Reviewerly/exHarmony huggingface dataset:
What have you read? based Multi-Document Summarization

Due to the tremendous amount of data available today, extracting essential information from such a large volume of data is quite tough. Particularly in the case of text documents, which need a significant amount of time from the user to read the material and extract useful information. The major problem is identifying the user's relevant documents, removing the most significant pieces of information, determining document relevancy, excluding extraneous information, reducing details, and generating a compact, consistent report. For all these issues, we proposed a novel technique that solves the problem of extracting important information from a huge amount of text data and using previously read documents to generate summaries of new documents. Our technique is more focused on extracting topics (also known as topic signatures) from the previously read documents and then selecting the sentences that are more relevant to these topics based on update summary generation. Besides this, the concept of overlapping value is used that digs out the meaningful words and word similarities. Another thing that makes our work better is the Dice Coefficient which measures the intersection of words between document sets and helps to eliminate redundancy. The summary generated is based on more diverse and highly representative sentences with an average length. Empirically, we have observed that our proposed novel technique performed better with baseline competitors on the real-world TAC2008 dataset.
WREP: A lightweight event real-time processing engine for IoT

The rapid development of the Internet of Things makes it benefit all aspects of human society and greatly facilitate people’s lives. At the same time, the amount of data generated by the Internet of Things is also increasing, and the data content is becoming more and more complex. Nowadays, people are no longer satisfied that the devices in the Internet of Things work individually, so the Trigger-Action platform came into being, which allows users to set up corresponding rules to allow other platforms or devices to work together. However, many IoT platforms cannot open API interfaces for the Trigger-Action platform, and many IoT platforms also need to set rules for devices. At this time, the scalability and portability of many Trigger-Action platforms, such as IFTTT, are insufficient enough to meet this demand.To solve this problem, we designed a lightweight event real-time processing engine architecture and developed a prototype system called WREP. This prototype system can be easily integrated into other IoT platforms, with excellent scalability and portability. While ensuring lightweight, it also has good performance.
Edge-Oriented Social Distance Monitoring System Based on MTCNN

Social distance monitoring (SDM) systems are vital in fighting the spread of the coronavirus (COVID-19). Existing SDM systems employ bounding box-method, which imposes inaccurate distance estimation due to the high variance in its output coordinates. To solve this problem, an SDM system based on multitask cascaded convolutional neural networks (MTCNN) is proposed. Instead of using bounding box coordinates, face detection and facial landmarks localization of MTCNN are used to provide fixed coordinates and increase the distance estimation accuracy of SDM. However, while the accuracy issue is solved by using MTCNN, the SDM system suffer from large computational requirements due to the cascaded networks added on top of the distance estimation process. To deal with this challenge, a constrained optimization technique is employed to each stage of MTCNN with the goal of reducing its hardware requirements while keeping the same reliability as the original implementation. Experimental results show that the SDM system based on the optimized MTCNN achieves higher accuracy performance with reduced computational requirements as compared with conventional SDM systems. This allows the proposed SDM system using optimized MTCNN to be deployed efficiently on edge devices.
Addressing Data Concerns and Usage through Law of Attraction on using Intelligent Voice Assistants in Language Classrooms

Data-mining done by data companies on ICT tools and the law of attraction comes out with a similar idea in terms of the outcome produced.In order to comprehend the idea, this paper dwells into the data rights, privacy policies and securities of the target application (Google Assistant and Amazon Alexa) that are used in the process of language teaching and learning.A lot of researches focus on the implication of ICT tools in language classrooms and so this paper reports on the ethical considerations that must be looked into before implementation of any ICT tools in language classrooms.Reasons for and against the use of Voice Assistants in terms of data privacy have been discussed with detailing on the similarities between the theory of law of attraction and the effect of data mining.The findings suggest that the constant and productive use of ICT tools in language classrooms will alter the students 'data pattern towards his/her area of study.The research suggests that it is the duty and obligation of the implementer to verify the tools to be inculcated and use it productively.
Pap-smear image classification by using a fusion of texture features

In this paper we address the problem of pap-smear image classification. These images have great medical importance to diagnose and prevent uterine cervix cancer and have been intensively studied in computer vision research. We evaluated 19 texture features on their ability to discriminate between two classes (normal and abnormal) of pap-smear images. We performed the classification of these feature using three different approaches: K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Linear Discriminant Data (LDA). We conducted this evaluation considering each texture method independently and their concatenation with others. Results show combining methods improves the accuracy, surpassing most of the compared methods, including some deep learning approaches.
Development of Self Esteem-Oriented Micro Teaching Materials for IAIN Palopo English Education Students

