nils-herrmann/clean_scirepeval huggingface dataset:
Mining Student data by Ensemble Classification and Clustering for Profiling and Prediction of Student Academic Performance

Applying Data Mining (DM) in education is an emerging interdisciplinary research field also known as Educational Data Mining (EDM). Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of prediction. In this paper, we present a hybrid procedure based on ensemble classification and clustering that enables academicians to firstly predict students’ academic performance and then place each student in a well-defined cluster for further advising. Additionally, it endows instructors an anticipated estimation of their students’ capabilities during team forming and in-class participation. For ensemble classification, we use multiple classifiers (Decision Trees-J48, Naïve Bayes and Random Forest) to improve the quality of student data by eliminating noisy instances, and hence improving predictive accuracy. We then use the approach of bootstrap (sampling with replacement) averaging, which consists of running k-means clustering algorithm to convergence of the training data and averaging similar cluster centroids to obtain a single model. We empirically compare our technique with other ensemble techniques on real world education datasets.
Anaesthetic efficacy of MS-222 and AQUI-S in advanced size fry of rohu, Labeo rohita, (Hamilton-Buchanan)

Anaesthetic agents are very useful for reducing the stress caused by handling, sorting, transportation, artificial reproduction, tagging, administration of vaccines and surgical procedures in fish. The efficacy of two anaesthetics: MS-222 and AQUI-S were tested on rohu, Labeo rohita advanced size fry. The lowest effective doses that produced induction in 3 min or less and recovery times 5 min or less and meet the most criteria of good anaesthetic characteristics were 125 mg L 1 of MS-222, and 30 mg L 1 of AQUI-S in rohu, Labeo rohita advanced size fry. Induction times were significantly decreased with increased in the concentrations of any of the two tested anaesthetic agents. The lowest doses suitable for transportation of rohu advanced size fry observed were: 10–15 mg L 1 of MS-222 and 2.5 mg L 1 AQUI-S . Both anaesthetics showed promising to be used as anaesthetics for handling and transportation in rohu (Labeo rohita) advanced fry.
Alveolar bone thickness around maxillary central incisors of different inclination assessed with cone-beam computed tomography

To assess the labial and lingual alveolar bone thickness in adults with maxillary central incisors of different inclination by cone-beam computed tomography (CBCT).



Ninety maxillary central incisors from 45 patients were divided into three groups based on the maxillary central incisors to palatal plane angle; lingual-inclined, normal, and labial-inclined. Reformatted CBCT images were used to measure the labial and lingual alveolar bone thickness (ABT) at intervals corresponding to every 1/10 of the root length. The sum of labial ABT and lingual ABT at the level of the root apex was used to calculate the total ABT (TABT). The number of teeth exhibiting alveolar fenestration and dehiscence in each group was also tallied. One-way analysis of variance and Tukey's honestly significant difference test were applied for statistical analysis.



The labial ABT and TABT values at the root apex in the lingual-inclined group were significantly lower than in the other groups (p < 0.05). Lingual and labial ABT values were very low at the cervical level in the lingual-inclined and normal groups. There was a higher prevalence of alveolar fenestration in the lingual-inclined group.



Lingual-inclined maxillary central incisors have less bone support at the level of the root apex and a greater frequency of alveolar bone defects than normal maxillary central incisors. The bone plate at the marginal level is also very thin.
Patient delay in seeking help for potential breast cancer.

Patient delay in seeking medical attention could be a contributing cause in a substantial number of breast cancer deaths. The purpose of this study was to identify factors associated with long delay in order to identify specific groups in need of more intensive education regarding the signs of breast cancer and the importance of early treatment.



A study of 162 women with potential breast cancer symptoms was done in the area of Worcester, MA. Two methods of analysis were used. A case-control approach was used where the outcome variable was categorized into two groups of longer and shorter delay, and a survival analysis was used where the outcome variable was treated as a continuous variable.



It was found that women with increasing symptoms were more likely to delay than women whose symptoms either decreased or remained the same. Women performing monthly breast self-examination and/or receiving at least bi-annual mammograms were much less likely to delay than women who performed breast self-examination or received mammograms less often. It was also found that women using family practitioners were less likely to delay than women using other types of physicians.



