Reviewerly/exHarmony huggingface dataset:
Application of Big Data Technology in Computer Classroom Teaching

With the development of society, under the influence of high technology such as big data, cloud computing, network technology, and mobile Internet, education has gradually got rid of the traditional teaching methods and the limitations of traditional teaching methods. This article takes college students as the research theme. In order to obtain more complete survey data, the survey is based on an online questionnaire. In this online questionnaire survey, a total of 11 computer classroom teachers from colleges and universities at the city, county, and town levels were invited to supplement the paper questionnaire survey. After checking and verifying the retrieved questionnaire, the excel software technology and statistical data will be used to effectively analyze the questionnaire. Experimental research shows that 84% of teachers believe that big data education is related to computer teaching. They believe that the use of information technology to collect computer learning behaviors in colleges and universities can help improve the efficiency or quality of computer teaching, while only 16% of teachers are concerned about big data. The impact on computer teaching is unclear. Research confirms that high-data technology is related to computer classroom teaching, and the use of information technology to collect computer-based learning behavior can help improve the effectiveness or quality of computer classroom teaching. Big data technology has great potential in computer classroom teaching and will provide practical theoretical support and help for computer classroom teaching. Although the application of big data related technologies in the field of education in my country is still in the initial stage, it is still of great significance for deepening the reform of education and teaching.
VidQ

As the amount of recorded and stored videos on mobile devices increase, efficient techniques for searching video content become more and more important, especially for applications like searching for the moment of crime or other specific actions. When a user sends a query searching for a specific action in a large amount of data, the goal is to respond to the query accurately and fast. In this paper, we address the problem of responding to queries which search for specific actions in mobile devices in a timely manner by utilizing both visual and audio content processing approaches. We build a system, called VidQ, which consists of several stages and uses various Convolutional Neural Networks (CNNs) and Speech APIs to respond to such queries. As the state-of-the-art computer vision and speech algorithms are computationally intensive, we use servers with GPUs to assist mobile users in the process. After a query has been issued, we identify the possible different stages of processing that will take place. This is followed by identifying the order of these stages that build up the system. Finally, we distribute the process among the available network resources to further improve the performance by minimizing the processing time. Results show that VidQ reduces the completion time by at least 50% compared to other approaches.
Transparent and Interpretable Failure Prediction of Sensor Time Series Data with Convolutional Neural Networks

The application of neural networks in production scenarios has been growing over the last years. However, their innate black-box character and the lack of transparency and interpretability of their decision making hinders their application in safety critical scenarios and scenarios with high failure costs. We present an approach to facilitate transparency of failure predictions performed by convolutional neural networks processing sensory time series data from a deep drawing manufacturing process for predictive quality purposes. Our approach enables domain experts to comprehend why a failure is predicted, enabling them to better assess whether to intervene into the production process or not.
CAMPUS MITHRA: Design and implementation of voice based attender robot

Abstract A robot is a machine which is programmed by a computer and the movements and functions of which are controlled by an external or an embedded control. It has dynamic uses in all domains of life. A robot in a university setting can be used as an attender for passing circulars around instead of multiple attenders doing the task manually which is a cost and time consuming process. Parents often find it difficult to navigate through the unfamiliar university. In this paper, we have focused on a voice based attender robot with line following capabilities along with speech recognition that can be used at universities for a variety of purposes like passing around the circulars, interacting with parents and helping them navigate through the university through Spoken Natural Language. The main objectives of the proposed work is to reduce the burden of passing circulars, calling a student/faculty on the attender by designing a robot that is also competent enough to connect with human through spoken natural language such as English or Kannada, so that it interacts with parents who are new to the institution and do not know whom to approach. The main aim of this work is to introduce a robot that it is able to interact with human through the Spoken Natural Language. Here, the focus is on two languages; English and Kannada. This system uses a voice recognition module to recognize human voice and a voice playback module is used to reply back in either English or Kannada according to the user’s command. It can work in two modes, the voice recognition mode to answer to user queries or in line following mode to pass circulars, call student/faculty. In this way, the voice based attender robot finds its applications in the university setting. But it is not limited to only universities. It can also be further implemented in places like railway stations, bus stations, big factories and other similar surroundings.
Deep Learning Method for Detecting Fraudulent Motor Insurance Claims Using Unbalanced Data

This study investigates the use of deep learning methods in detecting fraudulent claims in the motor insurance industry. To help combat these problems, many researchers have come out with various machine learning algorithms to detect fraudulent claims. However, these claims suffer from the problem of imbalanced datasets. This is because fraudulent claims cases are rare compared to genuine ones. Therefore, the training data will have less instances of fraud cases as compared to the genuine cases. On the other hand, classification algorithms tend to learn more on the majority classes compared to the minority ones, and consequently fail to perform well on relatively rare cases of the minority classes. This research aims at using sampling techniques in the preprocessing stage to balance the datasets and minimize the issue class imbalances in deep learning. The outcome of deep learning with sampling is compared to unsampled cases. Results show that the deep learning approach performs perfectly well on the balanced datasets and the performance on imbalanced datasets is average.
Artificial Intelligence, Its Applications in Different Sectors and Challenges: Bangladesh Context

