Reviewerly/exHarmony huggingface dataset:
Automated People Monitoring System Using OpenCV and Raspberry Pi

Over the last decade, there has been a quantum leap in terms of the evolution of new methodologies to better our quest to understand artificial intelligence and machine learning. One such field, where there has been an unparalleled advancement, is computer vision. The paper aims to design and structure an automated monitoring system that automates the monitoring of the number of people in this COVID-19 scenario in a designated enclosure. We have deployed the system on Raspberry Pi module and integrated a HOG detector which transcends ordinary Haar cascades in terms of performance. This model can then subsequently be connected and integrated with other modules to further enhance its applicability and spectrum of usage.
Structure Optimization and Reconfigurable Design of CycleGAN

CycleGAN is an excellent Generative Adversarial Networks (GAN) in image style-transfer, but its complex network model consumes a lot of computation and storage. To simplify the generation network of CycleGAN, a hardware-friendly network structure named S-CycleGAN is proposed. In which, the generation network is replaced with a regular structural Inception module, the parameters of convolution are reduced greatly. The knowledge distillation is introduced into further structural compression. To calculate the convolution and deconvolution in a unified architecture, and support flexible runtime switching, the S-CycleGAN is implemented based on the Dynamic Programmable Reconfigurable Array Processor (DPRAP). Verified with the apple2organge dataset, the experimental results show that, the PSNR (Peak Signal to Noise Ratio) of the generated image by the proposed S-CycleGAN is increased by 1.12% on average, the SSIM (Structural Similarity Index) is increased by 2.18% on average, and memory saving of 10.6%. With 150 MHz, the hardware resource consumption (LUT and FF) is reduced by 52% contrast to the other FPGA scheme.
Feature Engineering for Tal-Patra Manuscript Text Using Natural Language Processing Techniques

The Tal-Patra manuscript is one of the most precious documents which have to be preserved to explore the ancient secrets in the fields of medical fields such as Ayurveda, Siddha, historical moments, historic pandemics, art, astronomy, etc. In assessing and manipulating text from documents, Natural Language Processing plays a crucial role. In this article, the different feature engineering techniques were an experiment for Tal-Patra manuscript text such as Bag-of-Words (BoW), TF-IDF (Term Frequency-Inverse Document Frequency) and Word Embedding (Word2vec). The Tal-Patra manuscript of Kuzhanthai Pini Marunthu (Child-Related Medicine) was used as a dataset. As pre-processing steps include sentences and word tokenization, this research work began with tokenization. Then the part of speech (POS) was tagged, and then, feature engineering techniques were performed to analyze the text. For the Tamil Tal-Patra manuscript, word embedding techniques (Word2vec) performed well compared to the above-mentioned feature engineering techniques.
Causality Extraction Based on Dependency Syntactic and Relative Attention

Mining causal relation in text is a complex and critical natural language understanding task. Recently, many efforts focus on extracting causal event pairs in text by exploiting sequence labeling. However, few studies give the uniform definition of the annotation scheme and the labeling boundary of causal events. To address these issues, this paper proposes a novel causal event labeling scheme based on dependency syntactic, which can express the complete semantics of causal relation, as well as delineate the causal event boundaries explicitly. In addition, combined with the relative attention and dependency syntactic, we construct a causal event extraction model named DGLSTM-GRAT-CRF. Experimental results indicate that our model achieves better performance compared with state-of-the-art causality extraction models. Besides, we attempt to explore the influence of various additional features on causal extraction.
Classroom Informatization Construction Based on Electronic Schoolbag

As an important practical carrier of educational informatization, e-schoolbags have broken through the limitations of time and content of traditional education with their openness and interactivity, injected fresh blood into subject teaching, and also provided a powerful impetus for the construction of classroom informatization.While recognizing that e-schoolbags bring convenience to teaching, we should dialectically examine the factors restricting the popularization of e-schoolbags and explore their core values to further promote the construction of classroom informatization.
From Pencils to iPads: Evolution in Instructional Technology

In nutshell, there has been a gradual yet vital evolution in the field of instructional techniques being backed by a change in the technology, medium, concepts and the approach. The invention of computers and the internet has been the game changer. With the changed time has changed the most sought after skill too. Today, the most important learning has become how to learn quickly and on oneself. The most sought after skill has become disruption which involves rethinking processes, concepts &amp; outcomes, challenging them and building a better model. The experience of working with people from different communities, cultures and countries has become the key to survive in MNCs, which is being developed at the school and college level itself by doing international tie-ups and by offering international secondments. The evolution of artificial intelligence and machine learning has paved way to new horizons. An immense change is knocking on the door and it is believed that such a change will redefine the field of education.
Analysis and Countermeasure Design on Adversarial Patch Attacks