Patient delay continues to be a major problem in breast cancer, as 16% of the women here delayed at least two months before seeking help. This study presented a new and improved method for defining patient delay, which should be explored further in larger studies.
Smart environmental monitoring beacon

The purpose of this paper is to present a beacon created for monitoring the environmental conditions, like weather parameters, air pollution, sound levels and UV radiation index. The data is stored in the cloud through an internet connection using a GSM module. The statistics regarding the weather evolution conditions and life quality in the areas where the beacon is installed are available on natively designed iOS and Android applications. The beacon is intended for the use of the local authorities, who can obtain and manage the statistics or for personal use. The users can access the history of the statistics, they can write reviews or they can notify the local authorities if they seize a problem in their neighborhoods.
The Impact of Agency and Familiarity in Cooperative Multiplayer Games

Many online videogames make use of characters controlled by both humans (avatar) and computers (agent) to facilitate game play. However, the level of agency a teammate shows potentially produces differing levels of social presence during play, which in turn may impact on the player experience. To better understand these effects, two experimental studies were conducted utilising cooperative multiplayer games (Left 4 Dead 2 and Rocket League). In addition, the effect of familiarity between players was considered. The trend across the two studies show that playing with another human is more enjoyable, and facilitates greater connection, cooperation, presence and positive mood than play with a computer agent. The implications for multiplayer game design is discussed.
Suicide by prisoners. National clinical survey.

The number of suicides in prison has increased over recent years. This is the first study to describe the clinical care of a national sample of prison suicides.



To describe the clinical and social circumstances of self-inflicted deaths among prisoners.



A national clinical survey based on a 2-year sample of self-inflicted deaths in prisoners. Detailed clinical and social information was collected from prison governors and prison health care staff.



There were 172 self-inflicted deaths: 85 (49%; 95% CI 42-57) were of prisoners on remand; 55 (32%; 95% CI 25-39) occurred within 7 days of reception into prison. The commonest method was hanging or self-strangulation (92%; 95% CI 88-96). A total of 110 (72%; 95% CI 65-79) had a history of mental disorder. The commonest primary diagnosis was drug dependence (39, 27%; 95% CI 20-35). Eighty-nine (57%; 95% CI 49-64) had symptoms suggestive of mental disorder at reception into prison.



Suicide prevention measures should be concentrated in the period immediately following reception into prison. Because hanging is the commonest method of suicide, removal of potential ligature points from cells should be a priority.
Object Detection in Videos by High Quality Object Linking

Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to exploit temporal contexts by linking the same object across video to form tubelets and aggregating classification scores in the tubelets. In this paper, we focus on obtaining high quality object linking results for better classification. Unlike previous methods that link objects by checking boxes between neighboring frames, we propose to link in the same frame. To achieve this goal, we extend prior methods in following aspects: (1) a cuboid proposal network that extracts spatio-temporal candidate cuboids which bound the movement of objects; (2) a short tubelet detection network that detects short tubelets in short video segments; (3) a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets. Experiments on the ImageNet VID dataset show that our method outperforms both the static image detector and the previous state of the art. In particular, our method improves results by 8.8% over the static image detector for fast moving objects.
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.
IT Module Based Test Automation Framework

This paper presents a case study of the design of the IT Module-Based Test Automation Framework (ITAF). ITAF is designed to allow IT project teams to share a standardized test automation framework or one of its modules within the.NET technology. This framework allows the project teams to easily automate the test cases, improve the efficiency and productivity, and reuse code and resources. The framework is extensible so that users can contribute and add value to it. Each module of the framework represents one typical technology or an application layer. Each module can be decoupled from the framework and used independently to fulfill an automation goal for a specific type of application.
Family and the Risky Behaviors of High School Students

Family plays an important role in helping adolescent acquiring skills or strengthening their characters.



We aimed to evaluate the influences of family factors, risky and protective, on adolescent health-risk behavior (HRB).



In this cross-sectional study, students of high schools in Kerman, Iran at all levels participated, during November 2011 till December 2011. The research sample included 1024 students (588 females and 436 males) aged 15 to 19 years. A CTC (Communities That Care Youth Survey) questionnaire was designed in order to collect the profile of the students' risky behaviors. Stratified cluster sampling method was used to collect the data.



