Reviewerly/exHarmony huggingface dataset:
Learn from one data set to classify all – A multi-target domain adaptation approach for white blood cell classification

Background and objective: Traditional machine learning methods assume that both training and test data come from the same distribution. In this way, it becomes possible to achieve high successes when modelling on the same domain. Unfortunately, in real-world problems, direct transfer between domains is adversely affected due to differences in the data collection process and the internal dynamics of the data. In order to cope with such drawbacks, researchers use a method called “domain adaptation”, which enables the successful transfer of information learned in one domain to other domains. In this study, a model that can be used in the classification of white blood cells (WBC) and is not affected by domain differences was proposed. Methods: Only one data set was used as source domain, and an adaptation process was created that made possible the learned knowledge to be used effectively in other domains (multi-target domain adaptation). While constructing the model, we employed data augmentation, data generation and fine-tuning processes, respectively. Results: The proposed model has been able to extract “domain-invariant” features and achieved high success rates in the tests performed on nine different data sets. Multi-target domain adaptation accuracy was measured as %98.09. Conclusions: At the end of the study, it has been observed that the proposed model ignores the domain differences and it can adapt in a successful way to target domains. In this way, it becomes possible to classify unlabeled samples rapidly by using only a few number of labeled ones.
A study of blockchain technology development and military application prospects

Abstract Once blockchain technology is successfully applied, it will certainly trigger revolutionary changes in the military development and combat mode, which are beyond the traditional military command and management scope. In the future, blockchain technology, by combining with military artificial intelligence, Internet of things, cloud computing and big data, will have priority to be applied in military management, support, security and even command. It is of great significance to excavate the military application potential of blockchain technology and scientifically predict its impact and influence on the military field, so as to improve the combat effectiveness of the army and promote its transformation and development.
A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media

The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020.
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.
Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.
Software-Based Approach towards Automated Authorship Acknowledgement—Chi-Square Test on One Consonant Group

A one-consonant group approach to the authorship attribution has been proposed. The approach is based on determining, by the chi-square test, the consonant group in which the difference between the texts by different authors is statistically significant. The developed model determines author-differentiating capability of each consonant group in a relation of the number of comparisons, in which the difference between the texts by two authors is statistically significant to the total number of comparisons. The determined general author-differentiating capability of the group of stop consonants, which is a statistical parameter of the authorial style, is the highest in the comparisons of texts from the publicist and belles-lettres styles. The one-consonant group approach simplifies the whole process of authorship attribution and ensures a higher level of automation. The conducted experiments on the Java programming language have proved that the chi-square test is a powerful nonparametric statistical test that can be used for author identification on the level of English consonants with a test validity of 95%.
Are low LRs reliable?

<h2>Abstract</h2> To answer the question "Are low likelihood ratios reliable?" requires both a definition of reliable and then a test of whether low likelihood ratios (<i>LR</i>s) meet that definition. We offer, from a purely statistical standpoint, that reliability can be determined by assessing whether the rate of inclusionary support for non-donors over many cases is not larger than expected from the <i>LR</i> value. Thus, it is not the magnitude of the <i>LR</i> alone that determines reliability. Turing's rule is used to inform the expected rate of non-donor inclusionary support, where the rate of non-donor inclusionary support is at most the reciprocal of the <i>LR,</i> i.e. Pr(<i>LR</i> > <i>x</i>|<i>H<sub>a</sub></i>) ≤1/<i>x</i>. There are parallel concerns about whether the value of the evidence can be communicated. We do not discuss that in depth here although it is an important consideration to be addressed with training. In this paper, we use a mixture of real and simulated data to show that the rate of non-donor inclusionary support for these data is significantly lower than the upper bound given by Turing's rule. We take this as strong evidence that low <i>LR</i>s are reliable.
Blockchain Technology for Fraudulent Practices in Insurance Claim Process

On top of distributed computing, Bitcoin was implemented as cryptocurrency. Bitcoin originally came with blockchain technology to protect coins from misuse. Blockchain provided a distributed ledger of cryptocurrency transactions in an immutable form to protect data from malicious attacks. Thus blockchain complemented the security aspect of Bitcoin. In the later stages, blockchain evolved as a distributed ledger technology that is used in different domains like healthcare. There are different issues associated with different domains. For instance, in the insurance domain, there are issues related to false claims and the claims that are manipulated by competent authority illegally. This problem addressed in this paper by implementing an insurance application with blockchain technology. The consensus is used to ensure that the claim process of the insurance company will be carried out with integrity, accountability, and non-repudiation. Especially, every transaction is cryptographically signed and stored as a collection of blocks in the blockchain. This approach safeguards claim transactions and prevent any fraudulent attempts. A prototype application is built using the IBM blockchain platform and its underlying components. Experimental results showed that the proposed implementation prevents fraud claims in the insurance industry.
Recognition and Localisation of Pointing Gestures Using a RGB-D Camera