Adversarial patch is an image-independent patch that misleads deep neural networks to output a targeted class. Existing defense strategies mainly rely on patch detection based on the frequency or semantic gaps between the patch and clean image. But we found that they are effective because the gap is huge. This is because existing patch attacks only look for an effective patch instead of the optimized patch that minimizes the gap. We then propose two improved patches, enhanced and smoothed patches, to reduce the gap. Consequently, the decision boundary for adversarial examples of the existing defense means is successfully obscured. To cope with the improved patches, we propose a defense method based on image preprocessing. We leverage multi-scale Gaussian blur to amplify the reduced gap between the patch and clean image. Due to the dense information of patches, for a patch, the dissimilarities of Gaussian blurs with different scales are higher than that of clean images. By enhancing the local multi-scale details and weakening them in another scale set, we maximize its effect on patch with high-frequency information. In this way, our defense method can efficiently distort adversarial patches and cause only a negligible impact on clean images.
Measuring Difficulty of Novel Clockwork Puzzle Instances Using Evolutionary Algorithms

This study debuts the clockwork problem a novel type of combinatorial puzzle; the rules and general variations of the puzzle are explained in order to showcase the puzzle's interesting properties and motivate interest in the puzzle. Following this, the “Do What's Possible” representation for an evolutionary algorithm is used to solve instances of the clockwork puzzle. The results of the evolutionary algorithm across many instances are analyzed to create a difficulty measure for said instances. This type of computational intelligence is preferred as the evolutionary algorithm adequately mimics the approach of a human player who lacks prior knowledge about the puzzle.
Report on a Trial to Develop Data Science Education for All Students at Sasebo College

At National Institute of Technology (KOSEN), Sasebo College, new curriculum has been implemented since 2020, and we plan to take a major subject of data science education called Data Science Engineering. For this preparation, we give eight classes in a subject of old curriculum in each department. In this report, we introduce practical cases of data science education and discuss assessment of them at Sasebo College.
Shock Wave Measurement and Time Difference of Arrival Analysis for Impact Point Positioning

In a shock wave positioning system for light weapons, the key to accurate positioning estimation of the impact point lies in the extraction of the characteristic attributes of the shock wave signal reaching each sensor in the sensor array and the proper design and modeling of the sensor array. In this work, according to the analysis of aerodynamics and the characteristics of shock wave transmission in the designed sensor array, a positioning model is developed to estimate the impact point location based on the time difference of arrival (TDOA) analysis. A novel correction algorithm is suggested to address the influence of wind speed so as to improve the positioning accuracy. A smart data acquisition (DAQ) system is developed to measure and capture the time differences of the shot wave reaching different sensors. The effectiveness of the designed sensor array, modeling technique, and wind correction algorithm is verified by tests using a specifically developed experimental setup.
A Sequence-to-sequence Based Error Correction Model for Medical Automatic Speech Recognition

The use of Automatic Speech Recognition (ASR) systems in medical applications is receiving rapidly growing interest due to their ability to reduce distractions and the cognitive workload of physicians, particularly during critical medical procedures. However, state-of-the-art ASR systems still experience recognition errors, especially in noisy environments where speakers rely on medical-domain terminologies. This paper proposes a customized language model and a neural network based sequence-to-sequence (seq2seq) error correction module for medical ASR systems to provide domain adaptation and more reliable transcription results. Specifically, the error correction module learns the error patterns in noisy scenarios and is able to correct such errors during inference. Our experiments show that the proposed method can reduce the sentence error rate (SER) by up to 81% for formatted input and up to 31% SER for unformatted input in noisy environments.
Comparison of Image Restoration using Median, Wiener, and Gaussian Filtering Techniques based on Electrical Tree

Electrical treeing lead to a major cause of a breakdown in solid insulation. Thus reduced solid insulation performance by degrading the insulation. Hence, it is important to study the electrical treeing and learn the root cause of the treeing formation. In this paper, the performances of median, wiener, and gaussian filters in restoring noisy images are studied based on electrical tree images. The electrical tree colour images is being transform into grayscale images, noisy images using impulse noise (salt and pepper), and finally motion blur are applied. Even though, there are several number of filters available, this paper focus on median, wiener, gaussian, and combination of the filters. In the end, comparison between these filters is made to study the efficiency using PSNR, SNR, and MSE in graph form.
Analysis of work characteristics based on work probe system

