. This study aimed to determine the impact of the silaba method of learning assistance on the reading ability of elementary school-age children in Babadan 01 hamlet. The tutoring method used is the silaba method. To facilitate the program, learning media is used, namely letter box media. The method in this activity is to use an Asset-Based Community Development (ABCD) approach. The evaluation was carried out with tests given to children, as well as interviews with children's parents, to measure the results of the tutoring program that had been implemented. As a result of this service program, children who initially could not read became able to read. The implementation of this tutoring activity went smoothly without significant obstacles.
Meet the reverse flip journal: Q&amp;#038;A with Lisa Matthias

Lisa Matthias, ScholCommLab Visiting Scholar and co-author of a new paper on reverse-flip journals. It all started with a tweet. Scholarly communications analyst Najko Jahn was exploring the library catalogues of Springer Nature when he stumbled across something he hadn’t noticed before.
Managing research data: A beginner&amp;#8217;s guide

Data management is a core aspect of the research process, helping scholars organize their work, share it with others, and collaborate effectively.
Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5

&lt;p&gt;This project aims to develop a vision system that can detect traffic light&lt;br /&gt;counter and to recognise the numbers shown on it. The system used you only&lt;br /&gt;look once version 3 (YOLOv3) algorithm because of its robust performance&lt;br /&gt;and reliability and able to be implemented in Nvidia Jetson nano kit. A total&lt;br /&gt;of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another&lt;br /&gt;80% (1764) from the images are used for training and 20% (440) are used for&lt;br /&gt;testing. The results obtained from the training demonstrated Total&lt;br /&gt;precision=89%, Recall=99.2%, F1 score=70%, intersection over union&lt;br /&gt;(IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2%&lt;br /&gt;and the estimate total confidence rate for red and green are 98.4% and 99.3%&lt;br /&gt;respectively. The results were compared with the previous YOLOv5&lt;br /&gt;algorithm, and the results are substantially close to each other as the YOLOv5&lt;br /&gt;accuracy and recall at 97.5% and 97.5% respectively.&lt;/p&gt;
ScholCommLab November Conference Blitz 

It’s been a busy November for the ScholCommLab! This month, lab members took part in three conferences: the Canadian Science Policy Conference in Ottawa, the United Nations Open Science Conference, and the PKP 2019 International Scholarly Publishing Conference in Barcelona. For those who couldn’t make it, we’re sharing highlights from each event, right here on the blog.
ScholCommLab co-director receives funding to analyze patterns of how research data is cited and reused 

&lt;strong&gt; &lt;em&gt;Stefanie Haustein wins Alfred P. Sloan Foundation research grant of $200K US&lt;/em&gt;&lt;/strong&gt; Have you ever wondered what motivates researchers to reuse open data and what makes them cite (or not cite) datasets in their work? Or how sharing, reusing and citing open data differs between research areas or changes during a researcher’s career? ScholCommLab co-director Stefanie was awarded $199,929 US ($281,660 CDN) by the Alfred P.
Emojis in Scholarly Communication: 🔥 or 💩? 

“Should there be an emoji for everything?” asked journalist Sophie Haigney in a recent &lt;i&gt;New Yorker &lt;/i&gt;article. “What, exactly, do we want from our emojis?” The future of the emoji may be uncertain, but one thing is abundantly clear. Emojis are booming. From classic smiley faces to dancing “party parrots,” there are now almost 3,000 options to choose from. They fill our text messages, our Slack chats, our emails—even some of our books.
Data Mining in Auditing: Challenges and Opportunities

Audit of financial statements is an activity that can usually be carried out to evaluate the fairness of a company's financial statements carried out by an independent party. Assessment of the fairness of financial statements is very important because of the conflict of interest between the preparers of financial reports and stakeholders. The audit usually uses sampling to obtain data related to the fairness assessment. The auditor uses sampling because of time and cost constraints. Sampling also has a risk that can lead to errors in assessing the fairness of financial statements. Therefore we need a method that can improve accuracy in data collection and processing, for example data mining. Data mining is a method that can be used to collect and process data more quickly and accurately. The use of data mining techniques that may develop in the future can have an impact on the audit process. Data mining in audits will be both an opportunity and a challenge for auditors in the future.