Reviewerly/exHarmony huggingface dataset:
MEFA-Net: A multi-scale edge features aggregate network for cervical cell semantic segmentation

Cervical cancer is one of the female diseases with the highest morbidity and mortality, Early screening of cervical cancer is the key to reducing the incidence of cervical cancer and timely treatment. Several studies have proposed a series of methods for cervical cell semantic segmentation. However, they ignored the importance of edge information. In this paper, a multi-scale edge features aggregate network for cervical cell semantic segmentation is proposed (MEFA-Net). Specifically, we use Canny operator and U-Net network to extract edge features, and use them as prior information in the encoding and decoding stages of MEFA-Net respectively. MEFA-Net uses U-Net as its basic architecture. Multi-scale feature extraction module (MFE) is designed as encoder to improve the multi-scale feature extraction ability of the network and make the network pay more attention to the spatial gradient change area. Secondly, edge feature aggregation module (EFA) is designed to aggregate semantic features and edge features on multiple scales.Experiments on the common data set CCEDD show that MEFA-Net achieves the most advanced performance.
Selected Developments in Biotechnology Law and the Biotechnology Industry

Biotechnology Law ReportVol. 42, No. 3 News BriefsSelected Developments in Biotechnology Law and the Biotechnology IndustryBy STEVEN J. ZWEIGBy STEVEN J. ZWEIGSearch for more papers by this authorPublished Online:9 Jun 2023https://doi.org/10.1089/blr.2023.29313.sjzAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 3Jun 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:By STEVEN J. ZWEIG.Selected Developments in Biotechnology Law and the Biotechnology Industry.Biotechnology Law Report.Jun 2023.115-117.http://doi.org/10.1089/blr.2023.29313.sjzPublished in Volume: 42 Issue 3: June 9, 2023Online Ahead of Print:May 30, 2023PDF download
Comparative Analysis Of On-Page And Off-Page White Hat Search Engine Optimization (SEO) Techniques On Website Popularity

Currently, the use of Search Engine Optimization (SEO) has become a necessity to achieve better ranking results from Search Engines known as Search Engine Optimization, which is a collection of techniques for positioning a site so that it can be indexed by search engines and get a position in search engine results—result Pages (SERPs). Based on Globalstatistics data, there has been an increase in internet users in Indonesia who search for products through the search engine market, which has reached 97.38%. This study aims to increase the visibility of a website or multi-umah web page in search results to get a higher ranking on the SERPs in the search results. search results using white hat SEO by comparing on-page techniques and off-page techniques through descriptive qualitative methods, then analyzed using Google Search Console and Google Analytics tools to understand keyword performance, behavior, and customer interests on the umahmulti.biz.id website&#x0D;
Task guided representation learning using compositional models for zero-shot domain adaptation

Zero-shot domain adaptation (ZDA) methods aim to transfer knowledge about a task learned in a source domain to a target domain, while task-relevant data from target domain are not available. In this work, we address learning feature representations which are invariant to and shared among different domains considering task characteristics for ZDA. To this end, we propose a method for task-guided ZDA (TG-ZDA) which employs multi-branch deep neural networks to learn feature representations exploiting their domain invariance and shareability properties. The proposed TG-ZDA models can be trained end-to-end without requiring synthetic tasks and data generated from estimated representations of target domains. The proposed TG-ZDA has been examined using benchmark ZDA tasks on image classification datasets. Experimental results show that our proposed TG-ZDA outperforms state-of-the-art ZDA methods for different domains and tasks.
Randomized Kaczmarz method with adaptive stepsizes for inconsistent linear systems

We investigate the randomized Kaczmarz method that adaptively updates the stepsize using readily available information for solving inconsistent linear systems. A novel geometric interpretation is provided which shows that the proposed method can be viewed as an orthogonal projection method in some sense. We prove that this method converges linearly in expectation to the unique minimum Euclidean norm least-squares solution of the linear system, and provide a tight upper bound for the convergence of the proposed method. Numerical experiments are also given to illustrate the theoretical results.
Real-Time Layered View Synthesis for Free-Viewpoint Video from Unreliable Depth Information

