nils-herrmann/clean_scirepeval huggingface dataset:
Software Development Processes for Games: A Systematic Literature Review

This paper describes the methodology and results from a Systematic Literature Review (SLR) of the software processes used in game development. A total of 404 papers were analyzed as part of the review and the various process models that are used in industry and academia/research are presented. Software Process Improvement (SPI) initiatives for game development are discussed. The factors that promote or deter the adoption of process models, and implementing SPI in practice are highlighted. Our findings indicate that there is no single model that serves as a best practice process model for game development and it is a matter of deciding which model is best suited for a particular game. Agile models such as Scrum and XP are suited to the knowledge intensive domain of game development where innovation and speed to market are vital. Hybrid approaches such as reuse can also be suitable for game development where the risk of the upfront investment in terms of time and cost is mitigated with a game that has stable requirements and a longer lifespan.
On-chip spectrum analyzer for analog built-in self test

This paper presents the design of an on-chip spectrum analyzer. A novel architecture is used to mitigate the problems encountered in trying to implement architectures employed in conventional stand-alone instruments on a chip. Specifically, it makes use of a very-low IF architecture, which leads to a highly compact design, that can be used for measuring the frequency content of high frequency on-chip signals. The architecture and design considerations along with an implementation in a 0.18 /spl mu/ CMOS process is described. The design takes up an area of approximately 0.384 mm/sup 2/ with a simulated frequency range of 33 MHz to 3 GHz and a dynamic range of 60 dB.
Design study of magnet shapes for axial Halbach arrays using 3D finite element analyses

Magnet material prices has become an uncertain factor for electric machine development. Most of all, the output of ironless axial flux motors equipped with Halbach magnet arrays depend on the elaborated magnetic flux. Therefore, possibilities to reduce the manufacturing cost without negatively affecting the performance are studied in this paper. Both magnetostatic and transient 3D finite element analyses are applied to compare flux density distribution, elaborated output torque and induced back EMF. It is shown, that the proposed magnet shapes and magnetization pattern meet the requirements. Together with the assembly and measurements of functional linear Halbach magnet arrays, the prerequisite for the manufacturing of axial magnet arrays for an ironless in-wheel hub motor are given.
Precipitation Titrations Determination of Chloride by the Mohr Method

and signals the end point. The color goes from a yellow to a brownish-yellow. The change can be detected most precisely when the color in the titration flask is compared to a reference color. A mixture containing CrO4 2indicator in a suspension of CaCO3, simulating AgCl precipitate, is used for this purpose. An indicator blank is prepared using a portion of this mixture. The blank provides a correction for the slight difference between the end point and the equivalence point, a systematic error.
Extreme learning machine for fault detection and isolation in wind turbine

This paper proposes a new scheme for fault detection and isolation (FDI) in variable speed wind turbine. The proposed scheme is based on an intelligent data-driven fault detection scheme using the extreme learning machine approach (ELM). The ELM is a kind of single hidden layer feed-forward neural network (SLFNN) with a fast learning. The basic idea is the use of a certain number n of ELM classifiers to deals with n types of faults affecting the wind turbine. Different parts of the process were investigated including actuators and sensors faults. The effectiveness of the proposed approach is illustrated through simulation.
Portfolio Selection in Stochastic Environments

In this article, I explicitly solve dynamic portfolio choice problems, up to the solution of an ordinary differential equation (ODE), when the asset returns are quadratic and the agent has a constant relative risk aversion (CRRA) coefficient. My solution includes as special cases many existing explicit solutions of dynamic portfolio choice problems. I also present three applications that are not in the literature. Application 1 is the bond portfolio selection problem when bond returns are described by ‘‘quadratic term structure models.’’ Application 2 is the stock portfolio selection problem when stock return volatility is stochastic as in Heston model. Application 3 is a bond and stock portfolio selection problem when the interest rate is stochastic and stock returns display stochastic volatility.
Adaptive Concurrent Query Execution Framework for an Analytical In-Memory Database System