Bangladesh is home to 160 million people and is the most densely populated country in the world. Once, this country was dependent only on agriculture. But with the change of time, the advent of advanced technology has taken place in every field of this country. Now Bangladesh is being transformed into a modern technology-based country. Automation and control technology is being applied in various industries. Artificial Intelligence (AI), Internet of Things (IoT), Big Data, Blockchain, etc. have become very popular in Bangladesh. Although late, the impact of these technologies has begun to be felt in different sectors of the country. For the effective implementation of AI, several specific sectors such as services, transportation, education, agriculture, health, and the environment have been identified in Bangladesh. Overall in Bangladesh, we see extensive use of AI technologies, for example, ride-sharing, natural language processing (NLP) for Bengali, ChatBots, booking hotels, buying air tickets and real-time mapping, etc. As about 34% of youth are technology-driven in the country now, the successful integration of AI technologies will lead Bangladesh towards a prosperous future. For implementing AI technology, the government of Bangladesh should have to undertake huge preparation. So there will be many challenges if the technology is adopted without any proper preparation. This challenge is not only for Bangladesh to adopting AI, every country has to go through these issues. So the Bangladesh government needs to have preparation such as infrastructural development and technology enhancement and some other relevant issues to control the AI. This chapter briefly discusses the current technological situations in Bangladesh along with the concept of AI, its applications in different sectors, and the relevant challenges. The chapter also suggests the steps to be taken in the use of AI technology to maintain certain aspects such as policy, information privacy, security, and regulations.
Knowledge graph mining for realty domain using dependency parsing and QAT models

The real estate business has a lot of risks, and in order to minimize them, you need a lot of information from different sources. Systems based on natural language processing can help customers find this information more easily: question answering, information retrieval, etc. The existing method of question answering requires data aligned with possible questions, which are not easy to obtain, in contrast, the knowledge-graph provides structured information. In this paper, we propose semi-automated ontology generation for the realty domain and a subsequent method for information retrieval related to the knowledge-graph of this ontology. The first contribution is the method for relation extraction method based on dependency-parsing and semantic similarity evaluation, which allows us to form ontology for a particular domain. The second contribution is knowledge-graph completion method based on question answering over text neural network. Our experimental analysis shows the efficiency of the proposed approaches.
Unsupervised induction of inflectional families

This paper proposes a model for inducing inflectional families from text, without any specific language knowledge. The method is based on a combination of word embeddings and network theory, inspired by Bybee's network model for morphology. Experiments were conducted on English whose morphology is simple, and German, which has relatively rich morphology. The proposed method outperforms FastText, a state-of-the-art algorithm employing subword embeddings to model morphology, and achieves F1 scores higher by more than 14% and 10%, for English and German, respectively.
Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment

In this paper we provide an overview of the approach we used as team Trial&Error for the ACM RecSys Challenge 2021. The competition, organized by Twitter, addresses the problem of predicting different categories of user engagements (Like, Reply, Retweet and Retweet with Comment), given a dataset of previous interactions on the Twitter platform. Our proposed method relies on efficiently leveraging the massive amount of data, crafting a wide variety of features and designing a lightweight solution. This results in a significant reduction of computational resources requirements, both during the training and inference phase. The final model, an optimized LightGBM, allowed our team to reach the 4th position in the final leaderboard and to rank 1st among the academic teams.
Enhanced Unsupervised Data Augmentation for Emergency Events Detection and Classification

We propose to jointly detect and classify emergency events using a multi-class text classifier, which is a typical deep learning architecture with transformer modules and particularly employs Bidirectional Encoder Representations from Transformers (BERT). Deep learning requires a large number of labeled data to work. Meanwhile, deep learning often implements the semi-supervised learning (SSL) method, which is able to use massive unlabeled data to improve performance of supervised deep learning. As an effective SSL variant, unsupervised data augmentation (UDA) focuses on data augmentation techniques to improve the performance of deep learning. We present an enhanced version of UDA (EUDA) by mixing more data augmentation strategies and using a problem related prefilter. Our EUDA targets at emergency event detection and classification. Considering that emergency events always have time and location elements, text can be filtered based on this semantic feature. We propose to use semantic feature aided enhanced unsupervised data augmentation to solve the concerned problem. Empirical studies on the dataset prepared for the task validates that the proposed EUDA can achieve significantly better performance than supervised learning with a limited size of labeled data. Experiments are also carried out on a text classification task, which confirms that EUDA improves performance for BERT neural network.
Distilling Region-Wise and Channel-Wise Deep Structural Facial Relationships for FAU (DSR-FAU) Intensity Estimation