The purpose of this research is to produce teaching materials for Micro teaching subject based on Self Esteem to students of the English Education Study Program with dimensions of respect and self-respect, courage and ability in communication and confidence in one's own abilities. The elements in the module are tips and motivation, exercises/practices and summaries and formative tests in each chapter. The module was developed by taking into account the value of its validity, practicality and effectiveness. The approach used in the research is Research and Development research or development with the ADDIE development model. The trial was conducted on 5th semester students in the English Education study program, Palopo State Islamic Institute. The output in this research is a module for microteaching teaching and is published as an ISBN book by IKAPI member publishers.
Identification of out-of-distribution cases of CNN using class-based surprise adequacy

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and calibration.
Teaching Strategy of "Whole Book Reading" of Primary School Chinese under Core Literacy Teaching

Primary school is an important period for students' learning and growth.The improvement of Chinese teaching at this stage will directly affect the long-term learning quality of students in the future.At present, with the deepening of the new curriculum reform, the teaching of reading the whole Chinese book also needs to expand teaching ideas and methods with the advancement of the curriculum reform, and more attention needs to be paid to the development and progress of students' language ability, cultural awareness, emotion and other core qualities in the reading process, so as to lay the foundation for long-term Chinese learning.Therefore, this paper will discuss the problems existing in the current teaching of whole book reading in primary schools, and discuss the importance of whole book reading under the core literacy and the specific teaching strategies.
DAFM: A CTR Prediction Model Based on Attention Mechanism

In the development of deep learning recommendation algorithm, the click-through rate prediction task has always been one of the main research directions. Aiming at the problem that learning all interactive features of the existing click-through rate prediction model may amplify the influence of noise data and increase the difficulty of model training, a DAFM model integrating attention network is proposed based on DeepFM model. The model retains the FM network and DNN network contained in DeepFM, uses FM network to learn linear and second-order nonlinear information, and uses DNN network to learn higher-order nonlinear information. An attention network is added based on the input layer, so that an additional attention weight value is considered for the characteristics of the input vector in different dimensions, so that the model can fit more important feature information. Experiments show that the average AUC of DAFM model is increased by 2.06% compared with DeepFM model.
Research on the Design of Series Emoticons Based on the User Cognition Experiment

Under the background of the Visual Culture, the popularity of emoticons is important evidence of the Pictorial Turn in network communication. Previous research on emoticons mostly focuses on the fields of communication and semiotics, and there remains a blank about the design principle and user habits of emoticons from the perspective of design. Therefore, this paper is trying to explore the design principles of different emoticons via a User Cognition Experiment. Through exploratory statistical analysis methods such as multi-dimensional scale analysis and correspondence analysis, this paper explores corresponding emoticons classification and design principles. With the help of emoticon design principles, this original design practice will help designers do better emoticons design.
Exploiting a Statistical Body Model for Handover Interaction Primitives

When humans perform object handovers, the non-verbal communication implicit in the movement of the interaction partners mutually communicates information on how the handover will proceed. This intention communication allows both subjects to understand where the transfer of the object will occur, the speed of the gesture, and how careful the receiver of the object must be. In human-robot interaction, it is also desirable that the robot can read and transmit the same information. Bayesian Interaction Primitives (BIP) can be used to learn natural handover interactions from demonstrations performed between humans. In this work, we explore BIPs for handover interactions and compare a state representation obtained directly from a motion capture system with a representation using a statistical body pose model fitted to the motion capture data.
Application of AI technology based on customer service digitalization

In the process of studying China Telecom customer service system, this paper finds that customer service needs a lot of back-end staff support, which makes the support cost have to rise. In this paper, the intelligent transformation of the back-end service support, with system automation as the core, changes the way of relying on manual maintenance; Artificial intelligence technology is used to solve the problem tracing and early warning of artificial customer service in the service process; Finally, combined with the service scenario, we can intelligently build modular composite solutions in Chinatelecom customer service system, improve the full-service service capability, and achieve cost reduction and efficiency increase.
Audit Committee Director Localization and Accounting Conservatism- Based on the C-Score Model