There is a growing need for in-memory database analytic services, especially in cloud settings. Concurrent query execution is common in such environments. A crucial deployment requirement is to employ a concurrent query execution scheduling framework that is flexible, precise, and adaptive to meet specified deployment goals. In addition, the framework must also aim to use all the underlying hardware resources effectively (for high performance and high cost efficiency). This paper focuses on the design and evaluation of such a scheduler framework. Our scheduler framework incorporates a design in which the scheduling policies are cleanly separated from the scheduling mechanisms, allowing the scheduler to support a variety of policies, such as fair and priority scheduling. The scheduler also contains a novel learning component to monitor and quickly adapt to changing resource requirements of concurrent queries. In addition, the scheduler easily incorporates a load controller to protect the system from thrashing in situations when resources are scarce/oversubscribed. We have implemented our scheduling framework in an in-memory database engine, and using this implementation we also demonstrate the effectiveness of our approach. Collectively, we present the design and implementation of a scheduling framework for in-memory database services on contemporary hardware in modern deployment settings.
Construction of a Flexibility Analysis Model for Flexible High-Throughput Satellite Communication Systems With a Digital Channelizer

Recent years have seen the development of a satellite communication system called a high-throughput satellite (HTS), which enables large-capacity communication to cope with various communication demands. Current HTSs have a fixed allocation of communication resources and cannot flexibly change this allocation during operation. Thus, effectively allocating communication resources for communication demands with a bias is not possible. Therefore, technology is being developed to add flexibility to satellite communication systems, but there is no system analysis model available to quantitatively evaluate the flexibility performance. In this study, we constructed a system analysis model to quantitatively evaluate the flexibility of a satellite communication system and used it to analyze a satellite communication system equipped with a digital channelizer.
A multi-strategy approach for information extraction of financial report documents

Information extraction studies have been conducted to improve the efficiency ansd accuracy of information retrieval. We developed information extraction techniques to extract name of company, period of document, currency, revenue, and number of employee information from financial report documents automatically. Different with other works, we applied a multi-strategy approach for developing extraction techniques. We separated information based on its similar characteristics before designing extraction techniques. We assumed that the difference of characteristics owned by each information induces the difference of strategy applied. First strategy is constructing extraction techniques using rule-based extraction method on information, which has good regularity on orthographic and layout features such as name of company, period of document and currency. Second strategy is applying machine learning-based extraction method on information, which has rich contextual and list look-up features such as revenue and number of employee. On the first strategy, rule patterns are defined by combining orthographic, layout, and limited contextual features. Defined rule patterns succeed to extract information and gain precision, recall, and F1-measure more than 0.98. On the second strategy, we conducted extraction task as classification task. First, we built classification models using Naive Bayes and Support Vector Machines algorithms. Then, we extracted the most informative features to train the classification models. The best classification model is used for extraction task. Contextual and list look-up features play important role in improving extraction performance. Second strategy succeed to extract revenue and number of employee information and gains precision, recall, and F-1 measure more than 0.93.
Mining Efficient Association Rules Through Apriori Algorithm Using Attributes and Comparative Analysis of Various Association Rule Algorithms

In frequent pattern mining, there are several algorithms. Apriori is the classical and most famous algorithm. Objective of using Apriori algorithm is to find frequent itemsets and association between different itemsets i.e. association rule. In this paper, author considers data (bank data) and tries to obtain the result using Weka a data mining tool. Association rule algorithms are used to find out the best combination of different attributes in any data. In this paper author uses Apriori to find association rule. Here author consider three association rule algorithms: Apriori Association Rule, PredictiveApriori Association Rule and Tertius Association Rule. Author compares the result of these three algorithms and presents the result. According to the result obtained using data mining tool author find that Apriori Association algorithm performs better than the PredictiveApriori Association Rule and Tertius Association Rule algorithms.
Floyd-Warshall Reinforcement Learning Learning from Past Experiences to Reach New Goals

Consider mutli-goal tasks that involve static environments and dynamic goals. Examples of such tasks, such as goaldirected navigation and pick-and-place in robotics, abound. Two types of Reinforcement Learning (RL) algorithms are used for such tasks: model-free or model-based. Each of these approaches has limitations. Model-free RL struggles to transfer learned information when the goal location changes, but achieves high asymptotic accuracy in single goal tasks. Model-based RL can transfer learned information to new goal locations by retaining the explicitly learned state-dynamics, but is limited by the fact that small errors in modelling these dynamics accumulate over long-term planning. In this work, we improve upon the limitations of model-free RL in multigoal domains. We do this by adapting the Floyd-Warshall algorithm for RL and call the adaptation Floyd-Warshall RL (FWRL). The proposed algorithm learns a goal-conditioned action-value function by constraining the value of the optimal path between any two states to be greater than or equal to the value of paths via intermediary states. Experimentally, we show that FWRL is more sample-efficient and learns higher reward strategies in multi-goal tasks as compared to Q-learning, model-based RL and other relevant baselines in a tabular domain.
Design of a new, light and portable mechanism for knee CPM machine with a user-friendly interface