This paper describes two experiments in two warehouses, performance evaluation of a work element estimation method, and a cloud server for analysis of work characteristics. This paper measures seven employees in an apparel warehouse and a warehouse that stocks pet health products. Results of performance evaluation show that the average error of the work element estimation is about 3%. Next, the cloud server is implemented. A user can download an Excel file that visualizes analysis results. The file shows productivities related to pick of goods, walks, and finds of goods or location. Using the file, operations managers can intuitively understand productivity problem. Because the file also helps the managers in coming up with appropriate strategy, which may involve developing new competitive advantages or optimizing existing ones, the cloud server is useful in practice.
A Real-time Embedded Target Tracking System Based on Deep Learning Model

Target tracking has been arousing increasing attention in current production and life. Usually, target tracking algorithms are deployed on high-performance servers. There are significant challenges in deploying real-time target tracking in embedded systems. Therefore, a real-time embedded target tracking system is studied based on a deep learning model in this paper. Firstly, the hardware implementation of the proposed system is presented in detail. Then, to resolve the problems of high latency and low performance in embedded platforms, we offer an adaptive detection strategy that combines the advantages of deep network model inference and prediction. This strategy makes full use of the target relevance between the front and rear frames of video, reduces the frequency of calling the depth model, and improves the system's speed. Finally, Experiments have proved that the system can effectively reduce the time delays by more than 50% while ensuring the same detection effect. Meanwhile, the system's power consumption and resource occupancy rate are also significantly improved.
Working Effectively with Collection Functions

Collection functions are central to productive F# coding. Trying to code without them is as crazy as trying to play the guitar without learning chords. Though you don’t need to know all the chords to be a decent guitarist, you do need the basic ones to be able to use them instinctively. The same is true for using collection functions when coding in F#.
RepRetinaFace: Re-Parameterization RetinaFace for Edge Embedded Platform

With the continuous development of deep learning in the fields of security, smart retail, and smart home, etc., face detection algorithm has become a key technology and been widely used on the edge embedded platform. In this paper, a face detection algorithm based on re-parameterization technology (RepRetinaFace) is introduced, aiming at solving the problem that existing face detection algorithms consume too much inference time on the edge embedded platform. The proposed algorithm uses the re-parameterization technology to simplify the skip-connection structure in the RetinaFace network, and uses PAN structure at the meantime to improve the algorithm accuracy, after which, re-parameterization technology is reused to finally reduce the inference cost of the PAN structure. Compared with RetinaFace, the accuracy of RepRetinaFace on WIDER FACE dataset is equivalent, while the inference speed on the edge embedded device (Hi3516DV300) is increased by 81.5%. To further confirm the adaptability of the proposed algorithm, verification is performed on NVIDIA RTX2080 and Rockchip RK3288, and the result shows same advantage to RetinaFace. Furthermore, RepRetinaFace has already been applied in AISWare AI2 Edge, an all-in-one edge product of AsiaInfo.
Hadoop-MTA: a system for Multi Data-center Trillion Concepts Auto-ML atop Hadoop

The ever-growing computation capability distributed infrastructure brings tremendous opportunities for mining and analysis of data that was impossible otherwise. Meanwhile, the inherent computation model of distributed system also brings unique and non-trivial challenges for traditional Auto-ML, including the explosion of data dimensions, the expected absence of features, and the heterogeneity of information. This is especially the case in modern Internet enterprises, where data in the scale of trillions are stored in multiple data centers, and the discovery of subtle signals could incur significant impact in revenue and welfare. How can we best harness the large scale distributed machine learning, but without keeping engineers constantly in the loop? In this work, we present Hadoop-MTA, a system for Multi Data-center, Trillion Concepts, Auto-ML on top of the Hadoop distributed computation environment that leverages sparsity aware heterogeneous knowledge graph representation and dimensionality agnostic parallel learning. Through multiple large scale experiments, we find that Hadoop-MTA significantly output-performs competitive state of the art distributed learning algorithms and scales well to trillion scale data-sets. Our model is rolled out to Hadoop serving infrastructure in Yahoo covering billions of unique identities and shows improvements 129.5% accuracy and 106.5 % weighted F1-score (more than 2x) on key targeting use cases.
Analysis of Machine Learning Algorithms in Big Data Analytics

Massive information being a brilliant resource of information and understanding from structures to the different end users. However, managing this much quantity of information wishes automation, thus leading to a fashion of statistics processing along with gadget mastering techniques. Inside the ict region, as in various areas of trade and evaluation, structures and equipment have been furnished and advanced in assisting the specialists to deal with the information and study from it routinely. Maximum of the systems returned from large corporations such as Microsoft or Google, or from the apache foundation’s incubators. This evaluation reveals gadget mastering algorithms in big records analytics, and gadget mastering challenges us to make selections where it may be no recognized "right course" for the specified trouble based on the previous training and tallies a number of the headmost used gear to analyze and model massive-statistics.
RETRACTED CHAPTER: Towards End-to-End DNN-Based Identification of Individual Manta Rays from Sparse Imagery

