Reviewerly/exHarmony huggingface dataset:
Bi-ResNet: Fully Automated Classification of Unregistered Contralateral Mammograms

Motivated by the fact that the contralateral mammograms can provide the symmetrical difference of the left and right breasts to assist identify the breast cancer, we propose a bilateral residual neural network (Bi-ResNet) that can automatically classify the normality/abnormality based on the unregistered contralateral whole mammograms. Specifically, the parallel ResNet network is designed to simultaneously process a group of contralateral mammograms and respectively capture the discriminative representations from the left and right mammograms, and the concatenation strategy in the final is used to integrate the differentiated features for the abnormal classification task. The proposed Bi-ResNet can achieve reproducible and similar results based on different backbones and is superior to traditional contralateral analysis methods in both automation and performance. Finally, our proposed Bi-ResNet is greatly demonstrated on the publicly available DDSM dataset, a total of 10480 images, yielding the highest AUC of 0.908 on the abnormal classification task. Through the massive experiments, we deem our model is stable and robust, and has the potential to be recommended to clinical application in the future.
Algebraic construction of lattices via maximal quaternion orders

In this paper we propose a framework to construct algebraic lattices in dimensions 4n via ideals from maximal orders of a quaternion algebra whose center is a totally real number field. For n=1,2,3,4 and 6 it was possible to construct rotated versions of the densest lattices in their dimensions, D4,E8,K12,Λ16 and Λ24. We also present a family of lattices in dimension 2r from A=(−1,−1)Q(ζ2r+ζ2r−1) and a characterization of a maximal quaternion order of A by using the Chebyshev polynomials.
Re-finding Behaviour in Educational Search

One of the search tasks in Web search is repeat search behaviour to find out documents that users once visited, which is called re-finding. Although there have been several works in the context of general-purpose Web search addressing the latter phenomena, the problem is usually overlooked for vertical search engines. In this work, we report re-finding and newfinding behaviours of users in an educational search context and compare results with the findings in the literature for general-purpose web search. Our analysis shows that re-finding pattern of students differs from web search drastically as only $$26\%$$ of all queries indicate re-finding behaviour compared to $$40\%$$ in Web.
Study of Practices and Innovation of the Applied Personnel Training Model at Private Institutions of Higher Education

The applied personnel training model, which is centered on applications, oriented toward the market, and premised on abilities, has appeared at private institutions of higher education in a variety of formats, including the embedded format, custom order format, industry-education integration, and so on. However, the presence of blind emulation in goal orientation, theoretization of curriculum systems, homogenization of the composition of qualified teachers, formalization of personnel evaluations, and other issues has prevented the applied personnel training model from achieving effective linkage between private higher education and industry: only through cooperation between entities on all sides and constant innovation will it be possible to fully exercise the important functions of the applied personnel training model at private institutions of higher education.
Business Dynamism and Innovation Capability in the European Union Member States in 2018 Through the Prism of InterCriteria Analysis

Here we apply the intuitionistic fuzzy sets-based InterCriteria Analysis on the data from the Global Competitiveness Index of 2018, about the two best correlating pillars of competitiveness ‘11 Business Dynamism’ and ‘12 Innovation Capability’ based on the data of the 28 European Union Member States. We get a deeper look on how the eight subindicators of the countries’ business dynamism and the ten subindicators of their innovation capability correlate in between and among each other.
Design and Development Meeting Schedule Management Application using the RAD Method

Scheduling meetings in an agency of government agencies are important. The problem that has existed so far is that recording a meeting schedule that still uses books and is prone to loss is one cause. Schedules that clash between parts or areas with limited space are also the cause. In this paper propose the development of a meeting scheduling system that can help overcome these problems. The main objectives of this work are twofold: 1) to assist in meeting scheduling, and 2) to minimize the risk of document loss. System development method used is the Rapid Application Development method, it chose which because it adapts from the waterfall model. An advantage of RAD is that it emphasizes a short software development cycle, uses a component-based construction approach and creates full functionality in 60-90 days. System analysis uses UML such as use case diagrams and activity diagrams. In this paper, an architectural design process and interface design are also carried out. We do implementation using the PHP, HTML 5, and JavaScript as a programming language with the MYSQL database. Based on the results of black box testing that has been done, it can be concluded that all functions can run well and as expected.
Efficient oblivious transfer with membership verification

