Reviewerly/exHarmony huggingface dataset:
Cross-Projection for Embedding Translation in Knowledge Graph Completion

Knowledge graphs have been applied in many applications. However, they are incomplete. Previous completion works such as TransE, TransH and TransR/CTransR regard relation as translation between head entity and tail entity. They think that only entities are affected by relations, and change entities embeddings according to the corresponding relation. However, they neglect the influence between two entities. In this paper, we propose a new model named Cross-projected Translation Embedding model (CpTE) based on the translation theory. In CpTE, we assume that relation is affected by related entities and transmits effect between end-to-end entities. Head entity and tail entity are influenced by each other rather than their relation. Given a triple, we project each entity into another entity space and map relation into a union space constructed by entities. In Experiments, we evaluate our model on two typical tasks including link prediction and triple classification. Experiments results show that our approach outperforms than TransE, TransH and TransR/CTransR in link prediction task on evaluation sets WN18 and FB15K, and gets an improvement in triple classification on specific evaluation set FB15K.
The Fusion of Two NN Architectures for the Improvement of Image Classification

Content-Based Image Retrieval (CBIR) systems require image classification. This paper presents an application of neural networks (NN) to image segment classification for our CBIR system. A new NN architecture which connects a convolutional NN (CNN) with a shallow NN is proposed in order to improve the classification results. This amelioration is necessary due to the shortage of image samples to train a CNN separately. Thanks to the fusion of CNN and shallow NN (SNN) a 5-11% improvement has been obtained.
Thou Shalt Discuss Security

The importance of secure development of new technologies is unquestioned, yet the best methods to achieve this goal are far from certain. A key issue is that while significant effort is given to evaluating the outcomes of development (e.g., security of a given project), it is far more difficult to determine what organizational practices result in secure projects. In this paper, we quantitatively examine efforts to improve the consideration of security in Requests for Comments (RFCs)--- the design documents for the Internet and many related systems --- through the mandates and guidelines issued to RFC authors. We begin by identifying six metrics that quantify the quantity and quality of security informative content. We then apply these metrics longitudinally over 8,437 documents and 49 years of development to determine whether guidance to RFC authors changed these security metrics in later documents. We find that even a simply worded --- but effectively enforced --- mandate to explicitly consider security created a significant effect in increased discussion and topic coverage of security content both in and outside of a mandated security considerations section. We find that later guidelines with more detailed advice on security also improve both volume and quality of security informative content in RFCs. Our work demonstrates that even modest amounts of guidance can correlate to significant improvements in security focus in RFCs, indicating a promising approach for other network standards bodies.
Function-Structure Collaborative Mapping Induced by Universal Triple I Systems

The and/or/not function tree is one of the principal functional models of product concept design. How to model and solve the and/or/not function tree is a key issue in this field. In the past, it was mainly to establish a function tree by artificial means, and it was hard to get the desired effect. Aiming at this problem, a novel function tree modeling method is proposed based on the idea of fuzzy reasoning. Firstly, the function tree is extended to the scope of the fuzzy function tree. Secondly, starting from the universal triple I algorithm of fuzzy reasoning, the fuzzy system for fuzzy function tree modeling is constructed, which is infiltrated into the function-structure mapping of function tree modeling. Therefore, the function-structure collaborative mapping method based on the triple I system is proposed. Through the application examples of the magnetic levitation train, the effectiveness of the proposed method is confirmed, and the problem of function tree modeling is effectively solved, which promotes the development of product concept design.
Tri-Level Cross-Domain Sign Prediction for Complex Network

Sign prediction is a fundamental research issue in complex network mining, while the high cost of data collection leads to insufficient data for prediction. The transfer learning method can use the transferable knowledge in other networks to complete the learning tasks in the target network. However, when the inter-domain differences are large, it is difficult for existing methods to obtain useful transferable knowledge. We therefor propose a tri-level cross-domain model using inter-domain similarity and relativity to solve the sign prediction problem in complex networks (TCSP). The first level pre-classifies the source domain, the second level selects the key instances of the source domain, and the third level calculates the similarity between the source domain and the target domain to obtain the pseudo-labels of the target domain. These “labeled” instances are used to train the sign classifier and predict the sign in the target network. Experimental results on real complex network datasets verify the effectiveness of the proposed method.
Flipping Out with Many Flips: Hardness of Testing $k$-Monotonicity

