nils-herrmann/clean_scirepeval huggingface dataset:
Disrupting Industries with Blockchain: The Industry, Venture Capital Funding, and Regional Distribution of Blockchain Ventures

The blockchain (i.e., a decentralized and encrypted digital ledger) has the potential to disrupt many traditional business models. This study investigates the emerging blockchain business-application landscape by analyzing its industry, venture capital funding, and regional distribution. By matching four venture databases on blockchain-based startups we create a unique database to analyze the technology from a diffusion of innovation theoretical perspective. First, our results show that blockchain startups are present across all industry segments and are most prominently represented in the Finance & Insurance and Information & Communication industries. A finegrained analysis of financial services yields increasing novel applications in existing service offerings. Second, we find that mainly Finance & Insurance and Information & Communication industries are funded by venture capital, but that blockchain startups are present across all industries. Third, our regional distribution analysis of the emerging ventures identifies two leading geographical blockchain clusters (i.e., the US and UK).
Robot Companions as Case-Scenario for Assessing the “Subjectivity” of Autonomous Agents. Some Philosophical and Legal Remarks

In this paper the European flagship project proposal Robot Companion for Citizens (RCC), grounded on the idea of developing robot companions for citizens, is taken as a case scenario for investigating the feasibility of ascribing rights and duties to autonomous robots from a legal and philosophical standpoint. In talking about rights and duties with respect to robots endowed with autonomous decision capabilities, one should face the implications that inevitably these terms rise, especially in the field of law. The paper points out the technological problems related to the application of the notion of duty to robots and the problems deriving from attributing a legal subjectivity to nonhuman entities such as robot.
Automatic Detection of Fake News

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76%. In addition, we provide comparative analyses of the automatic and manual identification of fake news.
Activity recognition and monitoring for smart wheelchair users

In recent years, the elderly population is increasing enormously, from 9% in 1994 to 12% in 2014, and is expected to reach 21% by 2050. Elderly live often alone today and even conducting an independent daily life, some of them move with the aid of walkers or using wheelchairs. Monitoring elderly activity in mobility has become a major priority to provide them an effective care service. This paper focuses on an enhancement of a smart wheelchair based on pressure sensors to monitor users sitting on the wheelchair. If the wheelchair user assumes a dangerous posture, the system triggers audio/visual alarms to avoid critical consequences such as wheelchair overturn. The paper discusses the hardware design of the system, then analyzes and compares posture recognition methods that have been applied on pressure data we collected. The experiments demonstrate the effectiveness of the proposed method and 99.5% posture recognition accuracy has been observed.
Data Mining for Fraud Detection

The advent of new technologies and the fast base of technological development have created new possibilities as well as imposing new challenges. Fraud, which is one of the greatest challenges for businesses and organizations, has been equipped with new technologies to take new and unconventional forms that are stealthier and more difficult to recognize than the traditional forms of this crime. Therefore, the techniques for detecting fraud had to develop as well to provide more efficient protection. The most recognized and effective technology that has been implemented for fraud detection is data mining. This research paper will explore some of the most effective data mining techniques for detecting different types of fraud. These techniques will first be categorized according into supervised and unsupervised methods. Those techniques will be explored in the fields of mobile telecommunication fraud, credit card fraud, medical insurance fraud and computer systems intrusion detection. In addition, a real life case will be presented to illustrate the application of one of the techniques in credit card fraud detection.
Online Minimum-Energy Trajectory Planning and Control on a Straight-Line Path for Three-Wheeled Omnidirectional Mobile Robots

To maximize total operating time, an online minimum-energy translational and rotational velocity trajectory planning and control system is presented on a straight-line path for three-wheeled omnidirectional mobile robots (TOMRs). We suggest an efficient online trajectory planning algorithm, which minimizes a practical cost function as the energy that is drawn from batteries to motors, which is based on an accurate TOMR dynamic model that includes both the Coriolis force and the actuator motor dynamics. Using Pontryagin's minimum principle, we find the minimum-energy rotational velocity trajectory in analytic form. Then, the minimum-energy translational velocity trajectory is found using a novel algorithm that has a time complexity of O(n), which is based on a linearity condition on the state transition of TOMRs. Moreover, a trajectory control system is implemented using the resolved-acceleration control to validate the actual performance. Simulation and experiment results show that these minimum-energy trajectories can save energy up to 4.76% compared with the energy-efficient trapezoidal velocity profile and up to 5.29% compared with the loss-minimization control.
Substrate Integrated Waveguide-to-Microstrip Transition in Multilayer Substrate

