nils-herrmann/clean_scirepeval huggingface dataset:
NeuroLinear: From neural networks to oblique decision rules

We present NeuroLinear, a system for extracting oblique decision rules from neural networks that have been trained for classiication of patterns. Each condition of an oblique decision rule corresponds to a partition of the attribute space by a hyperplane that is not necessarily axis-parallel. Allowing a set of such hyperplanes to form the boundaries of the decision regions leads to a signiicant reduction in the number of rules generated while maintaining the accuracy rates of the networks. We describe the components of NeuroLinear in detail by way of two examples using artiicial datasets. Our experimental results on real-world datasets show that the system is eeective in extracting compact and comprehensible rules with high predictive accuracy from neural networks.
Screenmilker: How to Milk Your Android Screen for Secrets

With the rapid increase in Android device popularity, the capabilities that the diverse user base demands from Android have significantly exceeded its original design. As a result, people have to seek ways to obtain the permissions not directly offered to ordinary users. A typical way to do that is using the Android Debug Bridge (ADB), a developer tool that has been granted permissions to use critical system resources. Apps adopting this solution have combined tens of millions of downloads on Google Play. However, we found that such ADB-level capabilities are not well guarded by Android. A prominent example we investigated is the apps that perform programmatic screenshots, a much-needed capability Android fails to support. We found that all such apps in the market inadvertently expose this ADB capability to any party with the INTERNET permission on the same device. With this exposure, a malicious app can be built to stealthily and intelligently collect sensitive user data through screenshots. To understand the threat, we built Screenmilker, an app that can detect the right moment to monitor the screen and pick up a user’s password when she is typing in real time. We show that this can be done efficiently by leveraging the unique design of smartphone user interfaces and its public resources. Such an understanding also informs Android developers how to protect this screenshot capability, should they consider providing an interface to let third-party developers use it in the future, and more generally the security risks of the ADB workaround, a standard technique gaining popularity in app development. Based on the understanding, we present a mitigation mechanism that controls the exposure of the ADB capabilities only to authorized apps.
A smart watch-based gesture recognition system for assisting people with visual impairments

Modern mobile devices provide several functionalities and new ones are being added at a breakneck pace. Unfortunately browsing the menu and accessing the functions of a mobile phone is not a trivial task for visual impaired users. Low vision people typically rely on screen readers and voice commands. However, depending on the situations, screen readers are not ideal because blind people may need their hearing for safety, and automatic recognition of voice commands is challenging in noisy environments. Novel smart watches technologies provides an interesting opportunity to design new forms of user interaction with mobile phones. We present our first works towards the realization of a system, based on the combination of a mobile phone and a smart watch for gesture control, for assisting low vision people during daily life activities. More specifically we propose a novel approach for gesture recognition which is based on global alignment kernels and is shown to be effective in the challenging scenario of user independent recognition. This method is used to build a gesture-based user interaction module and is embedded into a system targeted to visually impaired which will also integrate several other modules. We present two of them: one for identifying wet floor signs, the other for automatic recognition of predefined logos.
A Review of Class Imbalance Problem

Class imbalance is one of the challenges of machine learning and data mining fields. Imbalance data sets degrades the performance of data mining and machine learning techniques as the overall accuracy and decision making be biased to the majority class, which lead to misclassifying the minority class samples or furthermore treated them as noise. This paper proposes a general survey for class imbalance problem solutions and the most significant investigations recently introduced by researchers.
Authoring Data-Driven Videos with DataClips

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven “clips” together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.
A Framework for the Analysis of Unevenly Spaced Time Series Data

This paper presents methods for analyzing and manipulating unevenly spaced time series without a transformation to equally spaced data. Processing and analyzing such data in its unaltered form avoids the biases and information loss caused by resampling. Care is taken to develop a framework consistent with a traditional analysis of equally spaced data, as in Brockwell and Davis (1991), Hamilton (1994) and Box, Jenkins, and Reinsel (2004).
Developmental mechanisms channeling cortical evolution

Increase in the area and neuron number of the cerebral cortex over evolutionary time systematically changes its computational properties. One of the fundamental developmental mechanisms generating the cortex is a conserved rostrocaudal gradient in duration of neuron production, coupled with distinct asymmetries in the patterns of axon extension and synaptogenesis on the same axis. A small set of conserved sensorimotor areas with well-defined thalamic input anchors the rostrocaudal axis. These core mechanisms organize the cortex into two contrasting topographic zones, while systematically amplifying hierarchical organization on the rostrocaudal axis in larger brains. Recent work has shown that variation in 'cognitive control' in multiple species correlates best with absolute brain size, and this may be the behavioral outcome of this progressive organizational change.
Mobile App Usability Index (MAUI) for improving mobile banking adoption

