Reviewerly/exHarmony huggingface dataset:
Improved Representations for Personalized Document-Level Sentiment Classification

Incorporating personalization into document-level sentiment classification has gained considerable attention due to its better performance on diverse domains. Current progress in this field is attributed to the developed mechanisms of effectively modeling the interaction among the three fundamental factors: users, items, and words. However, how to improve the representation learning of the three factors themselves is largely unexplored. To bridge this gap, we propose to enrich users, items, and words representations in the state-of-the-art personalized sentiment classification model with an end-to-end training fashion. Specifically, relations between users and items are respectively modeled by graph neural networks to enhance original user and item representations. We further promote word representation by utilizing powerful pre-trained language models. Comprehensive experiments on several public and widely-used datasets demonstrate the superiority of the proposed approach, validating the contribution of the improved representations.
Fast Retrieval Method of Image Data Based on Learning to Hash

Abstract Hash is a method that is widely used in nearest neighbor retrieval, and its goal is to convert high-dimensional image data into low-dimensional representations or into a set of ordered binary code. As one of the more efficient methods of data storage and retrieval, the hash method is widely used in the nearest neighbor retrieval of large-scale image data. The traditional hashing method generates a hash code by manually extracting features, so that the feature and the hash code do not have the best fit, so that the generated hash code is suboptimal. The rapid development of deep learning makes the computer have a good effect on image visual feature recognition. Combining the learning of hash function makes the performance better than the traditional hash method. In this paper, a deep hash method based on triple constraint is proposed to extract the similarity features of the same category and distinguish the features between different categories. Further learning the hash code makes the similarity of the image preserved. Experiments show that the Hash-based learning method has better performance on CIFAR-10 and NUS-WIDE than other methods.
Research Status and Future Expectation of Agricultural Big Data Technology

Currently, China has entered the era of digital economy at a high speed, and Big Data has become a new factor of Chinese national production. In order to promote the transformation and upgrading of agriculture in China and promote the all-round and rapid development of agricultural Big Data in China, this paper summarizes Big Data technology, especially the related technology of agricultural Big Data, studies the present situation of the development of agricultural Big Data technology in foreign countries, focuses on the overall situation of the development of agricultural Big Data technology in China based on the investigation and analysis of the relevant literature of agricultural Big Data at home and abroad. Finally, the task and direction of agricultural Big Data technology development in China in the future are discussed accordingly in this paper.
Event Recognition in Chinese Emergencies Corpus Using ALBERT-BiLSTM-CRF

The event recognition is an important foundation in the event extraction task. This paper uses Chinese Emergencies Corpus (CEC) as the raw corpus, and the vector representation of the text is obtained by embedding a pre-trained language model ALBERT(A Lite Bidirectional Encoder Representation from Transformers). Then the BiLSTM-CRF (Bidirectional Long Short Term Memory-Conditional Random Field) model is used to extract the text features and label-dependent features, the model finally outputs the predicted label sequence to realize the boundary and type recognition of the trigger word. The experimental results show that the micro-F1 value of the trigger word recognition on the CEC reaches 76.4%. In comparison with other event recognition methods, our approach achieves more outstanding performance, and have higher training efficiency.
Dynamic Trust Model of ARP Real-Time Intrusion Detection Based on Extended Subjective Logic

This paper analyzes the ARP protocol and its operating principle, as well as the current protection method for ARP spoofing attacks to understand their defense and detection features. To study the principle of realization and complexity for these methods, and summarize their proper environment, strength and weakness, based on the WinPcap research, this paper mentioned this Dynamic Trust Model of ARP Real-Time Intrusion Detection based on Extended Subjective Logic(DTMARID-ESL). This model introduces Jasang's Subjective logic to detect ARP Intrusion behavior, and extend its subjective logic to solve and detect ARP intrusion's dynamic trust problems, including basic elements such as base rate and un-certain factors, etc.) and dynamic evaluation from time and space. The simulation results show this model has high accuracy and efficiency.
Adoption of blockchain in the supply chain to improve quality of product and customer service in manufacturing sectors

Supply Chain being has become predominate in a world where markets are global, heterogeneous, and complex and in which costumers expect a high level of quality, this can be effectuated by incorporating blockchain and its correlated appliance in the supply chain, and consider the most important parameters and constraints in the is process to improve upon. Manufacturing firms strive to enhance the quality of products and to reduce the time involved in the composition of the products by the creation of grading units for different products and certifying it before it reaches the customer. Blockchain can be an effective tool in this process by initiating real-time data collection, as it stipulates data being updated automatically in the form of blocks from the initial stage to the final stage. Since blockchain consists of hash and algorithms, which has to granted or verified with the previous hash. Obtaining a final quality certificate on a particular product by satisfying all the parameters about blocks will be an effective and efficient technique to elevate upon. Considering the current scenario of products developed being deficient of the affirmed quality and leading to dejected customer gratification, this research can empower manufacturing sectors and to stay on par with the global competence and have better conception over customers.
Innovation as a Singular Enabler

