Reviewerly/exHarmony huggingface dataset:
A Novel Readability Complexity Score for Gujarati Idiomatic Text

Gujarati language is used for conversation by more than 55 million people worldwide and it is more than 1000 years old language.It is the chief language of the Indian state of Gujarat.There are many dialects of Gujarati like Standard Gujarati, Amdawadi Gujarati, Kathiawadi Gujarati, Kutchi Gujarati etc.The Gujarati language is very rich in morphology like other Indo-Aryan languages like Hindi.Many readability tests are available in the English language, but no readability complexity test is available for the Gujarati idiomatic text.The Complexity score is the sub concept of the readability test.In order to define complexity level of Gujarati text, complexity score of Gujarati text is calculated.We deployed a novel readability complexity score calculation method in which we considered the number of letters of each word, the number of diacritics of each word, Gujarati idiomatic text of n-gram where n=1 to 9, Gujarati idiomatic text of m-meaning idioms where m=1 to 7. The complexity score is calculated as the sum of word complexity score, diacritics complexity score, n-gram complexity score of Gujarati idioms and m-meaning complexity score of Gujarati idioms.We emphasized Gujarati idiomatic text for the calculation of complexity score as idioms make the text more complex to understand.This is an innovative and first of its kind work in the research community of Gujarati language.The results are hopeful enough to employ the suggested complexity score method for developing a readability test method for natural language processing tasks for the Gujarati language.
Improvement and Evaluation of Data Consistency Metric CIL for Software Engineering Data Sets

Software data sets derived from actual software products and their development processes are widely used for project planning, management, quality assurance and process improvement, etc. Although it is demonstrated that certain data sets are not fit for these purposes, the data quality of data sets is often not assessed before using them. The principal reason for this is that there are not many metrics quantifying fitness of software development data. In that respect, this study makes an effort to fill in the void in literature by devising a new and efficient assessment method of data quality. To that end, we start as a reference from Case Inconsistency Level (CIL), which counts the number of inconsistent project pairs in a data set to evaluate its consistency. Based on a follow-up evaluation with a large sample set, we depict that CIL is not effective in evaluating the quality of certain data sets. By studying the problems associated with CIL and eliminating them, we propose an improved metric called Similar Case Inconsistency Level (SCIL). Our empirical evaluation with 54 data samples derived from six large project data sets show that SCIL can distinguish between consistent and inconsistent data sets, and that prediction models for software development effort and productivity built from consistent data sets achieve indeed a relatively higher accuracy.
Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists

Kathleen Siminyu, Kibibi Mohamed Amran, Abdulrahman Ndegwa Karatu, Mnata Resani, Mwimbi Makobo Junior, Rebecca Ryakitimbo, Britone Mwasaru. Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages. 2022.
Calibration of Machine Reading Systems at Scale

In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system's confidence in the prediction.This confidence measure is usually uncalibrated; i.e. the system's confidence in the prediction does not match the true probability of the predicted output.In this paper, we present an investigation into calibrating open setting machine reading systems such as open-domain question answering and claim verification systems.We show that calibrating such complex systems which contain a discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings.We propose simple extensions to existing calibration approaches that allow us to adapt these callibrators to these settings.Our experimental results reveal that the joint callibration of the retriever and the reader outperforms the reader calibrator by a significant margin.We also show that the callibrator can be useful for selective prediction, e.g., when question answering systems are posed with unanswerable or out-ofthe-training distribution questions.
A Video Stitching System of Underwater Image

Stitching underwater videos captured by a hand-held camera can essentially enhance the entertainment experience. However, underwater video is difficult to stitch due to its shortcomings in definition, visibility, shakiness and parallax. In this paper, we propose a video stitching system for underwater videos captured by mobile cameras. Our method consists of three components, underwater image enhancement, unified video stitching and 3D projection. The underwater image enhancement is a component based on Relative Global Histogram Stretching (RGHS). After the processing of this component, videos with higher definition and better visibility can be obtained, which will benefit the subsequent video stitching. The unified video stitching is a component based on background identification. Through background identification, we divide video stitching into two steps: background video stitching and foreground video stitching. This makes the stitched panoramic video more stable and the picture more natural, which is the key to heavy shakiness and large parallax problem. In addition, we have also studied the playback of the panoramic video to make it more immersive. We test the proposed system on videos that are captured by hand-held cameras when diving, and use SSIM, UCIQE, UIQM, and stitching score to quantitatively evaluate the generated panoramic video. The experimental results prove the effectiveness and robustness of our method in various cases, and the better video stitching results than existing methods.
Joint Energy and Computation Workload Management for Geo-Distributed Data Centers in Smart Grid

