nils-herrmann/clean_scirepeval huggingface dataset:
Classification Model of Prediction for Placement of Students

Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students’ activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system. In this study, we collected the student’s data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student’s academics performance for Training and placement. This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.
SDN-based load balancing strategy for server cluster

Growing network traffic brings huge pressure to the server cluster. Using load balancing technology in server cluster becomes the choice of most enterprises. Because of many limitations, the development of the traditional load balancing technology has encountered bottlenecks. This has forced companies to find new load balancing method. Software Defined Network (SDN) provides a good method to solve the load balancing problem. In this paper, we implemented two load balancing algorithm that based on the latest SDN network architecture. The first one is a static scheduling algorithm and the second is a dynamic scheduling algorithm. Our experiments show that the performance of the dynamic algorithm is better than the static algorithm.
Learning Analytics : Enhancing the Quality of Higher Education

The regular gathering of student information has created a high level of complexity, and also an incredible opportunity for teachers to enhance student learning experience. The digital information that learners leave online about their interests, engagement and their preferences gives significant measures of information that can be mined to customise their learning experience better. The motivation behind this article is to inspect the quickly developing field of Learning Analytics and to study why and how enormous information will benefit teachers, institutes, online course developers and students as a whole. The research will discuss the advancement in Big Data and how is it useful in education, along with an overview of the importance of various stakeholders and the challenges that lie ahead. We also look into the tools and techniques that are put into practice to realize the benefits of Analytics in Education. Our results suggest that this field has the immense scope of development but ethical and privacy issues present a challenge.
Learning Shape Priors for Single-View 3D Completion And Reconstruction

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.
Chaos in the Stadium Quantum Billiards

An expansion method was used to write a MATHEMATICA program to compute the energy levels and eigenfunctions of a 2-D quantum billiard system with arbitrary shape and dirichlet boundary conditions. One integrable system, the full circle, and one non-integrable system, the stadium, were examined. Chaotic properties were sought in nearest-neighbor energy level spacing distributions (NND). It was observed that the classically non-chaotic Poisson function seemed to fit the circle’s NND better, while the classically chaotic Gaussian Orthogonal Ensemble function the stadium better. A detailed explanation of the theory and algorithm are provided, although a more rigorous energy-level analysis is desireable.
Motivational theory in practice at Tesco A Tesco case study

Since then, Tesco has expanded across the world. It now has over 2,200 stores including hypermarkets and Tesco Express outlets to meet different customer needs. As a conglomerate Tesco also offers alternative goods and services such as insurance, banking and online shopping. With net profits of around £3.4 billion Tesco has become the largest British retailer and one of the world's leading retail outlets on three continents. Tesco's growth has resulted in a worldwide workforce of over 468,000 employees.
An Overview of Grounded Theory Design in Educational Research

Over past decades, grounded theory is increasingly popular in a broad range of research primarily in educational research. The current paper aims to provide useful information for the new-comers and fit them well in grounded theory research. This paper starts with definitions, origin and applications of grounded theory, followed by types of grounded theory research designs and the key characteristics of grounded theory. Other aspects covered include data collection and data analysis, general steps, and ethical issues in grounded theory. Discussions on the strengths and limitations of grounded theory, as well as evaluation aspects, are found in the last part of this paper.
A Reinforcement Learning Approach to the View Planning Problem

We present a Reinforcement Learning (RL) solution to the view planning problem (VPP), which generates a sequence of view points that are capable of sensing all accessible area of a given object represented as a 3D model. In doing so, the goal is to minimize the number of view points, making the VPP a class of set covering optimization problem (SCOP). The SCOP is NP-hard, and the inapproximability results tell us that the greedy algorithm provides the best approximation that runs in polynomial time. In order to find a solution that is better than the greedy algorithm, (i) we introduce a novel score function by exploiting the geometry of the 3D model, (ii) we device an intuitive approach to VPP using this score function, and (iii) we cast VPP as a Markovian Decision Process (MDP), and solve the MDP in RL framework using well-known RL algorithms. In particular, we use SARSA, Watkins-Q and TD with function approximation to solve the MDP. We compare the results of our method with the baseline greedy algorithm in an extensive set of test objects, and show that we can outperform the baseline in almost all cases.
An Evaluation of PredPatt and Open IE via Stage 1 Semantic Role Labeling

