nils-herrmann/clean_scirepeval huggingface dataset:
The hB-Tree: A Multiattribute Indexing Method with Good Guaranteed Performance

A new multiattribute index structure called the hB-tree is introduced. It is derived from the K-D-B-tree of Robinson  but has additional desirable properties. The hB-tree internode search and growth processes are precisely analogous to the corresponding processes in B-trees . The intranode processes are unique. A k-d tree is used as the structure within nodes for very efficient searching. Node splitting requires that this k-d tree be split. This produces nodes which no longer represent brick-like regions in k-space, but that can be characterized as holey bricks, bricks in which subregions have been extracted. We present results that guarantee hB-tree users decent storage utilization, reasonable size index terms, and good search and insert performance. These results guarantee that the hB-tree copes well with arbitrary distributions of keys.
On visual similarity based 2D drawing retrieval

A large amount of 2D drawings have been produced in engineering fields. To reuse and share the available drawings efficiently, we propose two methods in this paper, namely 2.5D spherical harmonics transformation and 2D shape histogram, to retrieve 2D drawings by measuring their shape similarity. The first approach represents a drawing as a spherical function by transforming it from a 2D space into a 3D space. Then a fast spherical harmonics transformation is employed to get a rotation invariant descriptor. The second statistics-based approach represents the shape of a 2D drawing using a distance distribution between two randomly sampled points. To allow users to interactively emphasize certain local shapes that they are interested in, we have adopted a flexible sampling strategy by specifying a bias sampling density upon these local shapes. The two proposed methods have many valuable properties, including transform invariance, efficiency, and robustness. In addition, their insensitivity to noise allows for the user’s causal input, thus supporting a freehand sketch-based retrieval user interface. Experiments show that a better performance can be achieved by combining them together using weights.
Closed loop speed control of chopper fed DC motor for industrial drive application

In this paper a comparative transient and robustness analysis of closed loop speed control employing different linear controllers for the same dc motor using 4 quadrant chopper is investigated. The controller configurations can be broadly classified under (i) Integer order PID controller (ii) linear state space observer and (iii) fractional order PID controller. All of them exhibit superior performances, the first one is the conventional controller used in industries but the later two are modern controllers that have rich potential for industry use and have large advantage over the conventional controllers. The closed loop control of chopper fed DC motor is shown with the 1st quadrant operation of chopper circuit.
Beyond blacklists: learning to detect malicious web sites from suspicious URLs

Malicious Web sites are a cornerstone of Internet criminal activities. As a result, there has been broad interest in developing systems to prevent the end user from visiting such sites. In this paper, we describe an approach to this problem based on automated URL classification, using statistical methods to discover the tell-tale lexical and host-based properties of malicious Web site URLs. These methods are able to learn highly predictive models by extracting and automatically analyzing tens of thousands of features potentially indicative of suspicious URLs. The resulting classifiers obtain 95-99% accuracy, detecting large numbers of malicious Web sites from their URLs, with only modest false positives.
Mesofluidic actuation for articulated finger and hand prosthetics

The loss of fingers and hands severely limits career and lifestyle options for the amputee. Unfortunately, while there have been strides made in advancements of upper arm and leg prosthetics, the state of the art in prosthetic hands is lagging far behind. Options are generally limited to claw like devices that provide limited gripping capacity. The overall objective of this paper is to demonstrate a path towards a low-cost prosthetic hand with multiple articulated fingers and a thumb that rivals the human hand in terms of weight, size, dexterity, range of motion, force carrying capacity and speed. We begin with a description of the functional requirements for a human hand. When comparing requirements with actuation technologies, the fluid power approach has the potential to realize a prosthetic hand that rivals a human hand in size, strength and dexterity. We introduce a new actuation technology, mesofluidics, that focuses on miniaturization of fluid power to the meso-scale (mm to cm). As a novel demonstration of the potential for this technology, we describe a proof-of-principle mesofluidic finger that has intrinsic actuation and control (actuators and control valves within the volume of the finger). This finger weighs 63 grams, is sized to the 50th percentile male finger, has a total of 25 mechanical parts and is capable of providing 10 kg (22 lbs) of pinch force.
A comparison of implicit and explicit links for web page classification

