Reviewerly/exHarmony huggingface dataset:
The concordance in lesion detection and characteristics between the Anatomical Intelligence and conventional breast ultrasound Scan method

The aim of this study was to investigate the concordance in lesion detection, between conventional Handhold Ultrasound (HHUS) and The Anatomical Intelligence for Breast ultrasound scan method.The AI-breast showed the absolute agreement between the resident and an experienced breast radiologist. The ICC for the scan time, number, clockface location, distance to the nipple, largest diameter and mean diameter of the lesion obtained by a resident and an experienced breast radiologist were 0.7642, 0.7692, 0.8651, 0.8436, 0.7502, 0.8885, respectively. The ICC of the both practitioners of AI-breast were 0.7971, 0.7843, 0.9283, 0.8748, 0.7248, 0.8163, respectively. The k value of Anatomical Intelligence breast between experienced breast radiologist and resident in these image characteristics of boundary, morphology, aspect ratio, internal echo, and BI-RADS assessment were 0.7424, 0.7217, 0.6741, 0.6419, 0.6241, respectively. The k value of the two readers of AI-breast were 0.6531, 0.6762, 0.6439, 0.6137, 0.5981, respectively.The anatomical intelligent breast US scanning method has excellent reproducibility in recording the lesion location and the distance from the nipple, which may be utilized in the lesions surveillance in the future.
ADAPTIVE WEIGHTING OF IMAGE OBSERVATIONS FOR SELF-CALIBRATION WITH FISHEYE IMAGES

Abstract. Fisheye cameras have been widely used in photogrammetric applications, but conventional techniques must be adapted to consider specific features of fisheye images, such as nonuniform resolution in the images. This work presents experimental results of an adaptive weighting of the observation in a self-calibrating bundle adjustment to cope with the nonuniform resolution of fisheye images. GoPro Fusion and Ricoh Theta dual-fisheye systems were calibrated with bundle adjustment based on equisolid-angle projection model combined with Conrady-Brown distortion model. The image observations were weighted as a function of radial distance based on combining loss of resolution and blurring in fisheye images. The results were compared with a similar trial by considering the same standard deviation for all image observations. The use of adaptive weighting of image observations reduced the estimated standard deviation of unit weight by 30 % and 50 % with GoPro Fusion and Ricoh Theta images, respectively. The estimation of relative orientation parameters (ROPs) was also improved (∼50 %) when using adaptive weighting for image observations.
eMLM: A New Pre-training Objective for Emotion Related Tasks

Bidirectional Encoder Representations from Transformers (BERT) have been shown to be extremely effective on a wide variety of natural language processing tasks, including sentiment analysis and emotion detection.However, the proposed pre-training objectives of BERT do not induce any sentiment or emotion-specific biases into the model.In this paper, we present Emotion Masked Language Modeling, a variation of Masked Language Modeling, aimed at improving the BERT language representation model for emotion detection and sentiment analysis tasks.Using the same pre-training corpora as the original BERT model, Wikipedia and BookCorpus, our BERT variation manages to improve the downstream performance on 4 tasks for emotion detection and sentiment analysis by an average of 1.2% F1.Moreover, our approach shows an increased performance in our task-specific robustness tests.We make our code and pre-trained model available at https://github.com/tsosea2/eMLM.
Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction

Identifying relations from dialogues is more challenging than traditional sentence-level relation extraction (RE), since the difficulties of speaker information representation and the long-range semantic reasoning.Despite the successful efforts, existing methods do not fully consider the particularity of dialogues, making them difficult to truly understand the semantics between conversational arguments.In this paper, we propose two beneficial tasks, speaker prediction and trigger words prediction, to enhance the extraction of dialoguebased relations.Specifically, speaker prediction captures the characteristics of speakerrelated entities, and the trigger words prediction provides supportive contexts for relations between arguments.Extensive experiments on the DialogRE dataset show noticeable improvements compared to the baseline models, which achieves a new state-of-the-art performance with a 65.5% of F1 score and a 60.5% of F1 c score, respectively.* Work done during an internship at Tencent Cloud Xiaowei.
Building and Annotating a Codeswitched Hate Speech Corpora

