nils-herrmann/clean_scirepeval huggingface dataset:
Insights into layout patterns of mobile user interfaces by an automatic analysis of android apps

Mobile phones recently evolved into smartphones that provide a wide range of services. One aspect that differentiates smartphones from their predecessor is the app model. Users can easily install third party applications from central mobile application stores. In this paper we present a process to gain insights into mobile user interfaces on a large scale. Using the developed process we automatically disassemble and analyze the 400 most popular free Android applications. The results suggest that the complexity of the user interface differs between application categories. Further, we analyze interface layouts to determine the most frequent interface elements and identify combinations of interface widgets. The most common combination that consists of three nested elements covers 5.43% of all interface elements. It is more frequent than progress bars and checkboxes. The ten most frequent patterns together cover 21.13% of all interface elements. They are all more frequent than common widget including radio buttons and spinner. We argue that the combinations identified not only provide insights about current mobile interfaces, but also enable the development of new optimized widgets.
Hierarchical data visualization in desktop virtual reality

While desktop virtual reality (VR) offers a way to visualize structure in large information sets, there have been relatively few empirical investigations of visualization designs in this domain. This thesis reports the development and testing of a series of prototype desktop vR worlds, which were designed to support navigation during information visudization and retrievd. Four rnethods were used for data collection: search task scoring, subjective questionnaires, navigationai activity logging ruid analysis, and administration of tests for spatid and structure-learning ability. The combination of these research methods revealed significant effects of user abilities, information environment designs, and task learning. The first of four studies compared three versions of a stmctured virtuai landscape, finding significant differences in sense of presence, ease of use, and overall enjoyment; there was, however, no significant difference in performance among the three landscape versions. The second study found a hypertext interface to be superior to a VR interface for task performance, ease of use, and rated efficiency; nevertheless, the VR interface was rated as more enjoyable. The third study used a new layout aigorithrn; the resulting prototype was rated as easier to use and more efficient than the previous VR version. In the fourth study, a zoomable, rnap-like view of the newest VR prototype was developed. Experimental participants found the map-view superior to the 3D-view for task performance and rated efficiency. Overall, this research did not find a performance advantage for using 3D versions of VR. In addition, the results of the fourth study found that people in the lowest quartile of spatial ability had significantly lower search performance (relative to the highest three quartiles) in a VR world. This finding suggests that individual differences for traits such as spatial ability may be important in detennining the usability and acceptability of VR environments. In addition to the experimental results summarized above, this thesis dso developed and refined a methodology for investigating tasks, users, and software in 3D environments. This methodology included tests for spatial and structure-learning abilities, as well as logging and analysis of a user's navigational activi-
A Novel Transfer Learning Method Based on Common Space Mapping and Weighted Domain Matching

In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the common space, the target domain data points are weighted and matched to the target domain in term of distributions. The weighting terms of source domain data points and the target domain classification responses are also regularized by the local reconstruction coefficients. The novel transfer learning framework is evaluated over some benchmark cross-domain data sets, and it outperforms the existing state-of-the-art transfer learning methods.
Information extraction on novel text using machine learning and rule-based system

Novel consists of around 30,000 to 50,000 words in total. It usually tells a story about entities and its relation one another such as, Person, Location or Organization. In order to apprehend those information, reading the whole novel is compulsory. However, it is a time-consuming task. This research proposes a solution — automatic extraction of entity relation by means of Information Extraction (IE) technique. This technique is divided into two steps. First, all the entities are retrieved from the text input, by using Named Entity Recognition (NER). Afterward, all relations is extracted by Relation Extraction (RE) process. This research implements an IE system to both NER and RE, which employs supervised machine learning approach combined with rule-based system. The main purpose of this research is to determine which features and algorithm of the machine learning are adequate to acquire the best result, and which rules are the most suitable for novel characteristics.
An Extensible Framework for Verification of Numerical Claims

