Reviewerly/exHarmony huggingface dataset:
A Proposal for a Language Combining Biochemical Rules and Topological Structure for Systems Biology

For about twenty years, rule-based modelling has been widely used for Systems Biology issues. Most existing languages focus on biochemical reactions primarily, and to a lest extent, on the cell structure in compartments. BIOCHAM and Pathway Logic Assistant (PLA) are representative examples of such rule-based languages. They are equipped with tools providing great analysis capabilities. We propose to provide such biochemical languages with annotations relating to the compartments in which the biochemical reactions take place. We will make sure that biochemical rules always indicate the nature of the compartments involved and the neighbourhood relations between them. At the end, it suffices to specialize the generic rules according to a particular topological structure in order to obtain sets of localized rules. Thus, resulting models can be analysed by using either BIOCHAM or PLA.
IFVM Bridge: A Virtual Machine for IFML Models Execution in Combination with Domain Models

Many software systems are executable on various computing devices. No one denies that each of these devices has its own user interfaces. Nevertheless, this trend of computing everywhere is not accompanied by a solution that can be used to abstractly express the content, user interaction and control behavior of the software application front end without focusing on the implementation platform. Applying the concept of abstract models to user interfaces become a necessity. Accordingly, OMG adopted (in March 2013) the new Interaction Flow Modeling Language (IFML) for abstractly describing the system front end. It ensures executability in order to be mapped into executable applications for different kind of devices. In this paper, we propose a new model driven development approach to execute the logical description of UIs components and their interactions captured with IFML. We define IFVM, a virtual machine for executing IFML models with focus on the content-dependent navigation specification for passing parameters between the ViewElements, and the Data binding specification to specify the source of the published content.
Detecting Summary-Worthy Sentences: The Effect of Discourse Features

We examine the benefit of a variety of discourse and semantic features for the identification of summary-worthy content in narrative stories. Using logistic regression models, we find that the most informative features are those that relate to the narrative structure of a text. We show that automatic methods for feature extraction perform significantly worse than full manual annotation, but that with optimization, a fully automatic approach can outperform a variety of existing extractive approaches to summarization.
Analyzing the Linear and Nonlinear Transformations of AlexNet to Gain Insight into Its Performance

AlexNet, one of the earliest and successful deep learning networks, has given great performance in image classification task. There are some fundamental properties for good classification such as: the network preserves the important information of the input data; the network is able to see differently, points from different classes. In this work we experimentally verify that these core properties are followed by the AlexNet architecture. We analyze the effect of linear and nonlinear transformations on input data across the layers. The convolution filters are modeled as linear transformations. The verified results motivate to draw conclusions on the desirable properties of transformation matrix that aid in better classification.
A Novel Coding and Discrimination (CODIS) ‎Algorithm to Extract Features from Arabic Texts to ‎Discriminate Arabic Poems

This article proposes a new algorithm to ‎discriminate Arabic poems by inserting Arabic poems ‎texts and coding Arabic letters, extracting letters features ‎depending on letter shapes to construct a multidimensional ‎contingency table, and analyses the frequencies of letters in ‎the inserted texts statistically. The proposed coding and ‎discrimination (CODIS) algorithm could be applied for ‎different Arabic texts in any media. A sample of five poems ‎for six poets was examined to implement a CODIS algorithm. ‎A Chi-Square statistic is used to determine the relation between ‎the features and discriminate poems.‎
Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network

Breast cancer is presently one of the most common cancer among women and has high morbidity and mortality worldwide. The emergence of microcalcifications (MCs) is an important early sign of breast cancer. In this study, a computer-aided detection and diagnosis (CAD) system is developed to automatically detect MC clusters (MCCs) and further providing cancer likelihood prediction. Firstly, each individual MC is detected using our previously designed MC detection system, which includes preprocessing, MC enhancement, MC candidate detection, false positive (FP) reduction of MCs and regional clustering procedures. Secondly, a deep convolution neural network (DCNN) is trained on 394 clinical high-resolution full field digital mammograms (FFDMs) containing biopsy-proven MCCs to discriminate MCC lesions. For cluster-based detection evaluation, a 90% sensitivity is obtained with a FP rate of 0.2 FPs per image. The classification performance of the whole system is validated on 70 cases and tested on 71 cases, and for case-based diagnosis evaluation, the area under the receiver operating characteristic curve (AUC) on validation and testing sets are 0.945 and 0.932, respectively. Different from previous literatures committing to finding and selecting effective features, the proposed method replaces manual feature extraction step by using deep convolution neural network. The obtained results demonstrate that the proposed method is effective in the automatically detection and classification of MCCs.
Photo Automatic Classification System Based on Face Recognition