Using logistic regression, 7 variables enrolled; 4 of them were risk factors and 3 were protective factors. The risk factors were age, (linear effect, ORa = 1.20, P = 0.001), boys versus girls (ORa = 2.33, P = 0.001), family history of antisocial behavior (ORa = 2.29, P = 0.001), and parental attitudes favorable toward antisocial behavior (ORa = 1.72, P = 0.03). And, protective factors were family religiosity (ORa = 0.65, P = 0.001), father education (linear effect, ORa = 0.48, P = 0.001), and family attachment (ORa = 0.78, P = 0.001).



Our findings showed that family has a very significant role in protecting students against risky behaviors. The education level of the father, family religiosity, and attachment were the most important factors.
The Application of Mamdani Fuzzy Model for Auto Zoom Function of a Digital Camera

Mamdani Fuzzy Model is an important technique in Computational Intelligence (CI) study. This paper presents an implementation of a supervised learning method based on membership function training in the context of Mamdani fuzzy models. Specifically, auto zoom function of a digital camera is modelled using Mamdani technique. The performance of control method is verified through a series of simulation and numerical results are provided as illustrations.  Mamdani fuzzy model, fuzzy logic, auto zoom, digital camera
Yin-yang: concealing the deep embedding of DSLs

Deeply embedded domain-specific languages (EDSLs) intrinsically compromise programmer experience for improved program performance. Shallow EDSLs complement them by trading program performance for good programmer experience. We present Yin-Yang, a framework for DSL embedding that uses Scala macros to reliably translate shallow EDSL programs to the corresponding deep EDSL programs. The translation allows program prototyping and development in the user friendly shallow embedding, while the corresponding deep embedding is used where performance is important. The reliability of the translation completely conceals the deep em- bedding from the user. For the DSL author, Yin-Yang automatically generates the deep DSL embeddings from their shallow counterparts by reusing the core translation. This obviates the need for code duplication and leads to reliability by construction.
Predicting system performance for multi-tenant database workloads

Database consolidation is gaining wide acceptance as a means to reduce the cost and complexity of managing database systems. However, this new trend poses many interesting challenges for understanding and predicting system performance. The consolidated databases in multi-tenant settings share resources and compete with each other for these resources. In this work we present an experimental study to highlight how these interactions can be fairly complex. We argue that individual database staging or workload profiling is not an adequate approach to understanding the performance of the consolidated system. Our initial investigations suggest that machine learning approaches that use monitored data to model the system can work well for important tasks.
A framework for supply chain performance measurement

Supply chain management (SCM) has been a major component of competitive strategy to enhance organizational productivity and profitability. The literature on SCM that deals with strategies and technologies for effectively managing a supply chain is quite vast. In recent years, organizational performance measurement and metrics have received much attention from researchers and practitioners. The role of these measures and metrics in the success of an organization cannot be overstated because they affect strategic, tactical and operational planning and control. Performance measurement and metrics have an important role to play in setting objectives, evaluating performance, and determining future courses of actions. Performance measurement and metrics pertaining to SCM have not received adequate attention from researchers or practitioners. We developed a framework to promote a better understanding of the importance of SCM performance measurement and metrics. Using the current literature and the results of an empirical study of selected British companies, we developed the framework presented herein, in hopes that it would stimulate more interest in this important area. r 2003 Elsevier B.V. All rights reserved.
On the Security of A Dynamic ID-based Authentication Scheme for Telecare Medical Information Systems

Telecare medical information systems (TMISs) are increasingly popular technologies for healthcare applications. Using TMISs, physicians and caregivers can monitor the vital signs of patients remotely. Since the database of TMISs stores patients’ electronic medical records (EMRs), only authorized users should be granted the access to this information for the privacy concern. To keep the user anonymity, recently, Chen et al. proposed a dynamic ID-based authentication scheme for telecare medical information system. They claimed that their scheme is more secure and robust for use in a TMIS. However, we will demonstrate that their scheme fails to satisfy the user anonymity due to the dictionary attacks. It is also possible to derive a user password in case of smart card loss attacks. Additionally, an improved scheme eliminating these weaknesses is also presented.
Experimental study in Ku-band of the propagation inside Empty Substrate Integrated Waveguides