PredPatt is a pattern-based framework for predicate-argument extraction. While it works across languages and provides a well-formed syntax-semantics interface for NLP tasks, a large-scale and reproducible evaluation has been lacking, which prevents comparisons between PredPatt and other related systems, and inhibits the updates of the patterns in PredPatt. In this work, we improve and evaluate PredPatt by introducing a large set of high-quality annotations converted from PropBank, which can also be used as a benchmark for other predicate-argument extraction systems. We compare PredPatt with other prominent systems and shows that PredPatt achieves the best precision and recall.
Defending against phishing attacks: taxonomy of methods, current issues and future directions

Internet technology is so pervasive today, for example, from online social networking to online banking, it has made people’s lives more comfortable. Due the growth of Internet technology, security threats to systems and networks are relentlessly inventive. One such a serious threat is “phishing”, in which, attackers attempt to steal the user’s credentials using fake emails or websites or both. It is true that both industry and academia are working hard to develop solutions to combat against phishing threats. It is therefore very important that organisations to pay attention to end-user awareness in phishing threat prevention. Therefore, aim of our paper is twofold. First, we will discuss the history of phishing attacks and the attackers’ motivation in details. Then, we will provide taxonomy of various types of phishing attacks. Second, we will provide taxonomy of various solutions proposed in literature to protect users from phishing based on the attacks identified in our taxonomy. We conclude our paper discussing various issues and challenges that still exist in the literature, which are important to fight against with phishing threats.
Wound healing dressings and drug delivery systems: a review.

The variety of wound types has resulted in a wide range of wound dressings with new products frequently introduced to target different aspects of the wound healing process. The ideal dressing should achieve rapid healing at reasonable cost with minimal inconvenience to the patient. This article offers a review of the common wound management dressings and emerging technologies for achieving improved wound healing. It also reviews many of the dressings and novel polymers used for the delivery of drugs to acute, chronic and other types of wound. These include hydrocolloids, alginates, hydrogels, polyurethane, collagen, chitosan, pectin and hyaluronic acid. There is also a brief section on the use of biological polymers as tissue engineered scaffolds and skin grafts. Pharmacological agents such as antibiotics, vitamins, minerals, growth factors and other wound healing accelerators that take active part in the healing process are discussed. Direct delivery of these agents to the wound site is desirable, particularly when systemic delivery could cause organ damage due to toxicological concerns associated with the preferred agents. This review concerns the requirement for formulations with improved properties for effective and accurate delivery of the required therapeutic agents. General formulation approaches towards achieving optimum physical properties and controlled delivery characteristics for an active wound healing dosage form are also considered briefly.
Emotion recognition through facial expression analysis based on a neurofuzzy network

Extracting and validating emotional cues through analysis of users' facial expressions is of high importance for improving the level of interaction in man machine communication systems. Extraction of appropriate facial features and consequent recognition of the user's emotional state that can be robust to facial expression variations among different users is the topic of this paper. Facial animation parameters (FAPs) defined according to the ISO MPEG-4 standard are extracted by a robust facial analysis system, accompanied by appropriate confidence measures of the estimation accuracy. A novel neurofuzzy system is then created, based on rules that have been defined through analysis of FAP variations both at the discrete emotional space, as well as in the 2D continuous activation-evaluation one. The neurofuzzy system allows for further learning and adaptation to specific users' facial expression characteristics, measured though FAP estimation in real life application of the system, using analysis by clustering of the obtained FAP values. Experimental studies with emotionally expressive datasets, generated in the EC IST ERMIS project indicate the good performance and potential of the developed technologies.
Addressing Information Security Risks by Adopting Standards