With the increasing demands of data computation and storage for cloud-based services, the energy demand of data centers (DCs) is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the power load pressure of DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, benefiting both SGs and DCs at large. However, the geo-distributed property of DCs and SGs, as well as the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, to model the interactions between data center operator (DCO) and SGs, a Stackelberg game is formulated. The SG offers the DCO an electricity selling price to maximize the benefit. The DCO allocates the computation workload to geo-distributed DCs, aiming to minimize the costs of energy consumption and carbon emissions. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the proposed scheme, showing that it can achieve considerable cost savings over counterparts. The proposed approach and obtained results provide useful insights for helping the DCO developing rational management strategies.
A Wearable Fall Detection System Based on 1D CNN

The current wearable fall detection systems mostly use threshold method with long-distance communication such as 3G/4G or machine learning algorithm with short-distance communication such as Bluetooth and Wi-Fi. But the former method has the problem of low algorithm accuracy, and the latter has the problem of short transmission distance. In order to solve these problems, an Arduino Nano 33 BLE development board with built-in accelerometer sensor is introduced. A deep learning model trained by 1D CNN (one-dimensional convolutional neural network) is trained offline firstly and transformed into a suitable model for the above development board using TensorFlow Lite. After deployment of a fall detection algorithm in an embedded terminal, the model has improved the fall detection accuracy. The inertial data is collected and normalized firstly and used as input data set for 1D CNN. The fall detection result and GPS data will be uploaded to the cloud using the NB-IoT (Narrow Band Internet of Things), and a warning message will be sent to the relative person. The fall accuracy of the above training model reached 98.85&#x0025;, and the sensitivity and specificity were 98.86&#x0025; and 99.84&#x0025;, respectively.
Meningkatkan Kemampuan Membaca dan Menulis Berbasis Indigenousasi dengan Media Flash Card

Reading and writing are basic skills that humans must have because these competencies help humans in communicating and carrying out aspects of life. As learners, reading and writing competencies need to be taught as early as possible in preparation for receiving various lessons at school. It can be assumed that students who have not mastered reading and writing skills will not be able to follow the course of learning smoothly. As a result, the learning process will be hampered and learning objectives cannot be achieved. One of the causes of the low ability to read and write in elementary school students is influenced by intelligence factors and the learning process received by students. Indigenization aims to re-express authenticity which is usually native to get to know the local culture where students come from. This kind of cultural theme needs to be introduced to students in learning activities so that students can appreciate local wisdom. At the initial level of elementary school, training on reading and writing skills with a special theme, in this case culture, must be taught regularly, structured and interesting. Therefore, the use of learning media should be considered in developing reading and writing competence. Flash card is one of the effective media to help improve reading and writing skills, especially at the early elementary school level. Card-shaped media with this visual type have pictures and descriptions that can make students more interested in participating in learning. The application of Flash card media in the learning process is accompanied by cooperative strategies so that its use is more effective and can make students more actively involved in learning. With fun activities in the classroom, the goal of improving the reading and writing skills of grade I (one) students will be achieved.
THE EFFECT OF POPULATION, LABOR FORCE ON ECONOMIC GROWTH IN OIC COUNTRIES

The population has an important role in accelerating or slowing down the economic growth of a country. This study aims to examine the effect of the male and female population and labor force on economic growth. The objects of this study were 5 OIC countries in the period 2010-2019. The method used in this research is panel data regression analysis. Researchers found that population size has a significant negative impact on economic growth. Meanwhile, the number of the labor force has a significant positive effect on increasing economic growth. However, the male labor force has a significant negative effect and the female workforce has a significant positive effect in increasing economic growth in 5 OIC (Indonesia, Pakistan, Egypt, Nigeria, and Bangladesh) during the 2010-2019 period.
Towards radical synergy for more just and equitable futures