In the last years a great number of substrate-integrated circuits has been developed. These circuits are a compromise between the advantages of waveguides: high-quality factor and low losses, and the advantages of planar circuits: low-cost and easy compact integration. Recently, a new methodology for manufacturing empty waveguides integrated in a planar substrate has been proposed. To manufacture this Empty Substrate-Integrated Waveguide, a rectangular hole is emptied in the substrate and plated. Finally, the structure is closed with two metallic covers that are soldered above and below the substrate. Hence, the Empty Substrate-Integrated Waveguide could be modeled with an ideal rectangular waveguide. However, the degree of error that this assumption implies has not been evaluated yet. This evaluation is imperative, since this new manufacturing process strongly differs from that of a traditional single-piece rectangular waveguide. Then, in this work, for the first time, the propagation constant of an Empty Substrate-Integrated Waveguide has been measured, and compared to the propagation constant of an equivalent ideal rectangular waveguide, in order to determine how accurately an Empty Substrate-Integrated Waveguide can be modeled with a rectangular waveguide, and if the design methods for rectangular waveguide devices can be directly transferred to this novel substrate-integrated waveguide.
Policing cyber-neighbourhoods : tension monitoring and social media networks

We propose that late modern policing practices, that rely on neighbourhood intelligence, the monitoring of tensions, surveillance and policing by accommo-dation, need to be augmented in light of emerging ‘cyber-neighbourhoods’, namely social media networks. The 2011 riots in England were the first to evidence the widespread use of social media platforms to organise and respond to disorder. The police were ill-equipped to make use of the intelligence emerging from these non-terrestrial networks and were found to be at a disadvantage to the more tech-savvy rioters and the general public. In this paper, we outline the development of the ‘tension engine’ component of the Cardiff Online Social Media ObServatroy (COSMOS). This engine affords users with the ability to monitor social media data streams for signs of high tension which can be analysed in order to identify deviations from the ‘norm’ (levels of cohesion/low tension). This analysis can be overlaid onto a palimpsest of curated data, such as official statistics about neighbourhood crime, deprivation and demography, to provide a multidimensional picture of the ‘terrestrial’ and ‘cyber’ streets. As a consequence, this ‘neighbourhood informatics’ enables a means of questioning official constructions of civil unrest through reference to the user-generated accounts of social media and their relationship to other, curated, social and economic data.
A 65-nm 25.1-ns 30.7-fJ Robust Subthreshold Level Shifter With Wide Conversion Range

Level shifters (LS) are crucial interface circuits for multisupply voltage designs, and it is challenging to achieve both robust and efficient level conversion from subthreshold to aforementioned threshold. In this brief, we propose two circuit techniques for a novel subthreshold LS with wide conversion range. First, we introduce a novel LS circuit with NMOS-diode-based current limiter for current contention reduction to achieve robust and efficient level conversion. Second, we explore the inverse narrow width effect to increase the drivability of the pull-down devices for delay reduction. When implemented in a commercial 65-nm MTCMOS process, the proposed LS achieves robust conversion from deep subthreshold (sub-100 mV) to nominal supply voltage (1.2 V). For the target conversion from 0.3 to 1.2 V, the proposed LS shows on average 25.1-ns propagation delay, 30.7-fJ energy efficiency, and 2.5-nW leakage power across 25 test chips.
Dynamic traversal of large gaps by insects and legged robots reveals a template.

It is well known that animals can use neural and sensory feedback via vision, tactile sensing, and echolocation to negotiate obstacles. Similarly, most robots use deliberate or reactive planning to avoid obstacles, which relies on prior knowledge or high-fidelity sensing of the environment. However, during dynamic locomotion in complex, novel, 3D terrains, such as a forest floor and building rubble, sensing and planning suffer bandwidth limitation and large noise and are sometimes even impossible. Here, we study rapid locomotion over a large gap-a simple, ubiquitous obstacle-to begin to discover the general principles of the dynamic traversal of large 3D obstacles. We challenged the discoid cockroach and an open-loop six-legged robot to traverse a large gap of varying length. Both the animal and the robot could dynamically traverse a gap as large as one body length by bridging the gap with its head, but traversal probability decreased with gap length. Based on these observations, we developed a template that accurately captured body dynamics and quantitatively predicted traversal performance. Our template revealed that a high approach speed, initial body pitch, and initial body pitch angular velocity facilitated dynamic traversal, and successfully predicted a new strategy for using body pitch control that increased the robot's maximal traversal gap length by 50%. Our study established the first template of dynamic locomotion beyond planar surfaces, and is an important step in expanding terradynamics into complex 3D terrains.