Reviewerly/exHarmony huggingface dataset:
ON SOME JOINT LAWS OF THE FIELD OF GRAVITY AND ELECTROMETRY

Adapted laws of statics of electric and gravitational fields in case of dynamic processes are used to obtain common laws of electricity and gravity.The mathematical description is made in a possible range of velocities in the usual three-dimensional Euclidean space and physical time, but bypassing the theory of relativity.This takes into account the finite speed of light propagation of electric and gravitational fields.For certainty, the derivation of the obtained results of gravity is duplicated by the methods of classical mechanics.Wave equations are obtained.The necessary attention is paid to the ratio of dimensions of parallel electrical and mechanical quantities.
FISHnet: Learning to Segment the Silhouettes of Swimmers

We present a novel silhouette extraction algorithm designed for the binary segmentation of swimmers underwater. The intended use of this algorithm is within a 2D-to-3D pipeline for the markerless motion capture of swimmers, a task which has not been achieved satisfactorily, partly due to the absence of silhouette extraction methods that work well on images of swimmers. Our algorithm, FISHnet, was trained on the novel Scylla dataset, which contains 3,100 images (and corresponding hand-traced silhouettes) of swimmers underwater, and achieved a dice score of 0.9712 on its test data. Our algorithm uses a U-Net-like architecture and VGG16 as a backbone. It introduces two novel modules: a modified version of the Semantic Embedding Branch module from ExFuse, which increases the complexity of the features learned by the layers of the encoder; and the Spatial Resolution Enhancer module, which increases the spatial resolution of the features of the decoder before they are skip connected with the features of the encoder. The contribution of these two modules to the performance of our network was marginal, and we attribute this result to the lack of data on which our network was trained. Nevertheless, our model outperformed state-of-the-art silhouette extraction algorithms (namely DeepLabv3+) on Scylla, and it is the first algorithm developed specifically for the task of accurately segmenting the silhouettes of swimmers.
An Efficient On-chip Single-photon SWAP Gate for Entanglement Manipulation

We demonstrate an on-chip single photon SWAP gate between polarization and spatial-momentum qubit. Our SWAP gate shows gate fidelity of 97.44%, and polarization entanglement preservation of SWAP operation is confirmed with average visibility over 98%.
Learning Conditional Deformable Shape Templates for Brain Anatomy

A brain template that describes the anatomical layout of an “average” brain is an essential building block of neuroimage analysis pipelines. However, a single template is often not sufficient to fully capture the variability in a heterogeneous population. Brain structures have very different shapes and sizes in different clinical and demographic groups. In this paper, we develop a novel neural network model that captures this morphometric variability. Our model learns to compute an attribute-specific spatial deformation that warps a brain template. We train this model on individual brain MRI segmentations in an end-to-end fashion, allowing for fast inference during testing. We demonstrate the ability of our model to deform a brain template given a wide range of ages, presence of disease and different sexes. Detailed qualitative and quantitative experiments are provided in order to demonstrate the flexibility of our model. Finally, we study the surface of the deformed template’s hippocampus to show how our model can be used for shape analysis. The code is freely available at https://github.com/evanmy/conditional_deformation.
A Scale-Aware YOLO Model for Pedestrian Detection

Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection, it remains very challenging to leverage this method for real-time pedestrian detection. In this paper, we propose a new framework called SA YOLOv3, a scale-aware You Only Look Once framework which improves YOLOv3 in improving pedestrian detection of small scale pedestrian instances in a real-time manner. Our network introduces two sub-networks which detect pedestrians of different scales. Outputs from the sub-networks are then combined to generate robust detection results. Experimental results show that the proposed SA YOLOv3 framework outperforms the results of YOLOv3 on public datasets and run at an average of 11 fps on a GPU.
Harmonizing IT Frameworks and Agile Methods: Challenges and Solutions for the case of COBIT and Scrum