A function $f:\{0,1\}^n\rightarrow \{0,1\}$ is said to be $k$-monotone if it flips between 0 and 1 at most $k$ times on every ascending chain. Such functions represent a natural generalization of (1-)monotone functions, and have been recently studied in circuit complexity, PAC learning, and cryptography. Our work is part of a renewed focus in understanding testability of properties characterized by freeness of arbitrary order patterns as a generalization of monotonicity. Recently, Canonne et al. [Innovations in Theoretical Computer Science, Schloss-Dagstuhl--Leibniz-Zentrum für Informatik GmBH, Wadern, Germany, 2017, 29] initiate the study of $k$-monotone functions in the area of property testing, and Newman et al. [SODA, SIAM, Philadelphia, 2017, pp. 1582--1597] study testability of families characterized by freeness from order patterns on real-valued functions over the line $[n]$ domain. We study $k$-monotone functions in the more relaxed parametrized property testing model, introduced by Parnas, Ron, and Rubinfeld [J. Comput. System Sci., 72 (2006), pp. 1012--1042]. In this process we show strong lower bounds on testing $k$-monotonicity. Specifically, we show that testing 2-monotonicity on the hypercube nonadaptively with one-sided error requires an exponential in $\sqrt{n}$ number of queries. This behavior shows a stark contrast with testing (1-)monotonicity, which only needs $\tilde{O}\mleft(\sqrt{n}\mright)$ queries. Furthermore, even the apparently easier task of distinguishing 2-monotone functions from functions that are far from being $n^{.01}$-monotone also requires an exponential number of queries.
Autotator: Semi-Automatic Approach for Accelerating the Chart Image Annotation Process

Annotating chart images for training machine learning models is tedious and repetitive especially in that chart images often have a large number of visual elements to annotate. We present Autotator, a semi-automatic chart annotation system that automatically provides suggestions for three annotation tasks such as labeling a chart type, annotating bounding boxes, and associating a quantity. We also present a web-based interface that allows users to interact with the suggestions provided by the system. Finally, we demonstrate a use case of our system where an annotator builds a training corpus of bar charts.
Integrated Semi-Supervised Model for Learning and Classification

Labelled data are not only time consuming but often expensive and difficult to procure as it involves skilful inputs by humans to tag and annotate. Contrary to this unlabelled data is comparatively easier to procure but fewer methods exist to optimally use them. Semi-Supervised Learning overcomes this problem and assists to build better classifiers by using unlabelled data along with sufficient labelled data and may actually yield higher accuracy with considerably less human input effort. But if the labelled data set is inadequate in size then the Semi-Supervised techniques are also stuck. We propose a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model. The model retrains with the unlabelled data to fortify the learning mechanism. We show that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment.
ORGANIZATIONAL AND MANAGEMENT ACTIVITIES OF A UNIVERSITY TEACHER UNDER THE CONDITIONS OF REALIZATION OF MODERN TENDENCIES OF DEVELOPMENT OF HIGHER SCHOOL

In the article, the authors analyze the problem of training the teaching staff of universities to carry out organizational and managerial activities. This problem is considered in the context of modern trends in the development of higher education (democratization, internationalization, increasing the openness of educational systems, expansion of integration processes, informatization, transition from mono to inter and transdisciplinary research), which put forward new requirements for educational programs of higher education and cause their transformational changes. It is shown that the traditional approach to the management of educational programs is replaced by an innovative one, involving the delegation of the powers of the head of the educational program to a separate specialist competent in forecasting, implementation, reflection and making effective management decisions. In this regard, there is not only a substantial change and complication of the traditional types of professional activities of the teacher, but also the appearance of elements of the business process in it, in particular, an increase in the share of organizational and managerial functions in the processes of design, design, implementation, quality assessment and guarantee of competitiveness educational program. In the management of educational programs, the separation of management contours into academic management and administrative management is noticeable. Academic management is represented by the academic head of the educational program and the academic Council of the program, and administrative management is carried out by the Manager of the educational program.
Full-Field Microscale Strain Measurement of Carbon Fiber Reinforced Plastic Using 2-Pixel Sampling Moiré

Microscale strain distributions are essential parameters for evaluating the mechanical properties and instability behaviors of compositeComposites materials. In this study, a recently-developed reconstructed multiplication moiré method from 2-pixel sampling moiré fringes was used to investigate the microscale deformation performance of a carbon fiber reinforced plasticCarbon fiber reinforced plastic (CFRP) specimen. This method enables wide field of view and high displacement and strain sensitivities for deformation measurement of CFRP. A microgrid as the deformation carrier was fabricated on CFRPCarbon fiber reinforced plastic by ultraviolet nanoimprint lithography. The in situ full-field distributions of two-dimensional (2D) displacements, normal strains and shear strains of CFRP were quantitatively measured at different three-point bending loads under a laser microscope. The deformation distribution features of CFRPCarbon fiber reinforced plastic were analyzed, which is helpful to understand its potential damage mechanism.
Fast and accurate classification and identification of mass spectra using hybrid optical-electronic convolutional neural networks

