nils-herrmann/clean_scirepeval huggingface dataset:
Removing gamification from an enterprise SNS

Gamification, the use of game mechanics in non-gaming applications, has been applied to various systems to encourage desired user behaviors. In this paper, we examine patterns of user activity in an enterprise social network service after the removal of a points-based incentive system. Our results reveal that the removal of the incentive scheme did reduce overall participation via contribution within the SNS. We also describe the strategies by point leaders and observe that users geographically distant from headquarters tended to comment on profiles outside of their home country. Finally, we describe the implications of the removal of extrinsic rewards, such as points and badges, on social software systems, particularly those deployed within an enterprise.
A study of DES and Blowfish encryption algorithm

With the rapid growing of internet and networks applications, data security becomes more important than ever before. Encryption algorithms play a crucial role in information security systems. In this paper, we have a study of the two popular encryption algorithms: DES and Blowfish. We overviewed the base functions and analyzed the security for both algorithms. We also evaluated performance in execution speed based on different memory sizes and compared them. The experimental results show the relationship between function run speed and memory size.
Knowledge of the firm and the evolutionary theory of the multinational corporation

Correspondence: Bruce Kogut, INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France. Tel: +33 1607242 05 Fax: +33 1607455 00/01 E-mail: Bruce.KOGUT@insead.edu  Firms are social communities that specialize in the creation and internal transfer of knowledge. The multinational corporation arises not out of the failure of markets for the buying and selling of knowledge, but out of its superior efficiency as an organizational vehicle by which to transfer this knowledge across borders. We test the claim that firms specialize in the internal transfer of tacit knowledge by empirically examining the decision to transfer the capability to manufacture new products to wholly owned subsidiaries or to other parties. The empirical results show that the less codifiable and the harder to teach is the technology, the more likely the transfer will be to wholly owned operations. This result implies that the choice of transfer mode is determined by the efficiency of the multinational corporation in transferring knowledge relative to other firms, not relative to an abstract market transaction. The notion of the firm as specializing in the transfer and recombination of knowledge is the foundation to an evolutionary theory of the multinational corporation Journal of International Business Studies (2003) 34, 516–529. doi:10.1057/palgrave.
A voltage-source inverter using IGBTs for a 50 kHz 10 kV corona surface treater

The authors describe a 50 kHz 5 kVA voltage-source inverter using insulated gate bipolar transistors (IGBTs), a series-resonant circuit including a step-up transformer of turn ratio 1:10, and a corona surface treater. The series-resonant circuit is used as a matching circuit between the inverter of output voltage 250 V and the corona surface treater of input voltage 10 kV. Experimental results obtained from the prototype inverter system are shown to verify the stable inverter operation and proper corona discharge irrespective of load conditions. The estimated inverter efficiency is 95%, and the measured overall efficiency of the system is 74%.<<ETX>>
Scalable Semantic Parsing with Partial Ontologies

We consider the problem of building scalable semantic parsers for Freebase, and present a new approach for learning to do partial analyses that ground as much of the input text as possible without requiring that all content words be mapped to Freebase concepts. We study this problem on two newly introduced large-scale noun phrase datasets, and present a new semantic parsing model and semi-supervised learning approach for reasoning with partial ontological support. Experiments demonstrate strong performance on two tasks: referring expression resolution and entity attribute extraction. In both cases, the partial analyses allow us to improve precision over strong baselines, while parsing many phrases that would be ignored by existing techniques.
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Qiteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability.
Intelligent Traffic Information System Based on Integration of Internet of Things and Agent Technology

In recent years popularity of private cars is getting urban traffic more and more crowded. As result traffic is becoming one of important problems in big cities in all over the world. Some of the traffic concerns are congestions and accidents which have caused a huge waste of time, property damage and environmental pollution. This research paper presents a novel intelligent traffic administration system, based on Internet of Things, which is featured by low cost, high scalability, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously. The Internet of Things is based on the Internet, network wireless sensing and detection technologies to realize the intelligent recognition on the tagged traffic object, tracking, monitoring, managing and processed automatically. The paper proposes an architecture that integrates internet of things with agent technology into a single platform where the agent technology handles effective communication and interfaces among a large number of heterogeneous highly distributed, and decentralized devices within the IoT. The architecture introduces the use of an active radio-frequency identification (RFID), wireless sensor technologies, object ad-hoc networking, and Internet-based information systems in which tagged traffic objects can be automatically represented, tracked, and queried over a network. This research presents an overview of a framework distributed traffic simulation model within NetLogo, an agentbased environment, for IoT traffic monitoring system using mobile agent technology. Intelligent Traffic; Internet-of-Things; RFID; Wireless Sensor Networks; Agent Technology
A vision-based obstacle detection system for Unmanned Surface Vehicle

