Reviewerly/exHarmony huggingface dataset:
CONTEXTUALIZED INSTRUCTION FOR SPEECH COMMUNICATION

In the twenty-first century, educators must tackle the issue of the modern period. The results of the study will assist school administrators in providing guidance to all English teachers regarding the adoption of contextualized instruction in teaching students in order to achieve lifelong learning that can be applied in their real life. With a mean of 1.032, the fluency and accuracy of the 31 pupils fall into the category of needing improvement. The mental structure of the kids could not be sequential, which could skew the coherence of thinking units. Numerous observations, according to Labastida (5), demonstrated that kids struggle with thinking, expressing themselves, and communicating. These can be seen in class discussions when students are reluctant to participate in them. While 13 of the responders had good content in their responses. However, 18 of the pupils had content that needed to be improved. Table 2 displays the respondents' entry-level scores. Thirty-one (31) pupils fall under the "needs improvement" category. This has implications for organizing in that it allows students to write on a particular area of interest, but there are drawbacks to the way thoughts are put together. According to Howard (88), writing that is well-organized is crucial not just for learning but also because it serves knowledge first and communication second. Table 3 shows the respondents' post-test speaking scores following their exposure to contextualized instruction. There were 10 students whose accuracy and fluency fit the good group, and 20 students whose accuracy and fluency fit the required improvement category. With a mean score of 1.290, it is possible that kids' speech is slurred and their thoughts are disjointed. Wilkins, who was cited by Flores (11), and who asserted that nothing can be communicated without grammar and vocabulary, can be used to corroborate this. There were 14 respondents whose organization fell into the "good" category; however, 16 respondents, with a mean score of 1.419, fell into the "needs improvement" category. This indicates that students have a sense of organization in their writing, even though some organizational tools are only sporadically used. According to the data, the respondents' speaking score at the entrance level, specifically in terms of fluency and accuracy, was 0.032, whereas their post-test score was 1.290.
Pelayanan dan Inovasi Perpustakaan Universitas Pamulang Di Masa Pandemi COVID-19

In this current condition of distance learning, college student are not advised to visit the public libraries or university libraries. However, as we know, that information are always be needed and cannot be delayed. As one of the institutions that prioritizes services to satisfy the user, library must do an innovation to always give the best services during this pandemic. This research aims to study the activities of services and innovation during this pandemic Covid-19 that provided at university libraries. The location of this research was conducted at Pamulang University Library. The research method used is qualitative descriptive method, with data analysis techniques carried out by interviews, obsevation, and literature study. Based on the result of this research, it is known that during the pandemic, the central library of the Central Library of Pamulang University implemented various policies related to its service. Among them are operational hours end constructive visits, face-to-face services that are only permitted for borrowing books, correspondence, and submitting theses. In addition, Pamulang University Library has deficiencies in providing services during a pandemic, one of which is the incomplete collection. This innovation carried out by taking advantage of the opportunities available, namely by promoing and reactivating the digital library that developed by Pamulang University. Keywords: Library, College Library, Library Services, Library Innovation, Pandemic Covid-19
HyAlg: A Multi-algorithm Cooperation for Balancing Performance and Accuracy

Most of the existing text similarity algorithms aim to improve the accuracy, but this introduces a high overhead because of scanning repeatedly the text to collect the necessary features. In order to achieve a good balance between accuracy and performance overhead, this paper proposes a novel method based on multi-algorithm collaboration, which we call HyAlg, is the abbreviation of Hybrid Algorithm. Hy Alg consists of two stages which are general similarity determination and structure similarity determination. The former uses the general features of the text to quickly complete the similarity determination, and initially eliminates a large number of dissimilar texts, thus reducing the time cost. On the basis of the former filtering results, the latter further determines the similarity from the perspective of text structure to ensure the accuracy of similarity determination. Our experimental and analysis show that Hy Alg is able to effectively reduce the performance cost of the algorithm while ensuring the accuracy.
Construction of Indian Currency Identifier for Visually Impaired

Visually impaired people often face difficulty to identify a nominal of money. Indian currency notes are available in different sizes and colours with tactile qualities that enable the visually impaired to identify different currency notes, but these tactile makers are worn out with usage. Various smartphone-based apps are available for note identification but using a smartphone by a visually impaired person gets difficult. So, it is necessary to design a device that supports visually impaired people in note identification. So, to overcome this problem we have designed an Indian currency identifier that will help visually impaired and blind people to identify Indian banknotes easily in less time using a colour sensor and sound module that will give output in audio form. The device makes use of a colour sensor and Arduino Uno to differentiate between currency denominations. The RGB output from the colour sensor is matched with the pre-stored RGB value. If RGB values fall inside any denomination pre-stored value the mp3 module is activated and the output voice is played from the store mp3 according to the result obtained from the Arduino Uno by mp3 module. A prototype was made to see the feasibility which has a Sensitivity of 85.71% and a Specificity of 66.66% to identify Indian currency notes. The cost of the construction of this device would be around 700-800 1NR.
Effect of Environmental Factors on Bunker Temperature and Pressure; A Study from Region with Harsh Tropical Weather