Presidential campaign periods are a major trigger event for hate speech on social media in almost every country. A systematic review of previous studies indicates inadequate publicly available annotated datasets and hardly any evidence of theoretical underpinning for the annotation schemes used for hate speech identification. This situation stifles the development of empirically useful data for research, especially in supervised machine learning. This paper describes the methodology that was used to develop a multidimensional hate speech framework based on the duplex theory of hate [1] components that include distance, passion, commitment to hate, and hate as a story. Subsequently, an annotation scheme based on the framework was used to annotate a random sample of ~51k tweets from ~400k tweets that were collected during the August and October 2017 presidential campaign period in Kenya. This resulted in a goldstandard codeswitched dataset that could be used for comparative and empirical studies in supervised machine learning. The resulting classifiers trained on this dataset could be used to provide real-time monitoring of hate speech spikes on social media and inform data-driven decision-making by relevant security agencies in government.
Medical Image Key Area Protection Scheme Based on QR Code and Reversible Data Hiding

Medical image data, like most patient information, has high requirements for privacy and confidentiality. To improve the security of medical image transmission within the open network, we proposed a medical image key area protection algorithm based on reversible data hiding. First, the coefficient of variation is used to identify the key area, that is, the lesion area of the image. Then, the other regions are divided into blocks to analyze the texture complexity. Next, we propose a new reversible data hiding algorithm, which embeds the content of the key area into the high-texture regions. On this basis, a quick response (QR) code is generated using the ciphertext of the basic image information to replace the original lesion area. Experimental results show that this method can not only safely transmit sensitive patient information by hiding the content of the lesion, it can also store copyright information through QR code and achieve accurate image retrieval.
Implementing Analysis of Ordinal Regression Model on Student’s Feedback Response

Instruction is a multidimensional procedure including a quantity of features, e.g., tutor qualities, that occasionally are hard to assess. In certain points, education efficiency, that is a part of instructing, is affected by a combination of teacher features for example, capacity and clarity to encourage the students to make them study of his subjects, capacity to establish the lesson also with trainings and lectures. These aspects are not only attributable to motivate students, but also age, gender, prior experiences, As more and more the effectiveness of teaching is becoming even more significant in school evaluation system, it is, indeed, essential to discover how to assess it and find the significant related factors for giving the best rank of feedback to their tutors. This paper focuses the assessment of teaching effectiveness forum from students’ perspective “Feedback”, examine the questionnaires provided to the students of Cihan University at the end of their courses and discover the most effected teacher’s characteristics. The outcome variable (Student’s Feedback) was recorded on well-ordered, five-point scale of Likert provided by the students, to a set of independent variables to teacher level. The major techniques elaborated in the model fitting for ordinal regression were stating which independent variables are most likely to be kept in the model and selecting the link function such as, (logit link, complementary log-log link, negative log-log link and probit link) which verified the model suitability. Further to that, several statistical diagnoses have conducted like the model fitting, classification accuracy and the validity assumptions of the model, which is parallel lines, were fundamentally calculated in choosing the best fitted model. The dataset implemented in the analysis entails of almost (21566) respondents to the formed questionnaire in relation to courses of Cihan University for the 2018 – 2019 academic year.
Pandemic as a Service (PNDaaS) – A Cloud-Based Approach for Early Detection of Pandemic Diseases (like COVID-19) Through Smart Phone Sensors

Since December 2019 an unbroken pandemic due to the Corona virus disease 2019 (COVID-19) caused by the severe respiratory disease corona virus 2 (SARS-CoV-2), leading in millions of deaths across worldwide.On 30 January 2020 WHO (World Health Organization) proclaimed COVID 19 a "Public Health Emergency of International Concern (PHEIC)" and a pandemic on 11 March 2020.According to WHO, COVID-19 has little or no specific vaccines or antiviral treatments till now, so, the first task is to identify infected people and isolate them to protect against cross-infection.This paper proposes a new cloud-based "pandemic as a service" framework -for early detection of any pandemic disease (such as COVID-19) through smart phone sensors on individual smart phones from anywhere in the world, i.e., any smart phone user can do their preliminary home pandemic test to protect against cross-infection.The government may therefore take the measures as a mandatory test for all smart phone users with the requisite sensors, not only helping to identify the disease early, but also monitoring the users' current location and taking the protective action.
Concurrent neural tree and data preprocessing AutoML for image classification

Deep Neural Networks (DNN's) are a widely-used solution for a variety of machine learning problems. However, it is often necessary to invest a significant amount of a data scientist's time to pre-process input data, test different neural network architectures, and tune hyper-parameters for optimal performance. Automated machine learning (autoML) methods automatically search the architecture and hyper-parameter space for optimal neural networks. However, current state-of-the-art (SOTA) methods do not include traditional methods for manipulating input data as part of the algorithmic search space. We adapt the Evolutionary Multi-objective Algorithm Design Engine (EMADE), a multi-objective evolutionary search framework for traditional machine learning methods, to perform neural architecture search. We also integrate EMADE's signal processing and image processing primitives. These primitives allow EMADE to manipulate input data before ingestion into the simultaneously evolved DNN. We show that including these methods as part of the search space shows potential to provide benefits to performance on the CIFAR-10 image classification benchmark dataset.
Sensor Degradation Detection Using Visual Timeseries and Deep Convolutional Neural Networks