In this article, we introduce a new concept of oblivious transfer with membership verification that allows any legitimate group users to obtain services from a service provider in an oblivious manner. We present two oblivious transfer with membership verification schemes, differing in design. In the first scheme, a trusted group manager issues credentials for a pre-determined group of users so that the group of users with a valid group credential can obtain services from the service provider, while the choices made by group users remain oblivious to the service provider. The second scheme avoids the trusted group manager, which allows any user in the group to be a group manager, thus it is more suitable in distributed systems. In particular, we prove that the two oblivious transfer with membership verification schemes can achieve receiver’s privacy and sender’s privacy under a half-simulation model.
Between Effort and Security

With the increasing popularity of the smartphone, the number of people using it for financial transactions such as online shopping, online banking or mobile payment is also growing. Apps used in these contexts store sensitive and valuable data, creating a need for security measures. It has not yet been researched to what extent certain authentication mechanisms, which can be information-, biometric- as well as token-based, are suitable for individual apps and the respective data. The goal of this work is to assess how perceived security and estimated effort of using such mechanisms, as well as the degree to which app data is considered worth protecting, influence users' choices of appropriate measures to protect app categories. Therefore, we conducted a representative study (n=1024). On the one hand, our results show that a positive correlation between perceived security and effort exists for all investigated non-biometric authentication methods. On the other hand, the study sheds light on the differences between the investigated app categories and the users' choice of the appropriate security mechanisms for the particular category. In contrast to perceived security having a positive influence on a user's preference of mechanism, a relation can hardly be identified for effort. Moreover, app data sensitivity does not seem relevant for the users' choice of security mechanism.
Robust Map Registration for Building Online Glass Confidence Maps

Laser rangefinders (LRFs) are widely used in mobile robot localization. However, glass, which is common in indoor environments, can only be detected by LRFs in limited incident angles, instead of all incident angles like other objects. As common representations of the environments do not consider this property, glass can negatively influence the robot’s localization accuracy by causing a mismatch between measurements and the map even when locations are correct. A solution to this problem is to build a glass confidence map, which shows the probability of each object in the environment to be glass. If glass confidence maps want to be built online, it is important to consider pose uncertainty. Pose uncertainty can cause incorrect registration of glass probabilities, i.e., the incorrect grid is assigned the computed glass probability. In this work, we propose a robust registration method that explicitly considers pose uncertainty. The proposed method is verified experimentally, and results show that glass confidence maps can be built online successfully and with high accuracy.
Deep Cascade of Extra Trees

Deep neural networks have recently become popular because of their success in such domains as image and speech recognition, which has lead many to wonder whether other learners could benefit from deep, layered architectures. In this paper, we propose the Deep Cascade of Extra Trees (DCET) model. Representation learning in deep neural networks mostly relies on the layer-by-layer processing of raw features. Inspired by this, DCET uses a deep cascade of decision forests structure, where the cascade in each level receives the best feature information processed by the cascade of forests of its preceding level. Experiments show that its performance is quite robust regarding hyper-parameter settings; in most cases, even across different datasets from different domains, it is able to get excellent performance by using the same default setting.
An Information Entropy-Based Method of Evidential Source Separation and Refusion

D-S theory is widely used in decision fusion of multi-source information and uses multiplication rule to realize numerical fusion between different sensors. But it is also because of multiplication rules that D-S theory is difficult to apply to data fusion between multi-sensor information with complex correlation or failure. Therefore, based on D-S theory, this paper proposes a method to eliminate the correlation between evidence sources by using physical quantities (obtained by sensor detection). The algorithm convolutes the sensor's evaluation information entropy with the sensor's Gauss anomaly distribution function to deal with the anomaly. The algorithm expands the application of D-S evidence theory in multi-sensor information fusion, and has the ability to deal with sensors that report anomalies.
AR in production

The use of Augmented Reality (AR) in the production environment is still not widespread. The high heterogeneity of the user interfaces in standard Head-Mounted Displays (HMD) poses a challenge for developers to find an ergonomically high-quality solution for specific tasks within the production environment. The aim of the study is to develop a catalogue of sample solutions, so-called patterns, which are tailored to the generic tasks of these special target groups, such as quality inspectors or line managers. This is done on the basis of extensive task analyses and comparative empirical evaluations of common user interfaces in HMDs. From these, possible patterns were extracted in which alternative layouts were combined with the input modes "focus" and "gestures". The paper describes the results of an evaluation with AR-experienced usability engineers.
A Heterogeneous Domain Adversarial Neural Network for Trans-Domain Behavioral Targeting