It is very important to manage a large number of digital pictures for photography enthusiasts and training institutions. There are time-consuming, labor-intensive, and low-efficiency for classification using manual classification methods. They are used to categorize photos based on their shooting time or location when managing photos. It is difficult to find photos that can be of interest to the customer. Photo automatic classification system based on face recognition is proposed, which is convenient for users to find their own photos. Given a single photo of a person, the system first conducts self-deep learning to obtain the feature value of the face. And then the system can find all the photos in the given folder and copy the found photos including the sample face to the corresponding person name folder after reading the sample photo. At the same time, photos that cannot be recognized are also copied to the undetected face folder, so that the user can do a small amount of manual classification.
Development of teaching materials in the linear program of class XI

The aim of this study is to know the development of educational comic to improve students' understanding in a linear program in High School Class XI. This educational comic can be said valid to improve students' understanding of a linear program in High School grade XI, and students' response to learning by using comic media of education. The type of research used is qualitative research. Subjects in this study are the validation experts and students of class XI in SMA Negeri 8 Cirebon. Based on research conducted, the development of educational comic media has been in accordance with the expected goals. Validation performed by experts has a very valid validity rate with a percentage of 86.7%. Students' response to the following learning using educational comic media equal to 76% with strong criteria. So, the conclusion of this research is educational comic media has been successful in its development because of the valid and strong response of students in learning.
How Relevance Feedback is Framed Affects User Experience, but not Behaviour

Retrieval systems based on machine learning require both positive and negative examples to perform inference, which is usually obtained through relevance feedback. Unfortunately, explicit negative relevance feedback is thought to have poor user experience. Instead, systems typically rely on implicit negative feedback. In this study, we confirm that, in the case of binary relevance feedback, users prefer giving positive feedback (and implicit negative feedback) over negative feedback (and implicit positive feedback). These two feedback mechanisms are functionally equivalent, capturing the same information from the user, but differ in how they are framed. Despite users' preference for positive feedback, there were no significant differences in behaviour. As users were not shown how feedback influenced search results, we hypothesise that previously reported results could, at least in part, be due to cognitive biases related to user perception of negative feedback.
Mathematical Modeling Course Creation and Implementation Using Cocalc

Being commonly used worldwide, e-learning courses are only starting to evolve in regional Russian universities. Unlike the most Moodle-using colleagues, the authors created mathematical modeling e-course with not so well-known in Russia learning management system Cocalc. The biggest advantage of Cocalc is the ability to write and compile program code inside, with a lot of programming languages supported. Using statistical methods, the authors assessed the results of the course implementation, revealing that students began to perform tasks faster, learned to program fluently in R and Latex and increased their English level knowledge.
A Strategy of Encryption and Decryption based in a Low Memory Environment

This paper proposes an information encryption and decryption Strategy that can run smoothly in a low memory environment. It divides the information into blocks, which consumes less memory in encryption. And then it adds the identifiers into each block of cipher text. When cipher text is decrypted, it reads identifiers, and divides the cipher text into groups and decrypt it based on the group identifier to obtain the plaintext. In addition, it analyses the root causes of garbled plaintext after decryption, proposes a strategy of increasing cipher text disorder, and provide a method to make a difference between all identifiers and cipher text content. The test results show that the proposed strategy can implement more secure encryption in low memory system than normal strategy, and reduce the memory requirement for encryption and decryption, and increase the complexity of encrypted cipher text. Practices has proved that this strategy can be used in the field of mobile phone encryption and decryption [2], and has certain use value
Encrypted Data Transmission Model For Ethernet LANs

