Reviewerly/exHarmony huggingface dataset:
Multi-class classification of colorectal cancer tissues using pre-trained CNN models

Colorectal cancer (CRC) is a significant public health concern worldwide. Its early detection is critical since it dictates treatment options and significantly impacts survival time. A pathologist can make a histological diagnosis based on histological images obtained from a colonoscopy biopsy. The traditional visual assessment is time-consuming and highly unreliable because of the subjectivity of the evaluation. On the other hand, current approaches primarily rely on the use of diverse combinations of textual features and classifiers, as well as transfer learning, to classify distinct organizational types. However, the classification remains difficult since histological pictures comprise various tissue types and properties. In this work, we propose a deep learning technique based on pre-trained Convolutional Neural Networks (CNNs) for distinguishing eight classes of adenocarcinomas from healthy tissues. We explored multiple CNN architectures such as RESNET50, INCEPTIONV3, VGG 16, VGG19, RESNET152V2 and heuristically searched the best architecture for CRC detection by varying different optimizers. It was found that INCEPTIONV3 is the best performing model with 89.8% average accuracy for eight CRC classes.
Enhanced U-Net for Computer-aided Diagnosis with Cetacean Postmortem CT Scan

Postmortem computed tomography (PMCT) scan has long been used in the postmortem examination of cetaceans on Virtopsy to find out the cause of death such as diseases on internal organs. However, manual diagnosis using PMCT scans is labour-intensive and time consuming. Driven by the recent advances in deep learning, in this project, we develop a computer-aided diagnosis system based on U-Net, a convolutional neural network, for cetaceans. In addition, we propose two loss functions to resolve the bias due to the skewed distribution of the different areas in the PMCT-scans, thereby improving the system accuracy significantly.
Creducate: Blockchain-based Academic Record Management and Verification System Built in the Solana Network

Academic records are necessary requirements for employment and further studies. Despite being critical infor-mation that assesses one's eligibility in different disciplines, credentials are oftentimes difficult to acquire and inefficient to handle. Effectively, these manual and centralized processes make academic records vulnerable to fraudulent activities such as tampering and falsification. This project designed an academic record management and verification system using blockchain technology which offers enhanced security, immutability, trans-parency, and process improvement. Unlike previous works that used Bitcoin, Ethereum, or Hyperledger as their blockchain network, this project implemented its decentralized application (dApp) in the Solana network due to its faster processing speed, low-cost transaction fees, and expanding ecosystem. The dApp covers the generation, issuance, and verification of the most commonly requested credentials-the Transcript of Records (TORs) and diplomas. The intended users of this application are the universities, students, and third party entities that need to verify academic credentials for employment or other reasons. It was identified that the blockchain-based system provides a better means to (1) preserve the integrity of academic records, (2) transact between record handlers and requestors and (3) execute the processes of record generation and verification.
MFDN: Multiception Feature Distillation Network

Image super-resolution refers to obtaining high-resolution images with refined details and enhanced visual quality from low-resolution inputs with coarse details. Because of its wide range of applications, image super-resolution has attracted the attention of researchers in the image processing and computer vision community. Even though CNN and GAN-based approaches are quite successful, it is unfeasible to use them in edge devices due to their heavy computational complexity. This work is our attempt in this direction to develop a lightweight image super-resolution model which uses multiception convolution layers for feature distillation. Our inspiration in this work was Residual Feature Distillation Network (RFDN), where we substantially decreased the parameters while maintaining the PSNR metric. The proposed approach was able to achieve image super-resolution with enhanced visual quality as compared to the baseline approach.
Role of Behavioural Factors in Cyber Threat Intelligence

Cyber-attacks behave differently based on different targets. Therefore, the behavioural factor is vital to understanding these attacks. This paper aims to study the influence of behavioural factors in deriving cyber threat intelligence and Approach.
Combating high variance in Data-Scarce Implicit Hate Speech Classification

Hate speech classification has been a long-standing problem in natural language processing. However, even though there are numerous hate speech detection methods, they usually overlook a lot of hateful statements due to them being implicit in nature. Developing datasets to aid in the task of implicit hate speech classification comes with its own challenges; difficulties are nuances in language, varying definitions of what constitutes hate speech, and the labor-intensive process of annotating such data. This had led to a scarcity of data available to train and test such systems, which gives rise to high variance problems when parameter-heavy transformer-based models are used to address the problem. In this paper, we explore various optimization and regularization techniques and develop a novel RoBERTa-based model that achieves state-of-the-art performance.
Synchronous Boolean Finite Dynamical Systems on Directed Graphs over XOR Functions

