Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Enhancing Vietnamese Sentiment Classification

Sentiment analysis has become an essential and fundamental task in natural language processing that has many useful applications. There exist various methods for solving the sentiment classification problem using traditional machine learning models, deep neural networks, and transfer learning methods. It is still interesting for Vietnamese sentiment classification problems to understand the impact of different ensemble techniques and deep learning approaches for building the most suitable model. This work aims to study the Vietnamese sentiment classification on one public dataset used in the Vietnamese Sentiment Analysis Challenge 2019 and another large-scale dataset, namely “AISIA-Sent-002”, which was collected from the Vietnamese e-commerce websites by ourselves. We explore five distinct ensemble schemes, including the classic methods (Uniform Weighting and Linear Ensemble) and the feature importance-based advanced methods (Gating Network, Squeeze-Excitation Network, and Attention Network) for Vietnamese sentiment analysis. We do these ensemble techniques with five individual deep learning models: TextCNN, LSTM, GRU, LSTM + CNN, and GRU + CNN. Extensive experiments on two datasets show that the ensemble methods perform much better than any individual model and significantly outperform the competition’s winning solution with a large margin. Finally, we aim to publish our source codes to contribute to the current research community related to natural language processing.
Adaptive 3D Visual Servoing of a Scara Robot Manipulator with Unknown Dynamic and Vision System Parameters

In the present work, we develop an adaptive dynamic controller based on monocular vision for the tracking of objects with a three-degrees of freedom (DOF) Scara robot manipulator. The main characteristic of the proposed control scheme is that it considers the robot dynamics, the depth of the moving object, and the mounting of the fixed camera to be unknown. The design of the control algorithm is based on an adaptive kinematic visual servo controller whose objective is the tracking of moving objects even with uncertainties in the parameters of the camera and its mounting. The design also includes a dynamic controller in cascade with the former one whose objective is to compensate the dynamics of the manipulator by generating the final control actions to the robot even with uncertainties in the parameters of its dynamic model. Using Lyapunov’s theory, we analyze the two proposed adaptive controllers for stability properties, and, through simulations, the performance of the complete control scheme is shown.
Distributed fusion in harsh environments using multiple bearings-only sensors with out-of-sequence-refined measurements

This paper investigates a novel distributed fusion system for target tracking in three dimensional surveillance using multiple asynchronous BO sensors by simultaneously addressing the problems of target mis-detection and clutter disturbance as well as the out-of-sequence (OOS) information fusion. The proposed distributed fusion architecture consists of the local tracking and distributed fusion, wherein, the refined BO measurements output from the local tracking in each BO sensor are transmitted to the fusion center for subsequent distributed fusion. In the local tracking, instead of kinematic state, the target pseudo state is defined and tracked by operating the proposed pseudo state-integrated probabilistic data association (PS-IPDA) algorithm with an approximately linear pseudo state evolution model, resulting in greatly refined BO sensor measurements. In the fusion center, the sequential asynchronous IPDA-general one-step retrodiction update (SAIPDA-GORU) algorithm is proposed to effectively fuse the transmitted origin-unknown OOS refined measurements (OOSRMs) with the central tracks. The SAIPDA-GORU propagates the central track hybrid state to the OOS time by deploying a general one-step retrodiction technique, and mathematically formulates the OOSRMs-to-retrodicted central track association probabilities utilized to facilitate the fusing of currently filtered central track with OOSRMs. Numerical results show that the proposed PS-IPDA algorithm reduces the errors of target bearings measurement by around a half and eliminates more than 60% of the clutter measurements contained in raw detections, saving significant communication bandwidth between local sensors and fusion center. Furthermore, the proposed SAIPDA-GORU method tangibly improves the estimation accuracy and false track discrimination over the existing discarding method, with the potential to be implemented in real-time.
APES: Audiovisual Person Search in Untrimmed Video

Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person re-identification and retrieval domains, few works have developed audiovisual search strategies. In this paper, we present the Audiovisual Person Search dataset (APES), a new dataset composed of untrimmed videos whose audio (voices) and visual (faces) streams are densely annotated. APES contains over 1.9K identities labeled along 36 hours of video, making it the largest dataset available for untrimmed audiovisual person search. A key property of APES is that it includes dense temporal annotations that link faces to speech segments of the same identity. To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval. Our study shows that modeling audiovisual cues benefits the recognition of people’s identities.
Blockchain to the Rescue—Tokenization of Residential Real Estate in the Emerging Token Economy

Blockchain technology allowing mass tokenization of assets represents the fundamental element of the ongoing industrial revolution. It helps asset owners to draft unique and tailored product offerings with access to broad groups of investors, exit opportunities and limited risk of fraud. Nevertheless, potential hazards to the process and the very concept exist. The near perspective of a private stablecoin threatening the financial system puts pressure on governments, regulators and the banking system to adopt the revolution rather than oppose it. Tokenization of residential real estate assets opens vast opportunities for inclusion of traditional financial institutions in the process via oracle services, as the quality of data exposed to the asymmetry of information and other factors particular to this market segment is crucial. The author believes that retail banks should further leverage their role in the economy, given their business experience, local expertise or trust, and drafts a concept of the Oracle Bank operating at the crossroads of the physical and digital worlds. This organization should emerge from the unstoppable process of the financial paradigm shift. The discussion on the institutional adoption of residential property tokenization should not be limited to the security of transactions but reflect other aspects of the inevitable redefinition of the banking sector’s role in the emerging token economy, with residential real estate at the focus point of future socio-economic development plans.
Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments

We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online planning in non-cooperative environments, where each robot attempts to maximize its cumulative reward while interacting with other self-interested robots. Our algorithm adapts the centralized, perfect information, discrete-action space method from AlphaZero to a decentralized, partial information, continuous action space setting for multi-robot applications. Our method has three interacting components: (i) a centralized, perfect-information “expert” Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (ii) a decentralized, partial-information “learner” MCTS with small computation resources that runs in real-time and provides self-play examples, and (iii) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth. Our numerical experiments demonstrate Neural Tree Expansion's computational advantage by finding better solutions than a MCTS with 20 times more resources. The resulting policies are dynamically sophisticated, demonstrate coordination between robots, and play the Reach-Target-Avoid differential game significantly better than the state-of-the-art control-theoretic baseline for multi-robot, double-integrator systems. Our hardware experiments on an aerial swarm demonstrate the computational advantage of Neural Tree Expansion, enabling online planning at 20 Hz with effective policies in complex scenarios.
Instance-wise Causal Feature Selection for Model Interpretation

We formulate a causal extension to the recently introduced paradigm of instance-wise feature selection to explain black-box visual classifiers. Our method selects a subset of input features that has the greatest causal effect on the model’s output. We quantify the causal influence of a subset of features by the Relative Entropy Distance measure. Under certain assumptions this is equivalent to the conditional mutual information between the selected subset and the output variable. The resulting causal selections are sparser and cover salient objects in the scene. We show the efficacy of our approach on multiple vision datasets by measuring the post-hoc accuracy and Average Causal Effect of selected features on the model’s output.
Lower-Bound Synthesis Using Loop Specialization and Max-SMT

Abstract This paper presents a new framework to synthesize lower-bounds on the worst-case cost for non-deterministic integer loops. As in previous approaches, the analysis searches for a metering function that under-approximates the number of loop iterations. The key novelty of our framework is the specialization of loops, which is achieved by restricting their enabled transitions to a subset of the inputs combined with the narrowing of their transition scopes. Specialization allows us to find metering functions for complex loops that could not be handled before or be more precise than previous approaches. Technically, it is performed (1) by using quasi-invariants while searching for the metering function, (2) by strengthening the loop guards, and (3) by narrowing the space of non-deterministic choices. We also propose a Max-SMT encoding that takes advantage of the use of soft constraints to force the solver look for more accurate solutions. We show our accuracy gains on benchmarks extracted from the 2020 Termination and Complexity Competition by comparing our results to those obtained by the "Image missing"<!-- image only, no MathML or LaTex --> system.
Deblocking filtering in VVC