After a knee joint surgery, due to severe pain and immobility of the patient, the tissue around the knee become harder and knee stiffness will occur, which may causes many problems such as scar tissue swelling, bleeding, and fibrosis. A CPM (Continuous Passive Motion) machine is an apparatus that is being used to patient recovery, retrieving moving abilities of the knee, and reducing tissue swelling, after the knee joint surgery. This device prevents frozen joint syndrome (adhesive capsulitis), joint stiffness, and articular cartilage destruction by stimulating joint tissues, and flowing synovial fluid and blood around the knee joint. In this study, a new, light, and portable CPM machine with an appropriate interface, is designed and manufactured. The knee joint can be rotated from the range of -15° to 120° with a pace of 0.1 degree/sec to 1 degree/sec by this machine. One of the most important advantages of this new machine is its own user-friendly interface. This apparatus is controlled via an Android-based application; therefore, the users can use this machine easily via their own smartphones without the necessity to an extra controlling device. Besides, because of its apt size, this machine is a portable device. Smooth movement without any vibration and adjusting capability for different anatomies are other merits of this new CPM machine.
Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.
Towards cybernetic surgery: robotic and augmented reality-assisted liver segmentectomy

Augmented reality (AR) in surgery consists in the fusion of synthetic computer-generated images (3D virtual model) obtained from medical imaging preoperative workup and real-time patient images in order to visualize unapparent anatomical details. The 3D model could be used for a preoperative planning of the procedure. The potential of AR navigation as a tool to improve safety of the surgical dissection is outlined for robotic hepatectomy. Three patients underwent a fully robotic and AR-assisted hepatic segmentectomy. The 3D virtual anatomical model was obtained using a thoracoabdominal CT scan with a customary software (VR-RENDER®, IRCAD). The model was then processed using a VR-RENDER® plug-in application, the Virtual Surgical Planning (VSP®, IRCAD), to delineate surgical resection planes including the elective ligature of vascular structures. Deformations associated with pneumoperitoneum were also simulated. The virtual model was superimposed to the operative field. A computer scientist manually registered virtual and real images using a video mixer (MX 70; Panasonic, Secaucus, NJ) in real time. Two totally robotic AR segmentectomy V and one segmentectomy VI were performed. AR allowed for the precise and safe recognition of all major vascular structures during the procedure. Total time required to obtain AR was 8 min (range 6–10 min). Each registration (alignment of the vascular anatomy) required a few seconds. Hepatic pedicle clamping was never performed. At the end of the procedure, the remnant liver was correctly vascularized. Resection margins were negative in all cases. The postoperative period was uneventful without perioperative transfusion. AR is a valuable navigation tool which may enhance the ability to achieve safe surgical resection during robotic hepatectomy.
The role of multiple chemotactic mechanisms in a model of chemotaxis in C. elegans: different mechanisms are specialised for different environments

Unlike simpler organisms, C. elegans possesses several distinct chemosensory pathways and chemotactic mechanisms. These mechanisms and pathways are individually capable of driving chemotaxis in a chemical concentration gradient. However, it is not understood if they are redundant or co-operate in more sophisticated ways. Here we examine the specialisation of different chemotactic mechanisms in a model of chemotaxis to NaCl. We explore the performance of different chemotactic mechanisms in a range of chemical gradients and show that, in the model, far from being redundant, the mechanisms are specialised both for different environments and for distinct features within those environments. We also show that the chemotactic drive mediated by the ASE pathway is not robust to the presence of noise in the chemical gradient. This problem cannot be solved along the ASE pathway without destroying its ability to drive chemotaxis. Instead, we show that robustness to noise can be achieved by introducing a second, much slower NaCl-sensing pathway. This secondary pathway is simpler than the ASE pathway, in the sense that it can respond to either up-steps or down-steps in NaCl but not both, and could correspond to one of several candidates in the literature which we identify and evaluate. This work provides one possible explanation of why there are multiple NaCl sensing pathways and chemotactic mechanisms in C. elegans: rather than being redundant the different pathways and mechanism are specialised both for the characteristics of different environments and for distinct features within a single environment.
Design of a gel-less two-electrode ECG monitor

