Reviewerly/exHarmony huggingface dataset:
Discoursing Novel Procedure for Segmentation of Mammograms

Mammography plays a significant role in the early detection of breast cancers since it can demonstrate changes in the breast, years before a patient or physician can feel them. The research work conducted in the research paper highlights the process of segmentation of mammogram images intending to detect the presence of tumors in the breast at early stages so that such tumors can be timely cured and further damage could be prevented. The flowchart developed in the research paper defines a systematic approach adopted to perform segmentation on mammograms. This includes the use of techniques like Green Channel Complement, CLAHE (Contrast Limited Adaptive Histogram Equalization), Morphological operations, and FCM (Fuzzy C-Means). Mammogram images from the MIAS (Mammographic Image Analysis Society) database have been used for performing segmentation. The research paper features a detailed algorithm that discusses the detailed adopted approach. The GUI (Graphical User Interface) has been constructed with multiple windows to show the output received at each step after appropriate processing.Numerical readings have been obtained for the parameters like sensitivity, specificity, accuracy, positive predictive value, negative predictive value, false-negative rate, false-positive rate, etc. The obtained readings of different parameters prove the authenticity of conducted work. Segmentation enables the scrutinizing of any region within an image. The conducted research work can prove helpful in enhancing the mammogram image and focusing on the segmented image which indicates the presence of microcalcifications. The effectively conducted segmentation enables the radiologist to classify the tumor as benign (non-cancerous) or malignant (cancerous). Based on the obtained result the further treatment of the patient can be decided upon. The findings of the research paper are restricted to the segmentation phase of the mammogram images.
Augmentation Techniques for Sequential Clinical Data to Improve Deep Learning Prediction Techniques

Methods based on neural networks have become more and more attractive in the medical domain as Deep Learning frameworks mature and popularize. One application in this context refers to the use of recurrent networks to predict the most probable clinical conditions of a patient, given his/her history of hospital/medical admissions. The problem is that clinical data to support machine learning is scarce, mainly due to privacy matters and to ill-structured medical databases. In this work, we demonstrate that it is possible to augment clinical data to improve the performance of automatic predictive systems. We introduce two methods to create synthetic clinical histories (trajectories) based on existing data; the first one extracts subsequences of trajectories to emphasize the transition in between hospital admissions; the second method benefits from the hierarchical structure of standard diagnosis codes (like ICD-9) trajectories whose characteristics resemble those of real-world clinics. Our results demonstrate significant improvements in two datasets, demonstrating the feasibility of data augmentation in the clinical scenario. Our results shall inspire the augmentation of data in several other scenarios related to the medical domain.
Abstract Echo State Networks

Noisy or adverse input is a threat to the safe deployment of neural networks in production. To ensure the safe operations of such networks they need to be hardened to work under such conditions. Abstract interpretation, as a tool to formally verify properties of computations, can be used for this task. But, to date, this has mostly been studied for feed-forward networks, but not so for recurrent neural networks. For a subclass of recurrent neural networks, called echo state networks, we propose a new training algorithm using abstract interpretation and convex programming to increase the robustness against noisy inputs. Our empirical results show that the new training regime improves the performance of echo state networks in an open loop setup under high noise and generally improves their performance in closed loop setups.
A No-Reference Image Quality Comprehensive Assessment Method

On the basis of the research status of image quality comprehensive assessment, a no-reference image quality comprehensive assessment function model is proposed in this paper. First, the image quality is classified as contrast, sharpness, and signal-to-noise ratio (SNR), and the interrelation of each assessment index is researched and analyzed; second, the weights in the comprehensive assessment model are studied when only contrast, sharpness, and SNR are changed. Finally, on the basis of studying each kind of distortion separately, and considering the different types of image distortion, we studied how to determine the weights of each index in the comprehensive image quality assessment. The results show that the no-reference image quality comprehensive assessment function model proposed in this paper can better fit human visual perception, and it has a good correlation with Difference Mean Opinion Score (DMOS). Correlation Coefficient (CC) reached 0.8331, Spearman Rank Order Correlation Coefficient (SROCC) reached 0.8206, Mean Absolute Error (MAE) was only 0.0920, Root Mean Square Error (RMSE) was only 0.1122, Outlier Ratio (OR) was only 0.0365. The method proposed in this paper can be applied to photoelectric measurement equipment television system and give an accurate and reliable quality assessment to no reference television images.
An Automatically Verified Prototype of the Tokeneer ID Station Specification