The paper describes the novel aspects of deblocking filter in VVC. We demonstrate the insufficiency of HEVC deblocking filter when employing larger transform block sizes and describe the design changes made to enable reduction of the resulting blocking artifacts for both luma and chroma, while also allowing for parallel friendly processing. Additionally, VVC deblocking includes filtering based on local luma level to address blocking artifacts observed in high dynamic range content. The paper also describes modifications made in VVC deblocking to address blocking artifacts introduced by additional coding tools present in VVC (but not in HEVC). The additional coding tools result in prediction and transform block boundaries at disparate locations in a coded picture. Similar to previous generation of video coding standards, the deblocking in VVC, adaptively filters samples located at the block boundaries based on coding modes and local spatial activity.
Reliable Edge Intelligence in Unreliable Environment

A key challenge for deployment of artificial intelligence (AI) in real-time safety-critical systems at the edge is to ensure reliable performance even in unreliable environments. This paper will present a broad perspective on how to design AI platforms to achieve this unique goal. First, we will present examples of AI architecture and algorithm that can assist in improving robustness against input perturbations. Next, we will discuss examples of how to make AI platforms robust against hardware induced noise and variation. Finally, we will discuss the concept of using lightweight networks as reliability estimators to generate early warning of potential task failures.
Augmented Reality with Mask R-CNN (ARR-CNN) inspection for Intelligent Manufacturing

A machine is an essential factor for industrial production. Industry 4.0 is the revolution that causes improvement of machines to have higher efficiency. Accordingly, inspection and maintenance are becoming more important. However, most of factories are not changed the operating process, there is no data logging for evaluation and analysis for preventive maintenance. This research aims to develop a model for machine inspection using augmented reality with object detection and marker techniques on real world machines and mask R-CNN algorithm allowing inspector to perform inspections. This study, we demonstrate the process of development of the proposed model by showing steps of data acquisition from a machine in a factory. The dataset is images of machines in different perspectives, and they were used for training and testing the model. The testing is done on a mobile device of an inspector. With computer vision technique and the proposed model, the instant precision tracking and detection are provided. Then the trained model is transferred to the mobile devices for testing without any modification by an expert. Some images of machines are randomly selected to verify the accuracy of the model. The result shows that the efficiency of the model is acceptable in real usage.
Parameters Compressed Mechanism in Federated Learning for Edge Computing

With the continuous improvement of the performance of the IoT and mobile devices, the IoT technology brings convenience to people's lives but leaks out personal privacy at the same time. Federated learning is a kind of machine learning technology that can protect data privacy. Different from current machine learning methods, Federated learning is usually facing non-Identically Independently Distributions (no-IID) data and keeps data in local places. Therefore, the effectiveness of existing machine learning methods in federated learning non-IID issues decreases greatly. At the same time, federated learning needs to interact with multiple edge learning nodes. With the increasing number of edge nodes, the cost of communication becomes higher and higher, and the parameter weight calculation of more independent nodes is also a problem. In this paper, we propose to balance the federated learning model by using Earth Mover's Distance to calculate the weights of different node parameters. Our method can reduce the impact of data non-independent identically distributed problems on the model so that the federated learning model will not be biased to the distributed nodes in the data set. Besides, this paper also proposes a method to compress redundant communication between nodes and servers in the process of training. Experimental results show that this paper method improves the accuracy by about 4% compared with the traditional federal learning average algorithm, and the gradient communication is compressed to about 6% of the original communication times.
White blood cell evaluation in haematological malignancies using a web‐based digital microscopy platform

