Reviewerly/exHarmony huggingface dataset:
An Attack Surface Metric Suitable for Heterogeneous Redundant System with the Voting Mechanism

With the development of cyberspace security "asymmetry in attack and defense", heterogeneous redundancy design is gradually being widely used. Heterogeneous redundancy design can enhance the robustness and security of the system through the differentiation of heterogeneous components. But the complex structural design of the system makes it difficult to apply the existing attack surface metric to measure the security of heterogeneous redundant system. In this paper, based on the attack surface metric proposed by Manadhata, we provide a new attack surface metric suitable for heterogeneous redundant system with voting mechanism. In this new metric, we define a new notion named attack surface arbitration, and also propose a new method for quantifying result of arbitration. The experiment result shows that the new attack surface metric can properly describe the attack surface of a heterogeneous redundant system with voting mechanism, which changes with the adjustment of voting algorithm.
Digital Native Pre-Service Teachers’ Perceptions of using Commercial and Open Source Online Applications in Future Praxis after Completing Technology Integration Course

Exposure to new technologies is commonplace for most people lived in the 21st century. As technology evolves, so does a generation of learners whose identity is shaped by how they manage these technological tools. The participants of this study were digital native pre-service teachers (DNPST) who were exposed to technology since childhood and enrolled in a technology integration course at a border city university. This study investigates DNPSTâ€™s perceptions upon exposure to alternate models of technology tools and their intentions for implementing them into their future classroom repertoire. The method of data collection for this quantitative study consisted of pre and post-test surveys. Data analysis was completed using the Wilcoxon Rank Sum-Test. Results of this study indicated that the technology methods course did not have a significant effect on pre-service teachers' perceptions to adopt technology tools for future classroom instruction. This study was encouraged by the department of education and the results obtained through this study will allow curricular updates. https://doi.org/10.26803/ijlter.18.2.8
Critical Parameter Consensus for Efficient Distributed Bundle Adjustment

We present a critical parameter consensus framework to improve the efficiency of Distributed Bundle Adjustment (DBA). Existing DBA methods are based solely on either camera consensus or point consensus, often resulting in excessive local computation time or large data transmission overhead. To address this issue, we jointly partition points and cameras, and perform the consensus on both overlapping cameras and points. Our joint block partitioning method first initializes a non-overlapping block partition, maximizing local problem constraints and ensuring a uniform partition. Then overlapping cameras and points are added in a greedy manner to maximize the partition score that quantifies the efficiency of DBA for local blocks. Experimental results on public datasets show that we can achieve better computational efficiency without loss of accuracy.
Aplicação de Tecnologias da Web Semântica em Motores de Busca na Internet

databases, and in the elaboration of queries and the comprehension of the users' needs.We also identified different semantic search engines, usually aimed at specific purposes, rather than generic searches.It was thus verified that SW technologies are used for researchers oriented towards specific contexts, and that researchers with a general purpose use semantic approaches, but not based in the Semantic Web.
Detecting and Tracking Surgical Tools for Recognizing Phases of the Awake Brain Tumor Removal Surgery

In order to realize automatic recognition of surgical processes in surgical brain tumor removal using microscopic camera, we propose a method of detecting and tracking surgical tools by video analysis. The proposed method consists of a detection part and tracking part. In the detection part, object detection is performed for each frame of surgery video, and the category and bounding box are acquired frame by frame. The convolution layer strengthens the robustness using data augmentation (central cropping and random erasing). The tracking part uses SORT, which predicts and updates the acquired bounding box corrected by using Kalman Filter; next, the object ID is assigned to each corrected bounding box using the Hungarian algorithm. The accuracy of our proposed method is very high as follows. As a result of experiments on spatial detection. the mean average precision is 90.58%. the mean accuracy of frame label detection is 96.58%. These results are very promising for surgical phase recognition.
AN IMAGE-BASED CAPTCHA SYSTEM USING CLICK

CAPTCHA stand for "Completely Automated Public Turing test to tell Computers and Humans Apart".It is a technique of testing where only human can solve it and computer-generated system (bots) could not get through it.In this paper, a new type CAPTCHA system is proposed.The proposed scheme named ClickShape, developed by using a technique based on image-based CAPTCHA.ClickShape will ask user to click on the shape displayed by the system.It is quite similar to image-based CAPTCHA where user have to choose any specific image, for example image of a signboard.As for this captcha image of a shape is easier to be recognize by human.This type of CAPTCHA had developed by using HTML, JavaScript, PHP and Cascading Style Sheets (CSS).
Target Segmentation and Pose Estimation for Rapid Grabbing Using Depth Information

