Reviewerly/exHarmony huggingface dataset:
Bangla PDF Speaker : A Complete Computer Application to Convert Bangla PDF to Speech

In this paper, a complete computer application is presented that can convert Bangla PDF to Bangla Speech. According to the proposed technique, images are extracted from PDF and then after processing the images, they are sent to OCR engine to extract text. Extracted text are then normalized and sent to text to speech (TTS) engine to generate speech. Image processing is a key component of the developed application as it increases the efficiency of OCR engine to a great extent. We propose a novel threshold selection method that is able to detect type of noise in the extracted image and select threshold accordingly for binary transformation. Thus it solves the problem of selecting appropriate threshold of different images and it increases the overall accuracy and efficiency of the application. Another feature that has improved the performance of introduced computer application is text normalization. Normalization of the extracted text from the OCR engine makes the text more accurate to pronounce by the TTS engine depending on the context. Finally, we present experimental results that show 80.804% accuracy on text extraction from the PDF file and 3.92 score (out of 5) on the generated speech by human evaluation.
Learning distant cause and effect using only local and immediate credit assignment

We present a recurrent neural network memory that uses sparse coding to create a combinatoric encoding of sequential inputs. The network is trained using only local and immediate credit assignment. Despite this constraint, results are comparable to networks trained using deep backpropagation or BackProp Through Time (BPTT). With several examples, we show that the network can associate distant cause and effect in a discrete stochastic process, predict partially-observable higherorder sequences, and learn to generate many time-steps of video simulations. Typical memory consumption is 10-30x less than conventional RNNs, such as LSTM, trained by BPTT. One limitation of the memory is generalization to unseen input sequences. We additionally explore this limitation by measuring next-word prediction perplexity on the Penn Treebank dataset.
Gesture-based Sports Training Video Recording System

Today with the rapid growth of technology and usage of internet services, e-training has become one of the latest trends in the sports and education sectors. This study examines an innovative approach in the form of an intelligent cloud-based system to enhance the current e-training procedures for indoor sports such as table tennis and badminton. The proposed gesture-based sports training recording system is a cloud-based web application that uses intelligent techniques to provide an interactive e-training experience to users of the system. In the proposed system, a Pan-Tilt-Zoom (PTZ) IP camera setup recognizes the gestures of the trainer and trainees during the practice session. The camera intelligently identifies specific gestures performed by the trainer to position it towards the trainee with the aid of gesture recognition algorithms. This paper also presents an evaluation of the gesture recognition module. This is an innovative system capable of taking e-training to the next level as it provides a complete training experience and makes the whole learning and teaching process efficient and relaxing.
Influence of Input Devices on VR Sickness: Effect of Subtle Stimulation of the Sense of Balance on the Sensory Discrepancy

Simulator sickness is still an unresolved issue in the VR field. Especially in driving or flight simulators, a perceptual discrepancy often leads to nausea and dizziness. This study tested the assumption that a mild stimulus to the sense of balance prevents or reduces this discrepancy using a VRGo, which acts as a control in contrast to conventional tilt input devices. A trend towards reduction in both time to nausea and severity of symptoms of simulator sickness were found supporting this hypothesis.
Addressable Quantum Gates

We extend the circuit model of quantum computation so that the wiring between gates is soft-coded within registers inside the gates. The addresses in these registers can be manipulated and put into superpositions. This aims at capturing indefinite causal orders and making their geometrical layout explicit: we express the quantum switch and the polarizing beam-splitter within the model. In this context, our main contribution is a full characterization of the anonymity constraints. Indeed, the names used as addresses should not matter beyond the wiring they describe; i.e., quantum evolutions should commute with “renamings.” We show that these quantum evolutions can still act non-trivially upon the names. We specify the structure of “nameblind” matrices.
A Matter of Identity? Designing Personas for the Development of Makerspaces for Girls with Migration Background Considering Complex Social Identities

English – The present study deals with the construction of personas considering complex social identities. The context is a requirements analysis for the participatory development of makerspaces for 10-16 year old girls with migration background. With their hands-on approach, makerspaces offer a low threshold access to STEM subjects, especially for children and teenagers. However, these offers do not reach girls with migration background sufficiently yet. Ten semi-structured interviews were conducted with girls aged 10-14 years to gather data of the target group. Using a multiple step process considering complex social identities, we identified four identity structures. Personas were designed based on these identity structures. The process of persona creation is depicted exemplarily and further use of the personas in the context of “STEM education/makerspaces” is discussed.
A Comparative Study of the Foreign Students’ Perceptions Regarding Synchronous and Asynchronous Online Classes in a Liberal Arts Writing Course

