nils-herrmann/clean_scirepeval huggingface dataset:
Material Requirement Planning with fuzzy constraints and fuzzy coefficients

We propose a new fuzzy mathematical programming model for production planning under uncertainty in an industrial environment. This model considers fuzzy constraints related to the total costs, the market demand and the available capacity of the productive resources and fuzzy coefficients for the costs due to the backlog of demand and for the required capacity. The main goal is to determine the master production schedule of each product, the MRP (Material Requirement Planning) for each raw component in each period, stock levels, demand backlog, and capacity usage levels over a given planning horizon. Finally, the proposed model is tested by using data from an automobile seat assembler and compared with other fuzzy mathematical programming approaches. The experiment shows that the proposed model has not got a better behaviour than more simple fuzzy models, but the advantage is that both types of uncertainties, fuzziness and lack of knowledge or epistemic uncertainty can be considered in a model with fuzzy constraints and fuzzy coefficients.
Haptics: Perception, Devices, Control, and Applications

For gaining proficiency in physical human-robot interaction (pHRI), it is crucial for engineering students to be provided with the opportunity to physically interact with and gain hands-on experience on design and control of force-feedback robotic devices. We present a single degree of freedom educational robot that features series elastic actuation and relies on closed loop force control to achieve the desired level of safety and transparency during physical interactions. The proposed device complements the existing impedance-type Haptic Paddle designs by demonstrating the challenges involved in the synergistic design and control of admittance-type devices. We present integration of this device into pHRI education, by providing guidelines for the use of the device to allow students to experience the performance trade-offs inherent in force control systems, due to the non-collocation between the force sensor and the actuator. These exercises enable students to modify the mechanical design in addition to the controllers, by assigning different levels of stiffness values to the compliant element, and characterize the effects of these design choices on the closed-loop force control performance of the device. We also report initial evaluations of the efficacy of the device for
A statistical aimbot detection method for online FPS games

First Person Shooter (FPS) is a popular genre in online gaming, unfortunately not everyone plays the game fairly, and this hinders the growth of the industry. The aiming robot (aimbot) is a common cheating mechanism employed in this genre, it differs from many other common online bots in that there is a human operating alongside the bot, and thus the in-game data exhibit both human and bot-like behaviour. The aimbot users can aim much better than the average player. However, there are also a large number of highly skilled players who can aim much better than the average player, some of these players have in the past been banned from servers due to false accusations from their peers. Therefore, it would be interesting to find out if and where the honest player's and the bot user's behaviour differ. In this paper we investigate the difference between the aiming abilities of aimbot users and honest human players. We introduce two novel features and have conducted an experiment using a modified open source FPS game. Our data shows that there is significant difference between behaviours of honest players and aimbot users. We propose a voting scheme to improve aimbot detection in FPS based on distribution matching, and have achieved approximately 93% in both True positive and True negative rates with one of our features.
Improving the care of patients with severe open fractures of the tibia: the effect of the introduction of Major Trauma Networks and national guidelines.

The management of open lower limb fractures in the United Kingdom has evolved over the last ten years with the introduction of major trauma networks (MTNs), the publication of standards of care and the wide acceptance of a combined orthopaedic and plastic surgical approach to management. The aims of this study were to report recent changes in outcome of open tibial fractures following the implementation of these changes.



Data on all patients with an open tibial fracture presenting to a major trauma centre between 2011 and 2012 were collected prospectively. The treatment and outcomes of the 65 Gustilo Anderson Grade III B tibial fractures were compared with historical data from the same unit.



The volume of cases, the proportion of patients directly admitted and undergoing first debridement in a major trauma centre all increased. The rate of limb salvage was maintained at 94% and a successful limb reconstruction rate of 98.5% was achieved. The rate of deep bone infection improved to 1.6% (one patient) in the follow-up period.



The reasons for these improvements are multifactorial, but the major trauma network facilitating early presentation to the major trauma centre, senior orthopaedic and plastic surgical involvement at every stage and proactive microbiological management, may be important factors.


