Reviewerly/exHarmony huggingface dataset:
Chinese RoBERTa Distillation For Emotion Classification

Abstract Through knowledge distillation method, a student model can imitate the output of a teacher model to improve its generalization ability without changing the computational complexity. However, in existing knowledge distillation research, the efficiency of knowledge transfer is still not satisfactory, especially from pre-trained language models (PTMs) like Robustly optimized BERT approach (RoBERTa) to another structure student model. To address this issue, this paper proposes a prediction framework (RTLSTM) for Chinese emotion classification based on knowledge distillation. In RTLSTM, a new triple loss strategy is proposed for training a student ‘BiLSTM’, which combines supervised learning, distillation and word vector losses. This strategy enables the student to learn more fully from a teacher model RoBERTa and retains 99% of the teacher models’ language understanding capability. We carried out emotion classification experiments on five Chinese datasets to compare RTLSTM with baseline models. The experiment results show that RTLSTM outperforms the baseline models belonging to the RNN group in terms of prediction performance under similar numbers of parameters. Moreover, RTLSTM is superior to the PTMs group baseline models through 92% fewer parameters and 83% less prediction time under comparable prediction performance.
A Unified Strategy for Formal Derivation and Proof of Binary Tree Nonrecursive Algorithms

In the formal derivation and proof of binary tree algorithms, Dijkstra's weakest predicate method is commonly used. However, the method has some drawbacks, including a time-consuming derivation process, complicated loop invariants, and the inability to generate executable programs from the specification. This paper proposes a unified strategy for the formal derivation and proof of binary tree non-recursive algorithms to address these issues. First, binary tree problem solving sequences are decomposed into two types of recursive relations based on queue and stack, and two corresponding loop invariant templates are constructed. Second, high-reliability Apla (abstract programming language) programs are derived using recursive relations and loop invariants. Finally, Apla programs are converted automatically into C++ executable programs. Two types of problems with binary tree queue and stack recursive relations are used as examples, and their formal derivation and proof are performed to validate the proposed strategy's effectiveness. This strategy improves the efficiency and correctness of binary tree algorithm derivation.
A genetically-optimised artificial life algorithm for complexity-based synthetic dataset generation

Algorithmic evaluation is a vital step in developing new approaches to machine learning and relies on the availability of existing datasets. However, real-world datasets often do not cover the necessary complexity space required to understand an algorithm’s domains of competence. As such, the generation of synthetic datasets to fill gaps in the complexity space has gained attention, offering a means of evaluating algorithms when data is unavailable. Existing approaches to complexity-focused data generation are limited in their ability to generate solutions that invoke similar classification behaviour to real data. The present work proposes a novel method (Sy:Boid) for complexity-based synthetic data generation, adapting and extending the Boid algorithm that was originally intended for computer graphics simulations. Sy:Boid embeds the modified Boid algorithm within an evolutionary multi-objective optimisation algorithm to generate synthetic datasets which satisfy predefined magnitudes of complexity measures. Sy:Boid is evaluated and compared to labelling-based and sampling-based approaches to data generation to understand its ability to generate a wide variety of realistic datasets. Results demonstrate Sy:Boid is capable of generating datasets across a greater portion of the complexity space than existing approaches. Furthermore, the produced datasets were observed to invoke very similar classification behaviours to that of real data.
Differentially private generative decomposed adversarial network for vertically partitioned data sharing

This paper considers the problem of differentially private vertically partitioned data sharing. In particular, with the assistance of a semi-honest curator, the involved parties (i.e., data owners) each holds records regarding different features of the same set of individuals collectively generate a shared dataset while satisfying differential privacy. Motivated by the superiority of the generative adversarial network (GAN) in data synthesizing, we present a differentially private generative decomposed adversarial network (DPGDAN) approach for vertically partitioned data sharing. In DPGDAN, the discriminator in the initial GAN is decomposed into several local discriminators and two relational discriminators, i.e., a real relational discriminator and a fake relational discriminator. Each party is assigned with a local discriminator while the curator holds the generator and the two relational discriminators. By combining the sanitized feedback from the local discriminators and the outputs of the relational discriminators, the curator can update the generator to approximate the distribution of the integrated dataset without compromising each party’s privacy. Moreover, to promote the shared data’s utility, we design a snapshot aggregation method by aggregating the synthetic records produced by the generators acquired during training. Furthermore, we show that DPGDAN satisfies (∊,δ)- differential privacy. Extensive experiments validate the effectiveness of DPGDAN.
Proposed hybrid encryption framework for robust 3D image communication over wireless channels