Facial emotions are expressed through a combination of facial muscle movements, namely, the Facial Action Units (FAUs). FAU intensity estimation aims to estimate the intensity of a set of structurally dependent FAUs. Contrary to the existing works that focus on improving FAU intensity estimation performance, this study investigates how knowledge distillation (KD) incorporated into a training model can improve FAU intensity estimation efficiency while achieving the comparable level of performance. Given the intrinsic structural characteristics of FAU, it is desirable to distill deep structural relationships, namely, DSR-FAU, using heatmap regression. Our methodology is as follows: First, a feature map-level distillation loss is applied to ensure that the student network and the teacher network share similar feature distributions. Second, the region-wise and channel-wise relationship distillation loss functions are introduced to penalize the difference in structural relationships. Specifically, the region-wise relationship can be represented by the structural correlations across the facial features, whereas the channel-wise relationship is represented by the implicit FAU co-occurrence dependencies. Third, we compare the model performance of DSR-FAU with the state-of-the-art models, based on two benchmarking datasets. It is shown that our model achieves comparable performance, with a lower number of model parameters and lower computation complexities.
iSCREAM: a suite for Smart Contract REAdability assessMent

Blockchain is increasingly revolutionizing a variety of sectors, from finance to healthcare. Indeed, the availability of public blockchain platforms, such as Ethereum, has stimulated the development of hundreds of decentralized apps (dApps) that combine smart contract(s) and a front-end user interface. Smart contracts are software, as well, and, as traditional software, they require to be developed and maintained or evolved. Among all the quality properties that must be assessed and guaranteed, readability is a key aspect of source code: a highly readable code facilitates its maintainability, portability, and reusability. This is especially true when considering smart contracts, where code reuse is widely adopted. Indeed, smart contract developers often integrate code portions from other smart contracts in their artifacts. To help developers and researchers more easily estimating and monitoring the code readability of smart contracts, in this demo, we present iSCREAM. iSCREAM automatically inspects Solidity smart contracts and computes a set of metrics that previous research demonstrated being related to code readability. We evaluated iSCREAM on 90 real-world smart contract functions, showing that our tool correctly computes all the aforementioned metrics. Demo webpage: https://github.com/mfredella/iSCREAM
Incompressibility of Classical Distributions

In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blind</i> compression of quantum states, a sender Alice is given a specimen of a quantum state <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> drawn from a known ensemble (but without knowing what <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> is), and she transmits sufficient quantum data to a receiver Bob so that he can decode a near perfect specimen of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> . For many such states drawn iid from the ensemble, the asymptotically achievable rate is the number of qubits required to be transmitted per state. The Holevo information is a lower bound for the achievable rate, and is attained for pure state ensembles, or in the related scenario of entanglement-assisted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visible</i> compression of mixed states wherein Alice knows what state is drawn. In this paper, we prove a general and robust lower bound on the achievable rate for ensembles of classical states, which holds even in the least demanding setting when Alice and Bob share free entanglement and a constant per-copy error is allowed. We apply the bound to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">specific</i> ensemble of only two states and prove a near-maximal separation (saturating the dimension bound in leading order) between the best achievable rate and the Holevo information for constant error. This also implies that the ensemble is incompressible – compression does not reduce the communication cost by much. Since the states are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">classical</i> , the observed incompressibility is not fundamentally quantum mechanical. We lower bound the difference between the achievable rate and the Holevo information in terms of quantitative limitations to clone the specimen or to distinguish the two classical states.
Authenticated key-value stores with hardware enclaves

Authenticated data storage on an untrusted platform is an important computing paradigm for cloud applications ranging from data outsourcing, to cryptocurrency and general transparency logs. These modern applications increasingly feature update-intensive workloads, whereas existing authenticated data structures (ADSs) designed with in-place updates are inefficient to handle such workloads. This work addresses the issue and presents a novel authenticated log-structured merge tree (eLSM) based key-value store built on Intel SGX.
Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

Detect the expected, discover the unexpected was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sense-making process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.
Research Trending Topic Prediction as Cognitive Enhancement

The Internet has been identified in human enhancement scholarship as a powerful cognitive enhancement technology. Using the Internet as an external memory system has overall benefits if we have the skills to efficiently navigate, evaluate, and compare online information. Long-term prediction of research trending topics is a form of cognitive enhancement because it helps to efficiently navigate, evaluate scientific articles, identify promising directions, and focus efforts in these directions. This paper presents the results of a method designed to realize long-term prediction of research trending topics. Meaningful topics were identified among the words included in the titles of scientific articles. The title is the most important element of a scientific article and the main indication of the article's subject and topic. We treated the title words, which occur several times in cited articles of the analyzed collection, as the research trending topics. The longevity of the citation trend growth was the target for the machine learning algorithms. The CatBoost machine learning method, which is one of the best implementations of decision trees, was used. We conducted experiments on a scientific dataset including 5 million publications of top conferences in artificial intelligence and data mining areas to demonstrate the effectiveness of the proposed model. The accuracy rate of three-year forecasts for a number of experiments from 1997 to 2014 was about 60%.
GUM: The generalized upper model

GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling decisions for individual domains may need to be responsive to domain-specific criteria, a linguistically-motivated ontology offers a characterization that generalizes across domains because its design criteria are derived independently both of domain and of application. With respect to this mediating role, the use of GUM resembles (and partially predates) the adoption of upper ontologies as tools for mediating across domains and for supporting domain modeling. This paper briefly introduces the ontology, setting out its origins, design principles and applications. The example cases for this special issue are then described, illustrating particularly some of the principal differences and similarities of GUM to non-linguistically motivated upper ontologies.