Inequity and social injustice are omnipresent wicked problems, complex challenges for which there are no single solutions due to their cross-cultural, cross-disciplinary, and systemic nature. For example, the "green revolution" of the 1970s was supposed to solve world hunger. But we saw instead a rise in corporate control over agriculture (Pielke and Linner, 2019). The design of social media, widely touted as creating a harmonious global village in the 1980s, has instead partly turned into hatching grounds for a global white supremacist movement and other forms of extremism. We cannot afford to passively allow accidental synergies to create global disasters. Instead, we need to bring social, technological, economic, and environmental concerns, among other considerations (the gamut of analysis often abbreviated STEEPV) into a deliberate and reflective emergent process. We refer to this decolonial, emancipatory form of design emergence as "radical synergy." In this visual paper, we show three projects by graduate students and their partners that take steps toward radical synergy through facilitating community-based, designerly activities that promote generative justice, playful changes of perspectives, and initial-stage integrative analysis
PENGEMBANGAN ELEKTRONIK LEMBAR KERJA PESERTA DIDIK BERBASIS PROJECT BASED LEARNING PADA MATERI BENTUK MOLEKUL

The aims of this research were to determine the feasibility of Development Electronic Worksheet (e-worksheet) based on Project Based Learning on geometry molecule by the results of expert judgments and student responses on the results of field trials. The form of this research is research and development (R&amp;D) which adopts Kemp's development model. The subject of this research was Electronic Worksheet (e-worksheet) based on Project Based Learning on geometry molecule which was tested at 27 students of SMAN 10 Pontianak. The instruments of data collections were the feasibility questionnaire and student response questionnaire. The results of data processing showed that the e-worksheet that developed was suitable for use in learning in terms of the percentage of content material, presentment, linguistic, and media respectively 96.6%; 96.8%; 90.2% and 86.1%. The result percentage of the calculation student questionnaire responses to the material aspects was 83.33%, 79.31% to linguistic aspects and graphical aspects was 89.50%. Based on the average of the three aspects of student responses, it was found that the percentage of e-worksheet based on Project Based Learning on geometry molecule subject was 84.04%, which was included in the very high category.
exploRNN: teaching recurrent neural networks through visual exploration

Abstract Due to the success and growing job market of deep learning (DL), students and researchers from many areas are interested in learning about DL technologies. Visualization has been used as a modern medium during this learning process. However, despite the fact that sequential data tasks, such as text and function analysis, are at the forefront of DL research, there does not yet exist an educational visualization that covers recurrent neural networks (RNNs). Additionally, the benefits and trade-offs between using visualization environments and conventional learning material for DL have not yet been evaluated. To address these gaps, we propose exploRNN , the first interactively explorable educational visualization for RNNs. exploRNN is accessible online and provides an overview of the training process of RNNs at a coarse level, as well as detailed tools for the inspection of data flow within LSTM cells. In an empirical between-subjects study with 37 participants, we investigate the learning outcomes and cognitive load of exploRNN compared to a classic text-based learning environment. While learners in the text group are ahead in superficial knowledge acquisition, exploRNN is particularly helpful for deeper understanding. Additionally, learning with exploRNN is perceived as significantly easier and causes less extraneous load. In conclusion, for difficult learning material, such as neural networks that require deep understanding, interactive visualizations such as exploRNN can be helpful.
Finding Frequent Items: A Novel Method for Improving the Apriori Algorithm

In the current paper, we use an intelligent method for improved the Apriori algorithm in order to extract frequent itemsets. PAA (proposed Apriori algorithm) is twofold. First, it is not necessary to take only one data item at each step. In fact, all possible combinations of the items could be generated at each step. Secondly, we can scan only some transactions instead of scanning all the transactions to obtain frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC_Apriori algorithms. The results exhibit that due to the significant reduction in the number of transaction scans to obtain the itemset, the algorithm execution time is significantly reduced; as in the first experiment, the time spent to generate frequent items underwent a reduction by 52% compared to the algorithm in the first experiment. In the second experiment, the amount of time spent is equal to 65%, while in the third experiment, it is equal to 46%.
Restaurant Recommendation System Using User Based Collaborative Filtering