Information Technology (IT) is a complex domain.In order to properly manage IT related processes, several frameworks including ITIL (Information Technologies Infrastructure Library), COBIT (Control OBjectives for Information and related Technologies), IT Service CMMI (IT Service Capability Maturity Model) and many others have emerged in recent decades.Meanwhile, the prevalence of Agile methods has increased, posing the coexistence of Agile approach with different IT frameworks already adopted in organizations.More specifically, the pursuit of being agile in the area of digitalization pushes organizations to go for agile transformation while preserving full compliance to IT frameworks for the sake of their survival.The necessity for this coexistence, however, brings its own challenges and solutions for harmonizing the requirements of both parties.In this paper, we focus on harmonizing the requirements of COBIT and Scrum in a same organization, which is especially challenging when a full compliance to COBIT is expected.Therefore, this study aims to identifying the challenges of and possible solutions for the coexistence of Scrum and COBIT (version 4.1 in this case) in an organization, by considering two case studies: one from the literature and the case of Akbank delivered in this study.Thus, it extends the corresponding previous case study from two points: adds one more case study to enrich the results from the previous case study and provides more opportunity to make generalization by considering two independent cases.
Psychological determinacy of communication styles of aviation industry students

Abstract The article presents the results of a theoretical and empirical study of the problem of style peculiarities of the human life activities, namely the styles of communication of student youth aviation industry as a specific form of human activity, the essence of which is the interaction of partners, subject-subject relations. The style of life activities is considered as a way of existence of a person characterized by originality; it covers the entire system of human being, its internal and external manifestations, and provides activity of a certainty in the characterization and predictability of behavior in a wide range of situations. The authors proceed from the assumption that enhancement of the subject’s life activity requires taking into account the existing and expanding (or forming), in the given direction, style peculiarities of the activity in mutual connection to its personal qualities. The article reflects the basic theoretical foundations of the conducted research and the empirical data obtained on students’ communication styles and their connection with the mental qualities of the personality. It is revealed that modern student youth is characterized by use in communication of a wide range of means, among which, according to various classifications, prevail styles such as: altruistic, cooperative, collegial, liberal, dialogical, compromise, whichintheir content indicate the orientation of the subject on partner, cooperation with him, finding a common solution, mutual respect, etc., demonstrating the ability to communicate. The data obtained also indicate the need for students to expand the irarsenal of communication toolsanduse the ircap abilitiesin a variety of conditions and requirements of life activities. The study proved that there is a mutual connection between the communication styles with the mental (personality) qualities of students: personality qualities may lead to an inclination for a particular style of communication, and vice versa. The same testifies the possibility of their formation of certain styles on the basis of development (or creation) of corresponding personality qualities; extension of the same style peculiarities of interaction, the use by a person of specific styles in life will promote the development of personality’s associated with this mental formations.
AutoHAS: Efficient Hyperparameter and Architecture Search

Efficient hyperparameter or architecture search methods have shown remarkable results, but each of them is only applicable to searching for either hyperparameters (HPs) or architectures. In this work, we propose a unified pipeline, AutoHAS, to efficiently search for both architectures and hyperparameters. AutoHAS learns to alternately update the shared network weights and a reinforcement learning (RL) controller, which learns the probability distribution for the architecture candidates and HP candidates. A temporary weight is introduced to store the updated weight from the selected HPs (by the controller), and a validation accuracy based on this temporary weight serves as a reward to update the controller. In experiments, we show AutoHAS is efficient and generalizable to different search spaces, baselines and datasets. In particular, AutoHAS can improve the accuracy over popular network architectures, such as ResNet and EfficientNet, on CIFAR-10/100, ImageNet, and four more other datasets.
Software Defect Prediction Based on Feature Subset Selection and Ensemble Classification

Two primary issues have emerged in the machine learning and data mining community: how to deal with imbalanced data and how to choose appropriate features. These are of particular concern in the software engineering domain, and more specifically the field of software defect prediction. This research highlights a procedure which includes a feature selection technique to single out relevant attributes, and an ensemble technique to handle the class-imbalance issue. In order to determine the advantages of feature selection and ensemble methods we look at two potential scenarios: (1) Ensemble models constructed from the original datasets, without feature selection; (2) Ensemble models constructed from the reduced datasets after feature selection has been applied. Four feature selection techniques are employed: Principal Component Analysis (PCA), Pearson’s correlation, Greedy Stepwise Forward selection, and Information Gain (IG). The aim of this research is to assess the effectiveness of feature selection techniques using ensemble techniques. Five datasets, obtained from the PROMISE software depository, are analyzed; tentative results indicate that ensemble methods can improve the model's performance without the use of feature selection techniques. PCA feature selection and bagging based on K-NN perform better than both bagging based on SVM and boosting based on K-NN and SVM, and feature selection techniques including Pearson’s correlation, Greedy stepwise, and IG weaken the ensemble models’ performance.
Data Analysis Based on Knowledge Graph