In this work we present a novel approach for the generation in real time of synthetic views for free-viewpoint video. Our system is based on purely passive stereo cameras which, under the constraints of real-time operation yield unreliable depth maps, especially in the background areas. To solve this issue we propose a layered synthesis algorithm that combines offline and online sources of colour and depth information to increase the quality of the synthesized image. Foreground objects are synthesized using online depth and colour information. The background, however, is assumed to be stationary so its geometry can be reconstructed offline. However, foreground objects affect the colour of the background, so to render the background we carefully mix online and offline colour information. Finally, foreground and background are combined to form the final image.
Understanding Diversity in Session-based Recommendation

Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of diversity. In addition, the asserted “tradeoff” relationship between accuracy and diversity has been increasingly questioned in the literature. Toward the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and diversity, striving for better understanding of the diversity-related issues for SBRSs and providing guidance on designing diversified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and diversified SBRSs by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. The source code can be obtained via github.com/qyin863/Understanding-Diversity-in-SBRSs . Our empirical results unveil that (1) non-diversified methods can also obtain satisfying performance on diversity, which can even surpass diversified ones, and (2) the relationship between accuracy and diversity is quite complex. Besides the “tradeoff” relationship, they can be positively correlated with each other, that is, having a same-trend (win–win or lose–lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on diversity in SBRSs (i.e., granularity of item categorization, session diversity of datasets, and length of recommendation lists) and offer an intuitive guideline and a potential solution regarding learned item embeddings for more effective session-based recommendation.
Ship detection based on improved YOLO algorithm

Ship detection technology plays a major role in maritime traffic safety and waterway management. Current ship detection algorithms mainly rely on radar remote sensing images, which cannot achieve accurate and real-time ship detection. In order to solve this problem, this paper proposes a ship detection method based on an improved YOLO algorithm. Firstly, we introduce the ASPP module to enhance the model's ability to distinguish foreground from background by fusing detailed information of spatial features at different scales and reinforcing semantic information. Secondly, we introduce the CBAM attention mechanism, which makes the neural network pay more attention to the location and spatial information and more attention to the region of interest, thus reducing the attention to invalid features; through adaptive feature fusion, the network obtains features with richer layers, which improves the accuracy of the network in detecting ships. Finally, Focal Loss is used as the loss function to improve the detection accuracy of hard-to-identify targets and to alleviate the problem of unbalanced datasets. Our experiments on the ship dataset show that our improved YOLO algorithm outperforms other conventional methods in the ship detection task. In addition, we also performed a practical application of our method and found that it achieves an average accuracy mean of 96. 8% in real-time ship detection with high accuracy and reliability.
Improved contrastive learning with MoCo framework

Self-supervised learning typically suffers from lacking contrastive pairs and extracting unrepresentative vectors. To handle above mentioned challenges, this paper introduces a novel self-supervised learning framework that integrates the location-based sampling manner and a well-designed dimensionality reduction module. In the location-based sampling module, this paper embeds a multi-crop sampling paradigm into the memory bank-based framework. In the dimensionality reduction module, this paper introduces a principal component dimensionality reduction to capture the most comprehensive features. Experiments on popular datasets demonstrate the superior performance of our proposed method.
Dynamic Keystroke Technique for a Secure Authentication System based on Deep Belief Nets

The rapid growth of electronic assessment in various fields has led to the emergence of issues such as user identity fraud and cheating. One potential solution to these problems is to use a complementary authentication method, such as a behavioral biometric characteristic that is unique to each individual. One promising approach is keystroke dynamics, which involves analyzing the typing patterns of users. In this research, the Deep Belief Nets (DBN) model is used to implement a dynamic keystroke technique for secure e-assessment. The proposed system extracts various features from the pressure-time measurements, digraphs (dwell time and flight time), trigraphs, and n-graphs, and uses these features to classify the user's identity by applying the DBN algorithm to a dataset collected from participants who typed free text using a standard QWERTY keyboard in a neutral state without inducing specific emotions. The DBN model is designed to detect cheating attempts and is tested on a dataset collected from the proposed e-assessment system using free text. The implementation of the DBN results in an error rate of 5% and an accuracy of 95%, indicating that the system is effective in identifying users' identities and cheating, providing a secure e-assessment approach.
Learning Symbolic Timed Models from Concrete Timed Data