It is well known that Web-page classification can be enhanced by using hyperlinks that provide linkages between Web pages. However, in the Web space, hyperlinks are usually sparse, noisy and thus in many situations can only provide limited help in classification. In this paper, we extend the concept of linkages from explicit hyperlinks to implicit links built between Web pages. By observing that people who search the Web with the same queries often click on different, but related documents together, we draw implicit links between Web pages that are clicked after the same queries. Those pages are implicitly linked. We provide an approach for automatically building the implicit links between Web pages using Web query logs, together with a thorough comparison between the uses of implicit and explicit links in Web page classification. Our experimental results on a large dataset confirm that the use of the implicit links is better than using explicit links in classification performance, with an increase of more than 10.5% in terms of the Macro-F1 measurement.
Model Agnostic Supervised Local Explanations

Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability is designing explanation systems that can capture aspects of each of these explanation types, in order to develop a more thorough understanding of the model. We address this challenge in a novel model called MAPLE that uses local linear modeling techniques along with a dual interpretation of random forests (both as a supervised neighborhood approach and as a feature selection method). MAPLE has two fundamental advantages over existing interpretability systems. First, while it is effective as a black-box explanation system, MAPLE itself is a highly accurate predictive model that provides faithful self explanations, and thus sidesteps the typical accuracy-interpretability trade-off. Specifically, we demonstrate, on several UCI datasets, that MAPLE is at least as accurate as random forests and that it produces more faithful local explanations than LIME, a popular interpretability system. Second, MAPLE provides both example-based and local explanations and can detect global patterns, which allows it to diagnose limitations in its local explanations.
Early versus late fusion in semantic video analysis

Semantic analysis of multimodal video aims to index segments of interest at a conceptual level. In reaching this goal, it requires an analysis of several information streams. At some point in the analysis these streams need to be fused. In this paper, we consider two classes of fusion schemes, namely early fusion and late fusion. The former fuses modalities in feature space, the latter fuses modalities in semantic space. We show by experiment on 184 hours of broadcast video data and for 20 semantic concepts, that late fusion tends to give slightly better performance for most concepts. However, for those concepts where early fusion performs better the difference is more significant.
A High-Voltage Pulse Generator With Continuously Variable Pulsewidth Based on a Modified PFN

The amount of stored energy is an important issue in the low-droop and high-precision pulse generation. Pulse forming network (PFN) simulating an open-ended transmission line seems to be useful due to its minimum stored energy. The drawbacks of the PFN include low-output pulse quality and constant pulsewidth, which limit their applications. Another limitation of generating rectangular pulses using PFN is that half of the charged voltage appears in the output when delivering the entire stored energy to the matched load. This paper proposes a structure of PFN-based pulse generator that overcomes the above-mentioned limitations. For continuous pulsewidth adjustment, a lossless scheme is presented. By increasing the ratio of the load magnitude to the characteristic impedance, the amplitude of the output pulse voltage would be approximately equal to that of the charged voltage. Therefore, the semiconductor switches can be utilized in the PFN pulse generator easily. In addition, the pulse quality is enhanced and faster rising time is obtained in comparison with the conventional PFNs. In this proposal, the stored energy quantity is limited to three times the energy needed for the output pulse in the case of  $R_{L}=10Z_{0}$ . To verify the proposed structure, a prototype is simulated using PSPICE software, and the experimental results are presented.
Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots

Ambiguity and noise in natural language instructions create a significant barrier towards adopting autonomous systems into safety critical workflows involving humans and machines. In this paper, we propose to build on recent advances in electrophysiological monitoring methods and augmented reality technologies, to develop alternative modes of communication between humans and robots involved in large-scale proximal collaborative tasks. We will first introduce augmented reality techniques for projecting a robot’s intentions to its human teammate, who can interact with these cues to engage in realtime collaborative plan execution with the robot. We will then look at how electroencephalographic (EEG) feedback can be used to monitor human response to both discrete events, as well as longer term affective states while execution of a plan. These signals can be used by a learning agent, a.k.a an affective robot, to modify its policy. We will present an end-to-end system capable of demonstrating these modalities of interaction. We hope that the proposed system will inspire research in augmenting human-robot interactions with alternative forms of communications in the interests of safety, productivity, and fluency of teaming, particularly in engineered settings such as the factory floor or the assembly line in the manufacturing industry where the use of such wearables can be enforced.
Partial replay of long-running applications

