Reviewerly/exHarmony huggingface dataset:
Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images

Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to the limited sample sizes and heterogeneity in tumor presentation in medical images, CNN models suffer from training issues, including training from scratch, which leads to overfitting. Alternatively, a pre-trained neural network's transfer learning (TL) is used to derive tumor knowledge from medical image datasets using CNN that were designed for non-medical activations, alleviating the need for large datasets. This study proposes two ensemble learning techniques: E-CNN (product rule) and E-CNN (majority voting). These techniques are based on the adaptation of the pretrained CNN models to classify colon cancer histopathology images into various classes. In these ensembles, the individuals are, initially, constructed by adapting pretrained DenseNet121, MobileNetV2, InceptionV3, and VGG16 models. The adaptation of these models is based on a block-wise fine-tuning policy, in which a set of dense and dropout layers of these pretrained models is joined to explore the variation in the histology images. Then, the models' decisions are fused via product rule and majority voting aggregation methods. The proposed model was validated against the standard pretrained models and the most recent works on two publicly available benchmark colon histopathological image datasets: Stoean (357 images) and Kather colorectal histology (5,000 images). The results were 97.20% and 91.28% accurate, respectively. The achieved results outperformed the state-of-the-art studies and confirmed that the proposed E-CNNs could be extended to be used in various medical image applications.
Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies

Along with the development of technology, the film industry continues to increase, this can be seen from the number of films that appear both in cinemas and tv shows. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved in the films. Information contained on IMDB such as actor/actress, director, writer, to the soundtrack used. In addition, IMDb is the most popular and trusted source of information for movies, TV, and other celebrity content. In this case, the researcher will conduct research on the film with what title is the most popular among the public by looking at some of the parameters contained in IMDB such as the number on the rating, score, certificate, and votes obtained from the audience. The data used comes from the Kaggle.com website. The data mining method used is the K-Means clustering method. To find out the optimal cluster value, the Davies Bouldin index is used. The K-Means algorithm will group the data based on the centroid. The parameters used for clustering are runtime, IMDB rating, meta score, number of votes, and gross. The results of the study obtained that the average calculation of the highest attributes was 48.74 and the number of clusters formed was 4 clusters. The results of the evaluation using the confusion matrix obtained an accuracy value of 100%.
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels

Pre-trained language models derive substantial linguistic and factual knowledge from the massive corpora on which they are trained, and prompt engineering seeks to align these models to specific tasks. Unfortunately, existing prompt engineering methods require significant amounts of labeled data, access to model parameters, or both. We introduce a new method for selecting prompt templates \textit{without labeled examples} and \textit{without direct access to the model}. Specifically, over a set of candidate templates, we choose the template that maximizes the mutual information between the input and the corresponding model output. Across 8 datasets representing 7 distinct NLP tasks, we show that when a template has high mutual information, it also has high accuracy on the task. On the largest model, selecting prompts with our method gets 90\% of the way from the average prompt accuracy to the best prompt accuracy and requires no ground truth labels.
Bag-of-Words vs. Graph vs. Sequence in Text Classification: Questioning the Necessity of Text-Graphs and the Surprising Strength of a Wide MLP

Graph neural networks have triggered a resurgence of graph-based text classification methods, defining today's state of the art.We show that a wide multi-layer perceptron (MLP) using a Bag-of-Words (BoW) outperforms the recent graph-based models TextGCN and Hete-GCN in an inductive text classification setting and is comparable with HyperGAT.Moreover, we fine-tune a sequence-based BERT and a lightweight DistilBERT model, which both outperform all state-of-the-art models.These results question the importance of synthetic graphs used in modern text classifiers.In terms of efficiency, DistilBERT is still twice as large as our BoW-based wide MLP, while graph-based models like TextGCN require setting up an O(N 2 ) graph, where N is the vocabulary plus corpus size.Finally, since Transformers need to compute O(L 2 ) attention weights with sequence length L, the MLP models show higher training and inference speeds on datasets with long sequences.
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension

