Reviewerly/exHarmony huggingface dataset:
Construction Research and Applications of Industry Chain Knowledge Graphs

Research on listed companies is an important part of stock analysis. This study proposes an automatic construction method of the knowledge graph for the financial industry chain, which can better track and study the relationship between the production and operation of listed companies from the perspective of the graph. To solve the problems of high expert labor costs, late update and maintenance, and unstandardized and label-lacking datasets during the construction of vertical domain knowledge graphs, this study conducts knowledge extraction of unstructured text by integrating two dependency parsing methods, phrase structure trees, and dependency parse trees. Furthermore, this study uses automatically labeled datasets to train a deep learning-based named entity recognition model. This method, which integrates the two abovementioned methods, improves normalization ability and allows for the automatic construction of a financial industry chain knowledge graph.
From Whole Video to Frames: Weakly-Supervised Domain Adaptive Continuous-Time QoE Evaluation

Due to the rapid increase in video traffic and relatively limited delivery infrastructure, end users often experience dynamically varying quality over time when viewing streaming videos. The user quality-of-experience (QoE) must be continuously monitored to deliver an optimized service. However, modern approaches for continuous-time video QoE estimation require densely annotating the continuous-time QoE labels, which is labor-intensive and time-consuming. To cope with such limitations, we propose a novel weakly-supervised domain adaptation approach for continuous-time QoE evaluation, by making use of a small amount of continuously labeled data in the source domain and abundant weakly-labeled data (only containing the retrospective QoE labels) in the target domain. Specifically, given a pair of videos from source and target domains, effective spatiotemporal segment-level feature representation is first learned by a combination of 2D and 3D convolutional networks. Then, a multi-task prediction framework is developed to simultaneously achieve continuous-time and retrospective QoE predictions, where a quality attentive adaptation approach is investigated to effectively alleviate the domain discrepancy without hampering the prediction performance. This approach is enabled by explicitly attending to the video-level discrimination and segment-level transferability in terms of the domain discrepancy. Experiments on benchmark databases demonstrate that the proposed method significantly improves the prediction performance under the cross-domain setting.
Toward Authentication of Videos: Integer Transform Based Motion Vector Watermarking

Now a day, digital content like videos, audios and images are widely used as evidence in criminal courts and forensic labs. Due to advanced low-cost and easily available multimedia/ communication tools and softwares, manipulation of the content is a no-brain task. Thus, the protection of digital content originality is a challenge for the content owners and researchers before it can be produced in court or used for some other purpose. In this paper, the motion vector watermarking technique has been proposed that validate/authenticate videos. We are embedding the correlated watermark in the integer wavelet transform domain and the selection of embedding areas is based on the variation of motion vectors. The video frames are fully protected in both spatial and transform domains since the watermark is correlated with the approximation subbands of wavelet transform before embedding. The proposed technique can concisely determine the attacked regions. The results validate the performance of the proposed approach in terms of quality metrics like peak signal to noise ratio (PSNR), structural similarity index (SSIM), normalized coefficients (NC) and bit error rate (BER).
CCDC: A Chinese-Centric Cross Domain Contrastive Learning Framework

Unsupervised/Supervised SimCSE [5] achieves the SOTA performance of sentence-level semantic representation based on contrastive learning and dropout data augmentation. In particular, supervised SimCSE mines positive pairs and hard-negative pairs through Natural Language Inference (NLI) entailment/contradiction labels, which significantly outperforms other unsupervised/supervised models. As NLI data is scarce, can we construct pseudo-NLI data to improve the semantic representation of multi-domain sentences? This paper proposes a Chinese-centric Cross Domain Contrastive learning framework (CCDC), which provides a "Hard/Soft NLI Data Builder" to annotate entailment/contradiction pairs through Business Rules and Neural Classifiers, especially out-domain but semantic-alike sentences as hard-negative samples. Experiments show that the CCDC framework can achieve both intra-domain and cross-domain enhancement. Moreover, with the Soft NLI Data Builder, the CCDC framework can achieve the best results of all domains with one model, improving 34% and 11% in terms of the Spearman correlation coefficient compared with the baseline (BERT-base) and strong baseline (unsupervised SimCSE). And through empirical analysis, this framework effectively reduces the anisotropy of the pre-trained models and shows semantic clustering over unsupervised SimCSE.
Vision-Based Holistic Scene Understanding for Context-Aware Human-Robot Interaction