Background: Stabilization of weather factors especially temperature, pressure and humidity in bunkers that housed the Linear accelerators is necessary for safety of equipment and their efficient function for our patients. Daily variation of such factors can lessen the performance of Linear accelerators resulting to an error in their output and wrong dosimetry. Sokoto centre being located in the North-Western region of Nigeria with three unique seasons (Dry hot, Harmattan winter and Wet raining seasons) require a study of this kind to ascertain the influence of those harsh environmental factors on bunker's internal air condition. Methods: Daily records of Linac bunker temperature and pressure were measured using Precision mercury-in-glass thermometer and Opus barometer (OPUS 10 THI) from January 2019 to December 2021. Similarly, the corresponding daily external environmental temperature and pressure for the study period were obtained from Nigerian Meteorological Agency. The values were grouped according to the months of year, mean values of temperature and pressure for each month of the year were generated and graphs and bar-charts were plotted for both bunker and external environment. Similarly, an overall mean score for both temperature and pressure of each month of the three years period were generated and graphs were plotted with those values for bunker and external environment. Results: The overall mean external temperature for the study period was 35.7°C, ± 3.82 as standard deviation (SD) and with a range of 26.0 – 43.6 °C, while for the overall mean bunker temperature it was 27.1°C ±1.7 SD and a range of 23.1 - 31°C. For the external environmental pressure, the overall mean value was 78.1hpa ± 1.5 as SD and a range of 68.1– 83.1hpa, while for the bunker it was 977.2 hpa ± 2.55 as SD and a range of 966.9 – 988.7 hpa. Using the pearsons correlation, it indicate that there is a significant association between environmental and bunker temperature (p-value=0.002). Conclusion: External environmental temperature showed a significant influence on bunker temperature despite the presence of chillers. But for environmental and bunker pressure, the relationship is complex and non-significant. Similarly, the seasons of the year also influenced bunker temperature.
THE EFFECTS OF HIGH TEMPERATURE HEATING ON COLOUR AND WEIGHT CHANGES IN GIC AND RMGIC (A STUDY TO SUPPORT IDENTIFICATION OF FIRE VICTIMS)

Background: The temperature reached by fire disaster was varies. The disaster can cause fatalities and thus human identification is required. Dental restorative materials have properties that are resistant to high temperatures and play a role in identification process of fire victims. The identification process can be observed from the change in colour and weight of the restorative material. GIC and RMGIC are some of the most commonly used restorative material because they have good aesthetics properties, biocompatible, economical, and can release fluoride. Purpose: To determine the effect of high temperature heating on colour and weight changes in GIC and RMGIC as a support for forensic identification. Method: A total of 30 GIC and RMGIC samples used in this study were made in the form of cylinder with a size 3x2 mm. Each material was heated at four temperature groups of 30°C-200°C, 30°C-400°C, 30°C-800°C dan 30°C-1100°C using a furnace. The study was continued with the examination of colour using the Vitapan Classical Shade Guide and weight changes using a digital scale. Results: GIC and RMGIC experienced changes in colour and weight changes due to hight temperatures of 30°C-200°C, 30°C-400°C, 30°C-800°C dan 30°C-1100°C. High temperature heating shows the difference of each material. This is indicated by the p value &lt;0,001 in the Two Way Anova Test and Post Hoc LSD which means that there is a significant difference in weight difference between GIC and RMGIC at each temperature interval. Conclusion: High temperature heating affects the colour and weight changes of GIC and RMGIC. Keywords: Forensic identification, GIC, High temperature heating, RMGIC
THE EFFECT OF HIGH TEMPERATURE HEATING ON WEIGHT AND COLOR CHANGES IN COMPOSITE RESIN AS FORENSIC IDENTIFICATION