To realize trans-domain behavioral targeting, which targets interested potential users of a source domain (e.g. E-Commerce) based on their behaviors in a target domain (e.g. Ad-Network), heterogeneous transfer learning (HeTL) is a promising technique for modeling behavior linkage between domains. It is required for HeTL to learn three functionalities: representation alignment, distribution alignment, and classification. In our previous work, we prototyped and evaluated two typical transfer learning algorithms, but neither of them jointly learns the three desired functionalities. Recent advances in transfer learning include a domain-adversarial neural network (DANN), which jointly learns distribution alignment and classification. In this paper, we extended DANN to be able to learn representation alignment by simply replacing its shared encoder with domain-specific types, so that it jointly learns the three desired functionalities. We evaluated the effectiveness of the joint learning of the three functionalities using real-world data of two domains: E-Commerce, which is set as the source domain, and Ad Network, which is set as the target domain.
Gesture recognition method with acceleration data weighted by sEMG

In this paper, we propose a gesture recognition method with acceleration data weighted by sEMG. Acceleration and sEMG are collected as training data, and gestures are recognized using only acceleration as input data. The dynamic time warping (DTW) algorithm is used for the distance calculation. Three axis acceleration data and sEMG were collected for three types of baseball pitching forms: overarm, sidearm, and underarm beginning from three types of preliminary motions: no windup, quick, and windup. We investigate the changes in sEMG during pitching motion. The distance calculation method is changed according to the sEMG amplitude, reducing the influence of unstable motions. In evaluation experiments, the proposed method achieved higher accuracy than the comparison method that does not use sEMG for windup form, even if the number of training data changes.
Energy Optimization for Software-Defined Data Center Networks Based on Flow Allocation Strategies

Nowadays, energy consumption has become an important issue in data center networks. The most promising energy-saving schemes are those that shut down unnecessary network devices and links while meeting the demand of traffic loads. Existing research mainly focuses on the strategies of energy savings in software-defined data center networks (SD-DCN). Few studies have considered both energy savings and the quality of service (QoS) of the traffic load. In this paper, we investigate the energy savings guaranteed by traffic load satisfaction ratio. To ensure the minimum-power consumption in data centers, we formulate the SD-DCN energy consumption optimization problem as an Integer Linear Programming model. To achieve a high success rate for traffic transmission, we propose three flow scheduling strategies. On this foundation, we propose a strategy-based Minimum Energy Consumption (MEC) heuristic algorithm to ensure the QoS satisfaction ratio in the process of energy optimization. The results show that our algorithm can save energy efficiently under the conditions of low traffic load and medium traffic load. Under high traffic load, our algorithm can achieve better network performance than existing solutions in terms of quality of service satisfaction ratio of flow allocation.
Data Mining Algorithms Parallelization in Logic Programming Framework for Execution in Cluster

This article describes an approach to parallelizing of data mining algorithms in logical programming framework, for distributed data processing in cluster. As an example Naive Bayes algorithm implementation in Prolog framework, its conversion into parallel type and execution on cluster with MPI system are described.
Accurate Ship Detection via Paired Semantic Segmentation

Ship detection in high-resolution optical satellite imagery is a challenging task due to complex backgrounds and docked ships side by side. In this paper, we propose a new way to generate ship proposals, and introduce an approach based on a novel deep encoding-decoding framework. According to the symmetry of the shape of the ship, we eliminate the need for designing a set of anchor boxes commonly used in prior ship detections and extract ship bounding boxes from the top-right and bottom-left segmentation parts of ship without location regression. By detecting ships as paired segmentation parts, we can detect docked ship side by side that previous semantic segmentation can not detect. The network is composed of multiple layers of convolution and de-convolution operators. We take the state-of-the-art convolutional neural network ResNet18 as the encoder network, which extracts the abstraction feature of image contents. The decoder network is responsible for recovering the image details. At the same time, we introduce skip-layer connections between convolutional and de-convolutional layers. Experiments have demonstrated the effectiveness of our approach both in qualitative and qualitative performance compared with state-of-the-art ship detection methods.
Reinterpretation of the suture zone between the Itabuna-Salvador-Curaçá and Jequié blocks, of the São Francisco Craton, Bahia, Brazil, using structural and geophysical data