In the past few years, the automatic construction of large-scale knowledge graphs has received extensive attention from academia and industry. In this paper, we show the specific applications and typical cases of knowledge graphs in data analysis, and summarize the overall process of knowledge graphs from construction to data analysis. Moreover, some open problems and challenges are also presented for the future research.
Reversible Data Hiding in Encrypted Images Based on Reversible Integer Transformation and Quadtree-Based Partition

This paper presents an improved secure reversible data hiding scheme in encrypted images based on integer transformation, which does not need using a data hider key to protect the embedded secret data. We first segment the original image into blocks of various sizes based on the quadtree-based image partition. For each block, we reserve m least significant bits (LSBs) of each pixel as embedding room based on the reversible integer transformation. In order to improve the security of the image encryption, we pad the mLSBs of each pixel using the corresponding (8-m) most significant bits (MSBs) information after the transformation, which protects the security of the encryption key. Then, we encrypt the transformed image with a standard stream cipher. After the image encryption, the data hider embeds the secret data in the mLSBs of the encrypted images through an exclusive or operation. On the receiving side, the receiver can extract the secret data after the image decryption and recover the original image without loss of quality. The security analysis shows that the proposed scheme improves the security weakness of the scheme directly using adaptive integer transformation. The experimental results show that the proposed method achieves a higher embedding ratio compared with several relevant methods.
Remarks on Optimal Scores for Speaker Recognition

In this article, we first establish the theory of optimal scores for speaker recognition. Our analysis shows that the minimum Bayes risk (MBR) decisions for both the speaker identification and speaker verification tasks can be based on a normalized likelihood (NL). When the underlying generative model is a linear Gaussian, the NL score is mathematically equivalent to the PLDA likelihood ratio, and the empirical scores based on cosine distance and Euclidean distance can be seen as approximations of this linear Gaussian NL score under some conditions. We discuss a number of properties of the NL score and perform a simple simulation experiment to demonstrate the properties of the NL score.
Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework.
Generating Fuzzy Linguistic Summaries for Menstrual Cycles

This paper presents a method of generating linguistic summaries of women's menstrual cycles based on the set of concepts describing various aspects of the cycles.These concepts enable description of menstrual cycles that are readable for humans, but they also provide high-level information that can be used as control input for other data processing actions such as e.g.anomaly detection.The labels signifying these concepts are assigned to cycles by means of multivariate time series analysis.The corresponding algorithm is a subsystem of a bigger solution created as a part of an R&D project.
A Comb for Decompiled C Code

Decompilers are fundamental tools to perform security assessments of third-party software. The quality of decompiled code can be a game changer in order to reduce the time and effort required for analysis. This paper proposes a novel approach to restructure the control flow graph recovered from binary programs in a semantics-preserving fashion. The algorithm is designed from the ground up with the goal of producing C code that is both goto-free and drastically reducing the mental load required for an analyst to understand it. As a result, the code generated with this technique is well-structured, idiomatic, readable, easy to understand and fully exploits the expressiveness of C language. The algorithm has been implemented on top of the revng static binary analysis framework. The resulting decompiler, revngc, is compared on real-world binaries with state-of-the-art commercial and open source tools. The results show that our decompilation process introduces between 40% and 50% less extra cyclomatic complexity.
Frequency-multiplexed hybrid optical entangled source based on the Pockels effect

In the recent years important experimental advances in resonant electro-optic modulators as high-efficiency sources for coherent frequency combs and as devices for quantum information transfer have been realized, where strong optical and microwave mode coupling were achieved. These features suggest electro-optic-based devices as candidates for entangled optical frequency comb sources. In the present work, I study the generation of entangled optical frequency combs in millimeter-sized resonant electro-optic modulators. These devices profit from the experimentally proven advantages such as nearly constant optical free spectral ranges over several gigahertz, and high optical and microwave quality factors. The generation of frequency multiplexed quantum channels with spectral bandwidth in the MHz range for conservative parameter values paves the way towards novel uses in long-distance hybrid quantum networks, quantum key distribution, enhanced optical metrology, and quantum computing.
Research on Three Picking Arm Avoidance Algorithms for Agaricus Mushroom Picking Robot