This paper describes a vision-based obstacle detection system for Unmanned Surface Vehicle (USV) towards the aim of real-time and high performance obstacle detection on the sea surface. By using both the monocular and stereo vision methods, the system offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV which runs at speeds up to 12 knots. Field tests in the real scenes have been taken and the obstacle detection system for USV is proven to provide stable and satisfactory performance.
A Bluetooth signal strength based indoor localization method

Indoor location awareness is intended for many applications like patient monitoring and store navigation. Short range wireless protocols are usually preferred when other ways of location sensing methods such as Global Positioning Systems or Inertial Measurement Units may not be feasible to use indoors. Devices using Bluetooth protocol have the advantage of being low cost and also being widely available. In this paper an affordable indoor localization system is proposed where location evaluation is performed by measuring unknown positioned Bluetooth transmitters' signal levels at some known base stations. A localization method is proposed which uses calculated values of a defined error function to estimate the positions of unknown transmitters. The error function is based on a modified Root-Mean-Square-Error (RMSE) metric. The error function is calculated using Received Signal Strength Indicator (RSSI) measurements at each point under consideration. The error function is calculated at discrete points in the area of interest and the points with low calculated error values indicates locations where transmitters may be located with a high probability. The validity of the decision method is verified with a real-world experiment.
Reconfigurable Binding against FPGA Replay Attacks

The FPGA replay attack, where an attacker downgrades an FPGA-based system to the previous version with known vulnerabilities, has become a serious security and privacy concern for FPGA design. Current FPGA intellectual property (IP) protection mechanisms target the protection of FPGA configuration bitstreams by watermarking or encryption or binding. However, these mechanisms fail to prevent replay attacks. In this article, based on a recently reported PUF-FSM binding method that protects the usage of configuration bitstreams, we propose to reconfigure both the physical unclonable functions (PUFs) and the locking scheme of the finite state machine (FSM) in order to defeat the replay attack. We analyze the proposed scheme and demonstrate how replay attack would fail in attacking systems protected by the reconfigurable binding method. We implement two ways to build reconfigurable PUFs and propose two practical methods to reconfigure the locking scheme. Experimental results show that the two reconfigurable PUFs can generate significantly distinct responses with average reconfigurability of more than 40%. The reconfigurable locking schemes only incur a timing overhead less than 1%.
Antibacterial activity of graphite, graphite oxide, graphene oxide, and reduced graphene oxide: membrane and oxidative stress.

Health and environmental impacts of graphene-based materials need to be thoroughly evaluated before their potential applications. Graphene has strong cytotoxicity toward bacteria. To better understand its antimicrobial mechanism, we compared the antibacterial activity of four types of graphene-based materials (graphite (Gt), graphite oxide (GtO), graphene oxide (GO), and reduced graphene oxide (rGO)) toward a bacterial model-Escherichia coli. Under similar concentration and incubation conditions, GO dispersion shows the highest antibacterial activity, sequentially followed by rGO, Gt, and GtO. Scanning electron microscope (SEM) and dynamic light scattering analyses show that GO aggregates have the smallest average size among the four types of materials. SEM images display that the direct contacts with graphene nanosheets disrupt cell membrane. No superoxide anion (O(2)(•-)) induced reactive oxygen species (ROS) production is detected. However, the four types of materials can oxidize glutathione, which serves as redox state mediator in bacteria. Conductive rGO and Gt have higher oxidation capacities than insulating GO and GtO. Results suggest that antimicrobial actions are contributed by both membrane and oxidation stress. We propose that a three-step antimicrobial mechanism, previously used for carbon nanotubes, is applicable to graphene-based materials. It includes initial cell deposition on graphene-based materials, membrane stress caused by direct contact with sharp nanosheets, and the ensuing superoxide anion-independent oxidation. We envision that physicochemical properties of graphene-based materials, such as density of functional groups, size, and conductivity, can be precisely tailored to either reducing their health and environmental risks or increasing their application potentials.
Adversarial Generator-Encoder Networks

We present a new autoencoder-type architecture, that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the canonical distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex architectures.
Seeing things : consumer response to the visual domain in product design