This study aimed to find more effective ways to conduct online classes by comparing the foreign students’ perceptions regarding synchronous and asynchronous classes in a liberal arts writing course. While asynchronous online writing classes ran in the first semester, synchronous classes ran in the second semester of 2020 at A university. The survey of satisfaction, which included the perceptions of the foreign students, was given to students studying Korean at the intermediate and advanced levels after each semester. As a result of this survey, we learned that the difference in the style of the student’s online classes did not affect their perception of the teaching contents, nor their perception of their instructors. However, students perceived synchronous classes more positively than asynchronous classes. In particular, there was a significant difference when it came to interactions. For instance, the advanced students were more positive about the synchronous classes regarding their concentration levels, their “sense of presence,” and their overall understanding of their instructor’s feedback. However, both levels of students were positive about the asynchronous classes when it came to their management of the learning process. Through this study, we confirmed that the synchronous class is suitable for liberal arts writing courses, which implement writing practice and feedback. However, additional materials, such as prerecorded lectures, should be provided according to the students’ Korean level and other considerations.
Auxiliary Pathological Analysis Based on Classification and Recognition of Electronic Gastroscope Images

In this paper, a computer-aided lesion detection method based on gastroscope image is proposed to improve the efficiency and accuracy of conventional gastroscopy. Considering that the mainstream image processing algorithms are not suitable for the region extraction of gastroscope image, this paper adopts a simpler and faster color feature extraction method with the characteristics of large changes between pixels. At the same time, a variety of feature combinations are used to replace the single feature, and the two-level series classifier is used to perform the de-interference and lesion detection steps in series. Finally, the design and implementation of gastroscope pathology auxiliary recognition system is completed in the relevant test. The results show that the recognition method based on classification detection is significantly better than the traditional machine learning classification method in the classification and recognition of precancerous diseases, and it has a significant improvement in accuracy compared with the deep learning model
Key Enablers and Barriers of Solar Paver Technologies for the Advancement of Environmental Sustainability

The global initiatives to improve environmental sustainability have centered on reducing energy consumption and developing technological solutions for greener power generation. Current insights on innovations for environmental sustainability are primarily from developed countries, with limited studies originating from developing countries. This study focuses on solar paver technology, a potential innovation for sustainable generation of power. This technology lies in its dual-purpose ability to enable the use of solar roadways that can generate electricity to power other road infrastructure such as electric lights. To maximize the potential of success of deployment of solar pavers, it is important to investigate the practicalities of solar pavers and understand the perceptions of stakeholders that will be responsible for the implementation of solar pavers. This study addresses this gap in knowledge. Thirty construction industry stakeholders in Malaysia were interviewed through focus group discussions and in-depth interviews. Qualitative analysis identified two main themes and five sub-themes, namely (i) key enablers (motivation, opportunity, and ability); (ii) key barriers (challenges and weaknesses) of solar paver technology. This study provides fresh insights into a new form of sustainable solar paver technology engendering new streams of research in construction engineering and technology management. Implications for management and organizational research are discussed.
Semi-structured Document Annotation Using Entity and Relation Types

Semi-structured documents such as web-pages and reports contain text units with complex structure connecting these. We motivate and address the problem of annotating such semi-structured documents using a knowledge graph schema with entity and relation types. This poses significant challenges not addressed by the existing literature. The latent document structure needs to be recovered, and paths in the latent structure need to be jointly annotated with entities and relationships. We present a two stage solution. First, the most likely document structure is recovered by structure search using a probabilistic graphical model. Next, nodes and edges in the recovered document structure are jointly annotated using a probabilistic logic program, considering logical constraints as well as uncertainty. We additionally discover new entity and relation types beyond those in the specified schema. We perform experiments on real webpage and complex table data to show that our model outperforms existing table and webpage annotation models for entity and relation annotation.
Disagreement Options: Task Adaptation Through Temporally Extended Actions

Embodied AI, learning through interaction with a physical environment, typically requires large amounts of interaction with the environment in order to learn how to solve new tasks. Training can be done in parallel, using simulated environments. However, once deployed in e.g., a real-world setting, it is not yet clear how an agent can quickly adapt its knowledge to solve new tasks.
Augmented Reality Guidance for Car Repairs: Immediate and Long-Term Effects