The study area is located in the central portion of the São Francisco Craton (SFC), between two of the main blocks that compose this feature: the Jequié and the Itabuna-Salvador-Curaçá blocks. The main objective of this study was to reevaluate the transition zone between both blocks, employing airborne geophysical, geologic and structural data. The geophysical data are represented by gamma-ray spectrometry and magnetic surveys, which showed the structure of the lithotypes mapped and helped in the identification of their limits. The analysis of these images established five radiometric domains and six magnetic zones. The geophysical data interpreted along with mapping survey, with macroscopic description and structural data, allowed better identification of the suture zone between the referred blocks. The structural analysis identified four deformation phases, characteristic of a transpressional zone, uplifting the ISCB onto JB. The present study proposes the modification of the limits between these blocks to a more southern location and updates the regional geotectonic understanding of the central region of the state of Bahia.
Topic Modelling vs Distant Supervision: A Comparative Evaluation Based on the Classification of Parliamentary Enquiries

We investigate two different approaches to text classification, by categorising enquiries submitted to the House of Commons Library from elected Members of the UK Parliament. One is an unsupervised approach, i.e. topic modelling, and the other is a supervised approach based on weakly labelled data, i.e. distant supervision. Models were trained on two types of feature sets: one based only on bag of words, and the other combining bag of words with structured metadata attached to enquiries. Our results show that topic modelling obtains superior performance on this task, and that the incorporation of structured metadata as learning features contributes insignificantly to improved model performance.
Evaluation of the MRI Images Matching Using Normalized Mutual Information Method and Preprocessing Techniques

One of the common methods for medical diagnosis is Magnetic Resonance Imaging (MRI), a safe, non-invasive method. During each imaging session a patient’s position may be different, therefore comparison of two sequences can become difficult. The primary goal of this work is preparation of an optimal algorithm for co-registration of T1 and T2 weighted MRI images. To adjust co-registration sensitivity, different preprocessing methods to perform normalizations and edge detection were used. The obtained results allow to increase quality of the co-registration process.
Very high altitude micro air vehicle deployment method

The paper presents the original work and method for high altitude micro air vehicle deployment. The method is based on the scientific ballooning, and adapted for stratospheric flight of commercial off-the-shelf micro air vehicle in flying wing configuration. The High Altitude Micro Air Vehicle, built for this research, was deployed during a test experiment at the lower level of the stratosphere. The results of the experiment and technical details are presented. The paper concludes the experiment’s findings and delivers assumptions for the future work and next experiment.
Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation

On-orbit semantic segmentation can produce the target image tile or image description to reduce the pressure on transmission resources of satellites. In this paper, we propose a fully convolutional network for on-orbit semantic segmentation, namely light-weight edge enhanced network (LEN). For the model to be pruned, we present a new model pruning strategy based on unsupervised clustering. The method is performed according to the $$l_1$$ -norm of each filter in the convolutional layer. And it effectively guides the pruning of filters and corresponding feature maps in a short time. In addition, the LEN uses a trainable edge enhanced module called enhanced domain transform to further optimize segmentation performance. The module fully exploits multi-level information of the object to generate the edge map and performs edge-preserving filtering on the coarse segmentation. Experimental results suggest that the models produce competitive results while containing only 1.53 M and 1.66 M parameters respectively on two public datasets: Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset.
Robust Centralized Fusion Kalman Filters with Uncertain Noise Variances

This paper studies the problem of the designing the robust local and centralized fusion Kalman filters for multisensor system with uncertain noise variances. Using the minimax robust estimation principle, the centralized fusion robust time-varying Kalman filters are presented based on the worst-case conservative system with the conservative upper bound of noise variances. A Lyapunov approach is proposed for the robustness analysis and their robust accuracy relations are proved. It is proved that the robust accuracy of robust centralized fuser is higher than those of robust local Kalman filters. Specially, the corresponding steady-state robust local and centralized fusion Kalman filters are also proposed. A Monte-Carlo simulation example shows the robustness and accuracy relations.
Improvement of Gmapping Algorithm Based on Non-continuous System

With the development of science and technology, robots are gradually replacing simple human labor and become an indispensable part of life. In the process of grain transportation in the granary, we use the robot as the tools, whereas, there are a lot of non-continuous environments, which pose a challenge to the construction of real-time map for robots. In this paper, we propose a modeling of a noncontinuous environment based on Gazebo platform. Then we raise a modified algorithm according to the Markov chain. By building the model of non-continuous environments in Gazebo platform, the effectiveness of the model is verified by comparing the model of plane and slope. According to this, we can solve the problem of hard to obtain accurate data from the real-world scenario to modify algorithms, and the efficiency is greatly improved.