We present a technique for learning explainable timed automata from passive observations of a black-box function, such as an artificial intelligence system. Our method accepts a single, long, timed word with mixed input and output actions and learns a Mealy machine with one timer. The primary advantage of our approach is that it constructs a symbolic observation tree from a concrete timed word. This symbolic tree is then transformed into a human comprehensible automaton. We provide a prototype implementation and evaluate it by learning the controllers of two systems: a brick-sorter conveyor belt trained with reinforcement learning and a real-world derived smart traffic light controller. We compare different model generators using our symbolic observation tree as their input and achieve the best results using k-tails. In our experiments, we learn smaller and simpler automata than existing passive timed learners while maintaining accuracy.
TF-Net: Deep Learning Empowered Tiny Feature Network for Night-Time UAV Detection

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is required to that could efficiently tackle this problem. Infrared cameras are increasingly used for nighttime surveillance due to their wide applications in night vision equipment. This paper uses a deep learning-based TinyFeatureNet (TF-Net), which is an improved version of YOLOv5s, to accurately detect UAVs during the night using infrared (IR) images. In the proposed TF-Net, we introduce architectural changes in the neck and backbone of the YOLOv5s. We also simulated four different YOLOv5 models (s,m,n,l) and proposed TF-Net for a fair comparison. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s. TF-Net yielded the best results with 95.7% precision, 84% mAp, and 44.8% IoU.
Verifying Attention Robustness of Deep Neural Networks Against Semantic Perturbations

It is known that deep neural networks (DNNs) classify an input image by paying particular attention to certain specific pixels; a graphical representation of the magnitude of attention to each pixel is called a saliency-map. Saliency-maps are used to check the validity of the classification decision basis, e.g., it is not a valid basis for classification if a DNN pays more attention to the background rather than the subject of an image. Semantic perturbations can significantly change the saliency-map. In this work, we propose the first verification method for attention robustness, i.e., the local robustness of the changes in the saliency-map against combinations of semantic perturbations. Specifically, our method determines the range of the perturbation parameters (e.g., the brightness change) that maintains the difference between the actual saliency-map change and the expected saliency-map change below a given threshold value. Our method is based on activation region traversals, focusing on the outermost robust boundary for scalability on larger DNNs. We empirically evaluate the effectiveness and performance of our method on DNNs trained on popular image classification datasets.
Adversarial feature hybrid framework for steganography with shifted window local loss

Image steganography is a long-standing image security problem that aims at hiding information in cover images. In recent years, the application of deep learning to steganography has the tendency to outperform traditional methods. However, the vigorous development of CNN-based steganalyzers still have a serious threat to steganography methods. To address this gap, we present an end-to-end adversarial steganography framework based on CNN and Transformer learned by shifted window local loss, called StegoFormer, which contains Encoder, Decoder, and Discriminator. Encoder is a hybrid model based on U-shaped network and Transformer block, which effectively integrates high-resolution spatial features and global self-attention features. In particular, Shuffle Linear layer is suggested, which can enhance the linear layer’s competence to extract local features. Given the substantial error in the central patch of the stego image, we propose shifted window local loss learning to assist Encoder in generating accurate stego images via weighted local loss. Furthermore, Gaussian mask augmentation method is designed to augment data for Discriminator, which helps to improve the security of Encoder through adversarial training. Controlled experiments show that StegoFormer is superior to the existing advanced steganography methods in terms of anti-steganalysis ability, steganography effectiveness, and information restoration.
Clustering of Precipitation in the Black Sea Region with by Fuzzy C-Means and Silhouette Index Analysis

