Reviewerly/exHarmony huggingface dataset:
Displacement Generating Module Based End-to-end Micro-expression Recognition Network

With the rapid development of deep learning, the research and application of micro-expression recognition are more and more extensive. In the existing solutions, the methods using the difference between onset frame and apex frame have high accuracy and low computational cost. But their extraction of dynamic features is not integrated with classification network, resulting in the lack of feedback from classification loss to the extracted dynamic features. In this paper, we propose a novel Displacement Generating Module (DGM) which uses convolution module to generate the displacement feature between onset frame and apex frame instead of traditional optical flow or dynamic image. The new DGM is integrated with the existing LEARNet to form an end-to-end micro-expression recognition network, where the classification loss can feed backward to the parameters of DGM to obtain better displacement features. We also present a random selection method of apex frame to increase the amount of training data, and present a normalization operation for the displacement features with different scales. Our new approach has been tested on SAMM, SMIC, CASME II datasets with LOSO evaluation method, and achieves 0.737 on UF1 and 0.726 on UAR, which is obviously higher than existing networks with optical flow and dynamic imaging techniques.
TRGAN: Text to Image Generation Through Optimizing Initial Image

Generative Adversarial Networks (GANs) have shown success in text-to-image generation tasks. Most of the current methods use multi-stages to generate images, but the quality of the final images is largely dependent on the quality of the initial generated images, thus it is difficult to generate high-quality images in the end if the initial images in the first stage are of low quality, low resolution, irregular shape, strange color, and unrealistic entity relations. Therefore, in this paper, we propose to design a multi-stage generation model, and we address this problem by developing a novel generation model called Text-representation Generative Adversarial Network (TRGAN). TRGAN contains two modules: Joint attention stacked generation module (JASGM) and Text generation in the opposite direction and correction module (TGOCM). In the JASGM module, the detailed feature is extracted from word-level information and the images are generated based on the global sentence attention. In the TGOCM module, the text descriptions are generated reversely, which can improve the quality of the initial images by matching the word-level feature vector. Experimental results present that our proposed model TRGAN outperforms the compared state-of-the-art text-to-image generation methods on CUB and COCO datasets.
Inter-Observer Visual Congruency in Video-Viewing

There are individual differences in human visual attention between observers when viewing the same scene. Inter-observer visual congruency (IOVC) describes the dispersion between different people's visual attention areas when they observe the same stimulus. Research on the IOVC of video is interesting but lacking. In this paper, we first introduce the measurement to calculate the IOVC of video. And an eye-tracking experiment is conducted in a realistic movie-watching environment to establish a movie scene dataset. Then we propose a method to predict the IOVC of video, which employs a dual-channel network to extract and integrate content and optical flow features. The effectiveness of the proposed prediction model is validated on our dataset. And the correlation between inter-observer congruency and video emotion is analyzed.
Hybrid Client/Server Peer to Peer Multitier Video Streaming

Real-Time Video Distribution (RTVD) is now the focus of many applications, including video conferencing, surveillance, video broadcasting, etc. This paper introduces a multi-IP camera-based method for distributing video signals to several levels using a hybrid client/server with peer-to-peer. There are four primary functions in the proposed structure: firstly, all linked camera transmissions will be received by the central server, and video signals will be shown to all attached clients and servers in Level two. The second function means that the clients/servers in level two start viewing received video signals and rebroadcasts them to the next level. The third function is clients/servers in level three who receive the video signals from the upper level and rebroadcast them to the fourth level. The fourth level consists of many clients that start viewing received video signals from level three. Therefore, the planned architecture and mechanism of the proposed system can provide the admin the capability to concentrate on the frames obtained from IP cameras to/from the central server. Furthermore, this mechanism can register and store the stream of frames and encode these frames during transmission. The proposed system depended on VC# programming language as a dependent tool that relied on various architectures and algorithms.
DA<sup>2</sup>-Net : Diverse &amp; Adaptive Attention Convolutional Neural Network

Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network&#x2019;s performance. Instead, most existing methods follow an indirect approach of increasing or tuning the networks&#x2019; depth and width, which in many cases significantly increase the computational cost. Inspired by biological visual system, we proposes a Diverse and Adaptive Attention Convolutional Network (DA<sup>2</sup>-Net), which enables any feed-forward CNNs to explicitly capture diverse features and adaptively select and emphasize the most informative features to efficiently boost the network&#x2019;s performance. DA<sup>2</sup>-Net incurs negligible computational overhead and it is designed to be easily integrated with any CNN architecture. We extensively evaluated DA<sup>2</sup>-Net on benchmark datasets, including CIFAR100, SVHN, and ImageNet, with various CNN architectures. The experimental results show DA<sup>2</sup>-Net provides a significant performance improvement with very minimal computational overhead.
Vertical Federated Principal Component Analysis on Feature-Wise Distributed Data

