Reviewerly/exHarmony huggingface dataset:
Health technologies for falls prevention and detection in adult hospital in-patients: a scoping review protocol

Review objective/questions: The objective of this scoping review is to map the evidence relating to the reporting and evaluation of health technologies for the prevention and detection of falls in adult hospital in-patients. The following questions will guide this scoping review:
Deep high-order supervised hashing

Recently, deep hashing has achieved excellent performance in large-scale image retrieval by simultaneously learning deep features and hash function. However, state-of-the-art methods for this task have so far failed to explore feature statistics higher than first-order. To address this problem, we propose two novel deep high-order supervised hashing (DHoSH) architectures based on point-wise labels (DHoSH-PO) and pair-wise labels (DHoSH-PA), respectively. The core of DHoSH is a trainable layer of bilinear features incorporated into existing deep convolutional neural network (CNN) for end-to-end learning. This layer captures the interaction of local features by bilinear pooling, using correlation to model dependencies of features within the same layer or cross-correlation for features across different layers of the CNN. Furthermore, DHoSH systematically employs the high-order statistics of features of multiple layers. Extensive experiments on commonly used image retrieval benchmarks show that our DHoSH-PO and DHoSH-PA models yield improved accuracy over its first-order counterparts and achieve effective performance for these benchmarks.
Simplifying the Collections Budget to Maximize Flexibility and Increase Responsiveness to User Needs

This paper considers management of the collections budget via fund structures. A common approach in collections budget management is to distribute allocations via format (e.g., monograph and serials), and subject areas. While tracking spending at a granular level provides more information, it also generally results in a large number of funds. As more and more materials are purchased in consortial packages or other types of "big deals,” or are simply interdisciplinary in nature, the allocation and expenditure on specific and narrowly defined funds begins to become inaccurate and less useful. Library administrators from two large Association of Research Libraries (ARL) libraries describe their experiences reviewing and revising their budget structures for resources in order to focus greater time and effort on priorities that meet user community needs and university-wide priorities.
Leveraging Web Semantic Knowledge in Word Representation Learning

Much recent work focuses on leveraging semantic lexicons like WordNet to enhance word representation learning (WRL) and achieves promising performance on many NLP tasks. However, most existing methods might have limitations because they require high-quality, manually created, semantic lexicons or linguistic structures. In this paper, we propose to leverage semantic knowledge automatically mined from web structured data to enhance WRL. We first construct a semantic similarity graph, which is referred as semantic knowledge, based on a large collection of semantic lists extracted from the web using several pre-defined HTML tag patterns. Then we introduce an efficient joint word representation learning model to capture semantics from both semantic knowledge and text corpora. Compared with recent work on improving WRL with semantic resources, our approach is more general, and can be easily scaled with no additional effort. Extensive experimental results show that our approach outperforms the state-of-the-art methods on word similarity, word sense disambiguation, text classification and textual similarity tasks.
The ∆-calculus: Syntax and Types

We present the ∆-calculus, an explicitly typed λ-calculus with strong pairs, projections and explicit type coercions. The calculus can be parametrized with different intersection type theories T , e.g. the Coppo-Dezani, the Coppo-Dezani-Salle, the Coppo-Dezani-Venneri and the Barendregt-Coppo-Dezani ones, producing a family of ∆-calculi with related intersection typed systems. We prove the main properties like Church-Rosser, unicity of type, subject reduction, strong normalization, decidability of type checking and type reconstruction. We state the relationship between the intersection type assignment systems a la Curry and the corresponding intersection typed systems a la Church by means of an essence function translating an explicitly typed ∆-term into a pure λ-term one. We finally translate a ∆-term with type coercions into an equivalent one without them; the translation is proved to be coherent because its essence is the identity. The generic ∆-calculus can be parametrized to take into account other intersection type theories as the ones in the Barendregt et al. book.
IMPROVING STUDENTS’ ABILITY IN WRITING RECOUNT TEXT USING DIARY AT ONE OF JUNIOR HIGH SCHOOL IN CAMPAKA – CIANJUR

In mastering English, students find problems dealing with language skill and language components, most students in Indonesia consider English as a difficult subject which make them frustrated. Writing is one of the four language skills that play a very important role in second language learning. Writing skill is more complex and difficult to teach, requiring the mater not only the grammatical and theoretically devices but also the conceptual. One of the equipment aids that can be used in learning writing is a diary. The main objective of this study is to find out whether writing diary improves students’ writing skill or not. In this research, the researcher used the quantitative method, with classroom action research design. The population of this study was all students of class 8A that consisted of 30 students. Observation, interview, and test as the instruments of this research. The researcher compared the achievement of pre-test to identify the effectiveness of using diary writing.
Animated mesh simplification based on motion features in visual sensor networks