In this paper, we investigate the complexity of a number of computational problems defined on a synchronous boolean finite dynamical system, where update functions are chosen from a template set of exclusive-or and its negation. We first show that the reachability and path-intersection problems are solvable in logarithmic space-uniform AC1 if the objects execute permutations, while the reachability problem is known to be in P and the path-intersection problem to be in UP in general. We also explore the case where the reachability or intersection are tested on a subset of objects, and show that this hardens complexity of the problems: both problems become NP-complete, and even ${\Pi }^{p}_{2}$ -complete if we further require universality of the intersection. We next consider the exact cycle length problem, that is, determining whether there exists an initial configuration that yields a cycle in the configuration space having exactly a given length, and show that this problem is NP-complete. Lastly, we consider the t-predecessor and t-Garden of Eden problem, and prove that these are solvable in polynomial time even if the value of t is also given in binary as part of instance, and the two problems are in logarithmic space-uniform NC2 if the value of t is given in unary as part of instance.
DRFENet: An Improved Deep Learning Neural Network via Dilated Skip Convolution for Image Denoising Application

Deep learning technology dominates current research in image denoising. However, denoising performance is limited by target noise feature loss from information propagation in association with the depth of the network. This paper proposes a Dense Residual Feature Extraction Network (DRFENet) combined with a Dense Enhancement Block (DEB), a Residual Dilated Block (RDB), a Feature Enhancement Block (FEB), and a Simultaneous Iterative Reconstruction Block (SIRB). The DEB uses our proposed interval transmission strategy to enhance the extraction of noise features in the initial stage of the network. The RDB module uses a combination strategy of concatenated dilated convolution and a skip connection, and the local features are amplified through different perceptual dimensions. The FEB enhances local feature information. The SIRB uses an attention block to learn the noise distribution while using residual learning (RL) technology to reconstruct a denoised image. The combination strategy in DRFENet makes the neural network deeper to obtain higher fine-grained image information. We respectively examined the performance of DRFENet in gray image denoising on datasets BSD68 and SET12 and color image denoising on datasets McMaster, Kodak24, and CBSD68. The experimental results showed that the denoising accuracy of DRFENet is better than most existing image-denoising methods under PSNR and SSIM evaluation indicators.
Complete solution for vehicle Re-ID in surround-view camera system

Abstract Vehicle re-identification (Re-ID) is a critical component of the autonomous driving perception system, and research in this area has accelerated in recent years. However, there is yet no perfect solution to the vehicle re-identification issue associated with the car’s surround-view camera system. Our analysis identifies two significant issues in the aforementioned scenario: (1) It is difficult to identify the same vehicle in many picture frames due to the unique construction of the fisheye camera. (2) The appearance of the same vehicle when seen via the surround vision system’s several cameras is rather different. To overcome these issues, we suggest an integrative vehicle Re-ID solution method. On the one hand, we provide a technique for determining the consistency of the tracking box drift with respect to the target. On the other hand, we combine a Re-ID network based on the attention mechanism with spatial limitations to increase performance in situations involving multiple cameras. Finally, our approach combines state-of-the-art accuracy with real-time performance. We will soon make the source code and annotated fisheye dataset available.
Predictive Uncertainty Quantification of Deep Neural Networks using Dirichlet Distributions

Advancements in deep neural networks have made it a prominent approach for most of the complex computer vision tasks. A key aspect for the deployment of deep neural networks in several applications, like automotive and medical, has been its ability to estimate its uncertainty. A recent leading approach is using Dirichlet distributions to model the uncertainty, which results in real-time estimation of uncertainty. The intermediate layer variational inference has also been a promising approach to-enable real-time estimation of uncertainty, beating state-of-the-art approaches. In this work we introduce the incorporation of both approaches in order to improve the reliability of uncertainty estimation whilst maintaining the real-time capability. Our experiments on the Cityscapes dataset for the task of semantic segmentation showed a significant boost in the deep neural network’s uncertainty estimation capability, whilst also improving its segmentation performance.
More Secure Collaborative APIs resistant to Flush-Based Cache Attacks on Cortex-A9 Based Automotive System