In order to realize rapid grabbing of stacked objects, we propose a quick and accurate method to realize target segmentation and pose estimation using only depth information. We use filtering to remove the noises in detection. Then we detect the contours and use the averages and gradients of depth to find the contour of the top plane. We calculate the centroid and normal vector of the top plane to get the pose estimation. Thus we can realize the rapid grabbing of the top object, then achieve rapid grabbing of stacked objects one by one.
A Deep Learning Graphical User Interface Application on MATLAB

Deep learning, as a new era of machine learning techniques, shows the ability to generalize computational model through hierarchical layers by learning the features from a large amount of training data with ignorable human interventions.Deep learning is mainly implemented in areas of computer vision, audio, and speech processing.Moreover, deep learning has significantly improved state of the art in image classification.Image-based classification, the most classic but significant research topic, has been critically improved using deep learning architecture and outperformed the majority of state-of-the-art achievements.No bells and whistles, deep learning becomes the most popular research area.However, to train a comprehensive classifier, a large amount of data is inescapable, which apparently needs lots of computational time so that it requires fast GPUs to accomplish fast training.In general, powerful GPUs are expensive, and the process of solving compatibility problems in both hardware and software are reluctant due to very limited access to GPUs' implementations in most universities.The primary purpose of this paper is how to propagate principles of deep learning to students and build the bridge to make them learn and use deep learning more comfortable or more accessible.In this paper, we develop a Graphic User Interface (GUI) deep learning beginners to test classification results through CPU without massive training computation.We use the high-performance GPU to train the model, once finish the training, we save the parameters of training model, load it to the GUI and let the student do the testing.
Interpretable Reinforcement Learning via Differentiable Decision Trees.

Decision trees are ubiquitous in machine learning for their ease of use and interpretability. Yet, these models are not typically employed in reinforcement learning as they cannot be updated online via stochastic gradient descent. We overcome this limitation by allowing for a gradient update over the entire tree that improves sample complexity affords interpretable policy extraction. First, we include theoretical motivation on the need for policy-gradient learning by examining the properties of gradient descent over differentiable decision trees. Second, we demonstrate that our approach equals or outperforms a neural network on all domains and can learn discrete decision trees online with average rewards up to 7x higher than a batch-trained decision tree. Third, we conduct a user study to quantify the interpretability of a decision tree, rule list, and a neural network with statistically significant results ($p < 0.001$).
On Semantic Search Algorithm Optimization

In the article we consider, on the example of development of a relational database (RDB) information system for Tatneft oil and gas company, an approach to organization of effective search in large arrays of heterogeneous data, satisfying the following essential requirements. On the one hand, the data is integrated at the semantic level, i.e. the system supports the presentation of data, describing its semantic properties within an unified subject domain ontology. Accordingly, end user’s request are formulated exclusively in the subject domain terminology. On the other hand, the system generates unregulated SQL-queries, i.e. the full text of possible SQL-queries, not just values of particular parameters, predefined by the system developers. Considered approach includes both the possibilities of increasing the reactivity of the universal SQL queries generation scheme as well as more specific optimization possibilities, arising from the particular system usage context.
Design and Implementation of Super Mall System Based on SOA Distributed Architecture

A B2C platform e-commerce system is completed by analysing the current mainstream e-commerce system functions. The system uses a distributed architecture including search system, single sign-on system, backstage system, order system, shopping cart system, data analysis system. The use of RPC(Remote Procedure Call) protocol to achieve communication between the system. The message queue mechanism improves the speed of response to the user, using Nosql database Redis as a cache. The use of a large number of technologies achieve high cohesion low coupling principle Server to reduce the pressure on the database to read and write. The use of AOP(Aspect-Oriented Programming) ideas on the database achieve the database read and write separation. The system uses Tomcat as a Web application server, database using MYSQL, Nginx as a Web server load load balancing and reverse proxy. Each system pass Dubbo communication to achieve mutual cooperation function. The system pass functional testing. it has good stability. According to the results of the stress test, virtual machine server can do the number of concurrent 400 under the cycle request 100 times. It is the error rate of 5%. If the nodes are clustered, the compression effect will be better. The flexibility can be designed for different requests.
Investigating Critical Obstacles to Entrepreneurship in Emerging Economies: A Comparative Study between Males and Females in Qatar