In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge. We focused on simple numerical claims such as “population of Germany in 2015 was 80 million” which comprised a quarter of the test instances in the challenge, achieving 68% accuracy. Our system extends previous work on semantic parsing and claim identification to handle temporal expressions and knowledge bases consisting of multiple tables, while relying solely on automatically generated training data. We demonstrate the extensible nature of our system by evaluating it on relations used in previous work. We make our system publicly available so that it can be used and extended by the
Personal space in virtual reality

Improving the sense of “presence” is a common goal of three-dimensional (3D) display technology for film, television, and virtual reality. However, there are instances in which 3D presentations may elicit unanticipated negative responses. For example, it is well established that violations of interpersonal space cause discomfort in real-world situations. Here we ask if people respond similarly when viewing life-sized stereoscopic images. Observers rated their level of comfort in response to animate and inanimate objects in live and virtual (stereoscopic projection) viewing conditions. Electrodermal activity was also recorded to monitor their physiological response to these stimuli. Observers exhibited significant negative reactions to violations of interpersonal space in stereoscopic 3D displays, which were equivalent to those experienced in the natural environment. These data have important implications for the creation of 3D media and the use of virtual reality systems.
FPGA-Based Spectrum Analyzer with High Area Efficiency by Goertzel Algorithm

FFT algorithm is the popular software design for spectrum analyzer, but doesnpsilat work well for parallel hardware system due to complex calculation and huge memory requirement. Observing the key components of a spectrum analyzer are the intensities for respective frequencies, we propose a Goertzel algorithm to directly extract the intensity factors for respective frequency components in the input signal. Goertzel algorithm dispenses with the memory for z-1 and z-2 processing, and only needs two multipliers and three adders for real number calculation. In this paper, we present the spectrum extraction algorithm and implement a spectrum extractor with high speed and low area consumption in a FPGA (field programmable gate array) chip. It proves the feasibility of implementing a handheld concurrent multi-channel real-time spectrum analysis IP into a low gate counts and low power consumption CPLD (complex programmable logic device) chip.
Intraday stock price forecasting based on variational mode decomposition

This paper presents a hybrid predictive model for forecasting intraday stock prices. The proposed model hybridizes the variational mode decomposition (VMD) which is a new multiresolution technique with backpropagation neural network (BPNN). The VMD is used to decompose price series into a sum of variational modes (VM). The extracted VM are used to train BPNN. Besides, particle swarm optimization (PSO) is employed for BPNN initial weights optimization. Experimental results from a set of six stocks show the superiority of the hybrid VMD–PSO–BPNN predictive model over the baseline predictive model eywords: ariational mode decomposition rtificial neural networks article swarm optimization ntraday stock price which is a PSO–BPNN model trained with past prices.
Stress and Plasticity in the Limbic System

The adult nervous system is not static, but instead can change, can be reshaped by experience. Such plasticity has been demonstrated from the most reductive to the most integrated levels, and understanding the bases of this plasticity is a major challenge. It is apparent that stress can alter plasticity in the nervous system, particularly in the limbic system. This paper reviews that subject, concentrating on: a) the ability of severe and/or prolonged stress to impair hippocampal-dependent explicit learning and the plasticity that underlies it; b) the ability of mild and transient stress to facilitate such plasticity; c) the ability of a range of stressors to enhance implicit fear conditioning, and to enhance the amygdaloid plasticity that underlies it.
Feature selection and feature extraction for text categorization

The eeect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets. Good categorization performance was achieved using a statistical classiier and a proportional assignment strategy. The optimal feature set size for word-based indexing was found to be surprisingly low (10 to 15 features) despite the large training sets. The extraction of new text features by syntactic analysis and feature clustering was investigated on the Reuters data set. Syntactic indexing phrases, clusters of these phrases, and clusters of words were all found to provide less eeective representations than individual words.
Authentication of Quantum Messages