India has 790+ million active mobile connections and 80.57 million smartphone users. However, as per Reserve Bank of India, the number of transactions performed using smartphone based mobile banking applicationsis less than 12% of the overall banking transactions. One of the major reasons for such low numbers is the usability of the mobile banking app. In this paper, we focus on usability issues related tomobile banking apps and propose a Mobile App Usability Index (MAUI) for enhancing the usability of a mobile banking app. The proposed Index has been validatedwith mobile banking channel managers, chief information security officers, etc.
A Survey on Multi-Task Learning

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL. First, we classify different MTL algorithms into several categories: feature learning approach, low-rank approach, task clustering approach, task relation learning approach, dirty approach, multi-level approach and deep learning approach. In order to compare different approaches, we discuss the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, batch MTL models are difficult to handle this situation and online, parallel and distributed MTL models as well as feature hashing are reviewed to reveal the computational and storage advantages. Many real-world applications use MTL to boost their performance and we introduce some representative works. Finally, we present theoretical analyses and discuss several future directions for MTL.
Induction Heating Appliances: Toward More Flexible Cooking Surfaces

Efficient energy management in residential areas is a key issue in modern energy systems. In this scenario, induction heating (IH) becomes an alternative to classical heating technologies because of its advantages such as efficiency, quickness, safety, and accurate power control. In this article, the design of modern flexible cooking surfaces featuring IH technology is presented. The main advantages and technical challenges are given, and the design of the inductor system and the power electronic converter is detailed. The feasibility of the proposed system is verified through a laboratory prototype.
From machine learning to machine reasoning An essay

A plausible definition of “reasoning” could be “algebraically manipulating previously acquired knowledge in order to answer a new question”. This definition covers firstorder logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labelled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated “all-purpose” inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.
Undergraduates' attitudes to text messaging language use and intrusions of textisms into formal writing

Students’ increasing use of text messaging language has prompted concern that textisms (e.g., 2 for to, dont for don’t, ☺) will intrude into their formal written work. Eighty-six Australian and 150 Canadian undergraduates were asked to rate the appropriateness of textism use in various situations. Students distinguished between the appropriateness of using textisms in different writing modalities and to different recipients, rating textism use as inappropriate in formal exams and assignments, but appropriate in text messages, online chat and emails with friends and siblings. In a second study, we checked the examination papers of a separate sample of 153 Australian undergraduates for the presence of textisms. Only a negligible number were found. We conclude that, overall, university students recognise the different requirements of different recipients and modalities when considering textism use and that students are able to avoid textism use in exams despite media reports to the contrary.
Short text classification by detecting information path

Short text is becoming ubiquitous in many modern information systems. Due to the shortness and sparseness of short texts, there are less informative word co-occurrences among them, which naturally pose great difficulty for classification tasks on such data. To overcome this difficulty, this paper proposes a new way for effectively classifying the short texts. Our method is based on a key observation that there usually exists ordered subsets in short texts, which is termed ``information path'' in this work, and classification on each subset based on the classification results of some pervious subsets can yield higher overall accuracy than classifying the entire data set directly. We propose a method to detect the information path and employ it in short text classification. Different from the state-of-art methods, our method does not require any external knowledge or corpus that usually need careful fine-tuning, which makes our method easier and more robust on different data sets. Experiments on two real world data sets show the effectiveness of the proposed method and its superiority over the existing methods.
Concurrency and Privacy with Payment-Channel Networks

Permissionless blockchains protocols such as Bitcoin are inherently limited in transaction throughput and latency. Current efforts to address this key issue focus on off-chain payment channels that can be combined in a Payment-Channel Network (PCN) to enable an unlimited number of payments without requiring to access the blockchain other than to register the initial and final capacity of each channel. While this approach paves the way for low latency and high throughput of payments, its deployment in practice raises several privacy concerns as well as technical challenges related to the inherently concurrent nature of payments that have not been sufficiently studied so far. In this work, we lay the foundations for privacy and concurrency in PCNs, presenting a formal definition in the Universal Composability framework as well as practical and provably secure solutions. In particular, we present Fulgor and Rayo. Fulgor is the first payment protocol for PCNs that provides provable privacy guarantees for PCNs and is fully compatible with the Bitcoin scripting system. However, Fulgor is a blocking protocol and therefore prone to deadlocks of concurrent payments as in currently available PCNs. Instead, Rayo is the first protocol for PCNs that enforces non-blocking progress (i.e., at least one of the concurrent payments terminates). We show through a new impossibility result that non-blocking progress necessarily comes at the cost of weaker privacy. At the core of Fulgor and Rayo is Multi-Hop HTLC, a new smart contract, compatible with the Bitcoin scripting system, that provides conditional payments while reducing running time and communication overhead with respect to previous approaches. Our performance evaluation of Fulgor and Rayo shows that a payment with 10 intermediate users takes as few as 5 seconds, thereby demonstrating their feasibility to be deployed in practice.
A Detailed Rubric for Motion Segmentation