In this paper, an ECG amplifier design, specifically to interface two gel-less electrodes for low-power portable applications, is presented. The goal is to develop a circuit with performance sufficient to extract heart rate information reliably using digital signal processing techniques, when measuring a subject engaging in moderate physical activity (such as walking). This application, having no reference electrode, requires biasing of the input to avoid amplifier saturation, all the while maintaining a sufficiently high common-mode noise rejection. A baseline correction circuit and an isolated circuit ground are included in the proposed design to handle changes in DC voltage drift due to electrode movement as well as to minimize commonmode noise from mains.
Dimensional Synthesis of Evanescent-Mode Ridge Waveguide Bandpass Filters

This paper introduces a method giving dimensions of inline evanescent-mode ridge waveguide bandpass filters. Evanescent mode couplings are evaluated individually, without optimization of the entire filter. This is obtained through an improved network model of the evanescent-mode coupling, together with novel analytical formulas to correct the resonators slope parameters. Unlike prior works based on full-wave optimization of the overall structure, this method is fast and leads to accurate bandwidth results. Several filter examples are included to support the design method. A prototype filter has been manufactured and the RF measurements are in good agreement with theory.
FBNA: A Fully Binarized Neural Network Accelerator

In recent researches, binarized neural network (BNN) has been proposed to address the massive computations and large memory footprint problem of the convolutional neural network (CNN). Several works have designed specific BNN accelerators and showed very promising results. Nevertheless, only part of the neural network is binarized in their architecture and the benefits of binary operations were not fully exploited. In this work, we propose the first fully binarized convolutional neural network accelerator (FBNA) architecture, in which all convolutional operations are binarized and unified, even including the first layer and padding. The fully unified architecture provides more resource, parallelism and scalability optimization opportunities. Compared with the state-of-the-art BNN accelerator, our evaluation results show 3.1x performance, 5.4x resource efficiency and 4.9x power efficiency on CIFAR-10.
Autonomy and paternalism in medical e-commerce.

One of the overriding interests of the literature on health care economics is to discover where personal choice in market economies end and corrective government intervention should begin. Our study addresses this question in the context of John Stuart Mill's utilitarian principle of harm. Our primary objective is to determine whether public policy interventions concerning more than 35,000 online pharmacies worldwide are necessary and efficient compared to traditional market-oriented approaches. Secondly, we seek to determine whether government interference could enhance personal  utility maximization, despite its direct and indirect (unintended) costs on medical e-commerce. This study finds that containing the negative externalities of medical e-commerce provides the most compelling raison d'etre of government interference. It asserts that autonomy and paternalism need not be mutually exclusive, despite their direct and indirect consequences on individual choice and decision-making processes. Valuable insights derived from Mill's principle should enrich theory-building in health care economics and policy.
A link-correlation-aware cross-layer protocol for IoT devices

The Internet of Things (IoT) applications is envisioned to require higher throughput protocols because of the increasing data amount. To significantly enhance the network throughput between IoT devices, this paper proposes a new link-layer data forwarding technique that is aware of link correlation (LC) and supports receiver initiated acknowledgement (RI-ACK). We also propose a multicast communication protocol based on LC-aware forwarding and RI-ACKs to further enhance the throughput. In a simulation study, our protocol improves the throughput by 35%-55% comparing to a state-of-the-art baseline.
Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration

The fast filtering of massive point cloud data from light detection and ranging (LiDAR) systems is important for many applications, such as the automatic extraction of digital elevation models in urban areas. We propose a simple scan-line-based algorithm that detects local lowest points first and treats them as the seeds to grow into ground segments by using slope and elevation. The scan line segmentation algorithm can be naturally accelerated by parallel computing due to the independent processing of each line. Furthermore, modern graphics processing units (GPUs) can be used to speed up the parallel process significantly. Using a strip of a LiDAR point cloud, with up to 48 million points, we test the algorithm in terms of both error rate and time performance. The tests show that the method can produce satisfactory results in less than 0.6 s of processing time using the GPU acceleration.