different cryptosystems have been presented to protect images, videos, and data against several types of attacks. These encryption algorithms use small and medium keys with small and medium spaces, which limit the overall security. In this paper, a novel hybrid encryption framework depending on the Rubik’s cube algorithm is suggested to implement different encryption algorithms, Methods; The suggested hybrid encryption framework begins with Cipher Feed Back (CFB) mode for chaotic Baker map encryption which belongs to the permutation family, Advanced Encryption Standard (AES) and Ron's Code (RC6) technique as a first stage of encrypting for multiple images, separately. Then, in a second stage, the resulting images are randomized again with a Rubik’s cube technique. Chaotic, RC6 or AES encrypted images are utilized on the Rubik’s cube faces. From the image encryption concepts, the chaotic, AES, RC6, and Rubik’s cube technique introduce a degree encryption which is a mix of permutation and diffusion technique. The images after the encryption process are further transmitted through a wireless communication channel with different versions of Orthogonal Frequency Division Multiplexing (OFDM). Decryption is performed at the receiver side. The quality evaluation of the decrypted images at the receiver records good performance, especially when comparing with classical algorithms. The simulation results show that the suggested hybrid encryption framework presents strong security and robustness.
STV+AGR: Towards Verification of Strategic Ability Using Assume-Guarantee Reasoning

We present a substantially expanded version of the open source tool STV for strategy synthesis and verification of strategic abilities. The new version provides a web interface and support for assume-guarantee verification of multi-agent systems.
The spatial frequency domain designated watermarking framework uses linear blind source separation for intelligent visual signal processing

This paper develops a digital watermarking algorithm using an informed watermark retrieval architecture. The developed method uses the fractional Fourier transform to embed the watermark in the space-frequency domain and extracts the watermark using blind source separation techniques. The watermark embedding is further enhanced using a heuristic algorithm to increase the strength of the watermarking system. We use genetic algorithm to find the optimal fractional domain by minimizing the coefficient of RMSE between the input image and the watermarked image. The algorithm's performance against various common attacks, e.g., JPEG compression and Gaussian noise, is presented to estimate the algorithm's robustness.
An international collaborative approach to learning histology using a virtual microscope

Histology is often taught in higher education settings using online virtual microscopes (VM). This study aimed to develop and evaluate the use of VM in teaching on a BSc degree at the University of Nottingham by surveying students and staff. A key development was the use of an e-workbook so that students were actively engaged in creating their own bespoke revision material. Subsequently, this approach was used in a second study evaluating the use of VM in teaching the histology and pathology of the gastrointestinal (GI) tract via group work with students from two BSc courses at the University of Nottingham; one based at Derby (RDHC) and the other in Malaysia (UNMC). Students worked together in groups to complete an e-workbook, develop a presentation, and decide how to collaborate and communicate. An evaluation of these activities revealed advantages in developing transferrable skills, and good engagement with both the histology topic and group work. Analysis of assessment of the module at UNMC showed that student performance improved in the histology-based module after the intervention (p < 0.01) and that this improvement was not evident in other modules taken by the cohort. Furthermore, when interrogating the questions from the examination paper that asked students to identify features from histological images, fewer questions were seen as 'difficult' (p < 0.001) and more were seen as 'average' (p < 0.01). This study demonstrates that the use of VM in histology combined with active learning in creating a revision resource enhances engagement and depth of learning. When further combined with collaborative active group work, students developed a range of histology knowledge and transferrable skills, with notable improvement in examination performance relative to other contemporaneous modules.
Sidecar

In response to ossification and privacy concerns, post-TCP transport protocols such as QUIC are designed to be "paranoid"---opaque to meddling middleboxes by encrypting and authenticating the header and payload---making it impossible for Performance-Enhancing Proxies (PEPs) to provide the same assistance as before. We propose a research agenda towards an alternate approach to PEPs, creating a sidecar protocol that is loosely-coupled to the unchanged and opaque, underlying transport protocol. The key technical challenge to sidecar protocols is how to usefully refer to the packets of the underlying connection without ossification. We have made progress on this problem by creating a tool we call a quACK (quick ACK), a concise representation of a multiset of numbers that can be used to efficiently decode the randomly-encrypted packet contents a sidecar has received. We implement the quACK and discuss how to achieve several applications with this approach: alternate congestion control, ACK reduction, and PEP-to-PEP retransmission across a lossy subpath.
Measuring the impact of health research data in terms of data citations by scientific publications

