Reviewerly/exHarmony huggingface dataset:
Representation of Differential Learning Method for Mitosis Detection

The breast cancer microscopy images acquire information about the patient’s ailment, and the automated mitotic cell detection outcomes have generally been utilized to ease the massive amount of pathologist’s work and help the pathologists make clinical decisions quickly. Several previous methods were introduced to solve automated mitotic cell count problems. However, they failed to differentiate between mitotic and nonmitotic cells and come up with an imbalance problem, which affects the performance. This paper proposes a Representation Differential Learning Method (RDLM) for mitosis detection through deep learning to detect the accurate mitotic cell area on pathological images. Our proposed method has been divided into two parts: Global bank Feature Pyramid Network (GLB-FPN) and focal loss (FL). The GLB feature fusion method with FPN essentially makes the encoder-decoder pay attention, to further extract the region of interest (ROIs) for mitotic cells. On this basis, we extend the GLB-FPN with a focal loss to mitigate the data imbalance problem during the training stage. Extensive experiments have shown that RDLM significantly outperforms on visualization view and achieves the best performance in quantitative matrices than other proposed approaches on the MITOS-ATYPIA-14 contest dataset. Our framework reaches a 0.692 F1-score. Additionally, RDLM achieves 5% improvements than GLB with FPN in F1-score on the mitosis detection task.
Pruning the Index Contents for Memory Efficient Open-Domain QA.

This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches. Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, passage reranker, extractive reader, generative reader and a simple way to combine them. Furthermore, previous work often comes with a massive index of external documents that scales in the order of tens of GiB. This work presents a simple approach for pruning the contents of a massive index such that the open-domain QA system altogether with index, OS, and library components fits into 6GiB docker image while retaining only 8% of original index contents and losing only 3% EM accuracy.
Europol: the AI hacker threat to biometrics

BTT's Tim Ring reports on a deep-dive study into how cybercriminals are using deepfakes and other AI technology to defeat the face and voice recognition security used by banks, businesses and governments.
Research on driverless vehicle vision algorithm

Obstacle detection in complex urban traffic environment has become an important part of unmanned vehicle optimization, and its complexity brings great challenges to the reliability of unmanned target detection. YOLOv3 in deep learning algorithm has a good detection effect in target detection, but it has certain defects in detecting targets in complex urban traffic environment. In this paper, the spatial pyramid module is added to YOLOv3 to improve the extraction of data features of the deep model. Then, on the basis of optimized network, the target detection algorithm is streamlined by combining layer pruning and channel pruning. The streamlined algorithm is called YOLOv3-SPP3-Tiny. Comparing the experimental results of YOLOv3-SPP3-tiny and YOLOv3 on Street Scenes dataset, the Precision is improved by 2.77%, the average precision (mAP) is increased by 0.87%, the Total BFLOPS is reduced by 94.49%, and the Inference time is reduced by 80.39%. Experimental results show that the model YOLOv3-SPP3-tiny algorithm is more conducive to unmanned object detection in complex urban road environment.
Multi-face recognition and dynamic tracking based on reinforcement learning algorithm

Aiming at the problem that the current low accuracy rate of face detection and target tracking, a reinforcement learning algorithm is proposed, which integrates face detection technology and target tracking technology organically, adopts the face detection algorithm based on Multi-Task Convolutional Neural Network (MTCNN) and target tracking algorithm based on Kalman filtering, so as to realize face detection, multiplayer face recognition and dynamic tracking of personnel movement. In this paper, the configuration environment is Anaconda, the operating platform is PyCharm, the video-based face detection and dynamic capture and rapid identification system has been designed and developed. The system consists of two modules: face detection module and target tracking module. The optimized face detection and dynamic capture algorithm improved the detection success rate by about 11.5%, the face detection success rate by about 15.2%, the dynamic capture success rate increased by about 12.0%, and the optimized system has a wider practicality.
Formation of readiness for employment among university students