The Tokeneer project was an initiative set forth by the National Security Agency (NSA, USA) to be used as a demonstration that developing highly secure systems can be made by applying rigorous methods in a cost effective manner. Altran Praxis (UK) was selected by NSA to carry out the development of the Tokeneer ID Station. The company wrote a Z specification later implemented in the SPARK Ada programming language, which was verified using the SPARK Examiner toolset. In this paper, we show that the Z specification can be easily and naturally encoded in the {log} set constraint language, thus generating a functional prototype. Furthermore, we show that {log}'s automated proving capabilities can discharge all the proof obligations concerning state invariants as well as important security properties. As a consequence, the prototype can be regarded as correct with respect to the verified properties. This provides empirical evidence that Z users can use {log} to generate correct prototypes from their Z specifications. In turn, these prototypes enable or simplify some verificatio activities discussed in the paper.
Document-level machine translation evaluation project: methodology,effort and inter-annotator agreement

Recently, document-level (doc-level) human evaluation of machine translation(MT) has raised interest in the community after a few attempts have disprovedclaims of “human parity” (Toral et al.,2018; Laubli et al., 2018). However, lit- ¨tle is still known about best practices regarding doc-level human evaluation. Thisproject aims to identify methodologies tobetter cope with i) the current state-of-theart (SOTA) human metrics, ii) a possiblecomplexity when assigning a single scoreto a text consisted of ‘good’ and ‘bad’sentences, iii) a possible tiredness bias indoc-level set-ups, and iv) the difference ininter-annotator agreement (IAA) betweensentence and doc-level set-ups.
NICE: Neural Integrated Custom Engines.

In this paper, we present a machine translation system implemented by the Translation Centre for the Bodies of the European Union (CdT). The main goal of this project is to create domain-specific machine translation engines in order to support machine translation services and applications to the Translation Centre’s clients. In this article, we explain the entire implementation process of NICE: Neural Integrated Custom Engines. We describe the problems identified and the solutions provided, and present the final results for different language pairs. Finally, we describe the work that will be done on this project in the future.
Evaluation of Shape from Shading Surface Reconstruction Quality for Liver Phantom

Bas, Mateusz Spinczyk, DominikThe complexity and limitations of common laparoscopic procedures induce designing of solutions that allow for efficient and precise methods of guidance throughout the surgery. Using Shape from Shading methods for reconstruction of surfaces in biomedical engineering and medicine creates a whole new approach in diagnostic and therapeutic procedures. The goal of presented work is to assess the quality of phantom liver surface reconstruction from monocular laparoscopic image frames using Shape from Shading algorithm by comparing the acquired shape with ground-truth 3D model from Time-of-Flight camera using Singular Value Decomposition registration algorithm.
Extreme ROS Reality: A Representation Framework for Robots Using Image Dehazing and VR

It is necessary to work in response to various situations when using robots at disaster areas. So, the complete automation remote control technique of the robot is essential. In recent years, the remote control technique of the robot is mainly focused on recognizing the working environment of the robot from the 2D monitor and performing the operation. In order to operate the robot correctly, a corresponding technique and sufficiently trained engineers are required. To this end, a new remote control technology that can operate more easily is required. Also, depending on the disaster environment that working at night or indoors is assumed, it becomes difficult to recognize the work environment correctly from the image by the sensor such as the camera due to insufficient light source. In this chapter, in order to solve these problems, as a development of new remote control technology, we propose the method of reproducing the working environment and the image improvement method under the low light source environment. In this chapter, we reproduce the working environment of remote areas by using virtual reality. In addition, we improve the image under low light source environment using haze removal algorithm. The work environment of the robot is acquired by RGBD sensor. Image improvement processing is performed on the obtained color image, and the image data is transmitted to a remote place where the operator is located via WebSocket. The experiments show that our method reproduces the working environment of the robot as point group to the virtual reality space and examine its usefulness.
Coresets for the Nearest-Neighbor Rule

Given a training set P of labeled points, the nearest-neighbor rule predicts the class of an unlabeled query point as the label of its closest point in the set. To improve the time and space complexity of classification, a natural question is how to reduce the training set without significantly affecting the accuracy of the nearest-neighbor rule. Nearest-neighbor condensation deals with finding a subset R ⊆ P such that for every point p ∈ P, p’s nearest-neighbor in R has the same label as p. This relates to the concept of coresets, which can be broadly defined as subsets of the set, such that an exact result on the coreset corresponds to an approximate result on the original set. However, the guarantees of a coreset hold for any query point, and not only for the points of the training set.This paper introduces the concept of coresets for nearest-neighbor classification. We extend existing criteria used for condensation, and prove sufficient conditions to correctly classify any query point when using these subsets. Additionally, we prove that finding such subsets of minimum cardinality is NP-hard, and propose quadratic-time approximation algorithms with provable upper-bounds on the size of their selected subsets. Moreover, we show how to improve one of these algorithms to have subquadratic runtime, being the first of this kind for condensation.
Wearable Scene Classification System for Visually Impaired Individuals