Despite many research and development efforts in the field of data communication security, the security of the local area network (LAN's) is still not fully resolved. In this work, we proposed a model of encryption of the data field in the Ethernet frame to create secure Ethernet LANs. In this model, the data field in the Ethernet frame is encrypted and sent to the destination. The 1500-byte data field, defined as the standard for the Ethernet frame, is divided into 1497 bytes as the field used for the data. The remaining 2-bytes are defined as Message Body Length (MBL) and 1-byte as Message Number (MN). The message number is used to verify the encrypted data and the MBL is used for the length of the message. The proposed model provides secure data communication over Ethernet local area networks. With this model, safe data communication is provided on the Ethernet LAN. Even if attackers obtain the packet at the time of communication, the encrypted message is difficult to decipher.
Learning to Use the Fitness Equipment: Development and Evaluation of a Context-aware System with iBeacon Technology

To further study the context-aware learning approach, the current study, based on the technology of iBeacon and WeChat “Shake Nearby”, built a contextaware learning system on fitness equipment and fitness knowledge. The study designed a controlled experiment and related interviews to evaluate the effectiveness and superiority of the context-aware learning. Results showed that the experimental group learning by the context-aware system had a high level of technology acceptance, and a slightly higher level of learning grades and learning motivation and a slightly lower level of the cognitive load than the control group. It could be seen that context-aware learning could help students study fitness equipment using and fitness knowledge more effective and pleasant than ordinary paper study materials.
Design and Implementation of Power Grid Graph Data Management Platform Based on Distributed Storage

With the construction of smart grids, the number of power device has increased rapidly, and various types of services in power grid management have been interconnected and refined. This has made how to store and effectively manage the massive data generated in power grid information management has become a bottleneck. This paper designs and implements a power grid graph data management platform based on distributed database HBase. Experiments on power grid topology data show that the graph data management platform proposed in this paper only takes nearly 80% of the time based on the graph database Neo4j when the scale is large enough, which can realize the storage and analysis of large-scale power grid topology data, and meet the needs of power grid informationization.
Demonstrating Naviarm

We present a wearable haptic assistance robotic system for augmented motor learning called Naviarm. This system comprises two robotic arms that are mounted on a user's body and are used to transfer one person's motion to another offline. Naviarm prerecords the arm motion trajectories of an expert via the mounted robotic arms and then plays back these recorded trajectories to share the expert's body motion with a beginner. The Naviarm system is an ungrounded system and provides mobility for the user to conduct a variety of motions. In our demonstration, the user will experience the recording of arm movement with backpack-type robotic arm. Then, the recorded movement will replayed and the user can experience the haptic feedback.
A Novel Based Recommended System Regularized with User Trust and Item Rating Prediction

Singular Value Decomposition (SVD) is trust-based matrix factorization technique for recommendations is proposed. Trust SVD integrates multiple information sources into the recommendation model to reduce the data sparsity and cold start problems and their deterioration of recommendation performance. An analysis of social trust data from four real-world data sets suggests that both the explicit and the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Trust SVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ uses the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted and trusting users on the guess of items for an active user. The proposed technique extends SVD++ with social trust information. Experimental results on the four data sets demonstrate that Trust SVD achieves accuracy than other recommendation techniques.
Head pose estimation with neural networks from surveillant images

Estimating head pose of pedestrians is a crucial task in autonomous driving system. It plays a significant role in many research fields, such as pedestrian intention judgment and human-vehicle interaction, etc. While most of the current studies focus on driver’s-view images, we reckon that surveillant images are also worthy of attention since more global information can be obtained from them than driver’s-view images. In this paper, we propose a method for head pose estimation from surveillant images. This approach consists of two stages, head detection and pose estimation. Since the head of pedestrian takes up a very small number of pixels in a surveillant image, a two-step strategy is used to improve the performance in head detection. Firstly, we train a model to extract body region from the source image. Secondly, a head detector is trained to locate head position from the extracted body regions. We use YOLOv3 as our detection network for both body and head detection. For head pose estimation, we treat it as classification task of 10 categories. We use ResNet-50 as the backbone of the classifier, of which the input is the result of head detection. A serial of experiments demonstrate the good performance of our proposed method.
Robust random number generator based on field effect transistor

Compact random number generator (RNG) is presented and demonstrated on the basis of field effect transistor connected in a such a way that avalanche electron current emerges when an input voltage exceeds the threshold value. The avalanche character of this phenomenon provides true randomness and large noise potential in wide spectral band at extremely small feeding electrical power. Created device is robust and it consists of compact elements with the possibility of digital signal acquisition from standard internal or external analogous to digital transducer. Created RNG has been tested. One hundred sequences each with 300000 bits length were tested using 15 separate tests of National Institute of Standards and Technology (NIST) with application of simple “linear post processing”. The results of testing are the following: all 100 sequences had passed NIST test series.
Development of Training Activities to Improve the Socio-Psychological Climate in the Workforce