The article is devoted to one of the urgent problems of modern society вЂ“ the employment of graduates of higher educational institutions and the reduction of unemployment among young people. Thus, the object of the study is readiness for employment, the subject of the study is the process of formation of readiness for employment among students in the conditions of a higher professional educational institution. In the course of the study, the main reasons for the lack of employment of university graduates were identified and described. The article reveals the concept of "readiness for employment among students", describes the components of readiness that affect the independent solution of practical tasks in job search and employment. Special attention is paid to theoretical aspects in the formation of readiness for employment, the importance of organizing special conditions at the university aimed at forming students' readiness for employment in the process of obtaining professional education as an integral part of professional training of a specialist is separately shown. В The novelty of this study consists in describing the essence of the concept of "formation of readiness for employment among students", determining the reasons for the lack of employment of graduates, substantiating the conditions for the formation of readiness for employment. In the course of the study, the authors come to the conclusion that it is necessary to introduce an optional course "The Art of Employment" for senior students, which contains an algorithm for organizing and implementing the stages of forming students' readiness for employment. The revealed relationship between the use of this course and the formation of a competent, competitive graduate who is able to independently solve practical problems when looking for work and further employment has been confirmed in the course of experimental work. The article is of interest to managers, teachers and employees of employment departments of higher educational institutions, employees of employment centers and representatives of non-profit organizations.
Computer-aided Research on the Translation Ability Cultivation Model of Chinese College English Interdisciplinary Talents

Abstract The trend of global economic integration has intensified the Chinese society’s demand for English-speaking compound talents. Coupled with the rapid economic development of today’s society, my country’s economic and cultural progress has also become more international [1] . Companies in all walks of life in society that are in line with foreign companies have begun to attach importance to the cultivation of their employees’ English proficiency. Compound talent is also a basic term for English majors that people put forward during this period. For the present, the cultivation of talents of compound foreign language majors is an important task English majors facing in various schools in our country. In order to better explore the connotation of professional talents, the construction of a training model for English majors based on multiple dimensions is a necessary step at present. Among them, the cultivation of translation ability is the top priority.
Klasterisasi kinerja karyawan menggunakan algoritma fuzzy c-means

Reward and punishment are needed for assessting employees’ performace. Employee grouping based on their performance is one of several ways to enhance employees’ performance. This research discusses about grouping employess based on their performance using Fuzzy C-Means. Result from assessment comes from the total of each criteria that contains of presence, discipline, and task duration. Three groups of employees are formed, which are good, moderate, and bad. From 13 employees, 10 of them are in the good criteria, one is in moderate criteria, and two are in the bad one. We also use different values of fuzzy exponent to get the clustering results. The values 1.5 and 2 of fuzzy exponents give the same clustering results with the result from manager. Therefore, grouping with FCM could be used to cluster employees based on their performance.
Analysis and Evaluation of MPLS Network Performance

Currently, the increasing network connectivity is becoming more and more complex and the integration of several services continues to appear.These approaches need a guarantee in terms of throughput, the performance of which they potentially impact.So, the basic aim of this research is to get the best MPLS network performance towards linux and FreeBSD operating systems.This is why in this report, we have worked on MPLS network performance, which will help to identify performance metrics.To evaluate the performance, we used those operating systems implementing the MPLS architecture in order to define the best performing between the two on this domain.For this, we used scapy to measure the response times by varying the size of the packets sent and validate the measurements with the MATLAB Simulink.After all experiments, we realized that FreeBSD operating system is more reliable than linux in MPLS network base.
Underwater image enhancement algorithm based on dark channel prior and underwater imaging model

The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.
A Learning Theoretic Perspective on Local Explainability

In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditional problem of performance generalization and bound the test-time accuracy of a model using a notion of how locally explainable it is. Second, we explore the novel problem of explanation generalization which is an important concern for a growing class of finite sample-based local approximation explanations. Finally, we validate our theoretical results empirically and show that they reflect what can be seen in practice.
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting

We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM combines a mechanistic stochastic simulation model GLEAM with deep learning. It uses deep learning to learn the correction terms from GLEAM, which leads to improved performance. We further integrate various uncertainty quantification methods to generate confidence intervals. We demonstrate DeepGLEAM on real-world COVID-19 mortality forecasting tasks.
Using geochemical imaging data to map nickel sulfide deposits in Daxinganling, China