Training a referring expression comprehension (ReC) model for a new visual domain requires collecting referring expressions, and potentially corresponding bounding boxes, for images in the domain.While large-scale pre-trained models are useful for image classification across domains, it remains unclear if they can be applied in a zero-shot manner to more complex tasks like ReC.We present ReCLIP, a simple but strong zero-shot baseline that repurposes CLIP, a state-of-the-art large-scale model, for ReC.Motivated by the close connection between ReC and CLIP's contrastive pre-training objective, the first component of ReCLIP is a region-scoring method that isolates object proposals via cropping and blurring, and passes them to CLIP.However, through controlled experiments on a synthetic dataset, we find that CLIP is largely incapable of performing spatial reasoning off-the-shelf.Thus, the second component of ReCLIP is a spatial relation resolver that handles several types of spatial relations.We reduce the gap between zero-shot baselines from prior work and supervised models by as much as 29% on RefCOCOg, and on RefGTA (video game imagery), ReCLIP's relative improvement over supervised ReC models trained on real images is 8%.
QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> oE-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ideo bitrate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.
Trends and Prospects of Techniques for Haze Removal From Degraded Images: A Survey

For the last two decades, image processing techniques have been used frequently in computer vision applications. The most challenging task in image processing is restoring images that are degraded due to various weather conditions. Mainly, the visibility of outdoor images is corrupted due to adverse atmospheric effects. The visibility of acquired images is reduced in these circumstances. Haze is an atmospheric phenomenon that reduces the clarity of an image. Due to the presence of particles such as dust, dirt, soot, or smoke, there is significant decay in the color and contrast of captured images. Haze present in acquired images causes issues in a variety of computer vision applications. Therefore, enhancing the contrast of a hazy image and restoring the visibility of the scene is essential. Since clear images are required in every application, image dehazing is an important step. Hence, many researchers are working on it. Different methods have been presented in the literature for image dehazing. This study describes various traditional and deep learning techniques of image dehazing from an analytical perspective. The main intention behind this work is to provide an intuitive understanding of the major techniques that have made a relevant contribution to haze removal. In this paper, we have covered different types of contributions toward dehazing based on the traditional method as well as deep learning approaches. With a considerable amount of instinctive simplifications, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.
Self-supervised Graph Neural Networks for Multi-behavior Recommendation

Traditional recommendation usually focuses on utilizing only one target user behavior (e.g., purchase) but ignoring other auxiliary behaviors (e.g., click, add to cart). Early efforts of multi-behavior recommendation often emphasize the differences between multiple behaviors, i.e., they aim to extract useful information by distinguishing different behaviors. However, the commonality between them, which reflects user's common preference for items associated with different behaviors, is largely ignored. Meanwhile, the multi-behavior recommendation still severely suffers from limited supervision signal issue. In this paper, we propose a novel self-supervised graph collaborative filtering model for multi-behavior recommendation named S-MBRec. Specifically, for each behavior, we execute the GCNs to learn the user and item embeddings. Then we design a supervised task, distinguishing the importance of different behaviors, to capture the differences between embeddings. Meanwhile, we propose a star-style contrastive learning task to capture the embedding commonality between target and auxiliary behaviors, so as to alleviate the sparsity of supervision signal, reduce the redundancy among auxiliary behavior, and extract the most critical information. Finally, we jointly optimize the above two tasks. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of S-MBRec, where the maximum improvement can reach to 20%.
Return to University Classrooms With Blended Learning: A Possible Post-pandemic COVID-19 Scenario

After more than 2 years of the pandemic caused by COVID-19, a gradual return to face-to-face teaching has been taking place. Therefore, administrators need to establish procedures to facilitate and ensure the quality of teaching during this process. The purpose of this article is to describe the strengths and challenges of implementing Blended Learning (BL). The design used is consistent with a secondary investigation of a narrative review. As a result, several recommendations are presented for building institutional frameworks that enable the implementation of high-quality BL models in the context of a gradual return to face-to-face courses in higher education. From a theoretical and contextual perspective, considerations for transitioning to this model are discussed, based on lessons learned from emergency remote education. We conclude that the present post-pandemic scenario constitutes a pivotal moment for determining the way education is delivered in higher education.
Regularized CASPT2: an Intruder-State-Free Approach