Bugs in deployed software can be extremely difficult to track down. Invasive logging techniques, such as logging all non-deterministic inputs, can incur substantial runtime overheads. This paper shows how symbolic analysis can be used to re-create path equivalent executions for very long running programs such as databases and web servers. The goal is to help developers debug such long-running programs by allowing them to walk through an execution of the last few requests or transactions leading up to an error. The challenge is to provide this functionality without the high runtime overheads associated with traditional replay techniques based on input logging or memory snapshots. Our approach achieves this by recording a small amount of information about program execution, such as the direction of branches taken, and then using symbolic analysis to reconstruct the execution of the last few inputs processed by the application, as well as the state of memory before these inputs were executed.
 We implemented our technique in a new tool called bbr. In this paper, we show that it can be used to replay bugs in long-running single-threaded programs starting from the middle of an execution. We show that bbr incurs low recording overhead (avg. of 10%) during program execution, which is much less than existing replay schemes. We also show that it can reproduce real bugs from web servers, database systems, and other common utilities.
A Color Gamut Description Algorithm for Liquid Crystal Displays in CIELAB Space

Because the accuracy of gamut boundary description is significant for gamut mapping process, a gamut boundary calculating method for LCD monitors is proposed in this paper. Within most of the previous gamut boundary calculation algorithms, the gamut boundary is calculated in CIELAB space directly, and part of inside-gamut points are mistaken for the boundary points. While, in the new proposed algorithm, the points on the surface of RGB cube are selected as the boundary points, and then converted and described in CIELAB color space. Thus, in our algorithm, the true gamut boundary points are found and a more accurate gamut boundary is described. In experiment, a Toshiba LCD monitor's 3D CIELAB gamut for evaluation is firstly described which has regular-shaped outer surface, and then two 2D gamut boundaries ( CIE-a*b* boundary and CIE-C*L* boundary) are calculated which are often used in gamut mapping process. When our algorithm is compared with several famous gamut calculating algorithms, the gamut volumes are very close, which indicates that our algorithm's accuracy is precise and acceptable.
Self-Compassion and Body Dissatisfaction in Women : A Randomized Controlled Trial of a Brief Meditation Intervention

Body dissatisfaction is a major source of suffering among women of all ages. One potential factor that could mitigate body dissatisfaction is self-compassion, a construct that is garnering increasing research attention due to its strong association with psychological health. This study investigated whether a brief 3-week period of self-compassion meditation training would improve body satisfaction in a multigenerational group of women. Participants were randomized either to the meditation intervention group (N=98; M age=38.42) or to a waitlist control group (N=130; M age=36.42). Results suggested that compared to the control group, intervention participants experienced significantly greater reductions in body dissatisfaction, body shame, and contingent self-worth based on appearance, as well as greater gains in self-compassion and body appreciation. All improvements were maintained when assessed 3 months later. Self-compassion meditation may be a useful and cost-effective means of improving body image in
Controlling tensegrity robots through evolution

Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400% better than a hand-coded solution, while the multiagent evolution performs 800% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.
A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things

In recent times food safety has drawn upsurge of academic and commercial concerns. In supply chain area, with the rapid growth of internet technologies, a lot of emerging technologies have been applied in traceability systems. However, to date, nearly all of these systems are centralized which are monopolistic, asymmetric and opaque that could result in the trust problem, such as fraud, corruption, tampering and falsifying information. Besides, centralized system is vulnerable to collapse, since a single point of breakdown will lead the whole system to be crashed. Today, a new technology called the blockchain which is a ground-breaking innovation in decentralized information technology presents a whole new approach. However, since this technology is still in its early stages, it has some inherent defects, in which scalability become a primary and urgent one when we face the mass data in the real world. In this paper we will build a food supply chain traceability system for real-time food tracing based on HACCP (Hazard Analysis and Critical Control Points), blockchain and Internet of things, which could provide an information platform for all the supply chain members with openness, transparency, neutrality, reliability and security. Furthermore, we introduce a new concept BigchainDB to fill the gap in the decentralized systems at scale. The paper concludes with a description of a use case and the challenges to adopt blockchain technology in the future food supply chain traceability systems are discussed.
Towards a holistic analysis of mobile payments: A multiple perspectives approach

