nils-herrmann/clean_scirepeval huggingface dataset:
Combining Verbal and Nonverbal Features to Overcome the "Information Gap" in Task-Oriented Dialogue

Dialogue act modeling in task-oriented dialogue poses significant challenges. It is particularly challenging for corpora consisting of two interleaved communication streams: a dialogue stream and a task stream. In such corpora, information can be conveyed implicitly by the task stream, yielding a dialogue stream with seemingly missing information. A promising approach leverages rich resources from both the dialog and the task streams, combining verbal and non-verbal features. This paper presents work on dialogue act modeling that leverages body posture, which may be indicative of particular dialogue acts. Combining three information sources (dialogue exchanges, task context, and users’ posture), three types of machine learning frameworks were compared. The results indicate that some models better preserve the structure of task-oriented dialogue than others, and that automatically recognized postural features may help to disambiguate user dialogue moves.
Microstrip Antennas

Microstrip antennas have been one of the niost innovative topics in antenna theory and design in recent years, and are increasingly finding application in a wide range of modern microwave systems. This paper begins with a brief overview of the basic characteristics of microstrip antennas, and then concentrates on the most significant developments in microstrip antenna technology that have been made in the last several years. Emphasis is on new antenna configurations for improved electrical performance and manufacturability, and advances in the analytical modeling of microstrip antennas and arrays.
Toward More Efficient NoC Arbitration : A Deep Reinforcement Learning Approach

The network on-chip (NoC) is a critical resource shared by various on-chip components. An efficient NoC arbitration policy is crucial in providing global fairness and improving system performance. In this preliminary work, we demonstrate an idea of utilizing deep reinforcement learning to guide the design of more efficient NoC arbitration policies. We relate arbitration to a self-learning decision making process. Results show that the deep reinforcement learning approach can effectively reduce packet latency and has potential for identifying interesting features that could be utilized in more practical hardware designs.
An Associative Classification Data Mining Approach for Detecting Phishing Websites

Phishing websites are fake websites that are created by dishonest people to mimic webpages of real websites. Victims of phishing attacks may expose their financial sensitive information to the attacker whom might use this information for financial and criminal activities. Various approaches have been proposed to detect phishing websites, among which, approaches that utilize data mining techniques had shown to be more effective. The main goal of data mining is to analyze a large set of data to identify unsuspected relation and extract understandable useful patterns. Associative Classification (AC) is a promising data mining approach that integrates association rule and classification to build classification models (classifiers). This paper, proposes a new AC algorithm called Phishing Associative Classification (PAC), for detecting phishing websites. PAC employed a novel methodology in construction the classifier which results in generating moderate size classifiers. The algorithm improved the effectiveness and efficiency of a known algorithm called MCAR, by introducing a new prediction procedure and adopting a different rule pruning procedure. The conducted experiments compared PAC with 4 well-known data mining algorithms, these are: covering algorithm (Prism), decision tree (C4.5), associative Classification (CBA) and MCAR. Experiments are performed on a dataset that consists of 1010 website. Each Website is represented using 17 features categorized into 4 sets. The features are extracted from the website contents and URL. The results on each features set show that PAC is either equivalent or more effective than the compared algorithms. When all features are considered, PAC outperformed the compared algorithms and correctly identified 99.31% of the tested websites. Furthermore, PAC produced less number of rules than MCAR, and therefore, is more efficient.
Removal of Partial Occlusion from Single Images

This paper examines large partial occlusions in an image which occur near depth discontinuities when the foreground object is severely out of focus. We model these partial occlusions using matting, with the alpha value determined by the convolution of the blur kernel with a pinhole projection of the occluder. The main contribution is a method for removing the image contribution of the foreground occluder in regions of partial occlusion, which improves the visibility of the background scene. The method consists of three steps. First, the region of complete occlusion is estimated using a curve evolution method. Second, the alpha value at each pixel in the partly occluded region is estimated. Third, the intensity contribution of the foreground occluder is removed in regions of partial occlusion. Experiments demonstrate the method's ability to remove the effects of partial occlusion in single images with minimal user input.
On Resilience of Split-Architecture Networks

The split architecture network assumes a logically centralized controller, which is physically separated from a large set of data plane forwarding switches. When the control plane becomes decoupled from the data plane, the requirement to the failure resilience and recovery mechanisms changes. In this work we investigate one of the most important practical issues in split architecture deployment, the placement of controllers in a given network. We first demonstrate that the location of controllers have high impact on the network resilience using a real network topology. Motivated by such observation, we propose a min-cut based controller placement algorithm and compare it with greedy based approach. Our simulation results show significant reliability improvements with an intelligent placement strategy. Our work is the first attempt on the resilience properties of a split architecture network.
Nanofiber assembly by rotary jet-spinning.

