nils-herrmann/clean_scirepeval huggingface dataset:
Measuring privacy leaks in Online Social Networks

Online Social Networking has gained huge popularity amongst the masses. It is common for the users of Online Social Networks (OSNs) to share information with digital friends but in the bargain they loose privacy. Users are unaware of the privacy risks involved when they share their sensitive information in the network. The users should be aware of their privacy quotient and should know where they stand in the privacy measuring scale. In this paper we have described and calculated the Privacy Quotient i.e a privacy metric to measure the privacy of the user's profile using the naive approach. In the starting of the paper we have given the detailed analysis of the survey that we have carried out to know how well do people understand privacy in online social networks. At last we have proposed a model that will ensure privacy in the unstructured data. It will make use of the Item Response Theory model to measure the privacy leaks in the messages and text that is being posted by the users of the online social networking sites.
Charge-based capacitance measurement for bias-dependent capacitance

In this letter, charge-based capacitance measurement (CBCM) is applied to characterize bias-dependent capacitances in a CMOS transistor. Due to its special advantage of being free from the errors induced by charge injection, the operation of charge-injection-induced-error-free CBCM allows for the extraction of full-range gate capacitance from the accumulation region to the inversion region and the overlap capacitance of MOSFET devices with submicrometer dimensions.
Surface Multi-Purposes Low Power Wireless Electromyography (EMG) system Design

The progress in the field of electronics and technology as well as the processing of signals coupled with advance in the use of computer technology has given the opportunity to record and analyze the bio-electric signals from the human body in real time that requires dealing with many challenges according to the nature of the signal and its frequency. This could be up to 1 kHz, in addition to the need to transfer data from more than one channel at the same time. Moreover, another challenge is a high sensitivity and low noise measurements of the acquired bio-electric signals which may be tens of micro volts in amplitude. For these reasons, a low power wireless Electromyography (EMG) data transfer system is designed in order to meet these challenging demands. In this work, we are able to develop an EMG analogue signal processing hardware, along with computer based supporting software. In the development of the EMG analogue signal processing hardware, many important issues have been addressed. Some of these issues include noise and artifact problems, as well as the bias DC current. The computer based software enables the user to analyze the collected EMG data and plot them on graphs for visual decision making. The work accomplished in this study enables users to use the surface EMG device for recording EMG signals for various purposes in movement analysis in medical diagnosis, rehabilitation sports medicine and ergonomics. Results revealed that the proposed system transmit and receive the signal without any losing in the information of signals.
A Context-aware Natural Language Generation Dataset for Dialogue Systems

We present a novel dataset for natural language generation (NLG) in spoken dialogue systems which includes preceding context (user utterance) along with each system response to be generated, i.e., each pair of source meaning representation and target natural language paraphrase. We expect this to allow an NLG system to adapt (entrain) to the user’s way of speaking, thus creating more natural and potentially more successful responses. The dataset has been collected using crowdsourcing, with several stages to obtain natural user utterances and corresponding relevant, natural, and contextually bound system responses. The dataset is available for download under the Creative Commons 4.0 BY-SA license.
Trajectory Triangulation: 3D Reconstruction of Moving Points from a Monocular Image Sequence

ÐWe consider the problem of reconstructing the 3D coordinates of a moving point seen from a monocular moving camera, i.e., to reconstruct moving objects from line-of-sight measurements only. The task is feasible only when some constraints are placed on the shape of the trajectory of the moving point. We coin the family of such tasks as atrajectory triangulation.o We investigate the solutions for points moving along a straight-line and along conic-section trajectories. We show that if the point is moving along a straight line, then the parameters of the line (and, hence, the 3D position of the point at each time instant) can be uniquely recovered, and by linear methods, from at least five views. For the case of conic-shaped trajectory, we show that generally nine views are sufficient for a unique reconstruction of the moving point and fewer views when the conic is of a known type (like a circle in 3D Euclidean space for which seven views are sufficient). The paradigm of trajectory triangulation, in general, pushes the envelope of processing dynamic scenes forward. Thus static scenes become a particular case of a more general task of reconstructing scenes rich with moving objects (where an object could be a single point). Index TermsÐStructure from motion, multiple-view geometry, dynamic scenes.
Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological Features

