Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Transfer Learning based Models for Lung Cancer Histopathology Classification

Lung cancer is the deadliest form of cancer, which attracted a lot of attention in the past. Transfer learning is a very popular approach in deep learning as it can apply the knowledge obtained from a previous task to improve the performance in another. In this research, four transfer learning models with different complexity are utilized to detect lung cancer, which are AlexNet, VGG16, ResNet50 and Inception-v3. Since the early detection and histopathological diagnosis can considerably decrease the likelihood of mortality, the lung cancer histopathological images dataset is considered. This dataset contains histopathological images of 3 classes, all of them are considered in this study. Firstly, the four models are trained on the histopathological database from random initialization for 10 epochs. Next, the four models are first pre-trained on ImageNet and then trained on the histopathological dataset for 10 epochs. For each epoch, the testing accuracy is recorded so as to find the optimal number of epochs and determine whether transfer learning models are capable in lung cancer detection. Then, various evaluation metrics e.g., accuracy and precision are used to measure and compare the four models’ performance. The study’s finding shows that AlexNet, VGG16, ResNet50 and Inception-v3 pre-trained on ImageNet are adequate in lung cancer detection. There corresponding accuracy rates are 99.367%, 99.800%, 100% and 100% respectively, which are much higher than that trained from random initialization. Among the four transfer learning models, ResNet50 and Inception-v3 perform the best on lung cancer classification.
Aliasing Limits on Translating C to Safe Rust

The Rust language was created to provide safe low-level systems programming. There is both industrial and academic interest in the problem of (semi-)automatically translating C code to Rust in order to exploit Rust's safety guarantees. We study the effectiveness and limitations of existing techniques for automatically translating unsafe raw pointers (in Rust programs translated from C) into safe Rust references via ownership and lifetime inference. Our novel evaluation methodology enables our study to extend beyond prior studies, and to discover new information contradicting the conclusions of prior studies. We find that existing translation methods are severely limited by a lack of precision in the Rust compiler's safety checker, causing many safe pointer manipulations to be labeled as potentially unsafe. Leveraging this information, we propose methods for improving translation, based on encoding the results of a more precise analysis in a manner that is understandable to an unmodified Rust compiler. We implement one of our proposed methods, increasing the number of pointers that can be translated to safe Rust references by 75% over the baseline (from 12% to 21% of all pointers).
Digitization for Professional Training of Future Specialists in the Field of Vocational and Technical Education

The article analyzes the problems of digitalization for professional training of future specialists in the field of vocational (vocational and technical) education, which arose in connection with the development and expansion of digital technologies and increased requirements for future specialists. On the basis of such scientific methods for research, analysis and systematization to scientific sources, modern results of digitalization of professional training of future specialists in the field of vocational (vocational and technical) education in Ukraine and abroad were studied. Prospective directions for further modernization of vocational (vocational and technical) education in Ukraine in the conditions of a digital society are outlined. First of all, it is appropriate to equip vocational (vocational and technical) education institutions with modern digital technology (computer classrooms with modern software, Internet communication and communication tools; open spaces and makerspaces for teachers and students, etc.). It is important that the administration of the vocational (vocational and technical) education institution should have a clear idea and strategy regarding the further use of expensive and modern equipment. The digital educational environment created on the basis of this should include technical, hardware, software and organizational structures that ensure the adjustment and functioning of the equipment and the operation to information services of the educational institution. In institutions and in the entire system of vocational (vocational and technical) education, the digital competence of pedagogical workers should be constantly monitored and methods of its improvement should be implemented in the system of professional development. In addition, it is necessary to use all means of the digital educational environment of the institution to vocational (vocational and technical) education for the formation and development of digital competence for future specialists in the field of vocational (vocational and technical) education (students).
PICO: Pipeline Inference Framework for Versatile CNNs on Diverse Mobile Devices

Distributing the inference of convolutional neural network (CNN) to multiple mobile devices has been studied in recent years to achieve real-time inference without losing accuracy. However, how to map CNN to devices remains a challenge. On the one hand, scheduling the workload of state-of-the-art CNNs with multiple devices is NP-Hard because the structures of CNNs are directed acyclic graphs (DAG) rather than simple chains. On the other hand, distributing the inference workload suffers from expensive communication and unbalanced computation due to the wireless environment and heterogeneous devices. This paper presents PICO, a pipeline cooperation framework to accelerate the inference of versatile CNNs on diverse mobile devices. At its core, PICO features: (1) a generic graph partition algorithm that considers the characteristics of any given CNN and orchestrates it into a list of model pieces with suitable granularity, and (2) a many-to-many mapping algorithm that produces the best pipeline configuration for heterogeneous devices. In our experiment with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2 \sim 8$</tex-math></inline-formula> Raspberry-Pi devices, the throughput can be improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.8 \sim 6.8 \times$</tex-math></inline-formula> under different CPU frequencies.
Improving the quality of dental crown using a transformer-based method