This paper discusses consumer response to product visual form within the context of an integrated conceptual framework. Emphasis is placed on the aesthetic, semantic and symbolic aspects of cognitive response to design. The accompanying affective and behavioural responses are also discussed and the interaction between cognitive and affective response is considered. All aspects of response are presented as the final stage in a process of communication between the design team and the consumer. The role of external visual references is examined and the effects of moderating influences at each stage in the process of communication are discussed. In particular, the personal, situational and cultural factors that moderate response are considered. In concluding the paper, implications for design practice and design research are presented.
Reinforcement Learning for Autonomous Maneuvering in Highway Scenarios

In this work, we present a Reinforcement Learning (RL) based approach for autonomous driving in highway scenarios, including interaction with other vehicles. The method used is Fitted Q-iteration  with Extremely Randomized Trees  as a function approximator. We demonstrate that Reinforcement Learning based concepts can be successfully applied and can be used to teach a RL agent to drive autonomously in an intelligent way, by following traffic rules and ensuring safety. By combining RL with the already established control concepts, we managed to build an agent that achieved promising results in the realistic simulated environment.
An Integrated Vehicle Navigation System Utilizing Lane-Detection and Lateral Position Estimation Systems in Difficult Environments for GPS

A navigation filter combines measurements from sensors currently available on vehicles - Global Positioning System (GPS), inertial measurement unit, inertial measurement unit (IMU), camera, and light detection and ranging (lidar) - for achieving lane-level positioning in environments where stand-alone GPS can suffer or fail. Measurements from the camera and lidar are used in two lane-detection systems, and the calculated lateral distance (to the lane markings) estimates of both lane-detection systems are compared with centimeter-level truth to show decimeter-level accuracy. The navigation filter uses the lateral distance measurements from the lidar- and camera-based systems with a known waypoint-based map to provide global measurements for use in a GPS/Inertial Navigation System (INS) system. Experimental results show that the inclusion of lateral distance measurements and a height constraint from the map creates a fully observable system even with only two satellite observations and, as such, greatly enhances the robustness of the integrated system over GPS/INS alone. Various scenarios are presented, which affect the navigation filter, including satellite geometry, number of satellites, and loss of lateral distance measurements from the camera and lidar systems.
A Cultural Perspective on Negotiation : Progress , Pitfalls , and Prospects

Cet article passe en revue les vingt dernieÁ res anneÂ es de recherche sur la culture et la neÂ gociation et preÂ sente les progreÁ s qui ont eÂ teÂ faits, les pieÁ ges dont il faut se deÂ fier et les perspectives pour de futurs travaux. On a remarqueÂ que beaucoup de recherches avaient tendance aÁ suivre ces deux modeÁ les implicites: (1) l'influence de la culture sur les strateÂ gies et l'aboutissement de la neÂ gociation et/ou (2) l'interaction de la culture et d'autres aspects de la situation immeÂ diate sur les reÂ sultats de la neÂ gociation. Cette recherche a porteÂ sur un grand nombre de cultures et a mis en eÂ vidence plus d'un modeÁ le inteÂ ressant. Nous signalons cependant trois pieÁ ge caracteÂ ristiques de cette litteÂ rature, pieÁ ges qui nous ont handicapeÂ s. Tout d'abord, la plupart des travaux se satisfont de deÂ nominations geÂ ographiques pour deÂ signer les cultures et il est par suite souvent impossible de deÂ terminer les dimensions culturelles qui rendent compte des diffeÂ rences observeÂ es. Ensuite, beaucoup de recherches ignorent les processus psychologiques (c'est-aÁ -dire les motivations et les cognitions) qui sont en jeu dans les neÂ gociations prenant place dans des cultures diffeÂ rentes si bien que nous apprenons peu de choses aÁ propos de la psychologie de la neÂ gociation dans des contextes culturels diversifieÂ s. On se heurte ainsi aÁ une « boõÃ te noire » que les travaux sur la culture et la neÂ gociation se gardent geÂ neÂ ralement d'ouvrir. Enfin, notre travail n'a recenseÂ qu'un nombre restreint de variables situationnelles immeÂ diates intervenant dans des neÂ gociations prenant place dans des cultures diffeÂ rentes; notre compreÂ hension des effets modeÂ rateurs de la culture sur la neÂ gociation est donc limiteÂ e. Nous proposons un troisieÁ me modeÁ le, plus complet, de la culture et de la neÂ gociation, preÂ sentons quelques donneÂ es reÂ centes en sa faveur et esquissons quelques perspectives pour l'avenir.
A review of visual inertial odometry from filtering and optimisation perspectives