The three-dimensional animated model is widely used in scientific visualization, entertainment, and virtual applications, especially in visual sensor networks. The main purpose of simplification is to capture the shape sequence of an object with very few elements while preserving the overall shape. As three-dimensional animated mesh is time-varying in all frames, the trade-off between the temporal coherence and geometric distortion must be considered to develop simplification algorithm. In this article, a novel three-dimensional animated mesh simplification algorithm based on motion features is presented. Here, motion features are the connection areas of the relative movement consisted of vertices and edges. Motion feature extraction is to find a subgraph that has movement property. Dihedral angle of the edge through all frames is used to determine whether an edge is connected or not to the movement parts. Then, a rotation connected graph is defined to extract motion features. Traveling this graph, all motion features can be extracted. Based on the motion features, animated quadric error metric is created and quadric error matrix is built through all frames. Compared with the other methods, the important advantages of this method are high-efficiency simplification process and smoother simplification effects. It is suitable to be used in real-time applications. Experiment results show that the 3D animated mesh simplification effects by our method are satisfactory.
How Your Supporters and Opponents Define Your Interestingness

How can one determine whether a data mining method extracts interesting patterns? The paper deals with this core question in the context of unsupervised problems with binary data. We formalize the quality of a data mining method by identifying patterns – the supporters and opponents – which are related to a pattern extracted by a method. We define a typology offering a global picture of the methods based on two complementary criteria to evaluate and interpret their interests. The quality of a data mining method is quantified via an evaluation complexity analysis based on the number of supporters and opponents of a pattern extracted by the method. We provide an experimental study on the evaluation of the quality of the methods.
A mathematical programming formulation for the Hartree–Fock problem on open-shell systems

The solutions of the time-independent Schrödinger equation provide a quantum description of the stationary state of electrons in atoms and molecules. The Hartree–Fock problem consists in expressing these solutions by means of finite dimensional approximations thereof. These are themselves linear combinations of an existing linearly independent set; best approximations are obtained when a certain energy function is minimized. In Lavor et al. (Europhys Lett 5(77):50006p1–50006p5, 2007) we proposed a new mathematical programming (MP) approach which enhanced the likelihood of attaining globally optimal approximations, limited to closed-shell atomic systems. In this paper, we discuss an extension to open-shell systems: this is nontrivial as it requires the expression of a rank constraint within an MP formulation. We achieve this by explicitly modelling eigenvalues and requiring them to be nonzero. Although our approach might not necessarily scale well, we show it works on two open-shell systems (lithium and boron).
Volunteering Of University Students As A Resource For The Development Of Higher Education

Historically volunteerism in Russia has traditionally developed in the system of higher education. Nowadays Russian universities scientific potential is becoming more active due to the changing of the educational processes. Despite the fact that self-government bodies, business as well as academic mobility and local communities stimulate university community, social mission of the higher education is being replaced with pragmatic goals. Whereas university communitys initiative is gaining value, universities overall program is formed under the guidance of innovation and commercialization. Meanwhile the popularization of volunteerism among younger generations as a trend is reflected in support provided to organized youth groups that often function in universities and colleges. The purpose of this article is to assess the impact of volunteerism on the scientific activity of Russian university students and their innovative potential. The report presents the data of a survey of students of the Urals Federal District which was realized in 2017. The method of correlation analysis is applied. The following research solves such tasks as the evaluation of dependence of the self-assessment of the innovative culture on the degree of the Ural macroregion’s students’ involvement in volunteer activity. The specificity of the involvement of student volunteers in the innovative and scientific activity of the university is determined.
Detection of masses in mammographic breast cancer images using modified histogram based adaptive thresholding (MHAT) method

Breast cancer is the leading type of cancer diagnosed in women nowadays and for breast screening, mammography is preferred to detect and diagnose the cancer by detecting the masses with the help of Computer-aided Diagnosis (CAD) system. It helps to assist radiologists in getting accurate diagnosis. An approach is proposed to effectively detect the masses in mammographic breast cancer images by using Modified Histogram based adaptive thresholding (MHAT) method. The algorithm has been tested over with 100 mammographic images and the experimental results show that the detection method has a sensitivity of 98.3% at 0.78 false positives with accuracy of 99% per image. We evaluated the performance of our MHAT algorithm by comparing with respect to the ground-truth boundary drawn by an expert radiologist. The results are clinically relevant, according to the radiologists who evaluated the results.
Adaptive Face Tracking Based on Online Learning