Effective condition monitoring and prognostic analyses depend on accurate sensor data in order to estimate the health of an asset.Given the nature of many items of legacy equipment in the nuclear industry, this is often not the case, with degradation occurring due to the age and operation of sensors, communication routes as well as the asset itself.Detection of anomalous signals which do not reflect the behaviour of the asset is therefore essential, with the primary objective to repair or replace such components.This paper introduces a method of detection of sensor degradation using timeseries which have been converted to images, in order to leverage the powerful feature detection capabilities of modern deep convolutional neural networks.By converting 1-D time-series to 2-D representations via a Grammian Angular Field and using a small number of training examples, it is possible to train such a network to automatically identify features associated with faults.A case study is presented for a set of sensor types, demonstrating the capability of the model to generalise to previously unseen data from sensors of the similar type and identify faults at greater than 85% accuracy.The results demonstrate the benefits that can be derived from an unsupervised feature detection process for this type of problem and highlights the transferability of models trained on one sensor type and applied to previously unseen similar sensor types.
Comparison of 2D vs. 3D Unet Organ Segmentation in abdominal 3D CT images

A two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented.First each relevant organ's volume of interest is extracted as bounding box.The extracted volume acts as input for a second stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmentation as label mask.In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts.Our initial results indicate Dice improvements of about 6% at maximum.In this study to our surprise, liver and kidneys for instance were tackled significantly better using the faster and GPU-memory saving 2D U-Nets.For other abdominal key organs, there were no significant differences, but we observe highly significant advantages for the 2D U-Net in terms of GPU computational efforts for all organs under study.
RANCANG BANGUN SISTEM PRESENSI MAHASISWA DENGAN MENGGUNAKAN QR CODE BERBASIS ANDROID

Presence is an activity that aims to determine the level of attendance of a person in an agency, school or company. One of the problems that occurs is the student attendance system at STMIK Rosma Karawang due to cheating when making attendance at lecture activities begins. By looking at the problems contained in STMIK Rosma Karawang, a program is needed. To create a program requires a method. The method used is a waterfall as a software development method. The proposed database design is a DFD (Data Flow Diagram). Implementation of the program using Android Studio. With the design of the program is a solution to solve problems that exist in the presence system at STMIK Rosma Karawang, and can achieve an effective activity in supporting activities at STMIK Rosma Karawang.
Designing a Traceability System for Rice Distribution Process Using QR Code Bassed Android Application at Perum Bulog Subdivre III Surakarta

The state-owned Enterprises (SEO) that manages businesses in the food sector, especially rice is Perum Bulog. However, there were often reports from public about quality of Perum Bulog's rice. Such as the rice had contaminated with gravel or plastic and did not match with premium rice standard. Moreover, The distribution process of premium rice to outlet (RPK) found uncertainty over the arrival time of orders. It caused out of stock and lost sales at the outlet. This paper introduces a q traceability system rice distribution process based on Android with QR Code technology. Furthermore, this paper discusses the system architecture and the development of traceability system design using the company's business process data flow diagram. The developed prototype system shows the functional requirements of the system and can be used by stakeholders to monitor the production process and assist the decision-making process.
Concurrent Two-Factor Identify Verification Using Facial Identify and Facial Actions

Identity verification is ubiquitous in daily life. Its applications range from unlocking mobile device to accessing online account, boarding airplane or other types of transportation, recording times of arrival and leaving work, controlling access to a restricted area, facility, or vault, and many more. The traditional and the most popular identity verification is password authentication but with many challenges. Human biometric identifiers like fingerprint, retina scan, and 2D or 3D facial features have become popular alternatives. Some applications use two-factor or multi-factor authentication to increase system security, e.g., password and login code sent to a mobile device. All these identity verification methods have their challenges ranging from forgotten or stolen password to unaware or unintentional authentication and complexity and high costs. This paper presents a promising alternative that could be an improvement to the existing identity verification methods. This improved identity verification is a two-factor approach that concurrently analyzes facial features and unique facial actions. The user's facial features and facial actions must both match what have been stored in the system in order to pass identity verification. This two-factor verification requires only the frontal view of the face and authenticates facial features and facial actions concurrently. It generates an embedding of facial features and facial action in a short video for matching. We name this method Current Two-Factor Identity Verification (C2FIV). Two frameworks that use recurrent neural networks to learn the representation of facial features and actions. One uses an auto-encoder, and the other one uses metric learning. Experimental result shows that the metric learning model performs reliably with an average precision of 98.8%.
ALF