Health is a representative domain data-driven research since health research data are growingly generated at a massive scale. There is an intuitive logic that the degree to which disease burden and the number of data resources align. In order to figure out disease-specific data sharing and reuse level, we took the number of data records and their citations in the scientific literature in the Data Citation Index platform as approximate indicators. The results indicated that only a small percentage (7.5%) of health data records had received documented citations by scientific publications. We find the level of data sharing and reuse varies across diseases. Our study suggested that the more socioeconomic burden and the more research funding, the more likely scientific data for diseases will be produced and made available. But such a correlation could not be observed for the activity of data reuse. Secondary reuse of scientific data is a complex behavior.
LPNet: A lightweight CNN with discrete wavelet pooling strategies for colon polyps classification

Abstract The traditional process of disease diagnosis from medical images follows a manual process, which is tedious and arduous. A computer‐aided diagnosis (CADs) system can work as an assistive tool to improve the diagnosis process. In this pursuit, this article introduces a unique architecture LPNet for classifying colon polyps from the colonoscopy video frames. Colon polyps are abnormal growth of cells in the colon wall. Over time, untreated colon polyps may cause colorectal cancer. Different convolutional neural networks (CNNs) based systems have been developed in recent years. However, CNN uses pooling to reduce the number of parameters and expand the receptive field. On the other hand, pooling results in data loss and is deleterious to subsequent processes. Pooling strategies based on discrete wavelet operations have been proposed in our architecture as a solution to this problem, with the promise of achieving a better trade‐off between receptive field size and computing efficiency. The overall performance of this model is superior to the others, according to experimental results on a colonoscopy dataset. LPNet with bio‐orthogonal wavelet achieved the highest performance with an accuracy of 93.55%. It outperforms the other state‐of‐the‐art (SOTA) CNN models for the polyps classification task, and it is lightweight in terms of the number of learnable parameters compared with them, making the model easily deployable in edge devices.
Quality Evaluation for Reconstructing Chaotic Attractors

Dynamical systems are used in various applications, and their simulation is related with the type of mathematical operations used in their construction. The quality of the system is evaluated in terms of reconstructing the system, starting from its final point to the beginning (initial conditions). Deciphering a message has to be without loss, and this paper will serve to choose the proper dynamical system to be used in chaos-based cryptography. The characterization of the chaotic attractors is the most important information in order to obtain the desired behavior. Here, observability and singularity are the main notions to be used for introducing an original term: quality observability index (q.o.i.). This is an original contribution for measuring the quality of the chaotic attractors. In this paper, the q.o.i. is defined and computed in order to confirm its usability.
LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators

Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN). However, its generator has a limitation in producing fake data distributions that are similar to real data distributions. Since the real data distribution is frequently changing, the generator could not create adequate fake data. To overcome this problem, we present a novel approach for semi-supervised learning for text classification based on generative adversarial networks, Linguistically Informed SeMi-Supervised GAN with Multiple Generators, LMGAN. LMGAN uses trained bidirectional encoder representations from transformers (BERT) and the discriminator from GAN-BERT. In addition, LMGAN has multiple generators and utilizes the hidden layers of BERT. To reduce the discrepancy between the distribution of fake data and real data distribution, LMGAN uses fine-tuned BERT and the discriminator from GAN-BERT. However, since injecting fine-tuned BERT could induce incorrect fake data distribution, we utilize linguistically meaningful intermediate hidden layer outputs of BERT to enrich fake data distribution. Our model shows well-distributed fake data compared to the earlier GAN-based approach that failed to generate adequate high-quality fake data. Moreover, we can get better performances with extremely limited amounts of labeled data, up to 20.0%, compared to the baseline GAN-based model.
3D Model Retrieval Algorithm Based on DSP-SIFT Descriptor and Codebook Combination