As the mobile technologies and services are in constant evolution, many speculate on whether or not mobile payments will be a killer application for mobile commerce. To have a better understanding of the market, there is a need to analyze not only the technology but also the different actors that are involved. For this purpose, we propose to conduct two disruption analyses to draw the disruptiveness profile of mobile payment solutions compared to other payment instruments. Then, we try to discover what factors have hindered the technical and commercial development by using a DSS based on a multi-criteria decision making method called Electre I.
Real time action recognition using histograms of depth gradients and random decision forests

We propose an algorithm which combines the discriminative information from depth images as well as from 3D joint positions to achieve high action recognition accuracy. To avoid the suppression of subtle discriminative information and also to handle local occlusions, we compute a vector of many independent local features. Each feature encodes spatiotemporal variations of depth and depth gradients at a specific space-time location in the action volume. Moreover, we encode the dominant skeleton movements by computing a local 3D joint position difference histogram. For each joint, we compute a 3D space-time motion volume which we use as an importance indicator and incorporate in the feature vector for improved action discrimination. To retain only the discriminant features, we train a random decision forest (RDF). The proposed algorithm is evaluated on three standard datasets and compared with nine state-of-the-art algorithms. Experimental results show that, on the average, the proposed algorithm outperform all other algorithms in accuracy and have a processing speed of over 112 frames/second.
The Expressive Power of SPARQL

This paper studies the expressive power of SPARQL. The main result is that SPARQL and non-recursive safe Datalog with negation have equivalent expressive power, and hence, by classical results, SPARQL is equivalent from an expressive point of view to Relational Algebra. We present explicit generic rules of the transformations in both directions. Among other findings of the paper are the proof that negation can be simulated in SPARQL, that non-safe filters are superfluous, and that current SPARQL W3C semantics can be simplified to a standard
Bayesian Optimization with Robust Bayesian Neural Networks

Bayesian optimization is a prominent method for optimizing expensive-to-evaluate black-box functions that is widely applied to tuning the hyperparameters of machine learning algorithms. Despite its successes, the prototypical Bayesian optimization approach – using Gaussian process models – does not scale well to either many hyperparameters or many function evaluations. Attacking this lack of scalability and flexibility is thus one of the key challenges of the field. We present a general approach for using flexible parametric models (neural networks) for Bayesian optimization, staying as close to a truly Bayesian treatment as possible. We obtain scalability through stochastic gradient Hamiltonian Monte Carlo, whose robustness we improve via a scale adaptation. Experiments including multi-task Bayesian optimization with 21 tasks, parallel optimization of deep neural networks and deep reinforcement learning show the power and flexibility of this approach.
CATCH: A detecting algorithm for coalition attacks of hit inflation in internet advertising

As the Internet flourishes, online advertising becomes essential for marketing campaigns for business applications. To perform a marketing campaign, advertisers provide their advertisements to Internet publishers and commissions are paid to the publishers of the advertisements based on the clicks made for the posted advertisements or the purchases of the products of which advertisements posted. Since the payment given to a publisher is proportional to the amount of clicks received for the advertisements posted by the publisher, dishonest publishers are motivated to inflate the number of clicks on the advertisements hosted on their web sites. Since the click frauds are critical for online advertising to be reliable, the online advertisers make the efforts to prevent them effectively. However, the methods used for click frauds are also becoming more complex and sophisticated. In this paper, we study the problem of detecting coalition attacks of click frauds. The coalition attacks of click fraud is one of the latest sophisticated techniques utilized for click frauds because the fraudsters can obtain not only more gain but also less probability of being detected by joining a coalition. We introduce new definitions for the coalition and propose the novel algorithm called CATCH to find such coalitions. Extensive experiments with synthetic and real-life data sets confirm that our notion of coalition allows us to detect coalitions much more effectively than that of