Abstract Nickel sulfide deposits occur in ultramafic rocks in the Daxinganling area, China; however, the prospectivity of these deposits has received little attention. This study transformed rasterized regional 1:200,000 geochemical data into spectral-like data and then used hyperspectral tools of the spectral angle mapper (SAM) to classify possible ultramafic lithologies and the multirange spectral feature fitting (MRSFF) method to classify prospective areas that are similar to a typical Gaxian Ni deposit. The prospective area map generated by the MRSFF implies the possible occurrence of ultramafic rocks classified by the SAM method. These results confirm the suitability of this innovative approach for prospectivity mapping of Ni sulfide deposits.
A microservice-based framework for exploring data selection in cross-building knowledge transfer

Supervised deep learning has achieved remarkable success in various applications. Successful machine learning application however depends on the availability of sufficiently large amount of data. In the absence of data from the target domain, representative data collection from multiple sources is often needed. However, a model trained on existing multi-source data might generalize poorly on the unseen target domain. This problem is referred to as domain shift. In this paper, we explore the suitability of multi-source training data selection to tackle the domain shift challenge in the context of domain generalization. We also propose a microservice-oriented methodology for supporting this solution. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select energy consumption data.
The State of the Art of Cryptography-Based Cyber-Attacks

This paper objective was to investigate the state of the art of crypto-based cyber-attacks during 1989-2019. Documentary research was employed for data analysis. The findings found that cryptography-based cyber-attacks during 1989-2019 could be defined as any attacks mainly focus on encryption technology-related crimes, classified into five key groups-ransomware, cryptojacking, coin thief, illegal money transfer, and fake identity. Simultaneously, dirty fiat currency and dirty cryptocurrency were investigated in these categories as mediums of exchange in cybercrimes. In addition, profit and availability were two key incentives for cryptographybased cyber-attackers during 1989-2009. Cybercriminals would change cryptography-based tools and targeted groups or market back and forth due to rise and fall of profit from their attacks. For profit-oriented incentive, cryptojacking, ransomware, coin thief, and illegal money transfer were key cybercrimes. In addition, cybercriminals intentionally lessened availability of their targeted devices of victims with sabotage-oriented disk wipers. Last, all illegal activities-based classification of cryptography-based cyber-attacks could be categorized into eCSIRT’s cyberattacks as follows: Abusive Content, Malicious Code, Intrusion Attempts, Intrusions, Availability, Information Security, and Fraud.
A Statistical Linguistic Terms Interrelationship Approach to Query Expansion Based on Terms Selection Value

Query expansion has changed the information retrieval process to improve search performance. It aimed at improving the performance of information retrieval system to retrieve user information need. However, the term selection process still lacks precision results due to lexical ambiguity challenge. Many researchers have focused on pseudo-relevance feedback to select terms from the top-retrieved documents using some statistical linguistic techniques. However, their methods have limitations. This paper proposed a statistical linguistic terms interrelationship that exploits term selection in query expansion and retrieved relevance results. The proposed approach was tested on Malay, Hausa and Urdu Quran translated datasets and the results indicate that the proposed approach outperforms the previous method in retrieving relevance results. Future work should focus on the weighting score based on terms interrelationship to improve the query expansion performance.
BPFContain: Fixing the Soft Underbelly of Container Security

Linux containers currently provide limited isolation guarantees. While containers separate namespaces and partition resources, the patchwork of mechanisms used to ensure separation cannot guarantee consistent security semantics. Even worse, attempts to ensure complete coverage results in a mishmash of policies that are difficult to understand or audit. Here we present BPFContain, a new container confinement mechanism designed to integrate with existing container management systems. BPFContain combines a simple yet flexible policy language with an eBPF-based implementation that allows for deployment on virtually any Linux system running a recent kernel. In this paper, we present BPFContain's policy language, describe its current implementation as integrated into docker, and present benchmarks comparing it with current container confinement technologies.
The Effect of IFRS Adoption on Foreign Direct Investment in Africa