High-voltage electrical fields and low production rate limit electrospinning, the electrical charging of polymer liquids, as a means of nanofiber fabrication. Here, we show a facile method of fabrication of aligned three-dimensional nanofiber structures by utilizing high-speed, rotating polymer solution jets to extrude fibers. Termed rotary jet-spinning, fiber morphology, diameter, and web porosity can be controlled by varying nozzle geometry, rotation speed, and polymer solution properties. We demonstrate the utility of this technique for tissue engineering by building anisotropic arrays of biodegradable polymer fibers and seeding the constructs with neonatal rat ventricular cardiomyocytes. The myocytes used the aligned fibers to orient their contractile cytoskeleton and to self-organize into a beating, multicellular tissue that mimics the laminar, anisotropic architecture of the heart muscle. This technique may prove advantageous for building uniaxially aligned nanofiber structures for polymers which are not amenable to fabrication by electrospinning.
Inferencing in information extraction: Techniques and applications

Information extraction at Web scale has become one of the most important research topics in data management since major commercial search engines started incorporating knowledge in their search results a couple of years ago . Users increasingly expect structured knowledge as answers to their search needs. Using Bing as an example, the result page for “Lionel Messi” is full of structured knowledge facts, such as his birthday and awards. The research efforts towards improving the accuracy and coverage of such knowledge bases have led to significant advances in Information Extraction techniques , . As the initial challenge of accurately extracting facts for popular entities are being addressed, more difficult challenges have emerged such as extending knowledge coverage to long tail entities and domains, understanding interestingness and usefulness of facts within a given context, and addressing information-seeking needs more directly and accurately. In this tutorial, we will survey the recent research efforts and provide an introduction to the techniques that address those challenges, and the applications that benefit from the adoption of those techniques. In particular, this tutorial will focus on a variety of techniques that can be broadly viewed as knowledge inferencing, i.e., combining multiple data sources and extraction techniques to verify existing knowledge and derive new knowledge. More specifically, we focus on four main categories of inferencing techniques: 1) deep natural language processing using machine learning techniques, 2) data cleaning using integrity constraints, 3) large-scale probabilistic reasoning, and 4) leveraging human expertise for domain knowledge extraction.
Analysis and Defensive Tools for Social-Engineering Attacks on Computer Systems

The weakest link in an information-security chain is often the user because people can be manipulated. Attacking computer systems with information gained from social interactions is one form of social engineering (K. Mitnick, et al. 2002). It can be much easier to do than targeting the complex technological protections of systems (J. McDermott, Social engineering - the weakest link in information security). In an effort to formalize social engineering for cyberspace, we are building models of trust and attack. Models help in understanding the bewildering number of different tactics that can be employed. Social engineering attacks can be complex with multiple ploys and targets; our models function as subroutines that are called multiple times to accomplish attack goals in a coordinated plan. Models enable us to infer good countermeasures to social engineering
KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition

We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The technique developed in this paper adapts the model conservatively by forcing the senone distribution estimated from the adapted model to be close to that from the unadapted model. This constraint is realized by adding Kullback-Leibler divergence (KLD) regularization to the adaptation criterion. We show that applying this regularization is equivalent to changing the target distribution in the conventional backpropagation algorithm. Experiments on Xbox voice search, short message dictation, and Switchboard and lecture speech transcription tasks demonstrate that the proposed adaptation technique can provide 2%-30% relative error reduction against the already very strong speaker independent CD-DNN-HMM systems using different adaptation sets under both supervised and unsupervised adaptation setups.
Advances in Effects of Sound Waves on Plants