Designing a synthetic crown is a time-consuming, inconsistent, and labor-intensive process. In this work, we present a fully automatic method that not only learns human design dental crowns, but also improves the consistency, functionality, and esthetic of the crowns. Following success in point cloud completion using the transformer-based network, we tackle the problem of the crown generation as a point-cloud completion around a prepared tooth. To this end, we use a geometry-aware transformer to generate dental crowns. Our main contribution is to add a margin line information to the network, as the accuracy of generating a precise margin line directly, determines whether the designed crown and prepared tooth are closely matched to allow appropriate adhesion. Using our ground truth crown, we can extract the margin line as a spline and sample the spline into 1000 points. We feed the obtained margin line along with two neighbor teeth of the prepared tooth and three closest teeth in the opposing jaw. We also add the margin line points to our ground truth crown to increase the resolution at the margin line. Our experimental results show an improvement in the quality of the designed crown when considering the actual context composed of the prepared tooth along with the margin line compared with a crown generated in an empty space as was done by other studies in the literature. Our code is available at : “<a href="https://github.com/Golriz-code/shellGeneration/tree/main/Shell%20Generation">https://github.com/Golriz-code/shellGeneration/tree/main/Shell%20Generation</a>”
Transformer as a spatially-aware multi-instance learning framework to predict the risk of death for early-stage non-small cell lung cancer

In computational pathology, training and inference of conventional deep convolutional neural networks (CNN) are usually limited to patches of small sizes (e.g., 256 × 256) sampled from whole slide images. In practice, however, diagnostic and prognostic information could lie within the context of tumor microenvironment across multiple regions, far beyond the scope of individual patches. For instance, the spatial relationship of tumor-infiltrating lymphocytes (TIL) across regions of interest might be prognostic for non-small cell lung cancer (NSCLC). This poses a multi-instance learning (MIL) problem, and a single-patch-driven CNN typically fails to learn spatial information and context between multiple patches, especially their spatial relationship. In this work, we present a cell graph-based MIL framework to predict the risk of death for early-stage NSCLC by aggregating feature representation of TIL-enclosing patches according to their spatial relationship. Inspired by PATCHY-SAN, a graph-embedding framework for CNNs, we use graph kernel-based approaches to embed a bag of patches into a sequence with their spatial information encoded into the sequence order. A transformer model was then trained to aggregate patch-level features based on spatial information. We demonstrate the capability of this framework to predict the likelihood of the patient with NSCLC in two cohorts (n=240) to survive for more than 5 years. The training cohort (n=195) comprised hematoxylin and eosin (H&E)-stained whole slide images (WSI), while the testing cohort (n=45) comprised H&E-stained tumor microarrays (TMA). We show that, with the spatial context of multiple patches encoded as an ordered patch sequence, the performance in the testing cohort of our approach achieves an area under the receiver operating characteristic curve (AUC) of 0.836 (p=0.009; HR=5.62), as opposed to a baseline conventional CNN with an AUC of 0.542 (p=0.105; HR=1.66). The results suggest that the Transformer is a generic spatial information aware MIL framework that can learn the spatial relationship of multiple TIL-enclosing patches from the graph representation of immune cells.
Competition on robust deep learning

This perspective paper proposes a new adversarial training method based on large-scale pre-trained models to achieve state-of-the-art adversarial robustness on ImageNet.
Calibration Method for Fisheye Camera Based on Multi-checkerboard Detection

This paper proposes a fisheye camera calibration method based on multiple chessboard detection.To address the complexity and multiple-frame requirement of traditional calibration methods, this method detects multiple chessboard corner points using libcbdetect algorithm and obtains pixel coordinates.Then, it uses a depth-first search algorithm to obtain the world coordinates of the chessboard corners and calculates the homography matrix based on the world-pixel coordinates pair through RANSAC algorithm.Finally, the undistorted image is transformed into a bird's eye view (BEV) using the obtained homography matrix.This method is simple and effective, and can improve the accuracy of lane keeping functions, which is of practical significance in autonomous driving applications.
Efficient hardware implementation of SIMECK lightweight block cipher