A recommendation system is an application that can identify entities of interest for a person and provide suggestions based on the past record of person’s likes and preferences. The entity of interest can be anything, for example it can be a product, a movie or a news article. Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. Keeping such things into consideration, this recommendation system aims to recommend restaurants to users using their past preferences so they do not need to go through a list of choices. The recommender system adopts a user preference model by using the features of user's visited restaurants, and utilizes the location information of user via GPS(Global Positioning System) using LBS(Location Based System) and restaurants to dynamically generate the recommendation results using collaborative filtering technique. The suggestions will be based on the user preferences obtained from the past ratings and reviews given by the user, frequently visited cuisines of the user and the time preference of the user. Moreover, a brief analysis of reviews is also made to provide user a computed synopsis of the restaurant using text mining algorithm.
EMO-Learning: Towards an intelligent tutoring system to assess online students’ emotions

Due to the COVID-19 pandemic, most universities have adapted their learning infrastructure to an increasing demand for online training modalities. However, this type of learning, usually through Learning Management Systems (LMSs), suffer from a lack of direct feedback between students and the educational staff. For that reason, the present work introduces the EMO-learning project, whose key goal is to capture the emotions of students. This is done by means of a deep learning approach, able to timely analyse the face expressions of the students during online lectures. The module has been tested with different students during the academic year 2020-21, showing quite promising results.
Fast Parallel Bayesian Network Structure Learning

Bayesian networks (BNs) are a widely used graphical model in machine learning for representing knowledge with uncertainty. The mainstream BN structure learning methods require performing a large number of conditional independence (CI) tests. The learning process is very time-consuming, especially for high-dimensional problems, which hinders the adoption of BNs to more applications. Existing works attempt to accelerate the learning process with parallelism, but face issues including load unbalancing, costly atomic operations and dominant parallel overhead. In this paper, we propose a fast solution named Fast-BNS on multi-core CPUs to enhance the efficiency of the BN structure learning. Fast-BNS is powered by a series of efficiency optimizations including (i) designing a dynamic work pool to monitor the processing of edges and to better schedule the workloads among threads, (ii) grouping the CI tests of the edges with the same endpoints to reduce the number of unnecessary CI tests, (iii) using a cache-friendly data storage to improve the memory efficiency, and (iv) generating the conditioning sets on-the-fly to avoid extra memory consumption. A comprehensive experimental study shows that the sequential version of Fast-BNS is up to 50 times faster than its counterpart, and the parallel version of Fast-BNS achieves 4.8 to 24.5 times speedup over the state-of-the-art multi-threaded solution. Moreover, Fast-BNS has a good scalability to the network size as well as sample size. Fast-BNS source code is freely available at https://github.com/jjiantong/FastBN.
pFedGF: Enabling Personalized Federated Learning via Gradient Fusion

Data heterogeneity is one of the main challenges faced by federated learning (FL). Unlike traditional FL methods (e.g. FedAvg) which train a global model for all clients, personalized federated learning (PFL) can address the above problem by training a personalized model for each client. Current mainstream PFL researches first obtain a global model through collaborative training among all clients and then fine-tune the global model on each client's local data to obtain personalized models. However, this two-staged approach has a drawback: when the heterogeneity of different clients is large, the obtained final global model can deviate from the distributions of all clients, and therefore is not a good starting point for updating personalized models. In this paper, we propose pFedGF, a new PFL method based on gradient fusion. Different from traditional two-staged PFL, in each round of pFedGF, each client maintains two gradients simultaneously, a global gradient to capture information from all clients, and a local gradient that reflects the specific distribution of each client. The two gradients are fused to obtain the updated direction of the personalized model for each client. We carried out experiments on MNIST, FMNIST, and CIFAR-10 datasets. The results demonstrate that in the presence of data heterogeneity, pFedGF outperforms other PFL methods.