The article presents the results of an experiment on the development and application of training activities to improve the socio-psychological climate in the workforce of the educational organization. The theoretical substantiation of the interrelation of organizational culture and socio-psychological climate in the work collective is given, the preferred type of organizational culture for modern educational organizations is described. The results of diagnostics of the type of organizational culture and socio-psychological climate on the example of an educational organization are described, conclusions are drawn about the need for their development.&#x0D; A set of training measures to improve the socio-psychological climate and the development of organizational culture is described. The results of experimental work on the implementation of the described training activities are presented, and the effectiveness of their application for the development of the work collective is proved.
Research on Innovation of Course Teaching &amp;lt;i&amp;gt;Utilizing Technology of Construction Machinery&amp;lt;/i&amp;gt;

The effectiveness of teaching course, Utilizing Technology of Construction Machinery, is less-than-ideal when the teaching activities take place in a closed classroom.Then some measures for teaching innovation were presented and practice in the course teaching.And the measures include reformation of course content, changing teaching method, and innovation of teaching techniques.Finally, students acquired the technical abilities on construction machinery and the satisfying effect of cultivating compound talents were achieved after the course contents have been innovated and the teaching methods have been improved.
Applicability of coupling strength estimation for linear chains of restricted access*

The characterization of an unknown quantum system requires the Hamiltonian identification. The full access to the system, however, is usually restricted, hindering the direct retrieval of the relevant parameters, and a reliable indirect estimation is usually required. In this work, based on the reformulated form of the original algorithm of Burgarth et al. [ Phys. Rev. A 79 020305 (2009)], the robustness of the estimation scheme against numerous sources of errors during the actual measurement is analyzed. The scheme is numerically studied for sites with a chain structure, exploring its applicability against observational errors including the limited signal-noise ratio and the finite spectral width. The spectral distribution of the end site is shown to determine the applicability of the method, and reducing the influence from truncated spectral components is critical to realize the robust reconstruction of the coupling strengths.
A Semantic Analysis of Interface Description Models of Heterogeneous Vehicle Application Frameworks: An Approach Towards Synergy Exploration

As the world is getting more connected, the demands of services in automotive industry are increasing with the requirements such as IoT (Internet of Things) in cars, automated driving, etc. Eventually, the automotive industry has evolved to a complex network of services, where each organization depends on the other organizations, to satisfy its service requirements in different phases of the vehicle life cycle. Because of these heterogeneous and complex development environments, most of the vehicle component interface models need to be specified in various manifestations to satisfy the semantic and syntactic requirements, specific to different application development environments or frameworks. This paper describes an approach to semantic analysis of components interfaces description models of heterogeneous frameworks, that are used for vehicle applications. The proposed approach intends to ensure that interface description models of different service-based vehicle frameworks can be compared, correlated and re-used based on semantic synergies, across different vehicle platforms, development environments and organizations. The approach to semantic synergy exploration could further provide the knowledge base for the increase in interoperability, overall efficiency and development of an automotive domain specific general software solutions, by facilitating coexistence of components of heterogeneous frameworks in the same high-performance ECU for future vehicle software.
Counts-of-counts similarity for prediction and search in relational data

Defining appropriate distance functions is a crucial aspect of effective and efficient similarity-based prediction and retrieval. Relational data are especially challenging in this regard. By viewing relational data as multi-relational graphs, one can easily see that a distance between a pair of nodes can be defined in terms of a virtually unlimited class of features, including node attributes, attributes of node neighbors, structural aspects of the node neighborhood and arbitrary combinations of these properties. In this paper we propose a rich and flexible class of metrics on graph entities based on earth mover’s distance applied to a hierarchy of complex counts-of-counts statistics. We further propose an approximate version of the distance using sums of marginal earth mover’s distances. We show that the approximation is correct for many cases of practical interest and allows efficient nearest-neighbor retrieval when combined with a simple metric tree data structure. An experimental evaluation on two real-world scenarios highlights the flexibility of our framework for designing metrics representing different notions of similarity. Substantial improvements in similarity-based prediction are reported when compared to solutions based on state-of-the-art graph kernels.