This paper presents a novel transition between a microstrip line and a substrate integrated waveguide (SIW) in a multilayer substrate design environment. In order to achieve a low-loss broadband response, the transition, consisting of a tapered or multisectional ridged SIW and a tapered microstrip line, is modeled and designed by simultaneously considering both impedance matching and field matching. Characteristic impedance and guided wavelength calculated by using closed-form expressions based on a transverse resonant method are used to develop our design procedure. Effective broad bandwidth is obtained in two examples developed in this study, which are validated with simulated and measured results. This transition provides a simple way to design substrate integrated circuits with buried microstrip circuits in the multilayer substrate in which any ratio of impedance transform can be anticipated.
Comparing Phrase-based and Syntax-based Paraphrase Generation

Paraphrase generation can be regarded as machine translation where source and target language are the same. We use the Moses statistical machine translation toolkit for paraphrasing, comparing phrase-based to syntax-based approaches. Data is derived from a recently released, large scale (2.1M tokens) paraphrase corpus for Dutch. Preliminary results indicate that the phrase-based approach performs better in terms of NIST scores and produces paraphrases at a greater distance from the source.
Asymptotic Confidence Regions for High-Dimensional Structured Sparsity

In the setting of high-dimensional linear regression models, we propose two frameworks for constructing pointwise and group confidence sets for penalized estimators, which incorporate prior knowledge about the organization of the nonzero coefficients. This is done by desparsifying the estimator by S. van de Geer and B. Stucky and S. van de Geer <italic>et al.</italic>, then using an appropriate estimator for the precision matrix <inline-formula> <tex-math notation="LaTeX">$\Theta$</tex-math></inline-formula>. In order to estimate the precision matrix a corresponding structured matrix norm penalty has to be introduced. After normalization the result is an asymptotic pivot. The asymptotic behavior is studied and simulations are added to study the differences between the two schemes.
Centrality in valued graphs : A measure of betweenness based on network flow

A new measure of centrality, C,, is introduced. It is based on the concept of network flows. While conceptually similar to Freeman’s original measure, Ca, the new measure differs from the original in two important ways. First, C, is defined for both valued and non-valued graphs. This makes C, applicable to a wider variety of network datasets. Second, the computation of C, is not based on geodesic paths as is C, but on all the independent paths between all pairs of points in the network.
On Resampling Algorithms for Particle Filters

In this paper a comparison is made between four frequently encountered resampling algorithms for particle filters. A theoretical framework is introduced to be able to understand and explain the differences between the resampling algorithms. This facilitates a comparison of the algorithms with respect to their resampling quality and computational complexity. Using extensive Monte Carlo simulations the theoretical results are verified. It is found that systematic resampling is favourable, both in terms of resampling quality and computational complexity.
Why Are Consumers Willing to Pay for Privacy? An Application of the Privacy-freemium Model to Media Companies

Monetizing their users’ personal information instead of charging a fee has become an established revenue model for platform operators—a new form of media companies specialized in aggregating, managing, and distributing user-generated online content. However, the commodification of privacy leads to privacy concerns that might be a risk for such businesses. Thus, a new approach is to focus on consumers’ willingness to pay for privacy, assuming that monetizing privacy protection might be an alternative revenue model. Following the freemium idea, we developed an innovative research design, offering 553 online survey participants the opportunity to subscribe to a fictional premium version of Facebook with additional privacy control features in return for a monthly fee. Based on the theory of planned behavior, we developed and tested a research model to explain actual willingness to pay for privacy behavior. Our findings show that perceived usefulness and trust significantly affect willingness to pay. In contrast, perceived internet privacy risk was not found to have a significant influence. We thus conclude that consumers are willing to pay for privacy in the form of a privacy-freemium model, provided they perceive the premium version as offering added value and as trustworthy.
Memory-augmented Neural Machine Translation

Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This paper presents a novel memoryaugmented NMT (M-NMT) architecture, which stores knowledge about how words (usually infrequently encountered ones) should be translated in a memory and then utilizes them to assist the neural model. We use this memory mechanism to combine the knowledge learned from a conventional statistical machine translation system and the rules learned by an NMT system, and also propose a solution for out-of-vocabulary (OOV) words based on this framework. Our experiments on two Chinese-English translation tasks demonstrated that the M-NMT architecture outperformed the NMT baseline by 9.0 and 2.7 BLEU points on the two tasks, respectively. Additionally, we found this architecture resulted in a much more effective OOV treatment compared to competitive methods.
Ultrawideband Metasurface Lenses Based on Off-Shifted Opposite Layers

Metasurfaces have demonstrated to be a low-cost solution for the development of directive antennas at high frequency. One of the opportunities of metasurfaces is the possibility to produce planar lenses. However, these lenses usually present a narrow band of operation. This limitation on bandwidth is more restrictive when the required range of refractive index is high. Here, we present a novel implementation of metasurfaces with low dispersion that can be employed for the design of ultrawideband planar lenses. The implementation consists of two mirrored metasurfaces that are displaced exactly a half unit cell. This displacement produces two effects: It increases the equivalent refractive index of the fundamental mode and flattens its frequency dependence.
It's worth the hassle!: the added value of evaluating the usability of mobile systems in the field

The distinction between field and laboratory is classical in research methodology. In human-computer interaction, and in usability evaluation in particular, it has been a controversial topic for several years. The advent of mobile devices has revived this topic. Empirical studies that compare evaluations in the two settings are beginning to appear, but they provide very different results. This paper presents results from an experimental comparison of a field-based and a lab-based usability evaluation of a mobile system. The two evaluations were conducted in exactly the same way. The conclusion is that it is definitely worth the hassle to conduct usability evaluations in the field. In the field-based evaluation we identified significantly more usability problems and this setting revealed problems with interaction style and cognitive load that were not identified in the laboratory.
Multi-label automatic GrabCut for image segmentation

This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments without user intervention. Since multi-label segmentation is an NP-hard problem, the proposed algorithm converts the segmentation problem into multiple iterative piecewise binary label GrabCut segmentations. This implies separating one segment from the image, under consideration, per iteration. In this way, the proposed algorithm maintains the powerful advantage of the GrabCut to get the optimal solution for the segmentation problem. Evaluation of the segmentation results was carried out using different accuracy metrics from the literature. The evaluations were conducted with human ground truth segmentations from Berkeley benchmark dataset of natural images. Although human segmentations are semantically more meaningful, experiments showed that the proposed multi-label GrabCut provided matching segmentation results to that of individual humans with acceptable accuracy.
Modulation, control and capacitor voltage balancing of alternate arm modular multilevel converter with DC fault blocking capability

This paper provides an overview of DC side fault tolerance issues of VSC based HVDC system and the need for fault tolerant converters. The working principle and DC fault ride through capability of recently introduced Alternate Arm Modular Multilevel Converter(AAMMC) has been discussed. The capacitor voltage balancing issues of AAMMC is analyzed and a novel scheme for balancing capacitor voltages of the wave shaping circuit is presented in this paper. The voltage balancing of capacitors of wave shaping circuits in the arm is done by introducing an overlap period during zero voltage period. Using the proposed scheme, the magnitude and direction of the current during the overlap period can be controlled by varying the switching pattern. It helps in charging or discharging of the submodule capacitors to bring them to their reference value. At the end of the overlap period, the arm current is brought to zero before opening the director switch so as to avoid the spike across the arm inductor. The efficacy of the proposed control scheme has been validated using simulation study done in PSCAD/EMTDC.
Detection and Prevention of SQL Injection Attack : A Survey

SQL (structure query language) injection is one of threats to the applications, which are Web-based application, Mobile application and even desktop application, which are connected to the database. By implementing SQL injection, attacker can gain full access to the application or database so that it can remove or change significant data irresponsibly. Applications that do not properly validate the user’s input make them vulnerable against SQL injection. SQL Injection Attacks (SQLIA) occurs when an attacker is able to insert a series of malicious SQL statements into a ―query‖ through manipulating user input data for execution by the back-end database. Using this type of threats, applications could be hacked easily and steal the confidential data by the attacker. In this paper we present classical and modern types of SQLIA and display different existing technique and tools which are used to detect or prevent these attacks.  SQL injection, Database security, PDO, Web application, SQLite.
Feeling-based location privacy protection for location-based services