Qatar’s Entrepreneurship sector faces major obstacles that impede its growth prospects. While  Qatar  has  started  to  focus  more  on  improvement  of  the  entrepreneurial  environment through   semi-governmentalagencies   and   creation   of   economic   free   zones,   the   overall environment  is  still  riddled  with  numerous  problems  that  hinder  the  achievement  of  this  goal.Emergence  of  private  sector  as  a  main  force  behind  Qatar’s  economic  growth  will  depend heavily on country’s ability to catalyze positive change in this area.The  main  objective  of  this paper  is  to  explore  the  main  obstacles  face  Entrepreneurship  sector  in  Qataras  an  emerging economyand  to  find  out  if  any  differences  could  be  found  between  males  and  females  in assessing  these  obstacles.Amixed  approach  is  followed  starting  with  aqualitative studyto determine  obstacles  followed  by  a  quantitative  examination  of  differences  between  males  and females.Interviewswith  different  entrepreneurship  stakeholders  reveal  four  main  obstacles facing Qatari entrepreneurs. These obstacles include: bureaucratic requirements, limited access to funding, restrictive and biased legal conditions, and social constraints.Statistical results show that there is no difference between males and females in assessing these obstacles and that both genders face nearly the same barriers.
KeySFC

One of the main challenges in network functions virtualization (NFV) is how to dynamically steer traffic flows through a set of service functions (SFs). Fig. 1(a) shows the embedding of a service function chaining (SFC) request in a NFV Infrastructure (NFVI). The overlay layer represents logical connections between virtual machines (VMs), while the underlay layer represents connections between physical nodes.
CARESSES:The Flower that Taught Robots about Culture

The video describes the novel concept of “culturally competent robotics”, which is the main focus of the project CARESSES (Culturally-Aware Robots and Environmental Sensor Systems for Elderly Support). CARESSES a multidisciplinary project whose goal is to design the first socially assistive robots that can adapt to the culture of the older people they are taking care of. Socially assistive robots are required to help the users in many ways including reminding them to take their medication, encouraging them to keep active, helping them keep in touch with family and friends. The video describes a new generation of robots that will perform their actions with attention to the older person's customs, cultural practices and individual preferences.
Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1.

This paper proposes a representational model for image pair such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components. (1) The vector representations of local contents of images. (2) The matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Our experiments show that our model can learn to infer local motions. Moreover, the model can learn Gabor-like filter pairs of quadrature phases.
NEGES 2018: Workshop on Negation in Spanish

This paper presents the 2018 edition of NEGES, Workshop on Negation in Spanish, that took place on September 18 as part of the 34th International Conference of the Spanish Society for Natural Language Processing. In this edition, three tasks were proposed: Task 1: “Annotation guidelines”, Task 2: “Negation cues detection”, and Task 3: “Sentiment analysis”. The dataset used for Task 2 and Task 3 was the SFU ReviewSP-NEG corpus. About 10 teams showed interest in the tasks and 4 teams finally submitted results.
Self-Organizing is not Self-Managing: A Case Study about Governance Challenges in an Agile IT Unit and its Scrum Projects

This paper presents a case study on the internal governance of Scrum projects and their relationships with their organization's governance within a rich research setting: an IT agile unit and its mature Scrum project teams.This study reveals ambiguities about the meaning of self-organizing versus self-managing, and the associated challenges for governance processes, especially those related to HR governance, which can lead to unresolved issues and conflicts.Interestingly, these ambiguities are also found in the current IS literature, which rarely differentiates self-organizing from self-managing in agile projects.Thus, this paper enhances our knowledge of governance processes and associated challenges, particularly for mature Scrum project teams, which are still little covered in the IS literature.
A Reconstruction of Default Conditionals within Epistemic Logic