The internet of things (IoT) has recently expanded, resulting in a new world of smart gadgets with substantial security consequences. For their vital security role, lightweight block ciphers have gained a significant amount of development in low resource devices (LRDs). SIMECK is a new lightweight block cipher family that incorporates the finest aspect of both SIMON and SPECK. SIMECK is a more efficient block cipher than SIMON and SPECK cipher. These lightweight ciphers are especially referred to as an alternative to the AES for RCD. In this study, area optimised architecture is implemented for SIMECK lightweight block cipher with sizes: 64/128. For implementation on different platforms such as Sparton-6, Sparton-3, Virtex-7, Virtex-6, Virtex-5 and Virtex-4 FPGA are used to examine several properties such as block size, key scheduling, and throughput, among others. The proposed area optimised architecture have attained a maximum operating frequency of 496.429 MHz with 61 slices and a high throughput of 706.032 Mbps on the Virtex-7 platform.
A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization, and Coordinate Descent

In this paper, we present a unified analysis of methods for such a wide class of problems as variational inequalities, which includes minimization problems and saddle point problems. We develop our analysis on the modified Extra-Gradient method (the classic algorithm for variational inequalities) and consider the strongly monotone and monotone cases, which corresponds to strongly-convex-strongly-concave and convex-concave saddle point problems. The theoretical analysis is based on parametric assumptions about Extra-Gradient iterations. Therefore, it can serve as a strong basis for combining the already existing type methods and also for creating new algorithms. In particular, to show this we develop new robust methods, which include methods with quantization, coordinate methods, distributed randomized local methods, and others. Most of these approaches have never been considered in the generality of variational inequalities and have previously been used only for minimization problems. The robustness of the new methods is also confirmed by numerical experiments with GANs.
V-Light: Leveraging Edge Computing For The Design of Mobile Augmented Reality Games

We explore the future of synchronous, multiplayer mobile AR gaming through our game V-Light, which extends current mobile AR game capacities using edge computing. Mobile AR games are currently limited by on-board processing power, while offloading operations to the cloud introduces high latency costs. This is a critical issue for games needing real-time response to player input. V-Light demonstrates how mobile AR games can leverage the power of edge computing, bringing computational resources closer to the user, keeping latency low and bandwidth high. We share our development toolkit, analyze the design and development of V-Light through the lens of an existing model for shared-world mobile AR, and demonstrate that edge computing can provide a "time machine" that lets game designers prototype mobile AR games for devices that do not yet exist.
Constraints on Gaussian error channels and measurements for quantum communication

Joint Gaussian measurements of two quantum systems are important for quantum communication between remote parties and are often used in continuous-variable teleportation or entanglement-swapping protocols. Many of the errors in real-world implementations can be modeled by independent Gaussian error channels acting prior to measurement. In this work we study independent single-mode Gaussian error channels on two modes A and B that take place prior to a joint Gaussian measurement. We determine the set of pairs of such channels that render all Gaussian measurements separable, and therefore unsuitable for entanglement swapping or teleportation of arbitrary input states. For example, if the error channels are loss with parameters lA,lB followed by added noise with parameters nA,nB then all Gaussian measurements are separable if and only if lA+lB+nA+nB≥1.
Libraries and cyber security: the importance of the human factor in preventing cyber attacks

Purpose This paper aims to investigate cyber security awareness of the staff of Estonian libraries and gives an overview why libraries could be a target of cyber attacks and why librarians need cyber security at first place. Design/methodology/approach The data used in this paper is based on a review of relevant literature to provide an overview of the concept of cyber security, and the results of the original online survey created by the paper’s author, conducted among Estonian librarians. The online questionnaire was developed using the world-recognised human aspects of information security questionnaire (HAIS-Q), which is based on the knowledge-attitudes-behaviour (KAB) methodology. A total of 388 completed questionnaires were returned from employees of academic, specialised, public and school libraries. The results are interpreted on the basis of descriptive statistics and Kruger and Kearney approach. Findings The final score of library employees is 86, which is classified as good, but based on the result, two focus areas need more attention than previously, which are the use of devices and prevention and handling of incidents. Originality/value The cyber behaviour of library employees has not been widely studied in the world using HAIS-Q and KAB models, and to the best of the authors’ knowledge, no research has been previously carried out in the Estonian library context into cyber security awareness.
A dataset of domain events based on open-source military news

The text dataset of the military field is the basis for event extraction in the military field, and the datasets of high quality can effectively promote the study of event extraction in this field. However, the event extraction dataset commonly used in the real world (such as ACE2005, etc.) is oriented to the general field, and the text corpus resources on military events are scarce. Therefore, we collected a large amount of military news content from public military news websites. Firstly, on the basis of text content analysis, we first established an event model of military news including event types, entity types and entity relationship types. Secondly, we manually labeled the text data according to the event model, which was iteratively verified and corrected simultaneously. Finally, we obtained dataset of 13,000 high-quality military news events with a full variety of labels. We make this military news event dataset publicly available in this paper.
A Study on the Performances of AI Recruitment System: A Case Study on Domestic and Abroad Companies