Modern society depends on information technology in nearly every facet of human activity including, finance, transportation, education, government, and defense. Organizations are exposed to various and increasing kinds of risks, including information technology risks. Several standards, best practices, and frameworks have been created to help organizations manage these risks. The purpose of this research work is to highlight the challenges facing enterprises in their efforts to properly manage information security risks when adopting international standards and frameworks. To assist in selecting the best framework to use in risk management, the article presents an overview of the most popular and widely used standards and identifies selection criteria. It suggests an approach to proper implementation as well. A set of recommendations is put forward with further research opportunities on the subject. KeywordsInformation security; risk management; security frameworks; security standards; security management.
Indirect exposure to client trauma and the impact on trainee clinical psychologists: Secondary traumatic stress or vicarious traumatization?

The study investigated the relationship between exposure to trauma work and well-being (general psychological distress, trauma symptoms, and disrupted beliefs) in trainee clinical psychologists. It also assessed the contribution of individual and situational factors to well-being.



A Web-based survey was employed.



The survey comprised the General Health Questionnaire, Secondary Traumatic Stress Scale, Trauma and Attachment Belief Scale, Trauma Screening Questionnaire, and specific questions about exposure to trauma work and other individual and situational factors. The link to the online survey was sent via email to trainee clinical psychologists attending courses throughout the UK  Five hundred sixty-four trainee clinical psychologists participated. Most trainees had a caseload of one to two trauma cases in the previous 6 months; the most common trauma being sexual abuse. Exposure to trauma work was not related to general psychological distress or disrupted beliefs but was a significant predictor of trauma symptoms. Situational factors contributed to the variance in trauma symptoms; level of stress of clinical work and quality of trauma training were significant predictors of trauma symptoms. Individual and situational factors were also found to be significant predictors of general psychological distress and disrupted beliefs.



This study provides support for secondary traumatic stress but lacks evidence to support belief changes in vicarious traumatization or a relationship between exposure to trauma work and general psychological distress. The measurement and validity of vicarious traumatization is discussed along with clinical, theoretical implications, and suggestions for future research.



Secondary traumatic stress is a potential risk for trainee clinical psychologists. Training courses should (a) focus on quality of trauma training as it may be protective; (b) advocate coping strategies to reduce stress of clinical work, as the level of stress of clinical work may contribute to trauma symptoms.



Exposure to trauma work only uniquely explained a small proportion of variance in trauma symptoms. The study was cross-sectional in nature therefore cannot imply causality.
Multi-UAV collaborative monocular SLAM

With systems performing Simultaneous Localization And Mapping (SLAM) from a single robot reaching considerable maturity, the possibility of employing a team of robots to collaboratively perform a task has been attracting increasing interest. Promising great impact in a plethora of tasks ranging from industrial inspection to digitization of archaeological structures, collaborative scene perception and mapping are key in efficient and effective estimation. In this paper, we propose a novel, centralized architecture for collaborative monocular SLAM employing multiple small Unmanned Aerial Vehicles (UAVs) to act as agents. Each agent is able to independently explore the environment running limited-memory SLAM onboard, while sending all collected information to a central server, a ground station with increased computational resources. The server manages the maps of all agents, triggering loop closure, map fusion, optimization and distribution of information back to the agents. This allows an agent to incorporate observations from others in its SLAM estimates on the fly. We put the proposed framework to the test employing a nominal keyframe-based monocular SLAM algorithm, demonstrating the applicability of this system in multi-UAV scenarios.
Chip Controller Memristor Memory Subarray On-Chip Controller Memristor Memory Subarray Memristor Memory Subarray Memristor Memory Subarray Memristor Memory Array On-Chip Controller Summing Amplifier Memristor Computing Array Memristor Memory Array Processing Element Controller