Sound waves technology has been applied to different plants. It has been found that sound waves were at different frequencies, sound pressure levels (SPLs), exposure periods, and distances from the source of sound influence plant growth. Experiments have been conducted in the open field and under greenhouse growing conditions with different levels of audible sound frequencies and sound pressure levels. Sound waves at 1 kHz and 100 dB for 1 h within a distance of 0.20 m could significantly promote the division and cell wall fluidity of callus cells and also significantly enhance the activity of protective enzymes and endogenous hormones. Sound waves stimulation could increase the plant plasma-membrane H-ATPase activity, the contents of soluble sugar, soluble protein, and amylase activity of callus. Moreover, sound waves could increase the content of RNA and the level of transcription. Stress-induced genes could switch on under sound stimulation. Sound waves at 0.1-1 kHz and SPL of (70±5) dB for 3 h from plant acoustic frequency technology (PAFT) generator within a distance ranged from 30 to 60 m every other day significantly increased the yield of sweet pepper, cucumber and tomato by 30.05, 37.1 and 13.2%, respectively. Furthermore, the yield of lettuce, spinach, cotton, rice, and wheat were increased by 19.6, 22.7, 11.4, 5.7, and 17.0%, respectively. Sound waves may also strengthen plant immune systems. It has been proved that spider mite, aphids, gray mold, late blight and virus disease of tomatoes in the greenhouses decreased by 6.0, 8.0, 9.0, 11.0, and 8.0%, respectively, and the sheath blight of rice was reduced by 50%. This paper provides an overview of literature for the effects of sound waves on various growth parameters of plant at different growth stages.
Colour Dynamic Photometric Stereo for Textured Surfaces

In this paper we present a novel method to apply photometric stereo on textured dynamic surfaces. We aim at exploiting the high accuracy of photometric stereo and reconstruct local surface orientation from illumination changes. The main difficulty derives from the fact that photometric stereo requires varying illumination while the object remains still, which makes it quite impractical to use for dynamic surfaces. Using coloured lights gives a clear solution to this problem; however, the system of equations is still ill-posed and it is ambiguous whether the change of an observed surface colour is due to the change of the surface gradient or of the surface reflectance. In order to separate surface orientation from reflectance, our method tracks texture changes over time and exploits surface reflectance’s temporal constancy. This additional constraint allows us to reformulate the problem as an energy functional minimisation, solved by a standard quasi-Newton method. Our method is tested both on real and synthetic data, quantitatively evaluated and compared to a state-of-the-art method.
Autonomic Computing: An Overview

The increasing scale complexity, heterogeneity and dynamism of networks, systems and applications have made our computational and information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for system and application design, which is based on strategies used by biological systems to deal with similar challenges – a vision that has been referred to as autonomic computing. The overarching goal of autonomic computing is to realize computer and software systems and applications that can manage themselves in accordance with high-level guidance from humans. Meeting the grand challenges of autonomic computing requires scientific and technological advances in a wide variety of fields, as well as new software and system architectures that support the effective integration of the constituent technologies. This paper presents an introduction to autonomic computing, its challenges, and opportunities.
Fuzzing the ActionScript virtual machine

Fuzz testing is an automated testing technique where random data is used as an input to software systems in order to reveal security bugs/vulnerabilities. Fuzzed inputs must be binaries embedded with compiled bytecodes when testing against ActionScript virtual machines (AVMs). The current fuzzing method for JavaScript-like virtual machines is very limited when applied to compiler-involved AVMs. The complete source code should be both grammatically and semantically valid to allow execution by first passing through the compiler. In this paper, we present ScriptGene, an algorithmic approach to overcome the additional complexity of generating valid ActionScript programs. First, nearly-valid code snippets are randomly generated, with some controls on instruction flow. Second, we present a novel mutation method where the former code snippets are lexically analyzed and mutated with runtime information of the AVM, which helps us to build context for undefined behaviours against compiler-check and produce a high code coverage. Accordingly, we have implemented and evaluated ScriptGene on three different versions of Adobe AVMs. Results demonstrate that ScriptGene not only covers almost all the blocks of the official test suite (Tamarin), but also is capable of nearly twice the code coverage. The discovery of six bugs missed by the official test suite demonstrates the effectiveness, validity and novelty of ScriptGene.
Internet Use and Well-Being in Adolescence

Previous research suggests that Internet use may be associated with decreases in well-being among adolescents. However, there has been little investigation of the relationship between well-being and social aspects of Internet use. In the present study, 130 7th graders from a middle-class public school in California completed dispositional measures of well-being, and on three subsequent evenings they responded to questions regarding their Internet use (including detailed logs of instant messages) and daily well-being. Time spent on-line was not associated with dispositional or daily well-being. However, as suggested by intimacy theory, the closeness of instant message communication partners was associated with daily social anxiety and loneliness in school, above and beyond the contribution of dispositional measures.
Do Bots impact Twitter activity?