In this paper, we consider the value of knowledge in an innovation context and deliberate a contrary perspective from existing empiricisms to bring about better innovation efficiency within multi-agent arenas. To do this, we consider why, if innovation is key for developmental trajectories in a healthcare environment, and despite the resource utilised to examine its characteristics, the transfer of knowledge within healthcare, practitioner or organisational innovation domains remains a problematic event.We reflect on this duality with a doxastic attitude and draw on modal maps as underpinning structures to present a critique. Furthermore, we draw from these qualitative descriptions of conditional maps as a natural extension of contemporary KBF (Knowledge Belief Frame) models. Thus, from an innovation context, we can deliberate the parallelism between an agent who establishes belief in real time propositions, and a formal system from which they derive the proposition and reality. Uniquely, in doing so we build a legitimate frame of reference by highlighting managerial parallelisms, which synthesise key epistemic doyennes and, efficaciously underpin the plausibility of logical associations and decision-making drawn from a first-person architype of belief.
A New Advantage Actor-Critic Algorithm For Multi-Agent Environments

Reinforcement learning is one of the most researched fields of artificial intelligence right now. Newer and newer algorithms are being developed, especially for deep reinforcement learning, where the selected action is computed with the assist of a neural network. One of the subcategories of reinforcement learning is multi-agent reinforcement learning, where multiple agents are present in the world. In our paper, we modify an already existing algorithm, the Advantage Actor-Critic (A2C) to be suitable for multi-agent scenarios. Afterwards, we test the modified algorithm on our testbed, a cooperative-competitive pursuit-evasion environment.
A Multi Agent-Based Security Protocol for Securing Password Management Application

Password-based authentication is the most common authentication method for either online or offline system. Password composition policies become too burdensome and put the user in a state of struggle to remember their password. Thus, most of the user save their password on the browser or even list it down in their personal gadgets. Therefore, a multi agent-based password management application have been developed to helps user in keeping their password safely. However, multi-agent system facing security issues such as man in the middle attack, data modification and eavesdropping. This paper proposed a security protocol for multi agent-based architecture in order to reduce potential threats. The security protocol focuess on the authentication of mobile agents, data transmission and the data local protection. The communication channels are secured using cryptography techniques.
Key Matrix Design Based on OLED Display Technology

In order to realize the hardware universal design of key matrix, function software custom settings and the number of keys can be expanded in the human-computer interaction scenario. The OLED display technology are studied, and the key matrix based on OLED display technology is designed. According to study the driving principle of OLED display keys, the circuit schematic diagram of LPC1788 controller, realizes the configurability of the software for displaying text and pictures. The key matrix design based on OLED display technology is stable, reliable, clear, versatile, beautiful in appearance, friendly in man-machine interface, and can be widely used in man-machine interaction system for keyboard requirements of vehicle, ship and airborne.
Mutual Relation Detection for Complex Question Answering over Knowledge Graph

Question Answering over Knowledge Graph (KG-QA) becomes a convenient way to interact with the prevailing information. The user's information needs, i.e., input questions become more complex. We find that the comparison, relation, and opinion questions are witnessed a significant growth, especially in some domains. However, most of the current KG-QA methods cannot appropriately handle the inherent complex relation and coverage characteristics within the questions.
Reasoning about Cardinal Directions between 3-Dimensional Extended Objects using Answer Set Programming

Abstract We propose a novel formal framework (called 3D- N CDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set Programming (ASP). 3D- N CDC-ASP extends Cardinal Directional Calculus (CDC) with a new type of default constraints, and N CDC-ASP to 3D. 3D- N CDC-ASP provides a flexible platform offering different types of reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set of constraints on 3D cardinal directions between objects, explaining inconsistencies, and inferring missing CDC relations. We prove the soundness of 3D- N CDC-ASP, and illustrate its usefulness with applications.
Architecting Blockchain Systems