Despite the wide attention to federated learning (FL) in the literature, the existing studies mostly focus on supervised federated learning under the horizontally partitioned local dataset setting. This paper will study the unsupervised FL under the vertically partitioned dataset setting. Accordingly, we propose the vertically dataset partitioned federated principal component analysis (VFedPCA) method, which reduces the dimensionality across the joint datasets over all the clients and extracts the principal component feature information for downstream data analysis. VFedPCA features efficient local computation, communication efficiency, and privacy-preserving. Further, we study two communication topologies. The first is a server-client topology where a semi-trusted server coordinates the federated training, while the second is the fully-decentralized topology which eliminates the requirement of the server by allowing clients to communicate with their neighbors. Extensive experiments conducted on real-world datasets justify the efficacy of VFedPCA under vertical partitioned FL setting.
The Role of Digital Talents in the Transition to Circular Business Models for Environmental Conservation

The problem of environmental conservation and resource-efficient functioning of enterprises is currently one of the most urgent for developed and developing economies of the world. The opportunities for improving the efficiency of companies' activities in the field of environmental conservation in Industry 4.0 conditions are strongly connected with new business models and technologies of the digital economy. The use of digital solutions can significantly reduce emissions, increase the rational use of resources, improve energy efficiency, etc. Moreover, the whole concept of interaction of economic agents with each other and with the environment is changing. In general, this allows, firstly, deploying different solutions to improve the interaction of economic entities with the environment due to the application of different digital technologies (deep technologies, including Artificial intelligence, Industrial Internet of Things, Blockchain, Additive manufacturing, etc.). Secondly, to switch to new business models those are most effective in the context of human interaction with the environment. An example is the circular business model. However, the implementation of both options requires the availability of appropriate specialists in the field of the digital economy – digital talents. Now there is a significant gap in this area between demand and supply in the labor market. A shortage of digital talent exists. The distribution of digital talents in enterprises is unreasonable. The lack of digital talent, especially high-end technical specialists, is prominent, as is the lack of low-end technicians and technicians familiar with the industry. The core characteristic of digital talents is the ability to quickly and accurately find out novel information and new situations in the information field amidst the constant renewal of information technology. This requires the ability to learn continuously and independently. The training of these technicians in universities is essential. The solution to existing challenges is possible based on the development of effective interaction between educational institutions and the subjects of the labor market. The paper contains the main directions of digital talents cultivation in the context of transition to circular sustainable business models.
An approach framework of transfer learning, adversarial training and hierarchical multi-task learning - a case study of disinformation detection with offensive text

Abstract With society going online and disinformation getting accepted as a phenomena that we have to live with, there is a growing need to automatically detect offensive text on modern social media platforms. But the lack of enough balanced labeled data, constantly evolving socio-linguistic patterns and ever-changing definition of offensive text make it a challenging task. This is a common pattern witnessed in all disinformation detection tasks such as detection of propaganda, rumour, fake news, hate etc. The work described in this paper improves upon the existing body of techniques by bringing in an approach framework that can surpass the existing benchmarks. Firstly, it addresses the imbalanced and insufficient nature of available labeled dataset. Secondly, learning using relates tasks through multi-task learning has been proved to be an effective approach in this domain but it has the unrealistic requirement of labeled data for all related tasks. The framework presented here suitably uses transfer learning in lieu of multi-task learning to address this issue. Thirdly, it builds a model explicitly addressing the hierarchical nature in the taxonomy of disinformation being detected as that delivers a stronger error feedback to the learning tasks. Finally, the model is made more robust by adversarial training. The work presented in this paper uses offensive text detection as a case study and shows convincing results for the chosen approach. The framework adopted can be easily replicated in other similar learning tasks facing a similar set of challenges.
VAVM: A Flexible Technique for Variable-Angle Around View Monitor System Towards Articulated Engineering Vehicle