A review of visual inertial odometry from filtering and optimisation perspectives Jianjun Gui, Dongbing Gu, Sen Wang & Huosheng Hu To cite this article: Jianjun Gui, Dongbing Gu, Sen Wang & Huosheng Hu (2015) A review of visual inertial odometry from filtering and optimisation perspectives, Advanced Robotics, 29:20, 1289-1301,  To link to this article: http://dx.doi.org/10.1080/01691864.2015.1057616
Vehicle Speed Estimation Using Acoustic Wave Patterns

We estimate a vehicle's speed, its wheelbase length, and tire track length by jointly estimating its acoustic wave pattern with a single passive acoustic sensor that records the vehicle's drive-by noise. The acoustic wave pattern is determined using the vehicle's speed, the Doppler shift factor, the sensor's distance to the vehicle's closest-point-of-approach, and three envelope shape (ES) components, which approximate the shape variations of the received signal's power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM, the number of cylinders, and the vehicle's initial bearing, loudness and speed to form a vehicle profile vector. This vector provides a fingerprint that can be used for vehicle identification and classification. We also provide possible reasons why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.
Online 6 DOF Augmented Reality Registration from Natural Features

We present a complete scalable system for 6 d.o.f. camera tracking based on natural features. Crucially, the calculation is based only on pre-captured reference images and previous estimates of the camera pose and is hence suitable for online applications. We match natural features in the current frame to two spatially separated reference images. We overcome the wide baseline matching problem by matching to the previous frame and transferring point positions to the reference images. We then minimize deviations from the two-view and three-view constraints between the reference images and the current frame as a function of the camera position parameters. We stabilize this calculation using a recursive form of temporal regularization that is similar in spirit to the Kalman filter. We can track camera pose over hundreds of frames and realistically integrate virtual objects with only slight jitter.
Deterministic Word Segmentation Using Maximum Matching with Fully Lexicalized Rules

We present a fast algorithm of word segmentation that scans an input sentence in a deterministic manner just one time. The algorithm is based on simple maximum matching which includes execution of fully lexicalized transformational rules. Since the process of rule matching is incorporated into dictionary lookup, fast segmentation is achieved. We evaluated the proposed method on word segmentation of Japanese. Experimental results show that our segmenter runs considerably faster than the state-of-the-art systems and yields a practical accuracy when a more accurate segmenter or an annotated corpus is available.
Using ARM trustzone to build a trusted language runtime for mobile applications

This paper presents the design, implementation, and evaluation of the Trusted Language Runtime (TLR), a system that protects the confidentiality and integrity of .NET mobile applications from OS security breaches. TLR enables separating an application's security-sensitive logic from the rest of the application, and isolates it from the OS and other apps. TLR provides runtime support for the secure component based on a .NET implementation for embedded devices. TLR reduces the TCB of an open source .NET implementation by a factor of $78$ with a tolerable performance cost. The main benefit of the TLR is to bring the developer benefits of managed code to trusted computing. With the TLR, developers can build their trusted components with the productivity benefits of modern high level languages, such as strong typing and garbage collection.
A Survey of Various Machine Learning Techniques for Text Classification

Sentiments are expressions of one’s words in a sentence. Hence understanding the meaning of text in the sentence is of utmost importance to people of various fields like customer reviews in companies, movie reviews in movies, etc. It may involve huge text data to analyze and it becomes totally unviable for manually understanding the meaning of sentences. Classifier algorithms should be used to classify the various meaning of the sentences. By using pre-defined data to train our classifier and three different algorithms namely Naive Bayes, Support Vector Machines, Decision Trees, we can simplify the task of text classification. Using relevant results and examples we will prove that SVM is one of the better algorithms in providing higher accuracy over the other two algorithms i.e. Naive Bayes and Decision Tree.
Development of joint torque sensor applied to compensate crosstalk error

For force control of robot and collision detection with humans, robots that has joint torque sensors have been developed. However, existing torque sensors cannot measure correct torque because of crosstalk error. In order to solve this problem, we proposed a novel torque sensor that can measure the pure torque without crosstalk. The hexaform of the proposed sensor with truss structure increase deformation of the sensor and restoration, and the Wheatstone bridge circuit of strain gauge removes crosstalk error. Sensor performance is verified with FEM analysis.