Adoption of International Financial Reporting Standards (IFRS) is supposed to help enhance comparability of financial statement, improve the quality of financial reporting and accounting information of businesses in a country. This is expected to help improve Foreign Direct Investment (FDI) in the adopting countries. This study examined the effect of IFRS adoption on FDI inflows in Africa. Unlike previous studies that sample both adopting and non-adopting countries, this study sampled only Africa countries that have adopted IFRS to determine whether the adoption has improved FDI inflows. To achieve this objective, 20 African countries that have adopted IFRS were sampled covering a period 1980 to 2015. Data was sourced from The World Bank financial and Economic Data. Control variables such as GDP growth, openness of the economy, government debt and population growth were included in the model. The correlation and regression analysis showed that IFRS adoption has a positive and significant influence on FDI inflows in Africa. On the other hand, open economy, government debt and population growth had a positive and significant association with FDI. Overall, the results show that African countries that want to improve FDI inflows must improve the quality of their reporting environment by adopting IFRS.
Generation and Distribution Secret Encryption Keys with Parameter

this article describes a new way to generate and distribute secret encryption keys, in which the processes of generating a public key and formicating a secret encryption key are performed in algebra with a parameter, the secrecy of which provides increased durability of the key.
Medical Text Entity Recognition Based on Deep Learning

Abstract Named entity recognition is a very important basic task in natural language processing, and a basic technology for many high-level applications of natural language processing. Traditional methods to solve named entity recognition are mainly based on rules and statistics. The rule-based method requires strong linguistic knowledge and is poor in generality. Based on the above background, the purpose of this paper is to recognize medical text entities based on deep learning. This paper proposes a new LSTM framework that integrates dual-channel and sentence-level reading control gates. In the input part, double channels are added to obtain two kinds of semantic information from static word vectors and fine-tuned word vectors. Then, the read-in control gate is integrated inside the neural network to determine the propagation of the sentence representation vector. Finally, the CRF model is used to fully consider the dependency relationship between the types of context words when outputting tags. The F1 value of 89.49% was achieved on the BioCreative II GM corpus. In summary, this article applies two deep learning methods to improve the performance of biomedical named entity recognition tasks. Finally, without adding any artificial features and post-processing operations, this article has achieved 89.94% F1 value on the BioCreative II GM corpus, and it is 0.89% higher than the F1 value of the current best system.
Human capital development: development of professional competencies through soft skills

The goal of the article is to analyze the development of the professional competency of leadership through the development of soft skills in the students of Russian engineering universities on the example of the Moscow Polytechnic University. The most demanded and dynamic part of the labor market is currently represented by engineering specialists. However, successful professional and career growth, as well as the effective development of human capital as the main resource of the innovative development of modern society, requires a combination of engineering (hard skills) and humanitarian (soft skills) abilities in a specialist. The development of many socially significant processes in modern society demonstrates aggravated contradictions and the development of new realization trends many of which are associated with changes in the parameters of these processes which, in turn, is determined by the mass implementation of innovative digital technologies and globalization processes. Globalization as a process ensuring the accessibility and openness of information has a significant impact on the transformation of national values within a country in favor of the emergence of global social, moral, and legal values and social behavior norms in the field of political and state power, as well as the economy. Leadership as a social phenomenon bearing the main load of managing all socially significant processes is also changing. Leadership as a social phenomenon is subject to constant scientific study and analysis. For this purpose, the GLOBE (Global Leadership and Organization Behavior Effectiveness) International Research Program was created. The GLOBE program coordinates the study of leadership in more than 60 countries which also supports the relevance of researching the phenomenon of leadership. In the professional activity, leadership manifests through the “leadership qualities” competency.
PredDiff: Explanations and interactions from conditional expectations

PredDiff is a model-agnostic, local attribution method that is firmly rooted in probability theory. Its simple intuition is to measure prediction changes while marginalizing features. In this work, we clarify properties of PredDiff and its close connection to Shapley values. We stress important differences between classification and regression, which require a specific treatment within both formalisms. We extend PredDiff by introducing a new, well-founded measure for interaction effects between arbitrary feature subsets. The study of interaction effects represents an inevitable step towards a comprehensive understanding of black-box models and is particularly important for science applications. Equipped with our novel interaction measure, PredDiff is a promising model-agnostic approach for obtaining reliable, numerically inexpensive and theoretically sound attributions.