Human activity recognition systems from static images or video sequences are becoming more and more present in our life. Most computer vision applications such as human-computer interaction, virtual reality, public security, smart home monitoring, or autonomous robotics, to name a few, highly rely on human activity recognition. Of course, basic human activities, such as "walking" and "running", are relatively easy to recognize. On the other hand, identifying more complex activities is still a challenging task that could be solved by retrieving contextual information from the scene, such as objects, events, or concepts. Indeed, a careful analysis of the scene can help to recognize human activities taking place. In this work, we address a holistic video understanding task to provide a complete semantic level description of the scene. Our solution can bring significant improvements in human activity recognition tasks. Besides, it may allow equipping a robotic and autonomous system with contextual knowledge of the environment. In particular, we want to show how this vision module can be integrated into a social robot to build a more natural and realistic context-based Human-Robot Interaction. We think that social robots must be aware of the surrounding environment to react in a proper and socially acceptable way, according to the different scenarios.
SAND : A Tool for Creating Semantic Descriptions of Tabular Sources

Building semantic descriptions of tables is a vital step in data integration. However, this task is expensive and time-consuming as users often need to examine the table data, its metadata, and ontologies to find the most appropriate description. In this paper, we present SAND , a tool for creating semantic descriptions semi-automatically. SAND makes it easy to integrate with semantic modeling systems to predict or suggest semantic descriptions to the users, as well as to use different knowledge graphs (KGs). Besides its modeling capabilities, SAND is equipped with browsing/querying tools to enable users to explore data in the table and discover how it is often modeled in KGs.
Assured Deep Multi-Agent Reinforcement Learning for Safe Robotic Systems

Using multi-agent reinforcement learning to find solutions to complex decision-making problems in shared environments has become standard practice in many scenarios. However, this is not the case in safety-critical scenarios, where the reinforcement learning process, which uses stochastic mechanisms, could lead to highly unsafe outcomes. We proposed a novel, safe multi-agent reinforcement learning approach named Assured Multi-Agent Reinforcement Learning (AMARL) to address this issue. Distinct from other safe multi-agent reinforcement learning approaches, AMARL utilises quantitative verification, a model checking technique that guarantees agent compliance of safety, performance, and non-functional requirements, both during and after the learning process. We have previously evaluated AMARL in patrolling domains with various multi-agent reinforcement learning algorithms for both homogeneous and heterogeneous systems. In this work we extend AMARL through the use of deep multi-agent reinforcement learning. This approach is particularly appropriate for systems in which the rewards are sparse and hence extends the applicability of AMARL. We evaluate our approach within a new search and collection domain which demonstrates promising results in safety standards and performance compared to algorithms not using AMARL.
Reasoning About Smart Contracts Encoded in LTL

Smart contracts are programs deployed on blockchains that automatically execute when some conditions are verified to satisfy the terms of an agreement. They are usually programmed in procedural languages even if some recent proposals use logic for their specification and verification. However, existing logic-based initiatives only enable the formal checking of properties of a single smart contract independently from the other smart contracts running on the same blockchain. This paper proposes a framework, called SCRea, that works on a set of smart contracts encoded in Linear Temporal Logics. SCRea can work offline or at runtime to verify whether the execution of each smart contract is compatible with the execution of the others and, in case, provides a particular order of execution ensuring that the preconditions of each smart contract are verified before its execution.
Decentralized Federated Learning With Markov Chain Based Consensus for Industrial IoT Networks

Federated learning (FL) provides a novel framework to collaboratively train a shared model in a distribution fashion by virtue of a central server. However, FL is inappropriate for a serverless scenario and also suffers from some major drawbacks in Industrial Internet of Things (IIoT) networks, such as unresilience to network failures and communication bottleneck effect. In this article, we propose a novel decentralized federated learning (DFL) approach for IIoT devices to achieve model consensus by exchanging model parameters only with their neighbors rather than a central server. We firstly formulate the problem of model consensus in DFL as a fastest mixing Markov chain problem and then optimize the consensus matrix to improve the convergence rate. Meanwhile, a practical medium access control protocol with time slotted channel hopping is taken into account to implement the proposed approach. Furthermore, we also propose an accumulated update compression method to alleviate communication cost. Finally, extensive simulation results demonstrate that the proposed approach improves accuracy and reduces communication cost especially under the nonindependent identically distribution data distribution.
Multi-Oriented Object Detection in Aerial Images With Double Horizontal Rectangles