In this work we present a new approach to fix the intruder-state problem (ISP) in CASPT2 based on σp regularization. The resulting σp-CASPT2 method is compared to previous techniques, namely, the real and imaginary level shifts, on a theoretical basis and by performing a series of systematic calculations. The analysis is focused on two aspects, the effectiveness of σp-CASPT2 in removing the ISP and the sensitivity of the approach with respect to the input parameter. We found that σp-CASPT2 compares favorably with respect to previous approaches and that different versions, σ1-CASPT2 and σ2-CASPT2, have different potential application domains. This analysis also reveals the unsuitability of the real level shift technique as a general way to avoid the intruder-state problem.
Reframing Human-AI Collaboration for Generating Free-Text Explanations

Large language models are increasingly capable of generating fluent-appearing text with relatively little task-specific supervision.But can these models accurately explain classification decisions?We consider the task of generating free-text explanations using human-written examples in a few-shot manner.We find that (1) authoring higher quality prompts results in higher quality generations; and (2) surprisingly, in a head-to-head comparison, crowdworkers often prefer explanations generated by GPT-3 to crowdsourced explanations in existing datasets.Our human studies also show, however, that while models often produce factual, grammatical, and sufficient explanations, they have room to improve along axes such as providing novel information and supporting the label.We create a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop.Despite the intrinsic subjectivity of acceptability judgments, we demonstrate that acceptability is partially correlated with various fine-grained attributes of explanations.Our approach is able to consistently filter GPT-3-generated explanations deemed acceptable by humans.
Image Encryption Algorithm Based on Plane-Level Image Filtering and Discrete Logarithmic Transform

Image encryption is an effective way to protect image data. However, existing image encryption algorithms are still unable to strike a good balance between security and efficiency. To overcome the shortcomings of these algorithms, an image encryption algorithm based on plane-level image filtering and discrete logarithmic transformation (IEA-IF-DLT) is proposed. By utilizing the hash value more rationally, our proposed IEA-IF-DLT avoids the overhead caused by repeated generations of chaotic sequences and further improves the encryption efficiency through plane-level and three-dimensional (3D) encryption operations. Aiming at the problem that common modular addition and XOR operations are subject to differential attacks, IEA-IF-DLT additionally includes discrete logarithmic transformation to boost security. In IEA-IF-DLT, the plain image is first transformed into a 3D image, and then three rounds of plane-level permutation, plane-level pixel filtering, and 3D chaotic image superposition are performed. Next, after a discrete logarithmic transformation, a random pixel swapping is conducted to obtain the cipher image. To demonstrate the superiority of IEA-IF-DLT, we compared it with some state-of-the-art algorithms. The test and analysis results show that IEA-IF-DLT not only has better security performance, but also exhibits significant efficiency advantages.
A Survey on the Cyber Security of Small-to-Medium Businesses: Challenges, Research Focus and Recommendations

Small-to-medium sized businesses (SMBs) constitute a large fraction of many countries&#x2019; economies but according to the literature SMBs are not adequately implementing cyber security which leaves them susceptible to cyber-attacks. Furthermore, research in cyber security is rarely focused on SMBs, despite them representing a large proportion of businesses. In this paper we review recent research on the cyber security of SMBs, with a focus on the alignment of this research to the popular NIST Cyber Security Framework (CSF). From the literature we also summarise the key challenges SMBs face in implementing good cyber security and conclude with key recommendations on how to implement good cyber security. We find that research in SMB cyber security is mainly qualitative analysis and narrowly focused on the Identify and Protect functions of the NIST CSF with very little work on the other existing functions. SMBs should have the ability to detect, respond and recover from cyber-attacks, and if research lacks in those areas, then SMBs may have little guidance on how to act. Future research in SMB cyber security should be more balanced and researchers should adopt well-established powerful quantitative research approaches to refine and test research whilst governments and academia are urged to invest in incentivising researchers to expand their research focus.
Improved A* Path Planning Method Based on the Grid Map