The around view monitor system (AVM) has been widely used in passenger vehicles. However, for articulated engineering vehicles (AEV), AVM is difficult to directly apply due to the long body and the articulation angle. In this paper, a flexible technique for variable-angle around view monitor system (VAVM) towards the AEV is proposed, which includes static calibration and dynamic execution. In the static calibration stage, the camera poses in the vehicle coordinate system with the joint of AEV as the coordinate origin can be calibrated using irregularly placed multiple checkerboards without knowing their relative position. This flexible calibration approach makes the variable-angle mapping from the fisheye image to the bird&#x2019;s eye view image can be directly obtained by updating the camera poses on the tractor according to the articulation angle when the AEV turns. In the dynamic execution stage, an optimization algorithm based on minimizing the photometric error is designed to reduce the cumulative error of the angle sensor. Finally, all the bird&#x2019;s eye view images are mosaiced and fused to a variable-angle around view image (VAVI). The experimental results show that the VAVM can output globally balanced and seamless VAVIs under different angles and visual environment conditions. Furthermore, the practicability of VAVM is also demonstrated by a real AEV.
Subjective Influence of Camera-Gaze Angular Offset

The study aimed to evaluate the difficulty in maintaining eye contact during a teleconference for different camera-gaze angular offsets. Videoconferencing systems may compromise the eye contact between participants due to imperfect angular alignment between the center of the screen and the camera. During a teleconference, difficulty maintaining eye contact may be perceived as uncomfortable or unsatisfying by participants in the call. An experiment deploying 33 test subjects was performed and evaluated. Groups of three test subjects performed a videoconference call. The effect of camera-gaze angular offset was quantified by assessing Effectiveness and Engagement ratings. It was shown that even 10 degrees of misalignment cause a statistically significant drop in Effectiveness for business-type teleconference calls. Camera-gaze angular offset is an important parameter to be considered during videoconferencing equipment design or setup. Higher misalignments may cause significant drops in perceived call quality (Quality of Experience). Our research results can be practical in conferencing equipment design and setup, modern telecommunication equipment design, workplace ergonomics, or distance learning equipment setup.
Behavioral Parameter Field for Human Abnormal Behavior Recognition in Low-Resolution Thermal Imaging Video

In recent years, thermal imaging cameras are widely used in the field of intelligent surveillance because of their special imaging characteristics and better privacy protection properties. However, due to the low resolution and fixed location for current thermal imaging cameras, it is difficult to effectively identify human behavior using a single detection method based on skeletal keypoints. Therefore, a self-update learning method is proposed for fixed thermal imaging camera scenes, called the behavioral parameter field (BPF). This method can express the regularity of human behavior patterns concisely and directly. Firstly, the detection accuracy of small targets under low-resolution video is improved by optimizing the YOLOv4 network to obtain a human detection model under thermal imaging video. Secondly, the BPF model is designed to learn the human normal behavior features at each position. Finally, based on the learned BPF model, we propose to use metric modules, such as cosine similarity and intersection over union matching, to accomplish the classification of human abnormal behaviors. In the experimental stage, the living scene of the indoor elderly living alone is applied as our experimental case, and a variety of detection models are compared to the proposed method for verifying the effectiveness and practicability of the proposed behavioral parameter field in the self-collected thermal imaging dataset for the indoor elderly living alone.
Exploiting the Structure Information of Suppositional Mesh for Unsupervised Multi-View Stereo

In this paper, we propose the Suppositional Mesh-based Unsupervised Multi-View Stereo Network (SMU-MVSNet), a conceptually innovative method that overcomes the challenges when encountering object occlusions and textureless areas by exploiting the intrinsic structure information of the target scene. Particularly, we propose the Suppositional Mesh (SM) to approximate the scene surface. Based on SM, on one hand, we propose the Single-view Occlusion Reasoning with which the occlusion masks can be efficiently generated to handle the scene occlusions. On the other hand, we design Vertex-face Normal Consensus loss with the goal of regularizing the geometric constraint for intractable scene areas. Moreover, we propose to conduct all our contributions on an upsampled high-resolution depth maps, which further tap the potentials of SMU-MVSNet. We demonstrate the effectiveness of components in isolation and in combination on the DTU dataset and show the excellent generalization ability on the Tanks and Temples benchmark without any fine-tuning.
Thai Language Tweet Emotion Prediction based on Use of Emojis