Under the constant upgrading of the modern economy, the use of big data to analyze accounting information has become the norm, and accounting conservatism as an important principle of accounting confirmation and measurement, the use of econometric model to study accounting conservatism has a certain practical significance.Therefore, based on the C-Score measurement model, this paper conducts A regression analysis on the relevant data of China's listed companies in Shanghai and Shenzhen A-share markets from 2007 to 2018, and finds that the localization of the chairman of the audit committee is positively correlated with the accounting conservatism of listed companies.The research of this paper is helpful to further deepen the understanding of accounting conservatism, and has a certain enlightenment effect on improving corporate governance.
Life on Mars: First Person Speculation in the (Imaginary) Everyday

Recent development in space exploration has focused on the possibility of human life on Mars. How can we design for such an unexplored environment, and in turn what might such designs tell us about designing for the everyday on Earth? We make use of a process of speculative 1st person perspective design to create a material playground for producing a set of printed textile structures to support the experience of Martian daily wear, while paying attention to Earthly concerns such as material scarcity and locality of resources. This pictorial describes the outcomes of this process with a focus on the notions personal safety, empowerment, and comfort. The contribution is a design exemplar of first- person design in an extreme context that suggests the use of the playful speculation of space travel to ultimately explore human experience and values on everyday Earth.
An ANALYSIS OF SPEAKING PROBLEMS IN ONLINE ENGLISH PRESENTATION DURING COVID-19 PANDEMIC

Speaking is one way to convey information to others. Those speaking skills are the primary skills that need to be learned in English. To master English speaking skills, it is necessary to have sufficient habituation and practice to use English actively. A class presentation is one way to provide speaking training to students to become accustomed to using English in their daily lives. However, due to Covid-19, all learning activities are online through the platform, including the presentation class. This research investigates students' English language problems in online English presentations during the covid-19 pandemic. This research used qualitative phenomenological research, data taken from observations, questionnaires and semi-structured interviews obtained from eight participants. Data were analyzed using Nvivo 12 software to process data effectively and efficiently. The analysis results showed that the participants experienced linguistic problems (lack of grammar, pronunciation errors, and lack of vocabulary when speaking in online English presentations during the covid-19 pandemic. In addition, participants revealed that psychological problems (shyness, anxiety, and nervousness) made it difficult to speak English in online presentations. The findings also indicate that the causes of the problem were lack of practice, unsupportive environment, and mother tongue
Efficient Classification with Counterfactual Reasoning and Active Learning

Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision. However, applying data augmentation to tabular data is a challenging problem since it is hard to generate synthetic samples with labels. In this paper, we propose an efficient classifier with a novel data augmentation technique for tabular data. Our method called CCRAL combines causal reasoning to learn counterfactual samples for the original training samples and active learning to select useful counterfactual samples based on a region of uncertainty. By doing this, our method can maximize our model’s generalization on the unseen testing data. We validate our method analytically, and compare with the standard baselines. Our experimental results highlight that CCRAL achieves significantly better performance than those of the baselines across several real-world tabular datasets in terms of accuracy and AUC. Data and source code are available at: https://github.com/nphdang/CCRAL .
Poly Onions: Achieving Anonymity in the Presence of Churn

Onion routing is a popular approach towards anonymous communication. Practical implementations are widely used (for example, Tor has millions of users daily), but are vulnerable to various traffic correlation attacks, and the theoretical foundations, despite recent progress, still lag behind. In particular, all works that model onion routing protocols and prove their security only address a single run, where each party sends and receives a single message of fixed length, once. Moreover, they all assume a static network setting, where the parties are stable throughout the lifetime of the protocol. In contrast, real networks have a high rate of churn (nodes joining and exiting the network), real users want to send multiple messages, and realistic adversaries may observe multiple runs of the protocol. We initiate a formal treatment of onion routing in a setting with multiple runs over a dynamic network with churn. We provide definitions of both security and anonymity in this setting, and constructions that satisfy them. In particular, we define a new cryptographic primitive called Poly Onions and show that it can be used to realize our definitions.
Distributed Trajectory Planning for Multi-rotor UAVs with Signal Temporal Logic Objectives