Advancements in technology are made to make human life easier. According to survey made by the World Health Organization (WHO), millions of people are suffering from visual impairement. Individuals with visual impairment face many troubles in their daily life tasks. In this research, our objective is to design a cheap, secure, wearable, and portable system for visually impaired individuals. The proposed methodology includes the acquisition of indoor samples using Raspberry Pi Night Vision Camera and then preprocessing using the Retinex Filtering Technique and Low Light Image Enhancement Technique. Local Binary Pattern Features are uprooted from the preprocessed images which are then trained using a different set of classifiers including Fine Tree (FT), Quadratic Discriminant (QD), SVM-Quadratic (SVM-Q), Weighted-KNN (W-KNN) and Ensemble. Using 10-fold cross-validation, Quadratic Discriminant gives maximum accuracy of 92.0%.
Seismic Soundoff

Machine learning and artificial intelligence are two trendy but important topics in geophysics. In this episode, host Andrew Geary speaks with Distinguished Lecturer Aria Abubakar about his 2020 tour titled, “Potential and challenges of applying artificial intelligence and machine learning methods for geoscience.” This conversation explores machine learning and artificial intelligence from multiple angles. Listen to the full episode at https://seg.org/podcast/post/9101 .
Code, and Other Laws of Blockchain†

Abstract There has been burgeoning interest among legal scholars in recent years regarding the implications of blockchain technology for the law. Two thoughtful monographs that go beyond the hyped claims of enthusiasts and cynics are Primavera De Filippi and Aaron Wright’s Blockchain and the Law: The Rule of Code and Kevin Werbach’s Blockchain and the New Architecture of Trust. While the two books have different focal points, both contain a common Laurence-Lessig-inspired theme of ‘code as law’ in which decentralised blockchain networks are viewed as a regulatory ‘modality’ or ‘architecture’ with its own system of rules. However, as this article argues, blockchain is not outside the law or the existing legal system. Code necessarily interacts with other modes of regulation, namely the market, social norms and law, in constraining the operation of blockchain applications such as smart contracts. This argument also situates smart contracts in a relational analysis of real-world contracting practices.
A Study on Students’ Satisfaction with University English Programs

본 연구에서는 대학 교양 영어 프로그램에서 한국인 영어 교수자와 원어민 영어 교수자가 가르치는 수업에 따라 학습자의 수업 만족도에 차이가 있는지를 알아보고, 수업의 만족도에 영향을 미치는 가장 큰 요인이 무엇인지 살펴보고자 한다. 본 연구는 경상남도에 위치한 4년제 C대학교에서 2019학년도 2학기 <영어회화>를 수강한 학생 831명을 대상으로 2019학년도 2학기가 끝나는 시점에 설문조사 형식으로 진행되었다. 설문지는 총 8개의 문항으로 구성되어 있으며, 수업내용, 교수법, 전문성 및 열정, 성실성, 과제분량, 피드백, 평가기준 및 방법제시의 명확성, 그리고 마지막으로 전체 만족도 요인을 측정하였다. 학습자들은 한국인 교수자와 원어민 교수자의 영어 수업에 대체로 만족하였으며, 두 집단 간의 차이가 통계적으로 유의미하지는 않았다. 또한 원어민 영어 교수자의 수업과 한국인 영어 교수자의 수업 모두에서 교수자의 수업방법, 과제분량, 피드백, 평가기준 및 방법제시 항목이 전체 만족도 결과에 영향을 미치는 요인이었다. 하지만 한국인 교수자의 영어 수업에는 교수자와의 원활한 상호작용 정도를 보여주는 피드백 요인이, 원어민 교수자의 영어 수업에서는 교수자의 수업 방법이 얼마나 효과적이었는지에 관한 요인이 전체 만족도에 가장 큰 영향력을 행사하였다. 이러한 결과는 한국인 영어 교수자와 원어민 교수자의 영어 수업에서 학습자들은 교수자와의 활발한 의사소통과 상호작용을 중요시한다고 볼 수 있다.This study aims to investigate whether there is a difference in students’ satisfaction with university English programs taught by native English teachers and Korean English teachers and to ascertain what is the biggest variable affecting students’ satisfaction. This study presents the result of the survey conducted at the end of the fall semester 2019 at C University, which is located in Gyeongsangnam-do. A total of 831 students who opted for “English Communication” at the fall semester of 2019 participated in this study. The questionnaire consisted of eight questions, which included “content,” “teaching method,” “competence,” “attitude,” “amount of homework,” “feedback,” “clarity in assessment,” and “overall satisfaction.” The students were generally satisfied with the English courses of both the Korean and native English teachers, however the difference between the two groups was not statistically significant. Additionally, in the classes of both the native and Korean English teachers, “teaching method,” “amount of homework,” “feedback,” and “clarity in assessment” are statistically significant variables that affect the “overall satisfaction.” The most important variable of the students’ satisfaction with the English courses of Korean English teachers is “feedback,” which indicated how smooth the interaction with the teachers was. However, with the English classes of Native English teachers, the effectiveness of the “teaching method” was the most vital variable. These results suggest that the students emphasize active communication and interaction with teachers in the English classes of Korean and native teachers.
An Adaptive Grey Wolf Algorithm Based on Population System and Bacterial Foraging Algorithm

In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.
Science and Math Courses in a Danish Digital Learning Platform – What Makes Them More or Less Popular?

