Reviewerly/exHarmony huggingface dataset:
Weighted Matrix Mapped CNN model for Optimizing the Sentiment Prediction

Sentiment analysis is now being adopted by ecommerce system to predict the user interest, objections and complains. The feedback system is used by these ecommerce systems to take the opinions of the users. The textual sentiment analysis algorithm is applied to identify the satisfaction layer of customer. The actual prediction of sentiment can help the sale of a product, firm or business to the significant improvements. In this paper, one such sentiment prediction model is presented to review the popular amazon products. It is the improved CNN model in which the weighted matrix mapping is applied to explore the feature strength. This improved feature framed CNN model is trained with a merged wider training set. The experimental evaluation is conducted over existing machine learning and deep learning models. The proposed model achieved an average accuracy of 92.52% for all datasets, whereas the existing LSTM, CNN, RF and MLP achieved average accuracy of 87.86%, 87.3%, 54.77% and 63.59% respectively. The proposed model has improved the accuracy and reliability of the sentiment prediction for product reviews.
Predicting Chinese Phrase-Level Sentiment Intensity in Valence-Arousal Dimensions With Linguistic Dependency Features

Phrase-level sentiment intensity prediction is difficult due to the inclusion of linguistic modifiers (e.g., negators, degree adverbs, and modals) potentially resulting in an intensity shift or polarity reversal for the modified words. This study develops a graph-based Chinese parser based on the deep biaffine attention model to obtain dependency structures and relations. These obtained dependency features are then used in our proposed Weighted-sum Tree GRU network to predict phrase-level sentiment intensity in the valence-arousal dimensions. Dependency parsing results using the Sinica Treebank indicate that our graph-based model outperforms transition-based methods such as MLP and stack-LSTM with identical findings for English dependency parsing. Experimental results on the Chinese EmoBank indicate that our Weighted-sum Tree GRU network model outperforms other transformer-based neural networks such as BERT, ALBERT, XLNET and ELECTRA, reflecting the effectiveness of linguistic dependencies in phrase-level sentiment intensity predication tasks. In addition, our proposed model requires fewer parameters and less inference time for quantitative analysis, making the proposed model is relatively lightweight and efficient.
SiamON: Siamese Tracking by Online Update Module and Non-local Pyramid Module

The core idea of the object tracker based on Siamese network and its derivative is to transform the tracking problem into a template matching problem, cross-correlation the extracted object features and the convolution features of the search area, and achieve a good balance in tracking speed and accuracy. However, with the deepening of feature extraction network, the feature map contains less details, which is not conducive to object location. Secondly, the Siamese network can not adapt to the change of the object and obtain the context characteristics of the object, which makes the tracking effect worse when the object is occluded and deformed. In order to solve the above problems, we propose a non-local Siamese network–SiamON. Firstly, the non-local pyramid module is used to fuse the low-level and high-level network features, so that the convoluted features contain more details and semantic information, and enhance the object representation. Secondly, by adding an online update module, the error accumulation caused by template update is avoided, and the ability of the tracker to adapt to object changes online is improved, so that the tracker has strong robustness in object emission deformation and occlusion. Finally, extensive experiments are carried out on seven tracking datasets. The experimental results show that our tracker is better than the latest tracker and can run in real time.
Multi-Object Tracking with Adaptive Cost Matrix

Multi-object tracking (MOT) aims at detecting and assigning identities for objects in videos. Complicated scenes, severe occlusions, irregular motions, and ambiguous appearances of objects hinder the further advance, which occurs frequently in pedestrian tracking. To tackle these challenges, we present a simple yet effective MOT framework focusing on two-fold: a more robust motion feature and a proper association paradigm. The Hybrid Motion Feature (HMF) integrates Intersection over Union (IoU), Euclidean distance metric, and area ratio information, and the latter two resort to the historical average variations to improve the cost matrix construction. Moreover, the new association paradigm, namely the Adaptive Calculation Method (ACM) performs better by avoiding the manual weighting of the motion and appearance-based cost matrix. In addition, the new correction method, i.e., reinitializing the state of the Kalman Filter when a severe mismatch occurs between the ground truth and the predicted trajectory, mitigates the effect of irregular motions. We achieved 80.7 MOTA, 78.5 IDF1, and 64.0 HOTA, outperforming the state-of-the-art model ByteTrack on the public MOT17 benchmark.
Extensions of Receding Horizon Task Assignment for Area Coverage in Dynamic Environments