Background: Fire disaster is a non-natural disaster that often occurs. The impact is that victims are difficult to identify. One of the identification methods that can be used is dental restorative materials with observing changes in color and weight that occur in dental restorative materials due to exposure to high temperatures. Composite resin is a dental restorative material that is often used because it has good aesthetics, physical and mechanical properties. Purpose: This study to determine the effect of high temperature on weight and color changes in nanohybrid and bulkfill composite resin as a reference for forensic identification. Method: This research is an experimental laboratory with a pre and post-test control group design. A total of 30 nanohybrid composite resin samples and 30 bulkfill composite resin samples were divided into 5 groups consisting of 4 treatment groups and 1 control group. The treatment group was heated at four temperature groups, 30°C-200°C, 30°C- 400°C, 30°C-800°C and 30°C-1100°C using a furnace (Thermo Scientific, USA). Weight changes were measured using a digital scale (Ohaus,China) and color changes were observed using the Shade Guide (Vitapan Classical, Germany). Result: A change in color and weight of the nanohybrid composite resin and bulkfill after being heated at high temperatures. Two-Way ANOVA test and Post Hoc LSD test showed that there was a significant difference in weight between nanohybrid composite resin and bulk fill composite resin at each temperature interval (p&lt;0.001). Conclusions: High temperature heating affects the color and weight changes of nanohybrid and bulkfill composite resin. Keywords: Bulkfill composite resin, High temperature heating, Nanohybrid composite resin
Mattpod: A Design Proposal for a Multi-Sensory Solo Dining Experience

The consumption of a meal is not just a bodily requirement but can also carry significant symbolic meaning. Solo dining is often contrasted to a shared eating experience and portrayed as an inferior way of eating a meal due to lacking essential social and normative qualities. Human-computer interaction research increasingly explores different ways of enhancing the solo dining experience. However, a focus seems to be on recreating aspects essential to the shared eating experience, such as a dining companion being present, rather than trying to enhance aspects that solo diners enjoy and, therefore, contribute to a reverie in eating. Based on earlier research findings, we developed a design concept that includes sound and visual elements supporting the multi-sensory eating experience and encouraging the user to concentrate on the food rather than seeking distraction. The formative usability evaluation results indicate that the proposed design needs further refinement to evoke the anticipated effect.
Constructing a Knowledge Graph from OpenStatistical Data: The Case of Nova ScotiaDisease Datasets

Abstract The majority of available datasets in open government dataare statistical. They are widely published by various governments to beused by the public and data consumers. However, most open data por-tals do not provide the five-star Linked Data standard datasets. Thepublished datasets are isolated from one another while conceptually con-nected. Through this paper, a knowledge graph is constructed for thedisease-related datasets of a Canadian government data portal, NovaScotia Open Data. We leverage the Semantic Web technologies to trans-form the disease-related datasets into the Resource Description Frame-work (RDF) standard and enrich them with semantic rules. An RDFdata model using the RDF Cube vocabulary is designed in this work todevelop the graph that adheres to best practices and standards, allowingfor expansion, modification and flexible re-use 3. The study also discussesthe lessons learned during the cross-dimensional knowledge graph con-struction and integrating open statistical datasets from multiple sources.
A mobile app for dyslexia biomarker detection in children at home or at school: Feasibility, Acceptability, Economic impact, Pilot Study and Survey Results

The use of mobile apps in diagnosing health conditions has increased in recent years. We need a biomarker detection app for dyslexia because it potentially helps to prevent severe consequences through an early diagnosis that helps provide early intervention. No other research assessed the feasibility and acceptability of using this kind of mobile app to detect dyslexia at home or at school. Here we present a dyslexia biomarker detection app based on Z-scored QEEG data that can be used at home or school and has accomplished a high accuracy rate in diagnosing dyslexia. The mobile app can be used at home by parents or teachers at school. We have collected data from 207 children (96 of them have dyslexia, 111 of them are typically developing) between 7-10 years old for 60 sessions. The data consists of the eyes-open resting state 2-minute QEEG data from 14-channels. Using the ANN machine learning method, children with dyslexia/ typically developing children (TDC) classification has been done with a high accuracy rate (98.8%). ANN yields the highest accuracy results with standardized QEEG data in the literature. A survey is created to assess the dyslexia biomarker detection app’s feasibility, acceptability, and economic impact. The results have shown that the biomarker detection app is found feasible and acceptable by families, however, it is found expensive to use at home.
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation

Simulation-based testing of autonomous vehicles (AVs) has become an essential complement to road testing to ensure safety. Consequently, substantial research has focused on searching for failure scenarios in simulation. However, a fundamental question remains: are AV failure scenarios identified in simulation meaningful in reality, i.e., are they reproducible on the real system? Due to the sim-to-real gap arising from discrepancies between simulated and real sensor data, a failure scenario identified in simulation can be either a spurious artifact of the synthetic sensor data or an actual failure that persists with real sensor data. An approach to validate simulated failure scenarios is to identify instances of the scenario in a corpus of real data, and check if the failure persists on the real data. To this end, we propose a formal definition of what it means for a labelled data item to match an abstract scenario, encoded as a scenario program using the SCENIC probabilistic programming language. Using this definition, we develop a querying algorithm which, given a scenario program and a labelled dataset, finds the subset of data matching the scenario. Experiments demonstrate that our algorithm is accurate and efficient on a variety of realistic traffic scenarios, and scales to a reasonable number of agents.
Adaptive Optimization with Examplewise Gradients