Non-verbal communication is part of our regular conversation, and multiple gestures are used to exchange information. Among those gestures, pointing is the most important one. If such gestures cannot be perceived by other team members, e.g. by blind and visually impaired people (BVIP), they lack important information and can hardly participate in a lively workflow. Thus, this paper describes a system for detecting such pointing gestures to provide input for suitable output modalities to BVIP. Our system employs an RGB-D camera to recognize the pointing gestures performed by the users. The system also locates the target of pointing e.g. on a common workspace. We evaluated the system by conducting a user study with 26 users. The results show that the system has a success rate of 89.59 and 79.92 % for a $$2 \times 3$$ matrix using the left and right arm respectively, and 73.57 and 68.99% for $$3 \times 4$$ matrix using the left and right arm respectively.
A fuzzy analytical hierarchy process to prioritize the success factors of requirement change management in global software development

Abstract Planning and managing of requirement change management (RCM) process in global software development (GSD) are a complicated task, but the RCM plays a significant role in developing the quality software within time and budget. The key aim of this study is to prioritize the factors that could positively influence the RCM program in GSD context. To achieve the study objective, the questionnaire survey study was conducted to get the feedback of the practitioners concerning the success factors of RCM in GSD context. Moreover, the fuzzy analytical hierarchy process (FAHP) was applied. The application of FAHP is novel in this research domain as it has been effectively applied previously in various other research areas, for example, supplier selection, electronics and electrical, personnel selection, and agile software development. The results of this study will provide the prioritization‐based taxonomy of RCM success factors and also contribute by introducing the novel FAHP approach in the research domain of RCM in GSD. The FAHP approach assists the practitioners to reduce the uncertainty and vague opinions of RCM experts.
Calibration in Collaborative Filtering Recommender Systems

Recommender systems learn from past user preferences in order to predict future user interests and provide users with personalized suggestions. Previous research has demonstrated that biases in user profiles in the aggregate can influence the recommendations to users who do not share the majority preference. One consequence of this bias propagation effect is miscalibration, a mismatch between the types or categories of items that a user prefers and the items provided in recommendations. In this paper, we conduct a systematic analysis aimed at identifying key characteristics in user profiles that might lead to miscalibrated recommendations. We consider several categories of profile characteristics, including similarity to the average user, propensity towards popularity, profile diversity, and preference intensity. We develop predictive models of miscalibration and use these models to identify the most important features correlated with miscalibration, given different algorithms and dataset characteristics. Our analysis is intended to help system designers predict miscalibration effects and to develop recommendation algorithms with improved calibration properties.
Sequential Fusion for Multirate Multisensor Systems With Heavy-Tailed Noises and Unreliable Measurements

The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -distribution and the approximate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -filter. The performance of the proposed algorithm is analyzed and compared with the Gaussian Kalman filter-based sequential fusion and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -filter-based sequential fusion without detection technique. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective and robust to unreliable measurements. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -filter-based sequential fusion algorithm is shown to be the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm.
Augmented and virtual reality for physics: Experience of Kazakhstan secondary educational institutions

Abstract Currently, computer‐based training systems, such as virtual laboratories (VLs), are becoming more common not only in universities but also in secondary schools. For physics, VLs are of great importance, as they allow simulating processes that cannot be seen or reproduced. Technologies such as augmented reality (AR) and virtual reality (VR) help us to create such physical visualizations. This study presents a VL, which allows users to solve various physical practical tasks or problems with the help of three‐dimensional process visualizations performed in three different ways, namely desktop VR, AR, and VR using a Leap Motion controller. This study describes in detail the functionality and implementation of a VL. As an experiment, three schools were provided with the necessary equipment and software. Fifty pupils of the 9th grade used the developed VL for half of the academic year, after which they took part in a comprehensive survey. About 88% of them found the laboratory useful for studying physics. The children were satisfied with its functionality and usability. Interviews with teachers helped us to understand which of the three visualization methods is preferable for them and children in the present environment.
Semantic Interpretation of Top-N Recommendations