This study examines how AI technology is being used in the recruitment process through domestic and international case studies of AI recruitment systems such as Google, Softbank, Unilever, Amazon, Loreal, Samsung, Lotte, SK, KB Kookmin Bank, Hyundai and LG, and seeks to verify the introduction performance of AI recruitment systems, and presents matters to be considered when companies select AI recruitment systems. AI Recruitment system improves the time efficiency, work efficiency, objectivity and accuracy of the recruitment process, allows a wider pool of applicants, and provides more fair opportunities for evaluation to candidates. Despite these effect, the following points should be considered when companies want to introduce AI Recruitment system. 1) AI HRM cannot guarantee positive results for all companies, 2) and companies should have personnel managers who are in charge of AI Recruitment system and a system to apply Recruitment, 3) In addition, the purpose of introducing AI Recruitment system should be established. 4) Also, It should be recognized in advance that the application of AI Recruitment system may show unintended consequences such as prejudice, 5) AI Recruitment system cannot replace everything in the realm of human beings, 6) Finally, since it takes a long time to implement AI Recruitment system in order to examine its effectiveness, companies should review the need for AI Recruitment system as well as cost.
The design, experiment, analyse, and reproduce principle for experimentation in virtual reality

Conducting experiments in virtual reality (VR) requires a complex setup of hardware, software, experiment design and implementation, and data collection which is supported by frameworks that provide pre-determined features for scientists to implement their experiment in VR. These VR frameworks have proliferated exponentially since the start of the millennia, and unfortunately, they both only differ slightly from one another and often miss one or more of the key features required by the researcher. Therefore, it has become less clear to researchers which framework to choose for what task and to what benefit. I introduce the design, experiment, analyse, and reproduce (DEAR) principle to develop a new perspective on VR frameworks through a holistic approach to experimentation (i.e., the process of conducting an experiment). The DEAR principle lays out the core components that future frameworks should entail. Most previous VR frameworks have focussed on the design phase and sometimes on the experiment phase to help researchers create and conduct experiments. However, being able to create an experiment with a framework is not sufficient for wide adoption. Ultimately, I argue that it is important to take reproducibility seriously to overcome the limitations of current frameworks. Once experiments are fully reproducible through automation, the adaptation of new experiments becomes easier. Hopefully, researchers can find ways to converge in the use of frameworks or else frameworks may become a hindrance instead of a help.
Research on Visible Light Pedestrian Detection Algorithm Based on Improved YOLOv5m

Visible pedestrian detection technology is widely used in video surveillance, human behavior analysis, intelligent military and other fields. In order to further improve the robustness and detection accuracy of pedestrian detection algorithm, this paper improves on the basis of YOLOv5m algorithm. Firstly, K-means++ clustering is used to optimize the initial center point selection, which makes the algorithm easier to converge in the training process and improves the robustness of the algorithm; Secondly, SENet channel attention module is introduced into YOLOv5m to make the network pay more attention to highlighted targets and improve pedestrian detection accuracy. Finally, OTCBVS data set and INO data set are used for training and testing. The results show that compared with the traditional YOLOv5m algorithm, the proposed improved algorithm has faster convergence and stronger robustness, and the detection accuracy is improved to 93.0%, which can be effectively applied to pedestrian recognition in visible light.
XDC: Adaptive Cross Domain Short Text Clustering

Short text clustering is a challenging unsupervised learning task which requires a complex representation of each document to effectively model the semantics and syntactic structure of the text. Existing works have attempted to tackle this challenging task by incorporating additional information to the model, such as number of clusters, number of datapoints in each clusters, the distribution of the input data, and more. Unlike previous approaches, we propose to exploit an auxiliary dataset that is fully labeled to augment the quality of the learned representations. We also define the problem as cross domain clustering (XDC), which leverages adversarial learning to train an adaptive clustering model across text domains. Specifically, XDC jointly exploits a labeled source domain and an unlabeled target domain during model training. Owing to domain adversarial learning, the distribution shift across source and target domains could be mitigated. Moreover, XDC is implemented as a linkage-based clustering approach using graphs, which is agnostic of the number of clusters. We evaluate our XDC framework on three text datasets, and results show that it outperforms the state-of-the-art text clustering methods in most cases. Ablation studies and qualitative analysis also demonstrate the effectiveness of our framework.