Authentication is a well-studied area of classical cryptography: a sender and a receiver sharing a classical private key want to exchange a classical message with the guarantee that the message has not been modified or replaced by a dishonest party with control of the communication line. In this paper we study the authentication of messages composed of quantum states. We give a formal definition of authentication in the quantum setting. Assuming and have access to an insecure quantum channel and share a private, classical random key, we provide a non-interactive scheme that both enables to encrypt and authenticate (with unconditional security) an qubit message by encoding it into qubits, where the probability decreases exponentially in the security parameter . The scheme requires a private key of size . To achieve this, we give a highly efficient protocol for testing the purity of shared EPR pairs. It has long been known that learning information about a general quantum state will necessarily disturb it. We refine this result to show that such a disturbance can be done with few side effects, allowing it to circumvent cryptographic protections. Consequently, any scheme to authenticate quantum messages must also encrypt them. In contrast, no such constraint exists classically: authentication and encryption are independent tasks, and one can authenticate a message while leaving it publicly readable. This reasoning has two important consequences: On one hand, it allows us to give a lower bound of key bits for authenticating qubits, which makes our protocol asymptotically optimal. On the other hand, we use it to show that digitally signing quantum states is impossible, even with only computational security.
ADAM: A Testbed for Exploring the Use of Data Mining in Intrusion Detection

Intrusion detection systems have traditionally been based on the characterization of an attack and the tracking of the activity on the system to see if it matches that characterization. Recently, new intrusion detection systems based on data mining are making their appearance in the field. This paper describes the design and experiences with the ADAM (Audit Data Analysis and Mining) system, which we use as a testbed to study how useful data mining techniques can be in intrusion detection.
Educational mismatches versus skill mismatches : effects on wages , job satisfaction , and onthe-job search By

Education-job mismatches are reported to have serious effects on wages and other labour market outcomes. Such results are often cited in support of assignment theory, but can also be explained by institutional and human capital models. To test the assignment explanation, we examine the relation between educational mismatches and skill mismatches. In line with earlier research, educational mismatches affect wages strongly. Contrary to the assumptions of assignment theory, this effect is not explained by skill mismatches. Conversely, skill mismatches are much better predictors of job satisfaction and on-the-job search than are educational mismatches.
Performance Study of Class-E Power Amplifier With a Shunt Inductor at Subnominal Condition

This paper presents analytical expressions for the class-E power amplifier with a shunt inductor for satisfying the subnominal condition and 50% duty ratio. The subnominal condition means that only the zero-current switching condition (ZCS) is achieved, though the nominal conditions mean that both the ZCS and zero-current derivative switching (ZCDS) are satisfied. The design values for achieving the subnominal condition are expressed as a function of the phase shift between the input and output voltages. The class-E amplifier with subnominal condition increases one design degree of freedom compared with that with the nominal conditions. Because of the increase in the design degree of freedom, one more relationship can be specified as a design specification. It is shown analytically that the dc-supply voltage and the current are always proportional to the amplitude of the output voltage and the current as a function of the phase shift. Additionally, the output power capability is affected by the phase shift, and the peak switch voltage has influence on the phase shift as well. This paper gives a circuit design example based on our proposed design expression by specifying the peak switch voltage instead of the ZCDS condition. The measurement and PSpice simulation results agree with the analytical expressions quantitatively, which show the validity of our analytical expressions.
NOYB: privacy in online social networks

Increasingly, Internet users trade privacy for service. Facebook, Google, and others mine personal information to target advertising. This paper presents a preliminary and partial answer to the general question "Can users retain their privacy while still benefiting from these web services?". We propose NOYB, a novel approach that provides privacy while preserving some of the functionality provided by online services. We apply our approach to the Facebook online social networking website. Through a proof-of-concept implementation we demonstrate that NOYB is practical and incrementally deployable, requires no changes to or cooperation from an existing online service, and indeed can be non-trivial for the online service to detect.
Compliance-based dynamic steering for hexapods