This study presents a task reassignment strategy that can flexibly handle changes in dynamic environments. Most existing studies have only considered static environments where the mission is predetermined and unexpected events do not occur. However, in practice, the environment in which unmanned aerial vehicles perform their mission is complex and includes various pop-up events. Therefore, task reassignment is essential through re-optimization to adjust the previous plan, which is no longer optimal whenever dynamic events occur. The importance of this study is that the modified receding horizon task assignment algorithm is applied for accurate and efficient task reassignment. In addition, this study aims to provide a complete system architecture from operator input to motor commands for autonomous area coverage missions. Reliable performance validation is also performed through various simulations and a challenging outdoor flight experiment with real hardware implementation.
Towards Ontology-Based Validation of EA Principles

This research demonstrates the feasibility of using enterprise ontologies as representation formalism for automated validation of Enterprise Architecture (EA) principles. EA Principles are usually described in natural language, while the Enterprise Architecture is represented with graphical models. As both are intended for human interpretation, it requires humans to validate whether EA Principles are satisfied by an Enterprise Architecture. This is a complex and time-consuming process. We describe a step-by-step procedure for transforming the knowledge of Enterprise Architecture models and the vocabulary of EA Principles into the ArchiMEO ontology, which allows for automated validation. The approach is evaluated in two case studies. They confirm that an ontology-based validation of EA Principles is applicable in reality.
Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels

Matrix approximations are a key element in large-scale algebraic machine learning approaches. The recently pro-posed method MEKA [1] effectively employs two common assumptions in Hilbert spaces: the low-rank property of an inner product matrix obtained from a shift-invariant kernel function and a data compactness hypothesis by means of an inherent block-cluster structure. In this work, we extend MEKA to be applicable not only for shift-invariant kernels but also for non-stationary kernels like polynomial kernels and an extreme learning kernel. We also address in detail how to handle non-positive semi-definite kernel functions within MEKA, either caused by the approximation itself or by the intentional use of general kernel functions. We present a Lanczos-based estimation of a spectrum shift to develop a stable positive semi-definite MEKA approximation, also usable in classical convex optimization frameworks. Furthermore, we support our findings with theoretical considerations and a variety of experiments on synthetic and real-world data.
Secure Data Storage Scheme of Judicial System Based on Blockchain

Nowadays, the office systems of the judicial department are disconnected from each other, which is called "data silos". Under normal circumstances, judicial data is stored in the node, and it is the possibility of malicious modification. A judicial system based on blockchain can address these challenges. However, in the blockchain systems, each judicial node has a complete ledger, which also brings challenges to the secure storage of judicial data. Therefore, how to further enhance the security of judicial data on the blockchain is of great significance. With the study of the security of judicial blockchain data, the data security requirements of the system are analyzed, and a data security storage solution for judicial blockchain is proposed to enhance the storage security and availability of judicial data. Finally, the experiment is implemented by using cryptography and smart contract technology to prove its effectiveness.
Model Checking for Microarchitectural Data Sampling Security

A new class of transient execution-based attacks, as known as Microarchitectural Data Sampling (MDS), were discovered in recent studies, such as RIDL, Fallout, and ZombieLoad, which have imposed severe threats to general-purpose processors. In MDS attacks, the attacker can steal the information from privileged CPU internal buffers, such as store buffers, line fill buffers, and load ports. To prevent these buffers from leaking information, processor vendors proposed mitigation via microcode update that overwrites CPU internal buffers as soon as the privilege levels are changed. However, the problem if a given architecture is safe against MDS attacks is still to be fully solved.In this study, we propose a formal verification method based on model checking to verify the microarchitectural security against MDS attacks. We formulate the MDS attacks as operation sequences, and set up a behavior model to describe the microarchitectural design correlated MDS attacks. A operation sequence is considered as an MDS attack if the operation sequence can obtain transient illegal data leaked from internal buffers during the operations. We enumerate all the possible sequences by binary decision diagram (BDD)-based statechart traversal, and output all the counterexamples. The experimental results show that the proposed method can sufficiently check the MDS attacks, and a kind of attack is detected to be able to bypass the mitigation proposed by Intel, which was contemporarily proposed as CacheOut attack.
Improved Design of Bayesian Networks for Modelling Toxicity Risk in Breast Radiotherapy using Dynamic Discretization