Digital microscopy systems are beginning to replace traditional light microscopes for morphologic analysis of blood films, but these are geographically restricted to individual computers and technically limited by manufacturer's constraints. We explored the use of a scanner-agnostic web-based artificial intelligence (AI) system to assess the accuracy of white blood cell (WBC) differentials and blast identification in haematological malignancies.Digitized images of 20 normal and 124 abnormal peripheral blood films were uploaded to the web-based platform (Techcyte©) and WBC differentials performed using the online AI software. Digital images were viewed for accuracy and manual cell reassignment was performed where necessary. Results were correlated to the 'gold standard' of manual microscopy for each WBC class, and sensitivity and specificity of blast identification were calculated.The AI digital differential was very strongly correlated to microscopy (r > .8) for most normal cell types and did not require any manual reassignment. The AI digital differential was less reliable for abnormal blood films (r = .50-.87), but could be greatly improved by manual assessment of digital images for most cell types (r > .95) with the exception of immature granulocytes (r = .62). For blast identification, initial AI digital differentials showed 96% sensitivity and 25% specificity, which was improved to 99% and 84%, respectively, after manual digital review.The Techcyte platform allowed remote viewing and manual analysis of digitized slides that was comparable to microscopy. The AI software produced adequate WBC differentials for normal films and had high sensitivity for blast identification in malignant films.
Designing Human-Robot Coexistence Space

When the human-robot interactions become ubiquitous, the environment surrounding these interactions will have significant impact on the safety and comfort of the human and the effectiveness and efficiency of the robot. Although most robots are designed to work in the spaces created for humans, many environments, such as living rooms and offices, can be and should be redesigned to enhance and improve human-robot collaboration and interactions. This letter uses autonomous wheelchair as an example and investigates the computational design in the human-robot coexistence spaces. Given the room size and the objects O in the room, the proposed framework computes the optimal layouts of O that satisfy both human preferences and navigation constraints of the wheelchair. The key enabling technique is a motion planner that can efficiently evaluate hundreds of similar motion planning problems. Our implementation shows that the proposed framework can produce a design around three to five minutes on average comparing to 10 to 20 minutes without the proposed motion planner. Our results also show that the proposed method produces reasonable designs even for tight spaces and for users with different preferences.
Improving Knowledge Graph Completion Using Soft Rules and Adversarial Learning

Knowledge graph is a useful resources and tools for describing entities and relationships in natural language processing tasks. However, the existing knowledge graph are incomplete. Therefore, knowledge graph completion technology has become a research hotspot in the field of artificial intelligence, but the traditional knowledge graph embedding method does not fully take into account the role of logic rules and the effect of false negative samples on knowledge embedding. Based on the logic rules of knowledge and the role of adversarial learning in knowledge embedding, we proposes a model to improve the completion of knowledge graph: soft Rules and graph adversarial learning (RUGA). Firstly, the traditional knowledge graph embedding model is trained as generator and discriminator by using adversarial learning method, and high-quality negative samples are obtained. Then these negative samples and the existing positive samples together constitute the label triple in the injection rule model. The whole model will benefit from both high-quality samples and logical rules. In addition, we evaluated the performance of link prediction task and triple classification task on Freebase and Yago datasets respectively. Finally, the experimental results show that the model can effectively improve the effect of knowledge graph completion.
Sentiment classification based on dependency-relationship embedding and attention mechanism

Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM.
Multimodal soccer highlight identification using a sparse subset of frames integrating long-term sliding windows

The massive growth of audiences eager for sport content has substantially increased workers’ demand in this profitable segment. Highlight identification is vital for summarizing football matches. Decision support tools can significantly reduce the number of company employees required to tackle such a task, widely benefiting workforce resource allocation. This paper discusses the development of an automatic football highlight detector. The proposed system exploits discriminative low-level audio and video features extracted from a compact set of irregularly time–spaced frames that integrate a long-term sliding window. A new mixed wrapper-probabilistic algorithm leverages a cost-effective selection of the most significant frames submitted to a robust multi-frame consensus classification scheme. By considering a comprehensive database integrating 30 full matches, the proposed approach achieves a highlight identification rate of 100% (including all annotated goals), conjugated with a match-time compression rate of about 94%, when employing a Random Forest classifier.
Deploying blockchain technology for storing digital diplomas

Blockchain is a booming technology. More and more applications are being developed in the field of banking, security, document storage, smart contracts, etc. This article proposes an exploratory research of blockchain technology and of the companies involved in the field. The article also presents an overview of some solutions for storing certificates using blockchain technology. After analyzing these existing applications, the paper proposes an architectural design and working scenario for deploying a system to storing diplomas issued by the University Politehnica of Bucharest using the blockchain technology. Finally, the conclusions and future work are presented.