This paper proposes a novel dynamic gait of locomotion for hexapedal robots which enables them to crawl forward, backward, and rotate using a single actuator. The gait exploits the compliance difference between the two sides of the tripods, to generate clockwise or counter clockwise rotation by controlling the acceleration of the robot. The direction of turning depends on the configuration of the legs -tripod left of right- and the direction of the acceleration. Alternating acceleration in successive steps allows for continuous rotation in the desired direction. An analysis of the locomotion is presented as a function of the mechanical properties of the robot and the contact with the surface. A numerical simulation was performed for various conditions of locomotion. The results of the simulation and analysis were compared and found to be in excellent match.
Performance evaluation for Modbus/TCP using Network Simulator NS3

Modbus protocol is a de-facto standard protocol in industrial automation. As the size and complexity of industry systems increase rapidly, the importance of real-time communication protocols arises as well. In this paper, we analyze the performance of the Modbus/TCP communication protocol which is implemented using the Network Simulator version 3 (NS-3). The performance evaluation focuses on the response time depending on the number of nodes and topology.
the Bolivian Rainforest First Impressions From Faces Among U . S . and Culturally Isolated Tsimane ' People in

The authors examined the generalizability of first impressions from faces previously documented in industrialized cultures to the Tsimane’ people in the remote Bolivian rainforest. Tsimane’ as well as U.S. judges showed within-culture agreement in impressions of attractiveness, babyfaceness, and traits (healthy, intelligent/knowledgeable, dominant/respected, and sociable/warm) of own-culture faces. Both groups also showed within-culture agreement for impressions of otherculture faces, although it was weaker than for own-culture faces, particularly among Tsimane’ judges. Moreover, there was between-culture agreement, particularly for Tsimane’ faces. Use of facial attractiveness to judge traits contributed to agreement within and between cultures but did not fully explain it. Furthermore, Tsimane’, like U.S., judges showed a strong attractiveness halo in impressions of faces from both cultures as well as the babyface stereotype, albeit more weakly. In addition to cross-cultural similarities in trait impressions from faces, supporting a universal mechanism, some effects were moderated by perceiver and face culture, consistent with perceiver attunements conditioned by culturally specific perceptual learning.
Incremental training of support vector machines

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.
Cardinal Virtues: Extracting Relation Cardinalities from Text

Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.
Counting Crowded Moving Objects

In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.
Expokit: A Software Package for Computing Matrix Exponentials

Expokit provides a set of routines aimed at computing matrix exponentials. More precisely, it computes either a small matrix exponential in full, the action of a large sparse matrix exponential on an operand vector, or the solution of a system of linear OBEs with constant inhomogeneity. The backbone of the sparse routines consists of matrix-free Krylov subspace projection methods (Arnoldi and Lanczos processes), and that is why the toolkit is capable of coping with sparse matrices of large dimension. The software handles real and complex matrices and provides specific routines for symmetric and Hermitian matrices. The computation of matrix exponentials is a numerical issue of critical importance in the area of Markov chains and furthermore, the computed solution is subject to probabilistic constraints. In addition to addressing general matrix exponentials, a distinct attention is assigned to the computation of transient states of Markov chains.
Exact Recovery of Hard Thresholding Pursuit

The Hard Thresholding Pursuit (HTP) is a class of truncated gradient descent methods for finding sparse solutions of l0-constrained loss minimization problems. The HTP-style methods have been shown to have strong approximation guarantee and impressive numerical performance in high dimensional statistical learning applications. However, the current theoretical treatment of these methods has traditionally been restricted to the analysis of parameter estimation consistency. It remains an open problem to analyze the support recovery performance (a.k.a., sparsistency) of this type of methods for recovering the global minimizer of the original NP-hard problem. In this paper, we bridge this gap by showing, for the first time, that exact recovery of the global sparse minimizer is possible for HTP-style methods under restricted strong condition number bounding conditions. We further show that HTP-style methods are able to recover the support of certain relaxed sparse solutions without assuming bounded restricted strong condition number. Numerical results on simulated data confirms our theoretical predictions.