Default conditionals are statements that express a condition of normality, in the form ‘if φ then normally ψ’ and are of primary importance in Knowledge Representation. There exist modal approaches to the construction of conditional logics of normality. Most of them are built on notions of preferen ce among possible worlds, corresponding to the semantic intuition that φ ⇒ ψ is true in a situation if in the most preferred (most ‘normal’) situations in which φ is true, ψ is also true. It has been noticed that there exist natural epistemic readings of a default conditional, but this direction has not been thoroughly explored. A statement of the form ‘something known to be a bird, that can be consistently believed to fly, does fly’ involves well-known epistemic attitudes and allows the possibility of defining defaults within the rich framework of Epistemic Logic. We pursue this direction here and proceed to define conditionals within KBE, a recently introduced S4.2-based modal logic of knowledge, belief and estimation. In this logic, knowledge is a normal S4 operator, belief is a normal KD45 operator and estimation is a non-normal operator interpreted as a ‘majority’ quantifier over the set of epistemically alternative situations. We define and explore various conditionals using the epistemic operators of KBE, capturing φ ⇒ ψ in various ways, including ‘according to the agent’s knowledge, an estimation that φ is true implies the estimation that (φ∧ψ) is true’ or ‘if φ is known and there is no reason to believe ¬ψ then ψ can be plausibly inferred’. Overall, we define here three nonmonotonic default conditionals, one conditional satisfying monotonicity (strengthening the antecedent) and two nonmonotonic conditionals that do not satisfy the ubiquitous axiom ID (reflexivity). Our project provides concrete evidence that the machinery of epistemic logic can be exploited for the study of default conditionals.
Model and Design of Teaching Management Oriented to Outstanding Civil Engineer’s Training in Local Colleges and Universities Based on Internet-plus

In the progress of higher education's reformation, the Plan for Educating and Training Outstanding Engineers (PETOE) is an important scheme that can train engineering talents in the domain of engineering education, which is deeply affecting colleges and universities' teaching activities including the way and depth for teaching problems. Taking higher education of civil engineering as an example in the background of "Internet + Architecture", firstly this paper analyses some problems that encountered during the implementation of PETOE in local colleges and universities, and proposes the school-enterprise's distributed and collaborative teaching strategy oriented to outstanding civil engineer's training. Then it builds the network management model for teaching services oriented to outstanding civil engineer's training. Finally, the paper designs and implements this kind of network management system for teaching services. As enterprise participation is the key to the success of PETOE, and school-enterprise cooperation is the assurance of training qualified outstanding engineers, PETOE needs to participant enterprises to supply more teaching resources, which can satisfy the local colleges and universities' demands of teaching data from the real engineering environment. In the process of teaching services management, the active and customized services to teaching data can also be reflected in the kind of network management system for teaching services.
An $\ell_{1/2}$ -Norm Regularizer-Based Sparse Coding Framework for Gaze Prediction in First-Person Videos

Predicting human gaze is important for efficiently processing and understanding numerous incoming visual information from first-person videos (FPVs). Even though people continuously gaze in noisy environments, most existing gaze prediction algorithms are based on saliency mapping, which is sensitive to noisy surroundings in the real world. Sparsity-based saliency detection algorithms perform favorably against state-of-the-art methods. In this paper, we apply a novel saliency detection method based on sparse coding with the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> -norm for predicting human gaze in FPVs. Image boundaries are first extracted via superpixels as bases for a dictionary, from which a sparse representation model is constructed. For each superpixel, we first compute sparse reconstruction errors. Then, a saliency map is updated based on the reconstruction errors. To receive the sparse reconstruction errors, the most widely utilized sparse constraint is the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm. However, the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm leads to over-penalization of large components in a sparse vector. We employ the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> -norm for sparse coding, which can lead to a sparser solution for a more accurate gaze prediction than the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm. We transform the complex nonconvex optimization of sparse coding with the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sub> -norm to a number of one-dimensional minimization problems. In this way, we obtain the closed-form solutions efficiently. The experimental results using a real-world gaze dataset demonstrate that the proposed algorithm performs better than the state-of-the-art methods of gaze prediction for FPVs.
Exploring the Semantics for Visual Relationship Detection.

Scene graph construction / visual relationship detection from an image aims to give a precise structural description of the objects (nodes) and their relationships (edges). The mutual promotion of object detection and relationship detection is important for enhancing their individual performance. In this work, we propose a new framework, called semantics guided graph relation neural network (SGRN), for effective visual relationship detection. First, to boost the object detection accuracy, we introduce a source-target class cognoscitive transformation that transforms the features of the co-occurent objects to the target object domain to refine the visual features. Similarly, source-target cognoscitive transformations are used to refine features of objects from features of relations, and vice versa. Second, to boost the relation detection accuracy, besides the visual features of the paired objects, we embed the class probability of the object and subject separately to provide high level semantic information. In addition, to reduce the search space of relationships, we design a semantics-aware relationship filter to exclude those object pairs that have no relation. We evaluate our approach on the Visual Genome dataset and it achieves the state-of-the-art performance for visual relationship detection. Additionally, Our approach also significantly improves the object detection performance (i.e. 4.2\% in mAP accuracy).