Thai Language can be handled/considered in the same group of Chinese and Japanese where no explicit spaces exist between words. This article presents a work on the emotional identification of tweets based on the use of emojis which focuses on a Thai language context. The use of emojis in user tweets indicates the writer’s emotions. The first phase of this study was to collect Thai tweets, clean them, and then to make a primary classification of the emojis into groups using K-mean clustering. These group clusters are used as target outputs for the prediction of emoji classes. It was found that 22 is the appropriate K for considering 70 emojis for a collected set of tweets. The corpus includes any level of Thai language usage, which means that the processed data can consist of suffixes, slang, and unknown word from tokenization process. The vector representation advances the unknown accent. In sum, this research created a corpus of short messages collected from Twitter which were grouped into 22 emoji-classes. The corpus includes 7,825,857 messages prepared for classification based on emotions by applying 2 biLSTM layers. A table of emojis is proposed based on Ekman’s six basic emotions: anger, disgust, fear, joy, sadness, and surprise were evaluated in both objective and subjective tests. The results show that word vectors work well for the classification of emotions through the use of emojis.
A Blockchain-Based Approach for Trust Management in Collaborative Business Processes

Blockchain is becoming a powerful technology for re-engineering collaborative business processes implemented on Web-based distributed systems, spanning across enterprises. On the blockchain, cross-organisation Web services orchestrated to form collaborative business processes can be transparently deployed as smart contracts. However, proper methods and tools are required to guide the process designer for exploiting the blockchain technology. To preserve data and business logics ownership and to ensure performance/cost tradeoff, only data and process activities requiring transparency and trust among the distributed process actors should be stored as transactions on the blockchain and deployed as smart contracts. In this paper, we propose a methodology and a tool that rely on methodological steps to support blockchain-based trust management in Web-based collaborative business processes originally designed according to a centralised BPM strategy. The methodology and the tool are grounded on a set of criteria, properly enforced with metrics, to identify trust-demanding elements to be considered for their deployment on the blockchain. The approach has been validated on a real case study of food quality certification in the biological domain.
An Advanced Recommendation System by Combining Popularity-Based and User-Based Collaborative Filtering Using Machine Learning

Finding the similarity is one of the critical rules of a recommender system. Popularity-based filtering offers to generalize recommendations to every user based on its popularity. Recommendation system or recommender engine is one of the most discussed topics in machine learning for business. There are three types of the commonly used recommender system. They are Popularity-based, Content-based, and Collaborative filtering-based. In content-based, it recommends a similar item based on a particular item, where collaborative filtering recommends an item to a person based on other equivalent persons’ interests. This research work has developed an algorithm that combines Popularity-based and User-based collaborative filtering using machine learning. In this paperwork, we applied textual clustering to keep the similar item precisely. Simultaneously, we tried to optimize our recommendation search time complexity. To evaluate the approaches, we used the Deskdrop dataset [6], and in the final stage, we used some distance matrix-like Pearson correlation to find similar users. The main lacking in general user-based collaborative filtering is its laziness. We have reduced the time complexity by applying textual clustering and file hashing to implement the data through machine learning.
Recognition of Facial Images via Self-Organizing Landmark Location with Approximate Graph Matching

The paper proposes a new approach toward face recognition using approximate graph matching on a self-organizing map (SOM). A set of Gabor filters are used to determine landmark points in facial images. Self-organization of the landmark points generates face graph for each image. A modified SOM algorithm (called Bi-SOM) is used for this purpose. An approximate graph matching technique is introduced to match the Bi-SOM face graphs of the images. The experiments are carried out on ORL and FERET databases to analyze the performance of the proposed technique.
Analysis Part of Speech Tagging Using Hidden Markov Model on Qur'an Data

Part of Speech (POS) tagging is a part of Natural Language Processing to determine word labels correctly in a sentence from a given word input automatically. The development of POS tagging techniques for Arabic is still little done. This study discusses POS tagging using the Hidden Markov Model method on the text of the Qur'an. In this study the dataset consists of three categories of data originating from the corpus of the Qur'an consisting of 150 simple sentences, 50 sentences with more than one S/P/O/K (compound sentences) and 50 verses of the Qur'an. choice (Complex Sentence). Experimental data was carried out using the K-Fold Cross Validation technique. The dataset is divided into two, namely training data and test data. The training data is used to find emissions and transition probabilities, while the data testing uses the Viterbi algorithm to determine the best tag for each word. The experimental results obtained an average accuracy of 89.44% for the first category, namely simple sentence data, 74.18% for the second category, namely compound sentences, and 69.04% for the third category of complex sentence data.
Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification

Abstract Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.