Companies are gravitating more and more towards the use of blockchains in their systems, but it is not a silver bullet. Challenges are currently holding back blockchain's enormous potential, such as scalability issues and frustrating trade-offs, most notably in public decentralized blockchain systems. In this paper, we conduct a Systematic Review of Literature in order to explore the current challenges of blockchains while presenting possible solutions to each of these challenges. We conclude that current challenges can be summarized in three categories: Scalability issues, security issues and a choice of the consensus protocol. We also briefly discuss the use of blockchain in current systems, concluding that while blockchains current immaturity makes it hard to recommend for most projects, blockchains in their current state could be used in the Internet of Things.
Structural Fuzzy Multi-class Support Vector Machine

Abstract Support vector machine is a kind of generalized linear classifier that classifies data in a supervised learning manner, and its decision boundary is the maximum margin hyperplane for solving learning samples. It is used to solve the problem of binary classification. For multi-classification problems, a combination method of multiple binary classifiers is usually used to solve, but such methods are prone to generate inseparable regions of data. Therefore, on the basis of constructing a multi-class problem directly, using the pinball loss function, and introducing the structural information of different classes in the data and the role of different samples, a new support vector machine algorithm Pin-SFSimMSVM for solving multi-classification problems is proposed. It not only retains the advantages of avoiding the existence of inseparable regions and fast calculation speed of multiple types of data, but also is insensitive to noise and resampling data, and has greatly improved the accuracy. The effectiveness of the proposed algorithm is verified by experimenting on UCI standard data sets and comparing with some multi-classification algorithms.
Automated Visual Inspection of Multiple Missing Fastening Bolts on the Sides of High-Speed Train Body in Railway Maintenance

The rapid development of China Railway High-speed (CRH) has provided safe and comfortable transportation for many people. The fastening bolts (FB) on the sides of CRH fall off occasionally, which might cause serious traffic accidents. However, the automatic inspection of multiple missing FBs on the sides of CRH is seldom conducted. In this study, an automated visual inspection system based on space-scale normalization is proposed to inspect the status of FBs on the sides of CRH. To locate the missing FBs in sequence images, the exact alignment of the target image and its reference image must be ensured. This problem can be solved by normalizing the target image according to its reference image. First, the FBs are located in a specific CRH region, which is called the region of interest (ROI). The target image is cropped according to the size of the ROI. Second, the keypoints are extracted and matched by using the gradient-enhanced scale-invariant feature transform (GESIFT). Furthermore, the horizontal shift between the target image and its reference image is obtained during keypoint matching. Third, the target image and its neighboring image are stitched according to the horizontal shift. Then, the target image is resegmented according to its reference image to complete the normalization of the target image. Finally, the normalized target image can be exactly aligned with its reference image. We propose an image subtraction method to locate the potential missing FBs. The FBs that may be lost are finally determined according to prior knowledge, and the state of the FBs is determined by using high-level image understanding knowledge. Experimental results show that our presented method performs excellently in monitoring the missing FBs on the sides of CRH.
Chinese Developers’ Information Behaviors When Using Technical Documentation

This paper was presented at the Invited Panel session "Technical Communication in China". Technical documentation quality is an essential part of product help center quality. As user information behavior gains increasing attention in user-interface design, for help centers, understanding user's information behavior of documentation provides insights to better develop product resources. To improve help center quality, we focus on typical technical documentation usersdevelopers and their information behavior. We used combined sociological approaches and conducted a twophase user research: an in-depth interview was first performed to gain basic idea of developers' documentation acquiring and using habits; then, a questionnaire survey was conducted to validate former result and further explore the factors influence those behaviors. This paper present how developers need, acquire and use technical documentations, and discussed the related factors. According to the result, we found that developers have preference in documentation acquiring methods, besides, quickly locating target information is their common way to start using a documentation. Furthermore, gender and user documentation dependency both have impact on their information behaviors and documentation using experience. In conclusion, developers' information behaviors of technical documentation have patterns, and these patterns should be carefully considered in help centers' future development.
Image Classification Model Using Deep Learning on the Edge Device

Training and inference are a crucial stage of deep learning. For better performance, it is essential to use an optimized approach during the training stage, as the selection of a system, network, model, and fine-tuning the training and hyperparameters. In this paper, an optimized model of the deep learning training and inference is proposed using insects dataset. The dataset consists of 613 images belonging to 10 classes of insects. In order to accelerate the deep learning training model, this system implemented CPU optimization and hyperparameter tuning. TensorFlow was applied as a framework for the training model with InceptionV3 as the topology. The evaluation of the model is demonstrated by the confusion matrix, precision-recall curve, and receiver operating characteristic (ROC). On the edge part, the model is optimized by the model optimizer technique for a better prediction result and speed. Finally, a Raspberry Pi 4 is used to inference the predictive model as an edge device.
Scottish Higher Education Digital Library: the e-book journey