Car service technicians are challenged by the increasing variety and complexity of repair and maintenance tasks. Recent studies have shown benefits by using augmented reality (AR) for different industrial tasks. Most studies compared a very established support technology with the new AR support in just one initial exposure. The lack of routine in dealing with AR often creates a respective disadvantage. This paper looks into the question to which extent can AR head-mounted display (AR HMD) guidance provide benefits for an unknown car repair task. To investigate further than an initial exposure, the service technicians repeated the specific task every two weeks in total four times. The experiment under field conditions compares the efficiency, the repair quality, and the mental effort under AR HMD support to the traditional repair guideline. Results indicated that AR HMD support impacts positive the repair quality immediately and long term. Furthermore, the AR HMD support created a decent learning environment, that the high quality also remained without AR guidance. This shows opportunities to use AR HMDs for training and guidance of industrial tasks in practice.
Architecture Blockchain Technology with IoT for Monitoring Drug Warehouse

The supply chain process is a process that occurs from the manufacturing process to distribution to consumers. The part that is often overlooked is managing drug storage in industrial drugstores, distributors, hospitals, or pharmacies. Its current development combines the supply chain system with blockchain technology with IoT to increase the system's ability to help monitor the condition of drugs placed in the right place and ensure the drug's condition according to their needs. This study describes a blockchain layer architecture with IoT for real-time monitoring of drugstores. With real-time monitoring, the quality of the drugs produced is as expected. The supply of drugs from the start of production to consumers hands will increase consumer confidence in the quality of drugs on the market.
Tools and Techniques for Machine Translation

Machine translation has been a subject of exploration for last numerous years. A ton of observable work has been done in this field. Machine translation involved serious speculation some time before there were computers to apply to it. This paper aims to present different approaches, techniques, and algorithms used for machine translation. Challenges of ambiguity in machine translation are also discussed. The paper also outlines success reported in different categories by various approaches taken along with challenges faced.
Privacy-Preserving Fair Machine Learning Without Collecting Sensitive Demographic Data

With the rising concerns over privacy and fairness in machine learning, privacy-preserving fair machine learning has received tremendous attention in recent years. However, most existing fair models still need to collect sensitive demographic data, which may be impossible given privacy regulations. To address the dilemma between model fairness and sensitive data collection, we propose DicPF, a distributed and privacy-preserving fair learning framework that operates without collecting sensitive demographic data. In particular, DicPF assumes multiple local agents and a modeler are distributed, and sensitive demographic data is separately held by multiple local agents. To assist fair learning at the modeler, each agent learns a fair local dictionary and send it to the modeler. The modeler learns a fair model based on an aggregated dictionary. Under DicPF framework, we propose a private z-Sparse Fair Learner. Extensive experiments on three real-world datasets demonstrate the efficiency of the proposed model. Compared with the state-of-the-art fair learners, the proposed z-Sparse Fair Learner achieves superior fairness performance by lowering prediction disparity. We also develop a privacy inference model to demonstrate the excellent privacy-preserving performance of DicPF. Finally, we theoretically analyze z-Sparse Fair Learner and prove upper bounds on its model fairness and accuracy.
A Feature Aggregation Hourglass Network for Human Pose Estimation

The fast development of Convolutional Neural Network (CNN) models has greatly contributed to the advancement of human posture estimation. However, hard cases such as invisible keypoints, variable poses and complex background are still great challenges for human pose estimation tasks. To this end, we present a novel cross stage Feature Aggregation Hourglass (FAH) network which aims to improve human pose estimation accuracy. Specifically, we construct a new cascaded residual block for hourglass model and design a multistage hourglass network with cross stage feature aggregation to obtain contextual information from different stages. Moreover, we add transposed convolution layers over our FAH model for upsampling in order to generate heatmaps from deep and low resolution features. Extensive experiments on two common human pose datasets demonstrate the advantages of our FAH model over a broad range of existing human pose estimation methods.
Embedded Platform Architectures