Mass spectrometer is one of the most important instruments in the field of modern analysis. Despite efforts to increase efficiency, it remains a challenge to deploy convolutional neural networks in mass spectrometer due to tight power budgets. In this paper, we propose a hybrid optical-electronic convolutional neural network to achieve fast and accurate classification and identification of mass spectra. The optical convolutional layer is realized by a folded 4f system. Our prototype with one single convolutional layer achieves 96.5% classification accuracy in an experimentally-acquired lipid dataset. A more complicated prototype adding one fully-connected layer achieves 100% accuracy. The proposed hybrid optical-electronic convolutional neural networks might enable non-professionals to avoid the accumulation of experimental experience and complicated calculations.
Accelerate tree ensemble learning based on adaptive sampling

Gradient Boosting Decision Tree (GBDT) has been used extensively in machine learning applications due to its superiority in efficiency, accuracy and interpretability. Although there are already excellent and popular open source implementations such as XGBoost and LightGBM, etc., however, large data size tend to make scalable and efficient learning to be very difficult. Since sampling is an efficient technique for alleviate massive data analysis performance issues, we exploit sampling techniques to address this problem. In this paper, we propose the AdaGBDT approach which apply an adaptive sampling method based on Massart’s Inequality to build GBDT model and draws samples in an on-line manner without manually specifying sample size. AdaGBDT is implemented by integrating the adaptive sampling method into LightGBM. The experimental results showed that, AdaGBDT not only keeps a small sample size and has a better training performance than LightGBM, but also subject to the constraint of estimation accuracy and confidence.
Video quality assessment based on LOG filtering of videos and spatiotemporal slice images

Center-surrounded receptive fields, which can be well simulated by the Laplacian of Gaussian (LOG) filter, have been found in the cells of the retina and lateral geniculate nucleus (LGN). With center-surrounded receptive fields, the human visual system (HVS) can reduce the visual redundancy by extracting the edges and contours of objects. Furthermore, current researches on image quality assessment (IQA) have shown that human's perception of image quality can be estimated by the correlation degree between the extracted perceptual-aware features of the reference and test images. Thus, this paper assesses the quality of a video by measuring the similarity of perceptual-aware features from LOG filtering between the test video and reference video. Considering the spatial and temporal channel of the human visual system both include the second derivative of Gaussian function, we first construct a three-dimensional LOG (3D LOG) filter to simulate human visual filter and to extract the perceptual-aware features for the design of VQA algorithms. Moreover, since the correlation measuring based on 2D LOG filtering of video spatiotemporal slice (STS) images can capture the distortion of spatiotemporal motion structure accurately and effectively, then we apply the 2D LOG filtering to video STS images and using maximum pooling for distortion of vertical and horizontal STS images to improve prediction accuracy. The performance of proposed algorithms is validated on the LIVE VQA database. The Spearman’s rank correlation coefficients of the proposed algorithms are all above 0.82, which shows that our methods are better than that of most mainstream VQA methods.
Proposal and Evaluation of Problem-based Experiments using Accelerated Motion Experiments Support Software

The new Courses of Study requires that education methods be improved based on active learning. The Courses of Study for science education note the importance of problem-based learning (PBL), in which students perform their own observations and experiments, for increasing students' interest in science. In this paper, we propose problem-based experiments (PBEs), which are educational experiments that adopt the philosophy of PBL to promote students' interest in physics. In PBEs, students formulate hypotheses on problems given by their teacher, and then they test their hypotheses through independent activities such as performing measurements and calculations. Students can consider better ways to solve the problems based on their initial results. To assess the effectiveness of PBEs, we used support software to evaluate experiments on accelerated motion performed by high school students. According to the results of a comprehension test administered during the experiment, students had improved understanding of basic concepts and laws related to energy. Additionally, the results of a questionnaire given after the experiment showed that the PBE increased the interest of over 75% of the students in physics.
IndQuery - An Online Portal for Registering E-Complaints Integrated with Smart Chatbot