Motion segmentation is currently an active area of research in computer Vision. The task of comparing different methods of motion segmentation is complicated by the fact that researchers may use subtly different definitions of the problem. Questions such as ”Which objects are moving?”, ”, and ”How can we use motion of the camera to segment objects, whether they are static or moving?” are clearly related to each other, but lead to different algorithms, and imply different versions of the ground truth. This report has two goals. The first is to offer a precise definition of motion segmentation so that the intent of an algorithm is as welldefined as possible. The second is to report on new versions of three previously existing data sets that are compatible with this definition. We hope that this more detailed definition, and the three data sets that go with it, will allow more meaningful comparisons of certain motion segmentation methods.
WUW - wear Ur world: a wearable gestural interface

Information is traditionally confined to paper or digitally to a screen. In this paper, we introduce WUW, a wearable gestural interface, which attempts to bring information out into the tangible world. By using a tiny projector and a camera mounted on a hat or coupled in a pendant like wearable device, WUW sees what the user sees and visually augments surfaces or physical objects the user is interacting with. WUW projects information onto surfaces, walls, and physical objects around us, and lets the user interact with the projected information through natural hand gestures, arm movements or interaction with the object itself.
Modular Decomposition Method Based on Design Structure Matrix and Application

Modular decomposition is an effective means to achieve a complex system, but that of current part-component-based does not meet the needs of the positive development of the production.  Structure Matrix (DSM) can simultaneously reflect the sequence, iteration, and feedback information, and express the parallel, sequential, and coupled relationship between DSM elements. This article, a modular decomposition method, named Design Structure Matrix Clustering modularize method, is proposed, concerned procedures are define, based on sorting calculate and clustering analysis of DSM, according to the rules of rows exchanges and columns exchange with the same serial number. The purpose and effectiveness of DSM clustering modularize method are confirmed through case study of assembly and calibration system for the large equipment.
Cross-domain Effects of Music and Language Experience on the Representation of Pitch in the Human Auditory Brainstem

Neural encoding of pitch in the auditory brainstem is known to be shaped by long-term experience with language or music, implying that early sensory processing is subject to experience-dependent neural plasticity. In language, pitch patterns consist of sequences of continuous, curvilinear contours; in music, pitch patterns consist of relatively discrete, stair-stepped sequences of notes. The primary aim was to determine the influence of domain-specific experience (language vs. music) on the encoding of pitch in the brainstem. Frequency-following responses were recorded from the brainstem in native Chinese, English amateur musicians, and English nonmusicians in response to iterated rippled noise homologues of a musical pitch interval (major third; M3) and a lexical tone (Mandarin tone 2; T2) from the music and language domains, respectively. Pitch-tracking accuracy (whole contour) and pitch strength (50 msec sections) were computed from the brainstem responses using autocorrelation algorithms. Pitch-tracking accuracy was higher in the Chinese and musicians than in the nonmusicians across domains. Pitch strength was more robust across sections in musicians than in nonmusicians regardless of domain. In contrast, the Chinese showed larger pitch strength, relative to nonmusicians, only in those sections of T2 with rapid changes in pitch. Interestingly, musicians exhibited greater pitch strength than the Chinese in one section of M3, corresponding to the onset of the second musical note, and two sections within T2, corresponding to a note along the diatonic musical scale. We infer that experience-dependent plasticity of brainstem responses is shaped by the relative saliency of acoustic dimensions underlying the pitch patterns associated with a particular domain.
A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.
A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces

Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.
3D shape-based face recognition using automatically registered facial surfaces

We address the use of three-dimensional facial shape information for human face identification. We propose a new method to represent faces as 3D registered point clouds. Fine registration of facial surfaces is done by first automatically finding important facial landmarks and then, establishing a dense correspondence between points on the facial surface with the help of a 3D face template-aided thin plate spline algorithm. After the registration of facial surfaces, similarity between two faces is defined as a discrete approximation of the volume difference between facial surfaces. Experiments done on the 3D RMA dataset show that the proposed algorithm performs as good as the point signature method, and it is statistically superior to the point distribution model-based method and the 2D depth imagery technique. In terms of computational complexity, the proposed algorithm is faster than the point signature method.
Indoor Human Navigation Systems: A Survey

Whereas outdoor navigation systems typically rely upon GPS, indoor systems have to rely upon different techniques for localizing the user, as GPS signals cannot be received indoors. Over the past decade various indoor navigation systems have been developed. This paper provides a comprehensive overview of existing indoor navigation systems and analyzes the different techniques used for: (1) locating the user; (2) planning a path; (3) representing the environment; and (4) interacting with the user. Our survey identifies a number of research issues that could facilitate large scale deployment of indoor navigation systems.
A scoping study of electric and magnetic field energy harvesting for wireless sensor networks in power system applications

This paper explores the existing energy harvesting technologies, their stage of maturity and their feasibility for powering sensor nodes. It contains a study of the energy requirements of the sensor nodes that are a part of the commercial domain. Further, it investigates methods and concepts for harvesting the energy from electric and magnetic fields present near utility assets through laboratory experimentation. The flux concentrator based approach that scavenges the magnetic field was considered to be the most promising solution providing nearly 250mW of power sufficient to power a sensor node.