Object tracking can be used to localize objects in scenes, and also can be used for locating changes in the object’s appearance or shape over time. Most of the available object tracking methods tend to perform satisfactorily in controlled environments but tend to fail when the objects appearance or shape changes, or even when the illumination changes (e.g., when tracking non-rigid objects such as a human face). Also, in many available tracking methods, the tracking error tends to increase indefinitely when the target is missed. Therefore, tracking the target objects in long and uninterrupted video sequences tends to be quite challenging for these methods. This work proposes a face tracking algorithm that contains two operating modes. Both the operating modes are based on feature learning techniques that utilize the useful data accumulated during the face tracking and implements an incremental learning framework. To accumulate the training data, the quality of the test sample is checked before its utilization in the incremental and online training scheme. Furthermore, a novel error prediction scheme is proposed that is capable of estimating the tracking error during the execution of the tracking algorithm. Furthermore, an improvement in the Constrained Local Model (CLM), called weighted-CLM (W-CLM) is proposed that utilize the raining data to assign weights to the landmarks based on their consistency. These weights are used in the CLM search process to improve CLM search optimization process. The experimental results show that the proposed tracking method (both variants) perform better than the comparative state of the art methods in terms of Root Mean Squared Error (RMSE) and Center Location Error (CLE). In order to prove the efficiency of the proposed techniques, an application in yawning detection is presented.
Asynchronous Structure from Motion at Scale

Vision systems that provide a 360-degree view are becoming increasingly common in today’s vehicles. These systems are generally composed of several cameras pointing in different directions and rigidly connected to each other. The purpose of these systems is to provide driver assistance in the form of a display, for example by building a Bird’s eye view around the vehicle for parking assistance. In this context, and for reasons of cost and ease of integration, such cameras are generally not synchronized. If non-synchronization is not a problem when it comes to display only, it poses significant issues for more complex computer vision applications (3D reconstruction, motion estimation, etc.). In this article, we propose to use a network of asynchronous cameras to estimate the motion of the vehicle and to find the 3D structure of the scene around it (for example for obstacle detection). Our method relies on the use of at least three images from two adjacent cameras. The poses of the cameras are independently estimated by conventional visual odometry algorithms. Then we show that it is possible to find the absolute scale factor by hypothesizing that the motion of the vehicle is smooth. The results are then refined through a local bundle adjustment on the scale factor and 3D points only. We evaluated our method under real conditions on the KITTI database, and we showed that our method can be generalized to a larger network of cameras thanks to a system developed in our lab.
Three Node Tandem Communication Network Model with Duane Arrival Process having Phase Type Service

This paper introduces a three node Tandem communication network model in which the arrival process follows a Duane process such that the inter arrival times follow Weibull distribution.In this model it is assumed that intermediator departures after transmission from first and second nodes are allowed it is further assumed that the inter transmission times in each node follow Poisson process and the transmission strategy is Dynamic bandwidth allocation.The network behavior is analyzed by deriving expressions for average number of packets in each Queue.The mean delays in transmission, the throughput of the nodes, the Utilization of the transmitters and the variability of the content of buffers.Through Sensitivity analysis it is observed that the arrival process has significant influence on network performance measures.The strategy of allowing inter mediatory departures reduce conjunction in buffers and mean delay of transmission of packets.This model also includes some of the earlier models as particular cases for specific limiting for specific values of the parameter.This model is useful for design and monitoring self-Similarity networks.
BAYES AĞI VE BULANIK ÇIKARIM SİSTEMİ TABANLI BİR BİLGİ GÜVENLİĞİ RİSK DEĞERLENDİRME MODELİ

This study proposes a novel information security risk assessment approach based on Bayesian network and Fuzzy Inference System in order to evaluate and calculate both qualitative and / or quantitative risks. The proposed model is developed to analyse test processes for a software services company in order to evaluate the information security risks. Threats, vulnerabilities, risks, and their relations are constructed with a Bayesian network and marginal probabilities are calculated for each risk factor. Several fuzzy membership functions and fuzzy decision rules are designed and constructed for assets’ values, risks’ probabilities, and relative risk values. Finally, the impacts of risk values are calculated after the aggregation and defuzzification process. It is shown that this new model enables the business decision makers and managers to obtain more objective, reliable, and flexible information security risk assessment results.
Financing Small &amp; Medium Enterprises with Blockchain: An Exploratory Research of Stakeholders’ Attitudes