For the Agaricus mushroom picking robot independently developed, in order to improve the picking efficiency, a three picking arm design is used, But the three picking arms will collide each other when they work at the same time, which greatly affects the picking efficiency. To solve this problem from a control perspective, a three picking arm avoidance algorithm based on genetic algorithm is now proposed. On the basis of analyzing the geometric constraints of the picking arm space and the distribution of mushrooms in the field, the fitness function is defined by the number of collisions during the picking process of the three picking arms. Code which picking arm is assigned to each mushroom in the picking area to reduces the number of collisions during cooperative picking with three picking arms. The experimental results prove that this algorithm can completely avoid collision during the picking process, and it has been proved that the overall efficiency can be improved by 0.2-0.4 times through the experiment of mushroom cultivation base.
Not Judging a User by Their Cover

Social media has shaken the foundations of our society, unlikely as it may seem. Many of the popular tools used to moderate harmful digital content, however, have received widespread criticism from both the academic community and the public sphere for middling performance and lack of accountability. Though social media research is thought to center primarily on natural language processing, we demonstrate the need for the community to understand multimedia processing and its unique ethical considerations. Specifically, we identify statistical differences in the performance of Amazon Turk (MTurk) annotators when different modalities of information are provided and discuss the patterns of harm that arise from crowd-sourced human demographic prediction. Finally, we discuss the consequences of those biases through auditing the performance of a toxicity detector called Perspective API on the language of Twitter users across a variety of demographic categories.
Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes

Abstract Although gold-standard histological assessment is subjective it remains central to diagnosis and clinical trial protocols and is crucial for the evaluation of any preclinical disease model. Objectivity and reproducibility are enhanced by quantitative analysis of histological images but current methods require application-specific algorithm training and fail to extract understanding from the histological context of observable features. We reinterpret histopathological images as disease landscapes to describe a generalisable framework defining topographic relationships in tissue using geoscience approaches. The framework requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner but is adaptable and scalable, able to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification. We demonstrate application to inflammatory, fibrotic and neoplastic disease in multiple organs, including the detection and quantification of occult lobular enlargement in the liver secondary to hilar obstruction. We anticipate this approach will provide a robust class of histological data for trial stratification or endpoints, provide quantitative endorsement of experimental models of disease, and could be incorporated within advanced approaches to clinical diagnostic pathology.
The Application of Chatbot for Customer Service in E-Commerce

The use of intelligent machines such as chatbot have been increasing each day. The main idea that we wanted to address in our paper is that with the capabilities that intelligent machines have it will be able to replace humans in doing a certain task and be able to bring better advantages in the long run. The focus of this study is that we are able to manipulate the ability of chatbot to impersonate how humans interact with each other and will enable it to play a role as to handle the customer service in e-commerce websites or applications which will be an alternative to replace the traditional customer service
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In the article we propose a new compression method (to 2⌈log2⁡(q)⌉+3 bits) for the Fq2-points of an elliptic curve Eb:y2=x3+b (for b∈Fq2⁎) of j-invariant 0. It is based on Fq-rationality of some generalized Kummer surface GKb. This is the geometric quotient of the Weil restriction Rb:=RFq2/Fq(Eb) under the order 3 automorphism restricted from Eb. More precisely, we apply the theory of conic bundles (i.e., conics over the function field Fq(t)) to obtain explicit and quite simple formulas of a birational Fq-isomorphism between GKb and A2. Our point compression method consists in computation of these formulas. To recover (in the decompression stage) the original point from Eb(Fq2)=Rb(Fq) we find an inverse image of the natural map Rb→GKb of degree 3, i.e., we extract a cubic root in Fq. For q≢1(mod27) this is just a single exponentiation in Fq, hence the new method seems to be much faster than the classical one with x-coordinate, which requires two exponentiations in Fq.