Most existing methods adopt the quadrilateral or rotated rectangle representation to detect multi-oriented objects. Yet, the same oriented object may correspond to several different representations, due to different vertex ordering, or angular periodicity and edge exchangeability. To ensure the uniqueness of the representation, some engineered rules are usually added. This makes these methods suffer from discontinuity problem, resulting in degraded performance for objects around some orientation. In this article, we propose to encode the multi-oriented object with double horizontal rectangles (DHRec) to solve the discontinuity problem. Specifically, for an oriented object, we arrange the horizontal and vertical coordinates of its four vertices in left-right and top-down order, respectively. The first (resp. second) horizontal box is given by two diagonal points with smallest (resp. second) and third (resp. largest) coordinates in both horizontal and vertical dimensions. We then regress three factors given by area ratios between different regions, helping to guide the oriented object decoding from the predicted DHRec. Inherited from the uniqueness of horizontal rectangle representation, the proposed method is free of discontinuity issue, and can accurately detect objects of arbitrary orientation. Extensive experimental results show that the proposed method significantly improves the existing baseline representation, and outperforms state-of-the-art methods. The code is available at: https://github.com/lightbillow/DHRec.
Relation Prediction Based on Source-Entity Behavior Preference Modeling via Heterogeneous Graph Pooling

Recent years have witnessed great advance of representation learning (RL) based models for the knowledge graph relation prediction task. Most existing approaches represent graph nodes as vectors in a low-dimensional embedding space, ignoring the entity behavior preference and the beneficial semantic interactions in the real-world graphs. To address this challenge, this paper proposes a novel relation prediction model based on source-entity behavior preference modeling, which represents each source-entity as a heterogeneous graph released from their structure and semantic perspectives to better capture the behavior relatedness. Especially, a heterogeneous graph pooling method is leveraged for learning source-entity behavior embedding representation from this personalized heterogeneous graph. In our comprehensive experiments, we evaluate our model on real-world graphs, and the results demonstrate that the proposed model significantly outperforms existing state-of-the-art methods on benchmark datasets for the relation prediction and knowledge graph completion task.
Self-Supervised Learning With Segmental Masking for Speech Representation

Self-supervised learning has achieved remarkable success for learning speech representations from unlabeled data. The masking strategy plays an important role in the self-supervised learning algorithm. Most of the masking techniques operate at a frame level. In linguistics, phone is the smallest unit of sound. Hence, we believe that a masking technique that operates at a phoneme level will effectively encode the phonotactic and prosodic constraints of a spoken language, thus eventually benefits the downstream speech recognition tasks. In this work, we explore a novel segmental masking strategy. Specifically, we mask phonetically motivated speech segments according to the phonetic segmentation in an utterance. By doing so, we implicitly incorporate the properties of a spoken language, such as phonotactic constraints and duration of phonetic segments, into the pre-training. Through extensive experiments, we confirm that the segmental masking strategy consistently outperforms the frame-based masking counterpart. We also further investigate the effect of segmental masking unit size, i.e. phoneme, phoneme span, and lexical word. This work presents an important finding about masking strategy in speech representation learning.
SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts

Question answering (QA) systems have gained explosive attention in recent years. However, QA tasks in Vietnamese do not have many datasets. Significantly, there is mostly no dataset in the medical domain. Therefore, we built a Vietnamese Healthcare Question Answering dataset (ViHealthQA), including 10,015 question-answer passage pairs for this task, in which questions from health-interested users were asked on prestigious health websites and answers from highly qualified experts. This paper proposes a two-stage QA system based on Sentence-BERT (SBERT) using multiple negatives ranking (MNR) loss combined with BM25. Then, we conduct diverse experiments with many bag-of-words models to assess our system's performance. With the obtained results, this system achieves better performance than traditional methods.
A Relevance-Based CNN Trimming Method for Low-Resources Embedded Vision

A significant amount of Deep Learning research deals with the reduction of network complexity. In most scenarios the preservation of very high performance has priority over size reduction. However, when dealing with embedded systems, the limited amount of resources forces a switch in perspective. In fact, being able to dramatically reduce complexity could be a stronger requisite for overall feasibility than excellent performance. In this paper we propose a simple to implement yet effective method to largely reduce the size of Convolutional Neural Networks with minimal impact on their performance. The key idea is to assess the relevance of each kernel with respect to a representative dataset by computing the output of its activation function and to trim them accordingly. The resulting network becomes small enough to be adopted on embedded hardware, such as smart cameras or lightweight edge processing units. In order to assess the capability of our method with respect to real-world scenarios, we adopted it to shrink two different pre-trained networks to be hosted on general purpose low-end FPGA hardware to be found in embedded cameras. Our experiments demonstrated both the overall feasibility of the method and its superior performance when compared with similar size-reducing techniques introduced in recent literature.
Ontology Matching Through Absolute Orientation of Embedding Spaces

Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces. Next to the approach, the paper presents a first, preliminary evaluation using synthetic and real-world datasets. We find in experiments with synthetic data, that the approach works very well on similarly structured graphs; it handles alignment noise better than size and structural differences in the ontologies.
Cross-perspective Graph Contrastive Learning

Attributed graph representation has attracted increasing attention recently due to its broad applications such as node classification, link prediction and recommendation. Most existing methods adopt Graph Neural Network (GNN) or its variants to propagate the attributes over the structure network. However, the attribute information will be overshadowed by the structure perspective. To address the limitation and build a link between nodes features and network structure, we aim to learn a holistic representation from two perspectives: topology perspective and feature perspective. To be specific, we separately construct the feature graph and topology graph. Inspired by the network homophily, we argue that there is a deep correlation information between the network structure perspective and the node attributes perspective. Attempting to exploit the potential information between them, we extend our approaches by maximizing the consistency between structural perspective and attribute perspective. In addition, an information fusion module is presented to allow flexible information exchange and integration between the two perspectives. Experimental results on four benchmark datasets demonstrate the effectiveness of our proposed method on graph representation learning, compared with several representative baselines.
A Game-Based Evolutionary Clustering With Historical Information Aggregation for Personal Recommendation

In order to alleviate the network information overload, recommender system becomes widespread in personalized recommendation. However, due to the increase of the number of users and items in the network, the rating data gets increasingly sparse. At this time, it is necessary to use a variety of clustering algorithms to divide nodes into different communities, and then make recommendations in each community, which can improve the performance of recommendations and reduce the time complexity of algorithms. In this paper, we propose a game-based evolutionary clustering with historical information aggregation for personal recommendation. Firstly, a payoff function of game theory is introduced into the evolutionary clustering to accelerate the stability of the algorithm. In this clustering approach, the next state value of each node is related not only to the current state value, but also to the historical states, hence, it achieve better prediction results. Meanwhile, the clustering method is theoretically proved to be stable by Lyapunov stability theory. And then, we predict the possible ratings by the user-based collaborate filtering method, and recommend items for target users according to the preferences of neighbors. Finally, diverse experiments are executed on seven real recommendation datasets to verify our recommendation results are better than several compared algorithms.
iRestroom : A smart restroom cyberinfrastructure for elderly people

According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors who live alone must have their health state closely monitored to avoid unexpected events (such as a fall). This study explains the underlying principles, methodology, and research that went into developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure system, that we call as iRestroom to assess the frailty of elderly people for them to live a comfortable, independent, and secure life at home. The proposed restroom idea is based on the required situations, which are determined by user study, socio-cultural and technological trends, and user requirements. The iRestroom is designed as a multi-sensory place with interconnected devices where carriers of older persons can access interactive material and services throughout their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a constantly updated reference for the data originating from numerous sensors and devices installed in different locations throughout the restroom. A small sample of pilot data was obtained, as well as pertinent web data. The Institutional Review Board (IRB) has approved all the methods.
Unveiling the sign value of early potter’s wheels based on a 3-D morphometric analysis of Late Chalcolithic vessels from the southern Levant

The sign value of the first potter's wheels used in the southern Levant (second half of the 5th mill. BC) is explored through the production modalities of V-shaped bowls, the main category of vessels shaped on the wheel at that time. To this end, a morphometric approach is applied to V-shaped bowls from four sites through numerical methods available in a Pottery 3-D software used to extract shape parameters. The results obtained highlight a high variability in absolute dimensions and a low variability in profiles. This apparent contradiction is discussed in light of the variability of bowls made under three experimental conditions. The baseline data obtained suggests that the roughouts of the archaeological bowls were made by several hands, while the shaping of the bowls on the wheel was done by a single hand. This disjunction in the production process supports the hypothesis that the earliest rotary instruments found in the southern Levant were loaded with symbolic meaning that was then transferred to the vessels when they were shaped on the wheel.