Obtaining the necessary financing can be a challenging task for small and medium enterprises (SMEs). To obtain such financing, SMEs must go through a lengthy assessment procedure that is costly for them as well as for financiers. Experience shows issues of trust, administrative costs, repetition, and data sensitivity may be at the root of these costs. With the emergence of blockchain technology, an opportunity arises whereby a shared ledger could alleviate these issues and support the financing procedure. In this paper, we conduct an exploratory research into the appetite of the stakeholders involved in two traditional financing procedures, namely invoice factoring and inventory finance, for adopting the blockchain technology in their part(s) of the procedure, as well as the envisioned changes to their respective careers. The results show the potential of blockchain in the financing procedure, and highlight the areas where it would have the highest impact.
Efficient Interactive Multi-object Segmentation in Medical Images

In medical image segmentation, it is common to have several complex objects that are difficult to detect with simple models without user interaction. Hence, interactive graph-based methods are commonly used in this task, where the image is modeled as a connected graph, since graphs can naturally represent the objects and their relationships. In this work, we propose an efficient method for the multiple object segmentation of medical images. For each object, the method constructs an associated weighted digraph of superpixels, attending its individual high-level priors. Then, all individual digraphs are integrated into a hierarchical graph, considering structural relations of inclusion and exclusion. Finally, a single energy optimization is performed in the hierarchical weighted digraph satisfying all the constraints and leading to globally optimal results. The experimental evaluation on 2D medical images indicates promising results comparable to the state-of-the-art methods, with low computational complexity.
Investigating the Effect of Using the Mobile Educational App as Appropriate Method of Study and Learning on Students' Educational Achievement

Background: Students' educational success depends on various factors, one of the most important factor is applying appropriate study methods To achieve this, a mobile educational application called: Appropriate study and learning practices for was designed by the researchers to investigate the effect of using educational mobile application on study habits for students' educational success. Methods: In this quasi-experimental study, 2 groups consisting of 20 and 21 undergraduate students of Radiology at Ahvaz Jundishapur University of Medical Sciences, in the Southwest of Iran were selected as statistical samples which then they were divided into two groups of intervention group (20 persons) and the control group (21 persons). At the beginning of the first half of the academic year of 2017-2018, this application was given to the intervention group. As the next step, at the end of the second semester of the same academic year, the mean of the students final term score of intervention group[J1]  (Who used this app) was compared with the control group (which did not use this app). Descriptive and inferential statistics were run via SPSS version 22. Results: The results of the study showed that this application had a positive impact on students' educational improvement. Accordingly, the mean and standard deviation of the average final score of the students' academic term were 14.65 ± 1.70 for the control group and 16.95 ± 1.36 for the intervention group. These results showed a significant difference between the control group and the intervention group (P 0.005). Conclusion: Using the educational application of appropriate methods of study and learning had a positive impact on students' academic achievement, since it increased their final scores. Learning the appropriate methods of study and learning for students in the form of a curriculum or holding workshops and, of course, with regard to the popularity of smartphones among students, one benefit of mobile education apps can be  students' academic achievement.
Visual Scene Understanding for Self-Driving Cars Using Deep Learning and Stereovision

Poster presented at the 2018 Defence and Security Doctoral Symposium.Autonomous driving has been rapidly evolving for the last few years and there is a lot of fervour in increasing the intelligence of these vehicles. One key aspect of a self-driving car is its ability to sense the environment in order to be aware of its surrounding.Our interest lies in using computer vision and deep learning techniques to detect surrounding entities; localising and recognising them. Here, we present a novel deconvolutional neural network for semantic segmentation, combined with disparity map information to localise each vehicle in front of the ego-vehicle, including occluded instances, in an urban traffic environment. We also compare our approach with state-of-the-art instance segmentation methods. In the future, we will extend our work to other types of obstacles, to improve awareness and increase obstacle avoidance and path finding capabilities of a vehicle.
The race to innovate for development should not leave foundational data systems behind

Data is everywhere, constantly being created by humans and machines across the globe. But as half of the world seems to be drowning in data, too many people and places are still invisible in the numbers that drive decisions.
The Role of Playing Video Games on School Achievement

The subject of the present research was the possible effect that playing video games can have on students.The focus is on the role that playing video games can have on school grades and school achievement in general.Problems in the research of effects of video games were outlined in the paper.The paper also presents the results of previous research of the impact of video games and often other media on school achievement.Elementary schools and high schools students participated in the present research.The results are generally as expected considering previous studies, and certain positive effects were observed, such as the fact that students who play video games achieve better overall success when compared to students who do not play games at all.