We propose a new, more general approach to the design of stochastic gradient-based optimization methods for machine learning. In this new framework, optimizers assume access to a batch of gradient estimates per iteration, rather than a single estimate. This better reflects the information that is actually available in typical machine learning setups. To demonstrate the usefulness of this generalized approach, we develop Eve, an adaptation of the Adam optimizer which uses examplewise gradients to obtain more accurate second-moment estimates. We provide preliminary experiments, without hyperparameter tuning, which show that the new optimizer slightly outperforms Adam on a small scale benchmark and performs the same or worse on larger scale benchmarks. Further work is needed to refine the algorithm and tune hyperparameters.
How Parallel Circuit Execution Can Be Useful for NISQ Computing?

Quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware in the short term. Only small circuits can be executed reliably on a quantum machine due to the unavoidable noisy quantum operations on NISQ devices, leading to the under-utilization of hardware resources. With the growing demand to access quantum hardware, how to utilize it more efficiently while maintaining output fidelity is becoming a timely issue. A parallel circuit execution technique has been proposed to address this problem by executing multiple programs on hardware simultaneously. It can improve the hardware throughput and reduce the overall runtime. However, accumulative noises such as crosstalk can decrease the output fidelity in parallel workload execution. In this paper, we first give an in-depth overview of stateof-the-art parallel circuit execution methods. Second, we propose a Quantum Crosstalk-aware Parallel workload execution method (QuCP) without the overhead of crosstalk characterization. Third, we investigate the trade-off between hardware throughput and fidelity loss to explore the hardware limitation with parallel circuit execution. Finally, we apply parallel circuit execution to VQE and zero-noise extrapolation error mitigation method to showcase its various applications on advancing NISQ computing.
Hadamard-Coded Supervised Discrete Hashing on Complex and Quaternion Domain

This paper extends Hadamard-coded supervised discrete hashing on real domain (termed as ℝ-HCSDH) using a real-valued kernel trans-formation (ℝKT) to one on complex/quaternion domain (termed as ℂ-HCSDH/ℍ-HCSDH) using complex/quaternion-valued KTs (ℂKT/ℍKT). Supervised discrete hashing has recently been attracted for its efficiency in data retrieval. Efficient learning of a hashing function is at the core of SDH, and many methods have been proposed. Among them, HCSDH simplifies the learning process by introducing Hadamard codes and shows its efficiency. Although many studies on SDH, including HCSDH, focus on hashing function learning, KT, which is an initial step of SDH to generate a feature vector, also affects performance but has received less attention. This motivates us to establish more effective KTs in this work. Since conventional KTs are ℝKTs that only consider the distance be-tween the input data and each anchor chosen from a training dataset, it cannot distinguish two anchors being equidistant. To solve this problem, we introduce ℂKT/ℍKT to consider not only the distance but also the angle between the input data and each anchor. More-over, under the ℂKT and the ℍKT, we verify that Hadamard codes are still optimal for the HCSDH model. Experimental results show ℂ-HCSDH and ℍ-HCSDH outperform ℝ-HCSDH in (cross-modal) data retrieval.
What if Image Self-Similarity can be Better Exploited in Data Fidelity Terms?

In this work, we introduce a novel variational model for image restoration. In particular, we study the suitability of exploiting self-similarity of natural images in the fidelity term. Traditionally, this cue has been used for the regularization term, promoting to align similarities in the degraded image with similarities in the restored one. In contrast, our proposed non-local data-fidelity term penalizes deviations of patches after having suffered from the degradation process if they are similar in the degraded image. Experiments on super-resolution, denoising and depth filtering show the competitiveness of this new formulation with respect to traditional nonlocal regularization terms and recent learning-based methods.
Efficient Fine-Tuning of Deep Neural Networks with Effective Parameter Allocation

It’s commonplace in modern deep learning to achieve SOTA performance by fine-tuning a large, pretrained base model. Recent successes in natural language processing, attributed in part to knowledge transfer from large, pretrained, transformer-based language models, have sparked a similar revolution in computer vision via the introduction of Vision Transformers. As modern deep neural networks increase in performance, they also tend to increase in size. Key issues associated with fine-tuning such enormous models include storage overhead, as well as memory and / or latency requirements. Parameter efficient fine-tuning is a fairly recent paradigm which has been evolving alongside massive neural networks in part to address these issues. We showcase the effectiveness of parameter efficient fine-tuning of vision transformers, and introduce a simple yet effective method for learning a non-uniform parameter allocation given a fixed budget. We demonstrate our approach across a range of benchmark tasks in image classification and semantic segmentation.