In obstacle spatial path planning, the traditional A* algorithm has the problem of too many turning points and slow search speed. With this in mind, a path planning method that improves the A* (A-Star) algorithm is proposed. The mobile robot platform was equipped with a lidar and inertial measurement unit (IMU). The Hdl_graph_slam mapping algorithm was used to construct a two-dimensional grid map, and the improved A* algorithm was used for path planning of the mobile robot. The algorithm introduced the path smoothing strategy and safety protection mechanism, and it eliminated redundant points and minimal corner points by judging whether there were obstacles in the connection of two path nodes. The algorithm effectively improved the smoothness of the path and facilitated the robot to move in the actual operation. It could avoid the wear of the robot by expanding obstacles and improving the safety performance of the robot. Subsequently, the algorithm introduced the steering cost model and the adaptive cost function to improve the search efficiency, making the search purposeful and effective. Lastly, the effectiveness of the proposed algorithm was verified by experiments. The average path search time was reduced by 13%. The average search extension node was reduced by 11%. The problems of too many turning points and slow search speed of traditional A* algorithm in path planning were improved.
The role of strategic agility towards competitiveness with mediating effect of knowledge management

This study aims to examine the role of strategic agility to achieve competitiveness by analyzing the mediating effect of knowledge management construct in Jordanian’s public higher education institutions. The study reviewed the existing literature addressing the knowledge management and strategic features of the organizations. The resource‐based view theory is the lens of linking the knowledge management with strategic outcomes of the organizations. The hypothesized theoretical model was tested using a quantitative research method through questionnaire. A PLS-SEM approach was utilized to conduct the key analytical procedure and test the proposed research hypotheses. The results found a significant and positive effect of both strategic agility and knowledge management on achieving competitiveness. On the contrary, knowledge management showed no effect on competitiveness. But it had a mediation effect of the strategic agility on achieving competitiveness. Future research suggestions provided resulting from the research implications to provide different research methods and models.
The approaches to Web-based education of computer science bachelors in higher education institutions

The problem of organizing of Web-based education of bachelors, and the bachelors of computer science in particular, is relevant for higher education institutions. The IT industry puts forward new requirements for future IT professionals training. This, in its turn, requires the educational process modernization: content specification, updating of forms, methods and means of training to meet the demands of socio-economic development of the society in general and bachelors of computer science in particular. The article analyzes and clarifies the notion of Web-based education of bachelors; as well as a line of approaches, such as approaches to the organization of Web-based learning for A La Carte, Station Rotation, Lab Rotation, Individual Rotation, Flipped Learning scenario; the necessity of cloud computing and virtual classroom use as a component of Web-based learning is substantiated. It is established that with the advent of a large number of cloud-based services, augmented and virtual realities, new conditions are created for the development of skills to work with innovative systems. It is noted that the implementation of the approaches to the organization of student Web-based education is carried out on international level, in such projects as Erasmus+ “Curriculum for Blended Learning” and “Blended learning courses for teacher educators between Asia and Europe”. The article features the results of programming students survey on the use of Web-based technologies while learning, namely the results of a new approach to learning organization according to the formula – traditional (30%), distance (50%) and project (20%) training.
Robust Cyber-Physical System Enabled Smart Healthcare Unit Using Blockchain Technology

With the growing demand for smart, secure, and intelligent solutions, Industry 4.0 has emerged as the future of various applications. One of the primary sectors that are becoming more vulnerable to security assaults like ransomware is the healthcare sector. Researchers have proposed various mechanisms in smart and secure health care systems with this vision in mind. Existing systems are vulnerable to security attacks on medical data. It is required to build a real-time diagnosis device using a cyber-physical system with blockchain technology in a considerable manner. The proposed work’s main purpose is to build secure, real-time preservation and tamper-proof control of medical data. In this work, the Bayesian grey filter-based convolution neural network (BGF-CNN) approach is used to enhance accuracy and reduce time complexity and overhead. Additionally, PSO and GWO optimization techniques are used to improve network performance. As an outcome of the proposed work, the privacy preservation of medical data is improved with a high accuracy rate by a blockchain-based cyber-physical system using a deep neural network (BGF Blockchain). To summarize, the proposed system helps in the privacy preservation of medical data along with a reduction in communication overhead using the Bayesian Grey Filter–CNN.