TAKE HOME MESSAGE
This study demonstrates that a systemised trauma network combined with evidence based practice can lead to improvements in patient care.
Coverage enhancement techniques for machine-to-machine communications over LTE

The tremendous growth of machine-tomachine (M2M) applications has been a great attractor to cellular network operators to provide machine-type communication services. One of the important challenges for cellular systems supporting M2M terminals is coverage, because terminals can be located in spaces in buildings and structures suffering from significant penetration losses. Since these terminals are also often stationary, they are permanently without cellular coverage. To address this critical issue, the third generation partnership project (3GPP), and in particular its radio access network technical specification group, commenced work on coverage enhancement (CE) for long-term evolution (LTE) systems in June 2013. This article reviews the CE objectives defined for LTE machine-type communication and presents CE methods for LTE downlink and uplink channels discussed in this group. The presented methods achieve CE in a spectrally efficient manner and without notably affecting performance for legacy (non- M2M) devices.
Selection and pre-employment assessment in aviation security x-ray screening

This document concentrates on how aviation security can be largely improved by selecting the right people for the x-ray screening task. X-ray images dispose of a different colour range, single objects superimpose each other independently of whether they are in front or behind of each other, and the single objects look very different from what they look like under natural light. Therefore, individual visual abilities such as mental rotation, figure-ground segregation, or visual search of specific patterns are very important for the correct interpretation of x-ray images. In this paper we briefly discuss job and task analysis, as well as cognitive task analysis in x-ray screening. We then discuss tests used in pre-employment assessment, including a more detailed presentation of the X-Ray Object Recognition Test (X-Ray ORT). Finally, a new project is presented in which a cognitive test battery is developed to measure important visual abilities needed in x-ray screening and related tasks.
Evaluating the Usability and Usefulness of a Mobile App for Atrial Fibrillation Using Qualitative Methods: Exploratory Pilot Study

Atrial fibrillation (AFib) is the most common form of heart arrhythmia and a potent risk factor for stroke. Nonvitamin K antagonist oral anticoagulants (NOACs) are routinely prescribed to manage AFib stroke risk; however, nonadherence to treatment is a concern. Additional tools that support self-care and medication adherence may benefit patients with AFib.



The aim of this study was to evaluate the perceived usability and usefulness of a mobile app designed to support self-care and treatment adherence for AFib patients who are prescribed NOACs.



A mobile app to support AFib patients was previously developed based on early stage interview and usability test data from clinicians and patients. An exploratory pilot study consisting of naturalistic app use, surveys, and semistructured interviews was then conducted to examine patients' perceptions and everyday use of the app.



A total of 12 individuals with an existing diagnosis of nonvalvular AFib completed the 4-week study. The average age of participants was 59 years. All participants somewhat or strongly agreed that the app was easy to use, and 92% (11/12) reported being satisfied or very satisfied with the app. Participant feedback identified changes that may improve app usability and usefulness for patients with AFib. Areas of usability improvement were organized by three themes: app navigation, clarity of app instructions and design intent, and software bugs. Perceptions of app usefulness were grouped by three key variables: core needs of the patient segment, patient workflow while managing AFib, and the app's ability to support the patient's evolving needs.



The results of this study suggest that mobile tools that target self-care and treatment adherence may be helpful to AFib patients, particularly those who are newly diagnosed. Additionally, participant feedback provided insight into the varied needs and health experiences of AFib patients, which may improve the design and targeting of the intervention. Pilot studies that qualitatively examine patient perceptions of usability and usefulness are a valuable and often underutilized method for assessing the real-world acceptability of an intervention. Additional research evaluating the AFib Connect mobile app over a longer period, and including a larger, more diverse sample of AFib patients, will be helpful for understanding whether the app is perceived more broadly to be useful and effective in supporting patient self-care and medication adherence.
Attention estimation system via smart glasses

Attention plays a critical role in effective learning. By means of attention assessment, it helps learners improve and review their learning processes, and even discover Attention Deficit Hyperactivity Disorder (ADHD). Hence, this work employs modified smart glasses which have an inward facing camera for eye tracking, and an inertial measurement unit for head pose estimation. The proposed attention estimation system consists of eye movement detection, head pose estimation, and machine learning. In eye movement detection, the central point of the iris is found by the locally maximum curve via the Hough transform where the region of interest is derived by the identified left and right eye corners. The head pose estimation is based on the captured inertial data to generate physical features for machine learning. Here, the machine learning adopts Genetic Algorithm (GA)-Support Vector Machine (SVM) where the feature selection of Sequential Floating Forward Selection (SFFS) is employed to determine adequate features, and GA is to optimize the parameters of SVM. Our experiments reveal that the proposed attention estimation system can achieve the accuracy of 93.1% which is fairly good as compared to the conventional systems. Therefore, the proposed system embedded in smart glasses brings users mobile, convenient, and comfortable to assess their attention on learning or medical symptom checker.
CFG Construction Soundness in Control-Flow Integrity Gang