Recently, extensive research efforts have been dedicated to view-based 3D object retrieval, owing to its advantage of using a set of 2D images to represent 3D objects. Some existing image processing technologies can be employed. In this paper, we adopt Bag-of-Words for view-based 3D object retrieval. Instead of SIFT, DSP-SIFT is extracted from all images as object features. Moreover, two codebooks of the same size are generated by approximate k-means. Then, we combine two codebooks to correct the quantization artifacts and improve recall. Bayes merging is applied to address the codebook correlation (overlapping among different vocabularies) and to provide the benefit of high recall. Moreover, Approximate Nearest Neighbor (ANN) is used to quantization. Experimental results on ETH-80 datasets show that our method improves the performance significantly compared with the state-of-the-art approaches.
Co-Saliency Detection of RGBD Image Based on Superpixel and Hypergraph

For the co-saliency detection algorithm of an RGBD image that may have incomplete detection of common salient regions and unclear boundaries, we proposed an improved co-saliency detection method of RGBD images based on superpixels and hypergraphs. First, we optimized the depth map based on edge consistency, and introduced the optimized depth map into the SLIC algorithm to obtain the better superpixel segmentation results of RGBD images. Second, the color features, optimized depth features and global spatial features of superpixels were extracted to construct a weighted hypergraph model to generate saliency maps. Finally, we constructed a weighted hypergraph model for co-saliency detection based on the relationship of color features, global spatial features, optimized depth features and saliency features among images. In addition, in order to verify the impact of the symmetry of the optimized depth information on the co-saliency detection results, we compared the proposed method with two types of models, which included considering depth information and not considering depth information. The experimental results on Cosal150 and Coseg183 datasets showed that our improved algorithm had the advantages of suppressing the background and detecting the integrity of the common salient region, and outperformed other algorithms on the metrics of P-R curve, F-measure and MAE.
OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are used widely for data collection in wireless sensor networks (WSNs). UAVs visit the sensors to collect the data. UAV-aided data collection is a challenging problem because different paths of a UAV, i.e., visiting orders of sensors, affect energy consumption and data delivery times. The problem becomes more difficult when there are obstacles in the path of the UAV. Thus, the UAV needs to take a detour to avoid them, resulting in different travel distances and times. Therefore, this study formulated the obstacle-aware path planning problem of UAVs, i.e., the obstacle-constrained distance minimization (OCDM) problem, as an integer linear programming problem (ILP) to minimize the total traveling distances of all UAVs while considering the UAVs’ flight time constraints. First, a possible detour-points-selection algorithm called vector rotation-angle-based obstacle avoidance (VRAOA) is proposed to find the detour points around each obstacle in the environment. Then, a genetic algorithm with VRAOA (GA w/VRAOA)is developed to find the trajectories of the UAVs, using the VRAOA and Dijkstra algorithm to find a detour path if there is an obstacle between any two sensors. Finally, simulations were performed for algorithm variants, where GA w/VRAOA outperformed others.
LPCL: Localized prominence contrastive learning for self-supervised dense visual pre-training

Self-supervised pre-training has attracted increasing attention given its promising performance in training backbone networks without using labels. By far, most methods focus on image classification with datasets containing iconic objects and simple background, e.g. ImageNet. However, these methods show sub-optimal performance for dense prediction tasks (e.g. object detection and scene parsing) when directly pre-training on datasets (e.g. PASCAL VOC and COCO) with multiple objects and cluttered backgrounds. Researchers explored self-supervised dense pre-training methods by adapting recent image pre-training methods. Nevertheless, they require a large number of negative samples and a long training time to reach reasonable performance. In this paper, we propose LPCL, a novel self-supervised representation learning method for dense predictions to settle these issues. To guide the instance information in multi-instance datasets, we define an online object patch selection module to select the local patches with the high possibility of containing instance area in the augmented views efficiently during learning. After obtaining the patches, we present a novel multi-level contrastive learning method considering the instance representation of global-level, local-level and position-level without using negative samples. We conduct extensive experiments with LPCL directly pre-trained on PASCAL VOC and COCO. For PASCAL VOC image classification task, our model achieves state-of-the-art 86.2% accuracy pre-trained on COCO(+9.7% top-1 accuracy compared with baseline BYOL). On object detection, instance segmentation and semantic segmentation task, our proposed model also achieved competitive results compared with other state-of-the-art methods.
Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).