Topology and Weight Evolving Artificial Neural Network (TWEANN) is a concept to find the topology and weights of Artificial Neural Networks (ANNs) using genetic algorithms. However, a well-known downside is that TWEANN algorithms often evolve inefficient large ANNs for large-scale problems and require long runtimes.
WDTISeg: One-Stage Interactive Segmentation for Breast Ultrasound Image Using Weighted Distance Transform and Shape-Aware Compound Loss

Accurate tumor segmentation is important for aided diagnosis using breast ultrasound. Interactive segmentation methods can obtain highly accurate results by continuously optimizing the segmentation result via user interactions. However, traditional interactive segmentation methods usually require a large number of interactions to make the result meet the requirements due to the performance limitations of the underlying model. With greater ability in extracting image information, convolutional neural network (CNN)-based interactive segmentation methods have been shown to effectively reduce the number of user interactions. In this paper, we proposed a one-stage interactive segmentation framework (interactive segmentation using weighted distance transform, WDTISeg) for breast ultrasound image using weighted distance transform and shape-aware compound loss. First, we used a pre-trained CNN to attain an initial automatic segmentation, based on which the user provided interaction points of mis-segmented areas. Then, we combined Euclidean distance transform and geodesic distance transform to convert interaction points into weighted distance maps to transfer segmentation guidance information to the model. The same CNN accepted the input image, the initial segmentation, and weighted distance maps as a concatenation input and provided a refined result, without another additional segmentation network. In addition, a shape-aware compound loss function using prior knowledge was designed to reduce the number of user interactions. In the testing phase on 200 cases, our method achieved a dice of 82.86 ± 16.22 (%) for automatic segmentation task and a dice of 94.45 ± 3.26 (%) for interactive segmentation task after 8 interactions. The results of comparative experiments proved that our method could obtain higher accuracy with fewer simple interactions than other interactive segmentation methods.
Transfer Learning for Improving Results on Russian Sentiment Datasets

In this study, we test transfer learning approach on Russian sentiment benchmark datasets using additional train sample created with distant supervision technique.We compare several variants of combining additional data with benchmark train samples.The best results were achieved using three-step approach of sequential training on general, thematic and original train samples.For most datasets, the results were improved by more than 3% to the current state-of-the-art methods.The BERT-NLI model treating sentiment classification problem as a natural language inference task reached the human level of sentiment analysis on one of the datasets.
BLOCKCHAIN TECHNOLOGY AND THE IMPACT ON BUSINESS MODELS

Blockchain is an innovative technology, which emerged in 2008 and presents important possibilities for application. Thus, this article aims to analyze the main characteristics, of the business models, the companies that use the blockchain. To this end, a survey was carried out based on Crunchbase, which is a market intelligence platform. Resulting in a sample of 48 companies more relevant according to the site. The next step was to make a content analysis, with the help of the Iramuteq software, of the material collected on the websites of these companies. As a result of the process, three classes of business blockchain impact were found: a distributed value structure, a governance structure, and an application structure aimed at encrypted and tokens.
BHARAT TOWARDS ATMANIRBHARTA: A TWITTER BASED ANALYSIS USING NVIVO

It’s been over a year now and Covid-19 is still among us and most of the countries are still in fight against Covid-19. Lockdown was the only possible solution but the impact of lockdowns on economies was devastated. Atmanirbhar Bharat (Self-Reliant India) a Covid-19 pandemic economic package of twenty lakh crore rupees was announced by the Indian central government on the 12th of May, 2020. The package was released to make the country independent and to primarily empower the poor, labourers, and migrants who were adversely affected by Covid-19.
Computation of Trusted Short Weierstrass Elliptic Curves for Cryptography

Abstract Short Weierstrass elliptic curves with underlying hard Elliptic Curve Discrete Logarithm Problem (ECDLP) are widely used in cryptographic applications. A notion of security called Elliptic Curve Cryptography (ECC) security is also suggested in literature to safeguard the elliptic curve cryptosystems from their implementation flaws. In this paper, a new security notion called the “trusted security” is introduced for computational method of elliptic curves for cryptography. We propose three additional “trusted security acceptance criteria” which need to be met by the elliptic curves aimed for cryptography. Further, two cryptographically secure elliptic curves over 256 bit and 384 bit prime fields are demonstrated which are secure from ECDLP, ECC as well as trust perspectives. The proposed elliptic curves are successfully subjected to thorough security analysis and performance evaluation with respect to key generation and signing/verification and hence, proven for their cryptographic suitability and great feasibility for acceptance by the community.