Memristors have extended their influence beyond memory to logic and in-memory computing. Memristive logic design, the methodology of designing logic circuits using memristors, is an emerging concept whose growth is fueled by the quest for energy efficient computing systems. As a result, many memristive logic families have evolved with different attributes, and a mature comparison among them is needed to judge their merit. This paper presents a framework for comparing logic families by classifying them on the basis of fundamental properties such as statefulness, proximity (from the memory array), and flexibility of computation. We propose metrics to compare memristive logic families using analytic expressions for performance (latency), energy efficiency, and area. Then, we provide guidelines for a holistic comparison of logic families and set the stage for the evolution of new logic families.
Hydration-layer models for cryo-EM image simulation.

To compare cryo-EM images and 3D reconstructions with atomic structures in a quantitative way it is essential to model the electron scattering by solvent (water or ice) that surrounds protein assemblies. The most rigorous method for determining the density of solvating water atoms for this purpose has been to perform molecular-dynamics (MD) simulations of the protein-water system. In this paper we adapt the ideas of bulk-water modeling that are used in the refinement of X-ray crystal structures to the cryo-EM solvent-modeling problem. We present a continuum model for solvent density which matches MD-based results to within sampling errors. However, we also find that the simple binary-mask model of Jiang and Brünger (1994) performs nearly as well as the new model. We conclude that several methods are now available for rapid and accurate modeling of cryo-EM images and maps of solvated proteins.
3 C-GAN : A N CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY

We present 3C-GAN: a novel multiple generators structures, that contains one conditional generator that generates a semantic part of an image conditional on its input label, and one context generator generates the rest of an image. Compared to original GAN model, this model has multiple generators and gives control over what its generators should generate. Unlike previous multi-generator models use a subsequent generation process, that one layer is generated given the previous layer, our model uses a process of generating different part of the images together. This way the model contains fewer parameters and the generation speed is faster. Specifically, the model leverages the label information to separate the object from the image correctly. Since the model conditional on the label information does not restrict to generate other parts of an image, we proposed a cost function that encourages the model to generate only the succinct part of an image in terms of label discrimination. We also found an exclusive prior on the mask of the model help separate the object. The experiments on MNIST, SVHN, and CelebA datasets show 3C-GAN can generate different objects with different generators simultaneously, according to the labels given to each generator.
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction

A combined Long Short Term Memory and Convolutional Neural Networks (LSTM CNN+WF+PF) that exploits word embeddings and positional embeddings is proposed for cross-sentence n-ary relation extraction. The proposed model brings together the properties of LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture the most informative features, essential for cross-sentence n-ary relation extraction. The LSTM CNN+WF+PF model was evaluated using standard datasets for cross-sentence n-ary relation extraction, where the model significantly outperforms baseline CNN and LSTM model and a combined CNN LSTM model. The paper also shows that the LSTM CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction.
Biofilm formation in medicated root canals.

The hypothesis that Enterococcus faecalis resists common intracanal medications by forming biofilms was tested. E. faecalis colonization of 46 extracted, medicated roots was observed with scanning electron microscopy (SEM) and scanning confocal laser microscopy. SEM detected colonization of root canals medicated with calcium hydroxide points and the positive control within 2 days. SEM detected biofilms in canals medicated with calcium hydroxide paste in an average of 77 days. Scanning confocal laser microscopy analysis of two calcium hydroxide paste medicated roots showed viable colonies forming in a root canal infected for 86 days, whereas in a canal infected for 160 days, a mushroom-shape typical of a biofilm was observed. Analysis by sodium dodecyl sulfate polyacrylamide gel electrophoresis showed no differences between the protein profiles of bacteria in free-floating (planktonic) and inoculum cultures. Analysis of biofilm bacteria was inconclusive. These observations support potential E. faecalis biofilm formation in vivo in medicated root canals.