In recent years, there has been a noticeable increase in the number of disasters caused by the effects of global climate change. In this context, various studies are carried out in our country and in the world in order to reduce the effects of climate change. The classification of regions affected by climate change into similar classes in terms of climate parameters is important in terms of applying similar methods in studies to be carried out in these regions. Thus, a correct strategy will be determined in the studies to be carried out in order to reduce the effects of climate change. The observation records evaluated within the scope of the study were used from 31 stations in the Black Sea Region of the Turkish State Meteorological Service, covering the period between 1982 and 2020. Cluster analysis was carried out using the Fuzzy C-Means. As a result of the study, the optimum cluster among the clusters formed by Fuzzy C-Means was determined by Silhouette index analysis. The optimal number of clusters is suggested as 4.
Solution of SAT problems with the adaptive-bias quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a promising method for solving certain classical combinatorial optimization problems on near-term quantum devices. When employing the QAOA to 3-SAT and Max-3-SAT problems, the quantum cost exhibits an easy-hard-easy or easy-hard pattern, respectively, as the clause density is changed. The quantum resources needed in the hard-region problems are out of reach for current noisy intermediate-scale quantum (NISQ) devices. We show by numerical simulations with up to 14 variables and analytical arguments that the adaptive-bias QAOA (ab-QAOA) greatly improves performance in the hard region of the 3-SAT problems and hard region of the Max-3-SAT problems. For similar accuracy, on average, ab-QAOA needs 3 levels for 10-variable 3-SAT problems as compared to 22 for QAOA. For 10-variable Max-3-SAT problems, the numbers are 7 levels and 62 levels. The improvement comes from a more targeted and more limited generation of entanglement during the evolution. We demonstrate that classical optimization is not strictly necessary in the ab-QAOA since local fields are used to guide the evolution. This leads us to propose an optimization-free ab-QAOA that can solve the hard-region 3-SAT and Max-3-SAT problems effectively with significantly fewer quantum gates as compared to the original ab-QAOA. Our work paves the way for realizing quantum advantages for optimization problems on NISQ devices.
Student experience and new media to leverage an Infocommunicational case study model

The use of a student’s own mobile digital device for learning purposes has been driven by extremely different perspectives. On one side some educational actors advocating the integration and strategically well-planned use of such technologies. On the other end of this spectrum some actors just completely continue to deny the use of such technologies, with students obliged to leave their mobile technologies switched off and kept away in a specific place controlled by the teacher in the classroom. This paper reports an empirical study that used student’s smartphone experience narratives, in their daily lives, to design and validate an infocommunicational services model for a learning ecosystem. This work was developed in a school cluster at the north of Portugal in a case study research setup with 141 students, 49 teachers and 46 parents. A smartphone application prototype was designed as a research instrument to simulate the model’s characteristics and used to inquire the participants and validate the proposed model. The 8 different prototype task scenarios are explained and the final list of infocommunicational services that comprise the model proposed, designed and tested within this educational community, are a main outcome of this paper. The characteristics of the model are directly related to the case study community’s wishes and needs and due to this with generalizable constraints. On the other hand, the research process reveals potential to be adapted and applied to any other educational community, ecosystem, or school cluster.
Separating the blue cloud and the red sequence using Otsu’s method for image segmentation

The observed colour bimodality allows a classification of the galaxies into two distinct classes: the ‘blue cloud’ and the ‘red sequence’. Such classification is often carried out using empirical cuts in colour and other galaxy properties that lack solid mathematical justifications. We propose a method for separating the galaxies in the ‘blue cloud’ and the ‘red sequence’ using Otsu’s thresholding technique for image segmentation. We show that this technique is insensitive to the choice of binning. It provides a robust and parameter-free method for the classification of the red and blue galaxies based on the minimization of the intra-class variance and maximization of the inter-class variance. We also apply an iterative triclass thresholding technique based on Otsu’s method to improve the classification. The galaxy colour is known to depend on the stellar mass and the luminosity of galaxies. We obtain the dividing lines between the two populations in the colour–stellar mass plane and the colour–absolute magnitude plane by employing these methods in a number of independent stellar mass bins and absolute magnitude bins.
Pay "Attention" to Chart Images for What You Read on Text

Data visualization is changing how we understand data, by showing why's, how's, and what's behind important patterns/trends in almost every corner of the world, such as in academic papers, news articles, financial reports, etc. However, along with the increasing complexity and richness of data visualizations, given a text description (e.g., "fewer teens say they attended school completely online (8%)"), it becomes harder for users to pinpoint where to pay attention to on a chart (e.g., a grouped bar chart).