Control-Flow Integrity (CFI) is an intensively studied technique for hardening software security. It enforces a Control-Flow Graph (CFG) by inlining runtime checks into target programs. Many methods have been proposed to construct the enforced CFG, with different degrees of precision and sets of assumptions. However, past CFI work has not made attempt at justifying their CFG construction soundness using formal semantics and proofs. In this paper, we formalize the CFG construction in two major CFI systems, identify their assumptions, and prove their soundness; the soundness proof shows that their computed sets of targets for indirect calls are safe over-approximations.
A hybrid algorithm for synchronous generator parameter estimation

This paper presents a hybrid algorithm for parameter estimation of synchronous generator. For large-residual problems (i.e., f(x) is large or f(x) is severely nonlinear), the performance of the Gauss-Newton method and Levenberg-Marquardt method is usually poor, and the slow convergence even causes iteration emergence divergence. The Quasi-Newton method can superlinearly converge, but it is not robust in the global stage of the iteration. Hybrid algorithm combining the two methods above is proved globally convergent with a high convergence speed through the example of synchronous generator parameter identification.
Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs

A multitenant database management system (DBMS) in the cloud must continuously monitor the trade-off between efficient resource sharing among multiple application databases (tenants) and their performance. Considering the scale of \attn{hundreds to} thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable. A self-managing controller of a multitenant DBMS faces several challenges. For instance, how to characterize a tenant given its variety of workloads, how to reduce the impact of tenant colocation, and how to detect and mitigate a performance crisis where one or more tenants' desired service level objective (SLO) is not achieved.
 We present Delphi, a self-managing system controller for a multitenant DBMS, and Pythia, a technique to learn behavior through observation and supervision using DBMS-agnostic database level performance measures. Pythia accurately learns tenant behavior even when multiple tenants share a database process, learns good and bad tenant consolidation plans (or packings), and maintains a pertenant history to detect behavior changes. Delphi detects performance crises, and leverages Pythia to suggests remedial actions using a hill-climbing search algorithm to identify a new tenant placement strategy to mitigate violating SLOs. Our evaluation using a variety of tenant types and workloads shows that Pythia can learn a tenant's behavior with more than 92% accuracy and learn the quality of packings with more than 86% accuracy. During a performance crisis, Delphi is able to reduce 99th percentile latencies by 80%, and can consolidate 45% more tenants than a greedy baseline, which balances tenant load without modeling tenant behavior.
An FPGA-based floating-point Jacobi iterative solver

Within the parallel computing domain, field programmable gate arrays (FPGA) are no longer restricted to their traditional role as substitutes for application-specific integrated circuits-as hardware "hidden" from the end user. Several high performance computing vendors offer parallel re configurable computers employing user-programmable FPGAs. These exciting new architectures allow end-users to, in effect, create reconfigurable coprocessors targeting the computationally intensive parts of each problem. The increased capability of contemporary FPGAs coupled with the embarrassingly parallel nature of the Jacobi iterative method make the Jacobi method an ideal candidate for hardware acceleration. This paper introduces a parameterized design for a deeply pipelined, highly parallelized IEEE 64-bit floating-point version of the Jacobi method. A Jacobi circuit is implemented using a Xilinx Virtex-II Pro as the target FPGA device. Implementation statistics and performance estimates are presented.
OPTIMIZATION OF STEEL CATENARY RISERS USING BIO-INSPIRED ALGORITHMS

The design of a riser is very time consuming, since a large number of parameters (e.g.: thickness, top angle, and material properties) are involved and tight safety requirements must be met. This leads to the study of tools, such as optimization algorithms, that can speed up the process of elaborating a feasible riser project for certain conditions. Considering that some of the parameters in the design of a riser can assume a discrete set of values, the utilization of mathematical programming algorithms becomes unfeasible. It is then necessary to use metaheuristic algorithms, such as Genetic Algorithm and Particle Swarm Optimization. In this context, this paper presents a study on the application of bio-inspired algorithms, including GA and PSO, to the design optimization of steel catenary risers. The problem consists of finding the riser material and wall thickness that minimize the cost to fabricate a viable riser, in conformance with the requirements of technical standards. The main hypotheses that were adopted are presented, along with the description of the methodology employed. The results show that a significant reduction in riser cost is achieved when the riser is divided in multiple segments with different thickness and material. The efficiency of the utilized algorithms in finding an optimum riser design for the specified conditions is confirmed by the obtained numerical results.
Light-Weight Head Pose Invariant Gaze Tracking