As part of a Danish context of educational reforms since 2013, digital learning platforms (DLPs) are mandatory in primary and lower secondary education. Teacher-created courses are available in DLPs and can be shared, remixed, and reused by other teachers, providing a wide range of courses for systematic studies. In this article, we present an analysis of the Science and Math courses’ popularity. Based on previous research and didactical theory, we focus on the question: Can didactical variables discriminate the most and least popular Science and Math courses in the learning platform Meebook? If so, which variables? The study was based on the random collection and coding of the 102 courses. Multivariate analysis techniques detected that eight variables can differentiate the course's popularity with a success classification rate of about 75%. Visual attractiveness, easy implementation, and students’ work are the three aspects that teachers use to select courses. The study results have potential implications for platform designers, local and national authorities, as well as teachers who use the course builder daily.
Research on Strategies of Improving Students’ Learning Motivation in College Physical Teaching

College physical teaching is of great significance to the healthy growth of students. In physical teaching, students’ strong learning motivation has a great influence on teaching efficiency and quality. Colleges should fully consider the law of physical and mental development and learning needs of contemporary college students, and adopt various effective strategies to improve students’ learning motivation in physical teaching. This paper first briefly discusses the basic knowledge of learning motivation, then puts forward the key points of improving students’ learning motivation in college physical teaching, and then puts forward the basic strategies of improving students’ learning motivation in college physical teaching combined with physical teaching practice.
Path Planning Under Malicious Injections and Removals of Perceived Obstacles: A Probabilistic Programming Approach

An autonomous mobile robot may encounter adversarial environments in which an attacker tries to influence its decisions. Through physical or software-level attacks, some of the robot's sensors might be compromised-a special concern for self-driving vehicles. Motivated by this scenario, this letter introduces and studies the problem of planning kinematically feasible (and possibly efficient) paths with bounded collision probability in adversarial settings where the obstacles perceived online by the robot display two layers of uncertainty. The first is the “usual” Gaussian uncertainty one would obtain from a standard object tracker (e.g., an Extended Kalman Filter); the second is an additional layer of uncertainty that captures possible sensor attacks and describes the actual existence of groups of obstacles in the environment. We study the complexity of the problem and propose a general sampling-based solution framework that uses the Sequential Probability Ratio Test (SPRT) to check collision probability constraints along the computed trajectory. We also show how probabilistic programming languages (PPLs) can simplify programming common algorithms (such as RRT and Hybrid A*) for mixed uncertainty. In addition to providing an easy-to-use programming framework, our approach is shown to plan safer paths compared to a Naive Monte Carlo baseline when both approaches are allowed to use at most the same given number of samples to perform collision checks.
The Cryptocurrency Ecosystem

In the previous chapter, we surveyed the history of money and discussed how bitcoin changes the paradigm for currency creation and distribution. We also saw how bitcoin transactions differ from transactions in a conventional fiat money system. The advent of Bitcoin has spawned a large number of cryptocurrencies which are similar in varying degrees to it. They differ from Bitcoin in their characteristics, such as the total number of coins that can be produced, the speed and manner in which transactions are consummated, as well as the manner in which the cryptocurrency reaches consensus as to which transactions are valid. Furthermore, as we will see subsequently in this chapter, many cryptocurrencies are designed to handle domain-specific use cases.
Research and Application of Integration Solution for Enterprise-Level Heterogeneous Document Management Systems

Abstract This paper proposes an enterprise-level document management system integration solution, which can effectively solve the problems of the coexistence of multiple document management systems in enterprises and public institutions, the repeated collection of document data and the inefficient access application. The solution also can effectively support the data security and confidentiality of enterprises and public institutions. The core idea of the integration solution is to realize the application integration between the document integration application system and the document information management systems by taking the document classification collection management regulations as the management basis, taking the document information integration application system as the technical carrier, and adopting the SOA technology. The integration solution has been proved to be effective in practice, with significant application value and promotion value.
Cybercrimes and investigations

Investigations into cybercrimes is a specialty that requires expertise and knowledge of the law. There are many challenges investigating cybercrimes and privacy legislation that you should be knowledgeable of. Knowing these challenges will help you make wise decisions about your strategy and methodology. Investigators that are experienced in cyberinvestigations should be the ones investigating such crimes.