Anonymous location information may be correlated with restricted spaces such as home and office for subject re-identification. This makes it a great challenge to provide location privacy protection for users of location-based services. Existing work adopts traditional K-anonymity model and ensures that each location disclosed in service requests is a spatial region that has been visited by at least K users. This strategy requires a user to specify an appropriate value of K in order to achieve a desired level of privacy protection. This is problematic because privacy is about feeling, and it is awkward for one to scale her feeling using a number. In this paper, we propose a feeling-based privacy model. The model allows a user to express her privacy requirement by specifying a public region, which the user would feel comfortable if the region is reported as her location. The popularity of the public region, measured using entropy based on its visitors' footprints inside it, is then used as the user's desired level of privacy protection. With this model in place, we present a novel technique that allows a user's location information to be reported as accurate as possible while providing her sufficient location privacy protection. The new technique supports trajectory cloaking and can be used in application scenarios where a user needs to make frequent location updates along a trajectory that cannot be predicted. In addition to evaluating the effectiveness of the proposed technique under various conditions through simulation, we have also implemented an experimental system for location privacy-aware uses of location-based services.
Trends, challenges and needs for lattice-based cryptography implementations: special session

Advances in computing steadily erode computer security at its foundation, calling for fundamental innovations to strengthen the weakening cryptographic primitives and security protocols. At the same time, the emergence of new computing paradigms, such as Cloud Computing and Internet of Everything, demand that innovations in security extend beyond their foundational aspects, to the actual design and deployment of these primitives and protocols while satisfying emerging design constraints such as latency, compactness, energy efficiency, and agility. While many alternatives have been proposed for symmetric key cryptography and related protocols (e.g., lightweight ciphers and authenticated encryption), the alternatives for public key cryptography are limited to post-quantum cryptography primitives and their protocols. In particular, lattice-based cryptography is a promising candidate, both in terms of foundational properties, as well as its application to traditional security problems such as key exchange, digital signature, and encryption/decryption. We summarize trends in lattice-based cryptographic schemes, some fundamental recent proposals for the use of lattices in computer security, challenges for their implementation in software and hardware, and emerging needs.
Comparison of different designs of cylindrical printed quadrifilar Helix antennas

INMARSAT allocated frequencies are 1.525 GHz to 1.559 GHz as downlink and 1.625 GHz to 1.6605 GHz as uplink GPS allocated frequencies 1.57542 GHz in L1 band. In this paper we design two types of Helix antenna as PQHA to operate in allocated frequencies for INMARSAT and GPS application. We used for their optimum design Electromagnetic simulations and were verified by fabrication and VSWR measurement.
All-dielectric subwavelength metasurface focusing lens.

We have proposed, designed, manufactured and tested low loss dielectric micro-lenses for infrared (IR) radiation based on a dielectric metamaterial layer. This metamaterial layer was created by patterning a dielectric surface and etching to sub-micron depths. For a proof-of-concept lens demonstration, we have chosen a fine patterned array of nano-pillars with variable diameters. Gradient index (GRIN) properties were achieved by engineering the nano-pattern characteristics across the lens, so that the effective optical density of the dielectric metamaterial layer peaks around the lens center, and gradually drops at the lens periphery. A set of lens designs with reduced reflection and tailorable phase gradients have been developed and tested, demonstrating focal distances of a few hundred microns, beam area contraction ratio up to three, and insertion losses as low as 11%.
An incentive mechanism with privacy protection in mobile crowdsourcing systems

In order to improve the efficiency and utility of mobile crowdsourcing systems, this paper proposes an incentive mechanism with privacy protection in mobile crowdsourcing systems. Combining the advantages of offline incentive mechanisms and online incentive mechanisms, this paper proposes an incentive mechanism that selects the worker candidates statically, and then dynamically selects winners after bidding. The proposed incentive mechanism includes two algorithms which are an improved two-stage auction algorithm (ITA) and a truthful online reputation updating algorithm (TORU). Through simulations, we verify the efficiency and effectiveness of the proposed incentive mechanism, which can solve the freeriding problem and improve the efficiency and utility of mobile crowdsourcing systems effectively.