This study investigates the dynamic discretization approach with the purpose of improving probabilistic causal models for the task of toxicity risk assessment in breast radiother-apy. We considered a probabilistic causal model such as Bayesian Networks, and implemented a modified version of Fenton & Neil's dynamic discretization algorithm to validate and analyze these models in terms of performance. Our implementation performs discretization at the data- or distribution-level. This approach is shown to provide significant improvement over static methods when assessing distribution fit via relative entropy error, as well as being very computationally efficient. Predictive performance was compared across four distinct datasets using Bayesian Networks, and dynamic discretization was not found to consistently outperform static techniques despite generating discretizations with significantly better fit to their underlying distributions.
An Efficient Soft Analytical Side-Channel Attack on Ascon

Lightweight cryptography is a subfield of cryptography, which is widely used in embedded systems, RFID, sensor networks, and so on. However, the leakage information during the operation of these IoT devices can be exploited by adversaries and subjected to side-channel attacks. Simultaneously, only a small number of previous works show these attacks. In this work, we perform the soft analytical side-channel attack (SASCA) on the encryption of Ascon. Since we construct a unique factor graph for Ascon, we can also use it to attack the masked implementations. The point of attack is the permutation function, one of Ascon’s most basic components. Our attack mainly consists of three steps. At the first, we run a side-channel template matching on the initialization phase. Then, we build a factor graph describing the intermediate computations in permutation, including the observed leakage for the intermediate variables. Third, we run a Belief Propagation (BP) algorithm that takes full advantage of these leakages. Through simulations, we show that the entire key can be successfully recovered by only using the leakage information of a few traces, and it also offers low time and memory complexity.
CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition

We describe a novel approach to indoor place recognition from RGB point clouds based on aggregating low-level colour and geometry features with high-level implicit semantic features. It uses a 2-stage deep learning framework, in which the first stage is trained for the auxiliary task of semantic segmentation and the second stage uses features from layers in the first stage to generate discriminate descriptors for place recognition. The auxiliary task encourages the features to be semantically meaningful, hence aggregating the geometry and colour in the RGB point cloud data with implicit semantic information. We use an indoor place recognition dataset derived from the ScanNet dataset for training and evaluation, with a test set comprising 3,608 point clouds generated from 100 different rooms. Comparison with a traditional feature-based method and four state-of-the-art deep learning methods demonstrate that our approach significantly outperforms all five methods, achieving, for example, a top-3 average recall rate of 75% compared with 41% for the closest rival method. Our code is available at: https://github.com/YuhangMing/Semantic-Indoor-Place-Recognition
Effects of Avatar Face Level of Detail Control on Social Presence in Augmented Reality Remote Collaboration

This study explored how avatar face Level of Detail (LOD) and task types affect users' social presence and interpersonal communication in Augmented Reality remote collaboration. In AR remote collaboration, users are represented as avatars, and an actual user's appearance inevitably changes. While certain situations require an avatar that resembles a user, it is challenging to reconstruct everything realistically due to its high cost. Since facial parts show relative significance compared to body parts, we proposed controlling face LOD as an approach to lowering the cost while maintaining social presence in AR environments. Our results showed that while Mid-LOD maintains social presence but reduces costs compared to High-LOD, Low-LOD negatively impacts social presence mainly due to emotional understanding. Based on our findings, we highlight the importance of 1) considering facial expressions and emotional understanding in managing face LOD, 2) figuring out the collaboration context with different effects of tasks and communication behaviors, and 3) providing additional communication cues for maintaining social presence while lowering the cost.
Decoupling Consensus and Storage in Consortium Blockchains by Erasure Codes