Flush-based cache attacks seriously threaten the security of automotive system based on ARM Cortex-A9 MPCore. Most of the proposed defense schemes have limited detection capabilities or can’t detect the malicious attacks fast enough. The method of permanently reducing the resolution of all time APIs in the system is not feasible because the high resolution time API is required for normal running of various applications. In this paper, we propose two more secure collaborative APIs—SafeFlush and SafeTime. In addition to the basic function of flushing a cache line, SafeFlush can also detect and handle the suspected flush-based cache attack process. More importantly, SafeFlush collaborates with SafeTime to effectively resist all flush-based cache attacks. That SafeTime adaptively reduces its resolution for a short time based on the signal sent by SafeFlush makes attacks fail. The attack experiment results show that the success rate of Flush+Reload and flush-based Spectre attacks using SafeFlush and SafeTime APIs can be reduced to less than 1%. Performance experiment results show that the access latency of SafeTime based on global timer is 14.5% slower than the original API and 18% slower based on PMCCNTR. The time consumption of SafeFlush is about 25.2% longer than the original cache flush API. Since SafeFlush and SafeTime are far more secure than the original APIs, their performance loss is acceptable.
Noise Removal from Images Using Adaptive Neuro/Network-Fuzzy Interface Systems

Any Information signal is best desirable without any external noise/ disturbances. Noise in any signal is the undesirable quantity present which deteriorates the signal's quality, thus compromising the information. Any signal, be it an image signal (2-D) or else a video signal (3-D) in the field of communication, if not always but most number of times prone to noise. In this paper, we would be dealing with removing types of noise on an image, using various filter techniques such as vector median filter, vector directional filter. Using the image processing tools in MATLAB, we could achieve this quite effortlessly. Looking at the prior approaches and keeping those factors in understanding, this paper would intend to path a more thoughtful way to bifurcate the image from its noise using the techniques of the neural networks. With thorough scrutiny and understanding of filters, this paper ensemble the performance of filters, through which we would be applying the most suitable filter out of the lot with the neural networks.
Self-supervised monocular depth estimation in fog

Self-supervised depth estimation has achieved remarkable results in sunny weather. However, in the foggy scenes, their performance is limited because of the low contrast and limited visibility caused by the fog. To address this problem, an end-to-end feature separation network for self-supervised depth estimation of fog images is proposed. We take paired clear and synthetic foggy images as input, separate the image information into interference information (illumination, fog, etc.) and invariant information (structure, texture, etc.) by a feature extractor with orthogonality loss. The invariant information is used to estimate depth. Meanwhile, similarity loss is introduced to constrain the fog image depth using the depth of the clear image as a pseudo-label, and an attention module and reconstruction loss are added to refine the output depth, so that better depth maps can be obtained. Then, real-world fog images are used for fine-tuning, which effectively reduces the domain gap between synthetic data and real data. Experiments show that our approach produces advanced results on both synthetic datasets and Cityscape datasets, demonstrating the superiority of our approach.
Persian Named Entity Recognition by Gray Wolf Optimization Algorithm

Named entity recognition (NER) is a subfield of natural language processing (NLP). It is able to identify proper nouns, such as person names, locations, and organizations, and has been widely used in various tasks. NER can be practical in extracting information from social media data. However, the unstructured and noisy nature of social media (such as grammatical errors and typos) causes new challenges for NER, especially for low-resource languages such as Persian, and existing NER methods mainly focus on formal texts and English social media. To overcome this challenge, we consider Persian NER as an optimization problem and use the binary Gray Wolf Optimization (GWO) algorithm to segment posts into small possible phrases of named entities. Later, named entities are recognized based on their score. Also, we prove that even human opinion can differ in the NER task and compare our method with other systems with the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">p</mi> <mo>_</mo> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">D</mi> <mo>_</mo> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">l</mi> <mn>01</mn> </math> dataset and the results show that our proposed system obtains a higher F1 score in comparison with other methods.
All minimal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e22" altimg="si7.svg"><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mn>9</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>-codes are hyperbolic quadrics

Minimal codes are being intensively studied in last years. [n,k]q-minimal linear codes are in bijection with strong blocking sets of size n in PG(k−1,q) and a lower bound for the size of strong blocking sets is given by (k−1)(q+1)≤n. In this note we show that all strong blocking sets of length 9 in PG(3,2) are the hyperbolic quadrics Q+(3,2).
Workload-Balanced Graph Attention Network Accelerator with Top-K Aggregation Candidates

Graph attention networks (GATs) are gaining attention for various transductive and inductive graph processing tasks due to their higher accuracy than conventional graph convolutional networks (GCNs). The power-law distribution of real-world graph-structured data, on the other hand, causes a severe workload imbalance problem for GAT accelerators. To reduce the degradation of PE utilization due to the workload imbalance, we present algorithm/hardware co-design results for a GAT accelerator that balances workload assigned to processing elements by allowing only K neighbor nodes to participate in aggregation phase. The proposed model selects the K neighbor nodes with high attention scores, which represent relevance between two nodes, to minimize accuracy drop. Experimental results show that our algorithm/hardware co-design of the GAT accelerator achieves higher processing speed and energy efficiency than the GAT accelerators using conventional workload balancing techniques. Furthermore, we demonstrate that the proposed GAT accelerators can be made faster than the GCN accelerators that typically process smaller number of computations.
Exploration and practice of the pilot reform of vocational education at the undergraduate level