The Scottish Higher Education Digital Library (SHEDL) is a strong brand in the publishing landscape, successfully negotiating access to over 3500 electronic journals for Scottish higher education institutions (HEIs), National Library of Scotland and National Museums Scotland. The fundamental basis of SHEDL is to provide content to all staff and students working in HEIs in Scotland. SHEDL has attempted to apply a similar model for publishers’ e-book output via a successfully run procurement framework. The SHEDL approach to the e-book journey has been innovative, with successes and challenges along the way as it collaboratively strives to provide quality academic e-book content in support of teaching and research.
MIVA: Multimodal Interactions for Facilitating Visual Analysis with Multiple Coordinated Views

Typically, people perform visual data analysis using mouse and touch interactions. While such interactions are often easy to use, they can be inadequate for users to express complex information and may require many steps to complete a task. Recently natural language interaction has emerged as a promising technique for supporting exploration with visualization, as the user can express a complex analytical question more easily. In this paper, we investigate how to synergistically combine language and mouse-based direct manipulations so that weakness of one modality can be complemented by the other. To this end, we have developed a novel system, named Multimodal Interactions System for Visual Analysis (MIVA), that allows user to provide input using both natural language (e.g., through speech) and direct manipulation (e.g., through mouse or touch) and presents the answer accordingly. To answer the current question in the context of past interactions, the system incorporates previous utterances and direct manipulations made by the user within a finite-state model. We tested the applicability of MIVA on several dashboards including a COVID-19 dashboard that visualizes coronavirus cases around the globe. Our demonstration provides initial indication that the MIVA system enhances the flow of visual analysis by enabling fluid, iterative exploration and refinement of data in a dashboard with multiple-coordinated views.
Personality Trait Classification Based on Co-occurrence Pattern Modeling with Convolutional Neural Network

In the modeling of impressions, a key factor for success is to extract nonverbal features that can be used to infer the target variable. To extract the effective features for capturing the relationship between the target subject which has the personality trait and other group members, Okada et al. propose a co-occurrence event-mining framework to explicitly extract the inter-modal and inter-personal features from multimodal interaction data. The framework is an unsupervised feature extraction algorithm by considering the relationship between nonverbal patterns of the target subject and group members. Though the label data of personality trait is useful to improve the accuracy, the valuable label data is not used for feature extraction. In this paper, we enhance the inter-modal and inter person feature extraction algorithm by using a deep neural network. We proposed a representation learning algorithm for capturing inter-modal and inter-person relationships by integrating using a convolutional neural network (CNN). In the experiment, we evaluate the effectiveness of the representation learning approach using the ELEA (Emerging Leadership Analysis) corpus, which includes 27 group interactions and is publicly available. We show that the proposed algorithm with CNN slightly improves the personality trait classification accuracy of the previous algorithms. In addition, we analyze which slice of multimodal time-series data is key descriptors to predict the personality trait using the proposed algorithm with CNN.
Exploring Crosslinguistic Frame Alignment

The FrameNet (FN) project at the International Computer Science Institute in Berkeley (ICSI), which documents the core vocabulary of contemporary English, was the first lexical resource based on Fillmore’s theory of Frame Semantics. Berkeley FrameNet has inspired related projects in roughly a dozen other languages, which have evolved somewhat independently; the current Multilingual FrameNet project (MLFN) is an attempt to find alignments between all of them. The alignment problem is complicated by the fact that these projects have adhered to the Berkeley FrameNet model to varying degrees, and they were also founded at different times, when different versions of the Berkeley FrameNet data were available. We describe several new methods for finding relations of similarity between semantic frames across languages. We will demonstrate ViToXF, a new tool which provides interactive visualizations of these cross-lingual relations, between frames, lexical units, and frame elements, based on resources such as multilingual dictionaries and on shared distributional vector spaces, making clear the strengths and weaknesses of different alignment methods.
Release-Aware Encryption Adjustment Query processing for Document Database

Providing confidentiality and privacy is a major security requirement for database systems. Private data is typically stored in the database in encrypted form. To query such data either it should be decrypted, leaving it vulnerable to server-side attacks, or special methods of querying directly over encrypted data should be applied. The latter direction for relational databases was taken in CryptDB approach [1] where encryption layers and onions were used to support execution a large class of SQL queries. Handling conjunctive and disjunctive queries in CryptDB has caused non-trivial trade-offs between security and efficiency. CryptDB-like approach has been extended later to other NoSQL types of databases, including graph databases [2] and document-based databases [3].