This paper presents an end-to-end deep learning approach for the fine-grained identification of individual manta rays (Manta alfredi) based on characteristic ventral coat patterns where training is restricted to sparse photographic sets of <10 ventral images per individual. The dataset is captured by divers in underwater habitats. Its content is challenging due to non-linear deformations (of the rays), perspective pattern distortions, partial occlusions, as well as lighting and noise-related acquisition issues. We show how a combination of data augmentation, encounter fusion, and transfer learning techniques can address the sparsity and noise challenges at hand so that deep learning pipelines can operate effectively in this uncompromising data environment. We demonstrate that using the proposed approach with an adapted InceptionV3 deep neural network (DNN) architecture significantly outperforms tested baselines including the Manta Matcher approach, the so-far best performing traditional, widely used method published for the application at hand.
Numerical Analyzation of Distributed Feedback Lasers with Different Phase Shift Structures

We optimize phase shift structures of distributed feedback (DFB) lasers. For the multi-distributed phase shift laser, the output slope can reach up to 51.90% when the optimal cavity length is 2.2 mm.
FEATURES OF THE USE OF THE WORD CLOUD IN THE LINGUISTIC AND LITERARY FIELD OF PRIMARY EDUCATION

The study reveals the possibilities of using the word cloud in the linguistic and literary field of primary education. The authors consider the possibility of using a cloud of words to visualize important educational information on the Ukrainian language and emphasize the semantic categories, the main idea, the features of the images of the work of art in the lessons of literary reading in primary school. The aim of the study is to substantiate the importance of using the word cloud as a didactic tool in the language and literature of primary education, to develop methodological support for reading activities of primary school students by intensifying their work with visualized keywords in the weighted list. The study implemented the following tasks: analyzed scientific sources on the problem of using the word cloud in education; the possibilities of using the word cloud in work with junior schoolchildren in Ukrainian language and reading lessons are characterized. The authors suggest using the word cloud from the standpoint of didactic heuristics, in particular for conducting a heuristic conversation, presenting the results of a school project, as a means of identifying the lesson topic, as visual material for associations, as accentuation of key words to explain new material. The article characterizes the didactic tool “cloud of words”, proves its effectiveness in the development of speech of primary school children, in particular in compiling descriptions, stories, arguments, as well as in editing their own tests. Visualization of language material, as well as images and the main idea of the work of art with the help of a cloud of words helps to establish associative connections, create a problem situation in the classroom, activate critical thinking and creativity of students. Reproduction of keywords in the form of a weighted list on a symbolic image on a certain topic contributes to the success of solving educational tasks, activates the cognitive activity of junior high school students, promotes the easy acquisition of knowledge.
An Object Detection Framework for Span Extraction in Question Answering

Machine Reading Comprehension(MRC), including a series of tasks that test the ability of models to understand natural language, has received quite a few attention in Natural Language Processing(NLP). Most existing works deal with MRC tasks by exploiting the expression capability of neural networks. Some of them have achieved impressive performance. Despite the rapid iteration of the models used, few work have focused on output layer and prediction method of answer span - also known as span extraction. In this paper, we focus on span extraction in the Question Answering(QA) task. A cross-sectional comparison of widely used span extraction methods is presented, with their strengths and weaknesses noted in detail. Furthermore, inspired by Faster R-CNN, we propose a brand new span extraction method. Experiment results show that our proposed method outperforms existing span extraction methods on both English and Chinese MRC tasks.
Timing Attack on Random Forests: Experimental Evaluation and Detailed Analysis

This paper proposes a novel implementation attack on machine learning. The threat of such attacks has recently become an problem in machine learning. These attacks include side-channel attacks that use information acquired from implemented devices and fault attacks that inject faults into implemented devices using external tools such as lasers. Thus far, these attacks have targeted mainly deep neural networks; however, other common methods such as random forests can also be targets. In this paper, we investigate the threat of implementation attacks to random forests. Specifically, we propose a novel timing attack that generates adversarial examples. Additionally, we experimentally evaluate and analyze its attack success rate. The proposed attack exploits a fundamental property of random forests: the response time from the input to the output depends on the number of conditional branches invoked during prediction. More precisely, we generate adversarial examples by optimizing the response time. This optimization affects predictions because changes in the response time indicate changes in the results of the conditional branches. For the optimization, we use an evolution strategy that tolerates measurement error in the response time. Experiments are conducted in a black-box setting where attackers can use only prediction labels and response times. Experimental results show that the proposed attack generates adversarial examples with higher probability than a state-of-the-art attack that uses only predicted labels. Detailed analysis of these results indicates an unfortunate trade-off that restricting tree depth of random forests may mitigate this attack but decrease prediction accuracy.