Over the years, model-based approaches have shown their effectiveness in computing recommendation lists in different domains and settings. By relying on the computation of latent factors, they can recommend items with a very high level of accuracy. Unfortunately, when moving to the latent space, even if the model embeds content-based information, we miss references to the actual semantics of the recommended item. It makes the interpretation of the recommendation process non-trivial. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from knowledge graphs to train an interpretable model, which is, in turn, able to provide recommendations with a high level of accuracy. In the presented approach, semantic features are injected into the learning process to retain the original informativeness of the items available in the dataset. By relying on the information encoded in the original knowledge graph, we also propose two metrics to evaluate the semantic accuracy and robustness of knowledge-aware interpretability. An extensive experimental evaluation on six different datasets shows the effectiveness of the interpretable model in terms of both accuracy and diversity of recommendation results and interpretability robustness.
Superpixel-Guided Label Softening for Medical Image Segmentation

Segmentation of objects of interest is one of the central tasks in medical image analysis, which is indispensable for quantitative analysis. When developing machine-learning based methods for automated segmentation, manual annotations are usually used as the ground truth toward which the models learn to mimic. While the bulky parts of the segmentation targets are relatively easy to label, the peripheral areas are often difficult to handle due to ambiguous boundaries and the partial volume effect, etc., and are likely to be labeled with uncertainty. This uncertainty in labeling may, in turn, result in unsatisfactory performance of the trained models. In this paper, we propose superpixel-based label softening to tackle the above issue. Generated by unsupervised over-segmentation, each superpixel is expected to represent a locally homogeneous area. If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area. Driven by this intuition, we soften labels in this area based on signed distances to the annotation boundary and assign probability values within [0, 1] to them, in comparison with the original “hard”, binary labels of either 0 or 1. The softened labels are then used to train the segmentation models together with the hard labels. Experimental results on a brain MRI dataset and an optical coherence tomography dataset demonstrate that this conceptually simple and implementation-wise easy method achieves overall superior segmentation performances to baseline and comparison methods for both 3D and 2D medical images.
Code2Inv: A Deep Learning Framework for Program Verification

We propose a general end-to-end deep learning framework Code2Inv, which takes a verification task and a proof checker as input, and automatically learns a valid proof for the verification task by interacting with the given checker. Code2Inv is parameterized with an embedding module and a grammar: the former encodes the verification task into numeric vectors while the latter describes the format of solutions Code2Inv should produce. We demonstrate the flexibility of Code2Inv by means of two small-scale yet expressive instances: a loop invariant synthesizer for C programs, and a Constrained Horn Clause (CHC) solver.
Research on Vehicle Object Detection Algorithm Based on Improved YOLOv3 Algorithm

Abstract Vehicle object detection is one of the important research directions in the field of computer vision. Aiming at solving the problems of low accuracy, slow speed, and unsatisfactory results of using traditional methods to detect the object of the vehicle in front of the driverless car on the road, this paper proposes an improved YOLOv3 vehicle target detection algorithm which we name it F-YOLOv3. First the multi-scale prediction network model is improved according to actual traffic conditions and efficiency requirements based on the original general object detection YOLOv3 algorithm. Then a scale prediction layer is added to improve the detection accuracy of large vehicles and improved k-means++ the algorithm is used to improve the effect of anchor box dimensional clustering and the detection speed. At last an experiment was conducted on a self-made dataset and compared with YOLOv3 in order to test the effectiveness of the F-YOLOv3 algorithm. The test results show that the improved F-YOLOv3 model has a precision mAP of 91.12% and a speed of 59FPS, which are better than the traditional general object detection YOLOv3 algorithm. Therefore, the algorithm has better performance and popularization prospect in vehicle object detection.
A Digital Pathology Solution to Resolve the Tissue Floater Conundrum

Context.— Pathologists may encounter extraneous pieces of tissue (tissue floaters) on glass slides because of specimen cross-contamination. Troubleshooting this problem, including performing molecular tests for tissue identification if available, is time consuming and often does not satisfactorily resolve the problem. Objective.— To demonstrate the feasibility of using an image search tool to resolve the tissue floater conundrum. Design.— A glass slide was produced containing 2 separate hematoxylin and eosin (H&amp;E)-stained tissue floaters. This fabricated slide was digitized along with the 2 slides containing the original tumors used to create these floaters. These slides were then embedded into a dataset of 2325 whole slide images comprising a wide variety of H&amp;E stained diagnostic entities. Digital slides were broken up into patches and the patch features converted into barcodes for indexing and easy retrieval. A deep learning-based image search tool was employed to extract features from patches via barcodes, hence enabling image matching to each tissue floater. Results.— There was a very high likelihood of finding a correct tumor match for the queried tissue floater when searching the digital database. Search results repeatedly yielded a correct match within the top 3 retrieved images. The retrieval accuracy improved when greater proportions of the floater were selected. The time to run a search was completed within several milliseconds. Conclusions.— Using an image search tool offers pathologists an additional method to rapidly resolve the tissue floater conundrum, especially for those laboratories that have transitioned to going fully digital for primary diagnosis.