Unconstrained remote gaze tracking using off-the-shelf cameras is a challenging problem. Recently, promising algorithms for appearance-based gaze estimation using convolutional neural networks (CNN) have been proposed. Improving their robustness to various confounding factors including variable head pose, subject identity, illumination and image quality remain open problems. In this work, we study the effect of variable head pose on machine learning regressors trained to estimate gaze direction. We propose a novel branched CNN architecture that improves the robustness of gaze classifiers to variable head pose, without increasing computational cost. We also present various procedures to effectively train our gaze network including transfer learning from the more closely related task of object viewpoint estimation and from a large high-fidelity synthetic gaze dataset, which enable our ten times faster gaze network to achieve competitive accuracy to its current state-of-the-art direct competitor.
Recommender Systems and their Security Concerns

Instead of simply using two-dimensional User × Item features, advanced recommender systems rely on more additional dimensions (e.g. time, location, social network) in order to provide better recommendation services. In the first part of this paper, we will survey a variety of dimension features and show how they are integrated into the recommendation process. When the service providers collect more and more personal information, it brings great privacy concerns to the public. On another side, the service providers could also suffer from attacks launched by malicious users who want to bias the recommendations. In the second part of this paper, we will survey attacks from and against recommender service providers, and existing solutions.
Academic abilities in children and adolescents with a history of autism spectrum disorders who have achieved optimal outcomes.

This study examines the academic abilities of children and adolescents who were once diagnosed with an autism spectrum disorder, but who no longer meet diagnostic criteria for this disorder. These individuals have achieved social and language skills within the average range for their ages, receive little or no school support, and are referred to as having achieved "optimal outcomes." Performance of 32 individuals who achieved optimal outcomes, 41 high-functioning individuals with a current autism spectrum disorder diagnosis (high-functioning autism), and 34 typically developing peers was compared on measures of decoding, reading comprehension, mathematical problem solving, and written expression. Groups were matched on age, sex, and nonverbal IQ; however, the high-functioning autism group scored significantly lower than the optimal outcome and typically developing groups on verbal IQ. All three groups performed in the average range on all subtests measured, and no significant differences were found in performance of the optimal outcome and typically developing groups. The high-functioning autism group scored significantly lower on subtests of reading comprehension and mathematical problem solving than the optimal outcome group. These findings suggest that the academic abilities of individuals who achieved optimal outcomes are similar to those of their typically developing peers, even in areas where individuals who have retained their autism spectrum disorder diagnoses exhibit some ongoing difficulty.
Correctly rounded floating-point division for DSP-enabled FPGAs

Floating-point division is a very costly operation in FPGA designs. High-frequency implementations of the classic digit-recurrence algorithms for division have long latencies (of the order of the number fraction bits) and consume large amounts of logic. Additionally, these implementations require important routing resources, making timing closure difficult in complete designs. In this paper we present two multiplier-based architectures for division which make efficient use of the DSP resources in recent Altera FPGAs. By balancing resource usage between logic, memory and DSP blocks, the presented architectures maintain high frequencies is full designs. Additionally, compared to classical algorithms, the proposed architectures have significantly lower latencies. The architectures target faithfully rounded results, similar to most elementary functions implementations for FPGAs but can also be transformed into correctly rounded architectures with a small overhead. The presented architectures are built using the Altera DSP Builder Advanced framework and will be part of the default blockset.
Distributional Smoothing by Virtual Adversarial Examples

We propose a novel regularization technique for supervised and semi-supervised training of large models like deep neural network. By including into objective function the local smoothness of predictive distribution around each training data point, not only were we able to extend the work of (Goodfellow et al. (2015)) to the setting of semi-supervised training, we were also able to eclipse current state of the art supervised and semi-supervised methods on the permutation invariant MNIST classification task.
Knowledge-rich, computer-assisted composition of Chinese couplets

Recent research effort in poem composition has focused on the use of automatic language generation to produce a polished poem. A less explored question is how effectively a computer can serve as an interactive assistant to a poet. For this purpose, we built a web application that combines rich linguistic knowledge from classical Chinese philology with statistical natural language processing techniques. The application assists users in composing a ‘couplet’—a pair of lines in a traditional Chinese poem—by making suggestions for the next and corresponding characters. A couplet must meet a complicated set of requirements on phonology, syntax, and parallelism, which are challenging for an amateur poet to master. The application checks conformance to these requirements and makes suggestions for characters based on lexical, syntactic, and semantic properties. A distinguishing feature of the application is its extensive use of linguistic knowledge, enabling it to inform users of specific phonological principles in detail, and to explicitly model semantic parallelism, an essential characteristic of Chinese poetry. We evaluate the quality of poems composed solely with characters suggested by the application, and the coverage of its character suggestions.