The WWW has seen massive growth in population of automated programs (bots) for a variety of exploits on online social networks (OSNs). In this paper we extend on our previous work to study the affects of bots on Twitter. By setting up a bot account on Twitter and conducting analysis on a click logs dataset from our web server, we show that despite bots being in smaller numbers, they exercise a profound impact on content popularity and activity on Twitter.
Sentiment Classification based on Latent Dirichlet Allocation

Opinion miningrefers to the use of natural language processing, text analysis and computational linguistics to identify and extract the subjective information.  Mining has become an indispensible part of online reviews which is in the present scenario. In the field of information retrieval, a various kinds of probabilistic topic modeling techniques have been used to analyze contents present in a document. A topic model is a generative technique for document.
Simultaneous Non-Myopic Optimization of UAV Guidance and Camera Gimbal Control for Target Tracking

Inexpensive lightweight gimbal-mounted cameras have become a viable option for replacing fixed camera systems mounted to a small UAVs for target tracking applications. The gimballed camera adds degrees of freedom, expanding the limited field of view of the camera. This is advantageous but the additional degrees of freedom equate to augmenting the control space to include the gimbal controls. While the addition of gimbal controls is not an insurmountable obstacle, the slew rate of the gimbal axis motors is often limited due to hardware performance capabilities or system definitions. This imposes constraints on the controls. Target-tracking scenarios often include sparsely distributed targets in which long-term tracking performance greatly benefits from non-myopic control. Under these considerations, the gimbal control constraints affect the reachable set of camera-aiming angles, in turn directly impacting the UAV guidance. To maximize the benefits provided by the gimbal, we formulate the problem of simultaneous UAV and camera gimbal control as a partially observable Markov decision process (POMDP), applying a Q-value approximation technique called nominal belief-state optimization (NBO). We combine a receding-horizon objective function with a heuristic expected cost-to-go (HECTG) to approximate target-tracking performance over an extended horizon at a reduced computational cost.
Estimation of IMU and MARG orientation using a gradient descent algorithm

This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.
Using predictive prefetching to improve World Wide Web latency

The long-term success of the World Wide Web depends on fast response time. People use the Web to access information from remote sites, but do not like to wait long for their results. The latency of retrieving a Web document depends on several factors such as the network bandwidth, propagation time and the speed of the server and client computers. Although several proposals have been made for reducing this latency, it is difficult to push it to the point where it becomes insignificant.This motivates our work, where we investigate a scheme for reducing the latency perceived by users by predicting and prefetching files that are likely to be requested soon, while the user is browsing through the currently displayed page. In our scheme the server, which gets to see requests from several clients, makes predictions while individual clients initiate prefetching. We evaluate our scheme based on trace-driven simulations of prefetching over both high-bandwidth and low-bandwidth links. Our results indicate that prefetching is quite beneficial in both cases, resulting in a significant reduction in the average access time at the cost of an increase in network traffic by a similar fraction. We expect prefetching to be particularly profitable over non-shared (dialup) links and high-bandwidth, high-latency (satellite) links.
Enterprise Data Analysis and Visualization: An Interview Study

Organizations rely on data analysts to model customer engagement, streamline operations, improve production, inform business decisions, and combat fraud. Though numerous analysis and visualization tools have been built to improve the scale and efficiency at which analysts can work, there has been little research on how analysis takes place within the social and organizational context of companies. To better understand the enterprise analysts' ecosystem, we conducted semi-structured interviews with 35 data analysts from 25 organizations across a variety of sectors, including healthcare, retail, marketing and finance. Based on our interview data, we characterize the process of industrial data analysis and document how organizational features of an enterprise impact it. We describe recurring pain points, outstanding challenges, and barriers to adoption for visual analytic tools. Finally, we discuss design implications and opportunities for visual analysis research.