Transparency is very significant for a successful e-governance. IndQuery is a complaint registering system used to improve the visibility of the actions taken on the complaints issued by the citizens. IndQuery is specifically developed for registering the complaints from the citizens. Also, when people need information regarding the house related services or finding difficulties in accessing those services provided by the government they can raise complaints and report it directly to the respective officials easily and effortlessly. Along with this, a chatbot is developed using artificial intelligence which is integrated with the website and many other social media platforms, to interact with the users dynamically. This Chatbot is mainly trained for reducing the official's workload and improvising the communication services. As it is difficult to find particular websites and contact details for reporting each problem, this website is considered vital for development. Also taking into consideration, the officials find difficulties for replying each and every complaint raised by thousands of citizens; Chatbot does the work for them. On the event of getting instantaneous replies people find it confident that their work is half done. The complaints can be submitted by the users from their respective portal which are stored in the database and further retrieved and displayed on the respective official's portal, by which the problems could be reviewed and resolved. Also, a feedback system is provided on which the users can rate the quality of service provided for them while the fixing of their complaints. This Chatbot can also be integrated with other platforms like Facebook messenger, Twitter, Telegram, Skype etc. to function conveniently with the users.
Sensory Worlds: Emotional Geography and Human-Centered Design in 360° VR Ethnographic Videos

This chapter assesses the usefulness of 360/VR video production in the creation of student VR videos. With the emergence of 360/VR documentary platforms in narrative and documentary film, empathy and presence are often touted as this format’s unique contribution beyond traditional documentary modes. However, the lack of an established grammar of VR filmmaking has left this worthy goal woefully under-theorized. This chapter explores spatialized storytelling techniques aimed at achieving viewer empathy by employing insights from the fields of emotional geography and human-centered design. How can these insights be employed to produce engaging VR experiences that effectively translate subjective worlds? This chapter argues that presence and empathy can be designed into the production of ethnographic VR experiences as long as they are grounded in an enhanced, collaborative ethnographic preproduction process.
Two-dimensional Categorical Data Collection Mechanism Satisfying Differential Privacy

In this paper, we propose a differentially private data collection mechanism for two-dimensional categorical data that is easy to implement. The mechanism is mainly composed of coding, noise addition, integer approximation, modulo and decoding. The method of round, round toward zero, ceil and floor are adopted to convert the data into decimal integer. We use accuracy rate, the ratio of the output data which is the same as the input data in the total output, to measure the utility of the mechanism. For privacy-preserving level, the measurement is based on local differential privacy. We define the privacy-preserving level of a randomized mechanism M which satisfies ϵ-local differential privacy as the minimum ϵ. We compare and analyze the utility and privacy-preserving level of each approximation method with the addition of relative noises to improve our mechanism. We find that round is the best approximation method for our mechanism and the utility and privacy-preserving level are related to the correlation coefficient of noises.
Communication Problems in Software Development — A Model and Its Industrial Application

Attaining effective communication within and across organizational units is among the most critical challenges for success in software development organizations. This paper presents a novel model, supporting analysis of problems in inter-departmental communication events. The model was developed and designed based on industrial needs emphasizing flexibility, applicability and scalability. The model covers central communication aspects in order to provide a useful approximation of communication problems rather than in-depth modeling on message-by message basis. Other event-specific information, such as costs, can then be attached to enrich analysis and understanding. To exemplify and evaluate the model and collect feedback from industry, it was applied to 16 events at a Swedish automotive manufacturer where communication between two departments had broken down during development of software-intensive systems. The evaluation showed that the model helped structure and conduct systematic data collection and analysis of dysfunctional communication patterns. We found that insufficient understanding of the matters being communicated was prevalent, but also more specifically, requirements were insufficiently balanced, detailed and specified over the full system development cycle. Besides, the long-term cost for the company was analyzed in depth for each event, yielding a total estimated cost for the analyzed communication events of 11.2MUS$.
Fusion of DPCA and ICA algorithms for mineral detection using Landsat-8 spectral bands

Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) algorithms was applied to some selected Landsat-8 mineral indices for mapping gossan and clay-rich zones for Zn-Pb exploration in the Franklinian Basin of North Greenland. This region contains a unique potential for exploration of world-class zinc deposits. Numerous potential zones for Zn-Pb deposits were identified using the fusion technique of DPCA/ICA. Their identification was based on detecting alteration mineral patterns consist of ferric iron, ferrous iron and clay minerals within a background of sedimentary rocks using Landsat-8 spectral bands. Several zones of gossan and clay mineral assemblages were identified in the trough sequences of the Citronen Fjord, Peary Land that may represent potential undiscovered CD Zn-Pb deposits and warrant further investigation. This investigation indicates that satellite remote sensing mapping techniques can aid in identifying unknown/undiscovered Zn-Pb sulfide deposits in the High Arctic Franklinian Basin by targeting the location of alteration zones that are feasible location for mineral occurrences.