This paper presents a novel iris recognition system using 1D log polar Gabor wavelet and Euler numbers. 1D log polar Gabor wavelet is used to extract the textural features, and Euler numbers are used to extract topological features of the iris. The proposed decision strategy uses these features to authenticate an individual’s identity while maintaining a low false rejection rate. The algorithm was tested on CASIA iris image database and found to perform better than existing approaches with an overall accuracy of 99.93%. Iris recognition, textural features, topological features.
Nonmetric Multidimensional Scaling: a Numerical Method

We describe the numerical methods required in our approach to multi-dimensional scaling. The rationale of this approach has appeared previously. 1.  We describe a numerical method for multidimensional scaling. In a companion paper  we describe the rationale for our approach to scaling, which is related to that of Shepard [9]. As the numerical methods required are largely unfamiliar to psychologists, and even have elements of novelty within the field of numerical analysis, it seems worthwhile to describe them. In  we suppose that there are n objects 1, · · · , n, and that we have experimental values 8;; of dissimilarity between them. For a configuration of points x1 , • • • , x .. in t:-dimensional space, with interpoint distances d;; , we defined the stress of the configuration by The stress is intendoo to be a measure of how well the configuration matches the data. More fully, it is supposed that the "true" dissimilarities result from some unknown monotone distortion of the interpoint distances of some "true" configuration, and that the observed dissimilarities differ from the true dissimilarities only because of random fluctuation. The stress is essentially the root-mean-square residual departure from this hypothesis. By definition, the best-fitting configuration in t-dimensional space, for a fixed value of t, is that configuration which minimizes the stress. The primary computational problem is to find that configuration. A secondary computational problem, of independent interest, is to find the values of
Rapid adaptation for deep neural networks through multi-task learning

We propose a novel approach to addressing the adaptation effectiveness issue in parameter adaptation for deep neural network (DNN) based acoustic models for automatic speech recognition by adding one or more small auxiliary output layers modeling broad acoustic units, such as mono-phones or tied-state (often called senone) clusters. In scenarios with a limited amount of available adaptation data, most senones are usually rarely seen or not observed, and consequently the ability to model them in a new condition is often not fully exploited. With the original senone classification task as the primary task, and adding auxiliary mono-phone/senone-cluster classification as the secondary tasks, multi-task learning (MTL) is employed to adapt the DNN parameters. With the proposed MTL adaptation framework, we improve the learning ability of the original DNN structure, then enlarge the coverage of the acoustic space to deal with the unseen senone problem, and thus enhance the discrimination power of the adapted DNN models. Experimental results on the 20,000-word open vocabulary WSJ task demonstrate that the proposed framework consistently outperforms the conventional linear hidden layer adaptation schemes without MTL by providing 3.2% relative word error rate reduction (WERR) with only 1 single adaptation utterance, and 10.7% WERR with 40 adaptation utterances against the un-adapted DNN models.
Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review

Recently, stream data mining applications has drawn vital attention from several research communities. Stream data is continuous form of data which is distinguished by its online nature. Traditionally, machine learning area has been developing learning algorithms that have certain assumptions on underlying distribution of data such as data should have predetermined distribution. Such constraints on the problem domain lead the way for development of smart learning algorithms performance is theoretically verifiable. Real-word situations are different than this restricted model. Applications usually suffers from problems such as unbalanced data distribution. Additionally, data picked from non-stationary environments are also usual in real world applications, resulting in the “concept drift” which is related with data stream examples. These issues have been separately addressed by the researchers, also, it is observed that joint problem of class imbalance and concept drift has got relatively little research. If the final objective of clever machine learning techniques is to be able to address a broad spectrum of real world applications, then the necessity for a universal framework for learning from and tailoring (adapting) to, environment where drift in concepts may occur and unbalanced data distribution is present can be hardly exaggerated. In this paper, we first present an overview of issues that are observed in stream data mining scenarios, followed by a complete review of recent research in dealing with each of the issue.
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages

With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes.
Modeling and Construction of a Furuta Pendulum Prototype

In order to provide a material that can facilitate the modeling and construction of a Furuta pendulum, this paper presents the deduction, step-by-step, of a Furuta pendulum mathematical model by using the Lagrange equations of motion. Later, a mechanical design of the Furuta pendulum is carried out via the software Solid Works and subsequently a prototype is built. Numerical simulations of the Furuta pendulum model are performed via Mat lab-Simulink. Furthermore, the Furuta pendulum prototype built is experimentally tested by using Mat lab-Simulink, Control Desk, and a DS1104 board from dSPACE.
Why students drop out CS1 course?

This study focuses on CS minor students' decisions to drop out from the CS1 course. The high level of drop out percentage has been a problem at Helsinki University of Technology for many years. This course has yearly enrolment of 500-600 students and the drop out percentage has varied from 30-50 percents.Since we did not have clear picture of drop out reasons we conducted a qualitative interview research in which 18 dropouts from the CS1 course were interviewed. The reasons of drop out were categorized and, in addition, each case was investigated individually. This procedure enabled us to both list the reasons and to reveal the cumulative nature of drop out reasons.The results indicate that several reasons affect students' decision to quit the CS1 course. The most frequent reasons were the lack of time and the lack of motivation. However, both of these reasons were in turn affected by factors, such as the perceived difficulty of the course, general difficulties with time managing and planning studies, or the decision to prefer something else. Furthermore, low comfort level and plagiarism played a role in drop out. In addition, drop out reasons cumulated.This study shows that the complexity and large variety of factors involved in students' decision to drop the course. This indicates that simple actions to improve teaching or organization on a CS1 course to reduce drop out may be ineffective. Efficient intervention to the problem apparently requires a combination of many different actions that take into consideration the versatile nature of reasons involved in drop out.
Structural stability and morphogenesis

The author has tried to show, in detail and with precision. just how the global regularities with which biology deals can be envisaged as structures within a many-dimensioned space. He not only has shown how such ideas as chreods, the epigenetic landscape, and switching points, which previously were expressed only in the unsophisticated language of biology, can be formulated more adequately in terms such as vector fields, attractors, catastrophes. and the like; going much further than this. he develops many highly original ideas, both strictly mathematical ones within the field of topology, and applications of these to very many aspects of biology and of other sciences. It would be quite wrong to give the impression that Thorn's book is exclusively devoted to biology, The subjects mentioned in his title, Structural Stability and Morphoge.esis, have a much wider reference: and he relates his topological system of thought to physical and indeed to general philosophical problems. In biology, Thorn not only uses topological modes of thought to provide formal definitions of concepts and a logical framework by which they can be related; he also makes a bold attempt at a direct comparison between topological structures within four-dimensional space-time, such as catastrophe hypersurfaces, and the physical structures found in developing embryos. The basic importance of this book is the introduction, in a massive and thorough way, of topological thinking as a framework for theoretical biology.
Linguistic Analysis of Toxic Behavior in an Online Video Game

In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and warn players who may become toxic and thus insulate potential victims from toxic playing in advance.
Automated Object Recognition in Baggage Screening using Multiple X-ray Views

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. The inspection process, however, is highly complex as hazardous items are very difficult to detect when placed in close packed bags, superimposed by other objects, and/or rotated showing an unrecognizable profile. In this paper, we review certain advances achieved by our research group in this field. Our methodology is based on multiple view analysis, because it can be a powerful tool for examining complex objects in cases in which uncertainty can lead to misinterpretation. In our approach, multiple views (taken from fixed points of view, or using an active vision approach in which the best views are automated selected) are analyzed in the detection of regular objects. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing guns, razor blades, pins, clips and springs in baggage inspection are presented achieving around 90% accuracy. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.
Controlled-Channel Attacks: Deterministic Side Channels for Untrusted Operating Systems