In this paper, we propose a novel framework in consortium blockchains, where the consensus and storage can be decoupled. For consensus, we propose a simplified version of Sinc HotStuff, in which the block equivocation can be avoided. With the help of a distributed storage system employing an erasure code, the storage is asymptotically constant on the size of the total blocks, but independent on the number of consensus nodes, such that the storage of the blockchain is reduced significantly. In addition, the storage system with (N, k) maximum-distance separable (MDS) codes has the ability to handle <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> - <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> crashes or N- k/2 Byzantine errors such that the new framework is more flexible.
Online Learning and the Pedagogy of Resilience, Agency and Protest: Lessons from the COVID-19 Experience

The global Covid-19 pandemic continues unabated with educational systems operating in crisis mode in an attempt to keep learning and teaching going through the various levels of lockdowns and the shutdowns of educational institutions. The ramifications of the pandemic cut across all sectors of society thwarting endeavours to achieve the Sustainable Development Goals. This chapter presents a critical overview of some of the challenges that impacted on the education sector. Notwithstanding these challenges, the chapter tells a positive story of students forming new digital communities that provide ways of overcoming the lack of a social presence and who show resilience in the face of hardship. Amidst the near calamity is a positive story of goodwill, ubuntu and resilience. The chapter reflects on the development of collective agency and resilience engendered in the digital space, enabling students to navigate the crises and supporting the momentum of learning. It focuses on the way in which the monitoring of the social media enables institutional responsiveness in catering for more than 400,000 university students and concludes with some reflections on education beyond the pandemic.
Research on YOLOv5 Vehicle Object Detection Algorithm Based on Attention Mechanism

In intelligent transportation systems, the study of vehicle object detection is of great importance. In order to improve the performance of object detection, an improved method based on YOLOv5 is used for vehicle object detection, and a YOLOv5x vehicle multi-object detection optimization algorithm based on attention mechanism is proposed by adding attention mechanism modules to different modules of the network for network optimization, and the algorithm is validated on the traffic dataset constructed in the paper to show the feasibility of the improved strategy. According to the experimental results, it can be seen that the improved detection algorithm improves the mAP by more than 10% compared with the original detection algorithm, which is better than the original detection algorithm.
Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation.We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://bit.ly/3jN1pJJ.
Memory-Token Transformer for Unsupervised Video Anomaly Detection

Video anomaly detection is crucial for behavior analysis, which has witnessed continuous progress in recent years with the auto-encoder based reconstruction framework. However, in some cases, abnormal frames may also be reconstructed well due to the strong representation ability of deep networks, increasing missed detection. To mitigate this issue, the existing methods usually the memory bank method. This method records normal patterns and assigns high errors for the reconstruction of abnormal frames into normal frames. In this paper, to better use the semantic information of normal videos recorded in the memory module, we introduce the Memory-Token Transformer (MTT) to boost the reconstruction performance on normal frames. We assume that the anomalies in a video mainly concentrate on the regions containing people and relevant objects. Therefore, during the decoding stage, we first extract the semantic concepts of a feature map and generate the corresponding semantic tokens. Then the tokens are combined with the proposed memory module. Last, we introduce a transformer to fuse the complex relationship among different tokens, and use 3D convolution with the pooling operator in our encoder to enhance spatio-temporal feature extraction as compared with 2D models. The experimental results obtained on various benchmarks demonstrate the effectiveness of the proposed method.
Generated Pseudo-Labels Guided by Background Skeletons for Overcoming Under-Segmentation in Overlapping Particle Objects

Unlike general image segmentation, highly complex particle images have significant challenges in labeling and segmentation due to the information occlusion and texture disturbance. Aiming at the highly under-segmentation problem caused by complex particle image segmentation, this paper proposes a Semi-supervised Hybrid-training Particle Segmentation framework (SHPS) based on skeleton-guided pseudo-labels. First, a pre-trained model is obtained by training a popular segmentation algorithm on partially labeled data. Then, a Background Skeleton-guided Pseudo-label generation algorithm (BSP) is proposed to generate pseudo-labels closer to the ground truth in terms of structural integrity based on coarse segmentation. The final segmentation model is obtained by training a mixed dataset consisting of labeled data and pseudo-labels from another partition on the pre-trained model. The skeleton differences of pseudo-labels and coarse segmentation are added to the loss function. Experimental results show that our method achieves 84.4% accuracy on mIoU with uniform label data distribution, which is 2.1% higher than the accuracy of UNet and reduces the degree of under-segmentation.