We develop a distributed motion planner for multi-robot systems with Signal Temporal Logic (STL) objectives. Existing approaches to STL-based motion planning are limited to fragments of STL that might not capture all desired behaviors or safety requirements. Focusing on the case of a fleet of multi-rotor Unmanned Aerial Vehicles (UAVs) jointly tasked with satisfying a given STL mission, we develop a distributed method, Fly-by-Distributed-Logic (FBDL), for trajectory planning. The proposed method generates trajectories that maximize the smooth robustness of STL specifications, where the associated optimization is solved in a distributed manner. This, to the best of our knowledge, is the first distributed method that can handle the full grammar of STL (bounded time). Simulation studies show the applicability of our approach to a variety of STL missions, including those with nested STL operators and the Until operator. We also compare its performance to a centralized approach and show that our method is computationally faster, but generally computes trajectories with lower robustness.
Single Image Haze Removal Techniques Based on Priors, Fusion and Deep Learning: A Comprehensive Survey

Image dehazing has a significant role in numerous applications related to computer vision. The primary purpose of this paper is to provide the outline of the existing deep learning algorithms (CNN based) associated with single image dehazing. Initially, the earlier issues found in the haze removal techniques based on multiple images are discussed. Then, the basic concept of atmospheric scattering model and Convolutional Neural Network (CNN) are discussed. The existing single image dehazing methods are decomposed into three categories i.e. prior based, image fusion based and deep learning based methods. Highlights and challenges of these dehazing techniques are discussed. The synthetic and real datasets utilized by different researchers in dehazing techniques are described along with implementation details. Performance metrics for image quality assessment are also mentioned in the paper.
Evaluating Computational Models of Similarity against a Human Rated Dataset

The first genuine human-rated similarity set for the Romanian language aligned with an English similarity dataset is presented.Two computational models automatically learn the similarity scores for the word pairs in the similarity set.The first model learns the similarity scores from language corpora.The second one assigns a similarity score based on the taxonomic structure of a semantic network.We studied what model captures human similarity scores best, the language influence on the perception of similarity, and the impact of parts of speech on similarity.
Improving Morpheme Segmentation Using BERT Embeddings

We offer a method of incorporating BERT embeddings into neural morpheme segmentation. We show that our method significantly improves over baseline on 6 typologically diverse languages (English, Finnish, Turkish, Estonian, Georgian and Zulu). Moreover, it establishes a new SOTA on 4 languages where language-specific models are available. We demonstrate that the key component of the performance is not only the BPE vocabulary of BERT, but also the embeddings themselves. Additionally, we show that a simpler pretraining task optimizing subword word2vec-like objective also reaches state-of-the-art performance on 4 of 6 languages considered.
Vendor Neutral Cloud Platform for 4D Digital Pathology

In the last decade, digital technologies have been transforming clinical pathology, dematerializing the processes, improving the workflow, and promoting the development of decision support tools. The glass slides can be digitized with high-quality scanners and sent to network archives, opening doors to data sharing and universal access. Despite these advancements, however, the adoption of digital pathology solutions in production environments has been slowed down by the lack of standardization in the available software. Among the improved visualization features in digital pathology is the ability to capture a series of images at the same spatial location with varying focal planes, also known as Z-stack. This article proposes a full-stack, standard solution that efficiently supports the visualization of these 4D images. It is a Cloud-based architecture that supports Z-stack image management and visualization. The solution is made vendor-neutral by providing a pipeline for the integration of different image vendors.
Style-Agnostic Reinforcement Learning

We present a novel method of learning style-agnostic representation using both style transfer and adversarial learning in the reinforcement learning framework. The style, here, refers to task-irrelevant details such as the color of the background in the images, where generalizing the learned policy across environments with different styles is still a challenge. Focusing on learning style-agnostic representations, our method trains the actor with diverse image styles generated from an inherent adversarial style perturbation generator, which plays a min-max game between the actor and the generator, without demanding expert knowledge for data augmentation or additional class labels for adversarial training. We verify that our method achieves competitive or better performances than the state-of-the-art approaches on Procgen and Distracting Control Suite benchmarks, and further investigate the features extracted from our model, showing that the model better captures the invariants and is less distracted by the shifted style. The code is available at https://github.com/POSTECH-CVLab/style-agnostic-RL .
An Incremental Load Balancing Algorithm in Federated Cloud Environment

With the development of cloud computing, load balancing issues have substantially become prominent, which is of concern as well to the industry as to academia. Load balancing contributes to a high degree of customer satisfaction and the use of resources in the cloud by verifying an accurate, secure, and equitable allocation of all computing resources. In this paper, we review the research progress on load balancing issues from the perspective of several incremental and dynamic algorithms in federated cloud computing. First, we present some obstacles to load balancing algorithms in cloud computing and suggest a corresponding incremental algorithm for load balancing in a federated cloud. Last, we offer current challenges and highlight possible future directions for research in this field.