The presence of large numbers of security vulnerabilities in popular feature-rich commodity operating systems has inspired a long line of work on excluding these operating systems from the trusted computing base of applications, while retaining many of their benefits. Legacy applications continue to run on the untrusted operating system, while a small hyper visor or trusted hardware prevents the operating system from accessing the applications' memory. In this paper, we introduce controlled-channel attacks, a new type of side-channel attack that allows an untrusted operating system to extract large amounts of sensitive information from protected applications on systems like Overshadow, Ink Tag or Haven. We implement the attacks on Haven and Ink Tag and demonstrate their power by extracting complete text documents and outlines of JPEG images from widely deployed application libraries. Given these attacks, it is unclear if Over shadow's vision of protecting unmodified legacy applications from legacy operating systems running on off-the-shelf hardware is still tenable.
Fully Homomorphic Encryption with Polylog Overhead

We show that homomorphic evaluation of (wide enough) arithmetic circuits can be accomplished with only polylogarithmic overhead. Namely, we present a construction of fully homomorphic encryption (FHE) schemes that for security parameter λ can evaluate any width-Ω(λ) circuit with t gates in time t · polylog(λ). To get low overhead, we use the recent batch homomorphic evaluation techniques of Smart-Vercauteren and Brakerski-Gentry-Vaikuntanathan, who showed that homomorphic operations can be applied to “packed” ciphertexts that encrypt vectors of plaintext elements. In this work, we introduce permuting/routing techniques to move plaintext elements across these vectors efficiently. Hence, we are able to implement general arithmetic circuit in a batched fashion without ever needing to “unpack” the
Personal tracking as lived informatics

This paper characterises the use of activity trackers as "lived informatics". This characterisation is contrasted with other discussions of personal informatics and the quantified self. The paper reports an interview study with activity tracker users. The study found: people do not logically organise, but interweave various activity trackers, sometimes with ostensibly the same functionality; that tracking is often social and collaborative rather than personal; that there are different styles of tracking, including goal driven tracking and documentary tracking; and that tracking information is often used and interpreted with reference to daily or short term goals and decision making. We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people's outlook on their future.
Optimization of a spherical mechanism for a minimally invasive surgical robot: theoretical and experimental approaches

With a focus on design methodology for developing a compact and lightweight minimally invasive surgery (MIS) robot manipulator, the goal of this study is progress toward a next-generation surgical robot system that will help surgeons deliver healthcare more effectively. Based on an extensive database of in-vivo surgical measurements, the workspace requirements were clearly defined. The pivot point constraint in MIS makes the spherical manipulator a natural candidate. An experimental evaluation process helped to more clearly understand the application and limitations of the spherical mechanism as an MIS robot manipulator. The best configuration consists of two serial manipulators in order to avoid collision problems. A complete kinematic analysis and optimization incorporating the requirements for MIS was performed to find the optimal link lengths of the manipulator. The results show that for the serial spherical 2-link manipulator used to guide the surgical tool, the optimal link lengths (angles) are (60/spl deg/, 50/spl deg/). A prototype 6-DOF surgical robot has been developed and will be the subject of further study.
Trading on Twitter: The Financial Information Content of Emotion in Social Media

We collected data from Twitter posts about firms in the S&P 500 and analyzed their cumulative emotional valence (i.e., whether the posts contained an overall positive or negative emotional sentiment). We compared this to the average daily stock market returns of firms in the S&P 500. Our results show that the cumulative emotional valence (positive or negative) of Twitter tweets about a specific firm was significantly related to that firm's stock returns. The emotional valence of tweets from users with many followers (more than the median) had a stronger impact on same day returns, as emotion was quickly disseminated and incorporated into stock prices. In contrast, the emotional valence of tweets from users with few followers had a stronger impact on future stock returns (10-day returns).