We have studied the current processor architectures and the direction in which they are advancing for higher performance. Data crunching needs are bringing advances in complex GPUs. Multi core and multi-threaded processor architectures are executing more number of instructions per clock and high level of integration is realized through Systems-on-chip. But processors cannot perform independently unless they are interfaced with external peripherals having similar performance. The capabilities of SoC devices have to be expanded through certain interfaces. The peripherals can be like Input/Output devices, hard disk storage, extended memory, cache and memory controllers and so on. So all SoC devices have in-built interfaces to extend their capabilities and interconnect to multiple types of peripherals. Though the peripheral controllers are fast, the way they interconnect with processors should also be efficient. Communication among cores, processor to peripherals is done through bus. Bus architectures are also advancing for high throughput, fast event response and bus extension capabilities. Bus connectivity has been standardized so that multiple heterogeneous peripherals can be interconnected seamlessly. In this chapter, we will study some important peripheral interconnects and bus architectures which lead to efficient embedded platforms. Introduction to bus and basic modes of data transfers across processors and peripherals are described in Sect. 13.1 and 13.2. Typical Arm™ plat form with AMBA bus is described in Sect. 13.3 and 13.4. Important IO interface standards like USB, Bluetooth etc. are described in Sect. 13.5. Emerging IoT platform for embedded systems is introduced in 13.6. To summarize, challenge lies in selecting proper plat forms for distributed embedded systems. It depends on the data throughputs and real time nature of networks.
ANP-W2V: Effects of Composition Methods for Embedding Adjective-Noun Pairs

Adjective-Noun Pairs (ANPs) are often used for affective computing in textual and visual domains. Due to the training cost of more current models (e.g., transformers) many approaches still rely on Word2Vec to compute ANP embeddings by combining individual word embeddings. However, when combining an adjective and a noun into an ANP, there can potentially be more complex interactions which cannot be accounted for by using Word2Vec alone. To solve these challenges, we propose ANP- W2V, an approach that puts adjectives and nouns in different embedding spaces and hence outperforms the baselines based on Word2Vec. In this paper, we do a comprehensive comparison, where we systematically evaluate the role of six different fusion methods in four different tasks with different embedding sizes. The chosen tasks not only gauge the external and internal relationships in the ANPs but can also check whether ANP embeddings capture more complex interactions between adjectives and nouns.
Um Estudo Comparativo Entre Algoritmos de Aprendizagem de Máquina Supervisionados para Predição de Solução de Reclamações no Procon

The use of machine learning algorithms is already a reality in companies and industries. Its useaims to improve processes, optimize its strategies and, consequently, maximize profits. Thisreality is already common in several companies around the world, such as Netflix, Facebook,Amazon, Walmart, among others. In Brazil, several agencies, companies and institutions stilllack the use of machine learning algorithms to leverage their results, as PROCON - Programade Protecao e Defesa do Consumidor. This agency is present in all Brazilian states and aimsto deal with conflicts between consumers and companies that sell products or provide services.In order to predict for new entries, if they will be solved or not, this work aims to conduct acomparative study between six machine learning algorithms on data of complaints made throughPROCON. An exploratory and descriptive methodology was used, together with the CRISP-DMmethodology, in order to assist in understanding the problem, interpreting and preparing the data,modeling, evaluating the results and publishing the solution. Results demonstrate that, for sometechniques, it is possible to obtain more than 70 % accuracy in the prediction of complaints’attendance, that is, if the complaint was answered or not. Among the applied techniques, thetextit Random Forest algorithm was the one that obtained the best results, reaching 73.34 % ofaccuracy.
An Empirical Study on Bilingual MOOC of Traditional Chinese Sports in the Context of International Communication

In contemporary society where technological empowerment prevails, the ever-upgrading, widespread application of information technology has also accelerated educational reform and development. The long-standing issue of lack of channels for the international Communication of traditional Chinese sports can hopefully be tackled by the popularization of MOOCs. This paper uses the literature research method, questionnaire survey method and experimental method to study the application of a MOOC course that disseminates traditional Chinese sports in bilingual (Chinese-English) way, thus designing a dedicated experimental MOOC course "Traditional Chinese Sports Culture" to use in practice. The course is expected to inspire the international Communication of traditional Chinese sports.
AdaDiff: Adaptive Gradient Descent with the Differential of Gradient

Abstract Optimization methods are crucial to train deep neural networks. Adaptive optimization methods, especially Adam, are wildly used because they aren’t sensitive to the selection of learning rate and converge fast. Recent work point out Adam has a performance gap with SGD and even not converge because of the unstable and extreme learning rates. Many variants of Adam are proposed to solve the problem, such as AMSGrad, AdaBound and AdaBelief. In this paper, we propose a new variant of Adam, called AdaDiff. AdaDiff computing gradient descent step size by the exponential moving average(EMA) of gradient and differential of gradients aiming to make the training of networks more stable. We compare our method with other optimizers on various tasks. The results show AdaDiff outperforms Adam and minimizes the performance gap with SGD.