In order to actively adapt to the needs of the development of the software industry in southern Zhejiang province, Cooperate with the first-class enterprises in the software technology field, Schools and enterprises to jointly formulate professional talent training programs, Promote the reform of talent training mode and curriculum system of vocational education at the undergraduate level, Building a quality assurance system for talent training, Explore the training path and law of high-level technical skills personnel with the deep integration of industry and education, Strive to achieve coordinated development in personnel training, scientific research and academic team building, To build a high-quality software technology application of high-level technical skills personnel training highland, Serving the economic transformation and upgrading in southern Zhejiang province, Lead the demonstration, software technology professionals training.
Construction and Practice of New Technology Course "Digital Design and Simulation of Production Line"

Digital Production Line Design and Simulation is a practical and comprehensive major course for electromechanical and automation majors in higher vocational colleges. This course is based on digital technology to construct automatic production lines, and uses virtual simulation and virtual reality combination technology for teaching, creating a new digital teaching ecology. In this paper, the teaching innovation means and methods are proposed, and specific exploration and practice are carried out for this course, which has achieved good teaching results.
Research on the cultivation path of marketing talents for vocational education undergraduates under the background of digital economy

The characteristic of higher vocational education is to pay attention to the cultivation of students' skills, practice and professionalism, and the construction of off-campus practical training base is the key to realize the above skills of students. Both off-campus training bases and on-campus training bases are important components of practical teaching in higher vocational colleges. There are great differences between them in basic functions, functions, operation and management modes. With the reform and rapid development of national vocational education, the advanced concepts of teaching, teaching materials and teaching methods are constantly updated. Under the basic background of ideological and political construction of the course, innovation and entrepreneurship drive, the reconstruction of teaching content, loose leaf teaching materials and project-based teaching method can be flexibly applied in the actual teaching of network marketing comprehensive practical training course. While improving the teaching effect on campus, higher vocational colleges should enhance their ability to serve the regional economy, integrate social services with curriculum construction, and improve teachers' social service ability by means of the integration platform of production and education.
Teaching Reform of Theory and Practice Course of Analog Electronic Technology under the New Engineering Background

In order to cultivate electronic information professionals with lifelong learning ability and independent innovation ability, and to meet the needs of fresh engineering construction, the paper reformed the teaching of Analog Electronic Technology from all aspects, such as teaching content, teaching methods, experimental teaching and assessment mechanism. The teaching content is project-oriented, the teaching methods are mixed, the innovative EDA and pocket laboratory are introduced, the comprehensive design experiment projects are added, and the outstanding ability cultivation and process-oriented evaluation mechanism are established.
High Quality Cultivation Mechanism of Graduate Research and Innovation Ability in Post-epidemic Era

In the post-epidemic era, it is of great significance to strengthen the training of clinical academic postgraduate students to enhance China’s overall national strength. However, in the new situation of epidemic prevention and control, the current training model for academic master’s in China cannot fully adapt to the needs of talent training in the post-epidemic era. Based on the research of relevant documents and literature, combined with the current situation of academic postgraduate training, this study summarizes the main problems existing in postgraduate training, and puts forward the following countermeasures and suggestions. We study the innovation training mechanism of graduate students from three aspects: teaching mode, tutor guidance and evaluation index. Finally, we combine the elements of scientific innovation with the characteristics of our professional background. In addition, we introduce academic innovation into the graduate training system to construct a high-quality academic graduate training mechanism in the post-epidemic era.
Research on the Innovation Path of Production-Education Integration in Higher Vocational Colleges Based on the "1+X" Certificate System

The piloting of the "1+X" certificate system is a must for the development of vocational education in China, and it is in line with the connotation of industry-education integration in terms of the demand for vocational talents, talent training mode, vocational education curriculum construction, practical training system reform and talent evaluation mechanism. At present, the integration of industry education under the "1+X" certificate system still faces difficulties such as insufficient depth, bi-directionalities, lack of advanced concepts and misconceptions, etc. It is necessary to adopt the "talent training model reconstruction as a fundamental, school-enterprise dual-coordination education, 'school, enterprise, certificate Three mutual integration, 'enrollment, teaching, management, employment' four common" model of innovative integration of industry and education development.