nils-herrmann/clean_scirepeval huggingface dataset:
Perceived Space in the HTC Vive

Underperception of egocentric distance in virtual reality has been a persistent concern for almost 20 years. Modern head-mounted displays (HMDs) appear to have begun to ameliorate underperception. The current study examined several aspects of perceived space in the HTC Vive. Blind-walking distance judgments, verbal distance judgments, and size judgments were measured in two distinct virtual environments (VEs)—a high-quality replica of a real classroom and an empty grass field—as well as the real classroom upon which the classroom VE was modeled. A brief walking interaction was also examined as an intervention for improving anticipated underperception in the VEs. Results from the Vive were compared to existing data using two older HMDs (nVisor SX111 and ST50). Blind-walking judgments were more accurate in the Vive compared to the older displays, and did not differ substantially from the real world nor across VEs. Size judgments were more accurate in the classroom VE than the grass VE and in the Vive compared to the older displays. Verbal judgments were significantly smaller in the classroom VE compared to the real classroom and did not significantly differ across VEs. Blind-walking and size judgments were more accurate after walking interaction, but verbal judgments were unaffected. The results indicate that underperception of distance in the HTC Vive is less than in older displays but has not yet been completely resolved. With more accurate space perception afforded by modern HMDs, alternative methods for improving judgments of perceived space—such as walking interaction—may no longer be necessary.
A 20-V four-quadrant CMOS analog multiplier

A novel technique is presented for performing the analog multiplication in CMOS technology. The circuit handles a wide range of input voltages. The MSO version of Gilbert's six-transistor cell (GSTC) is the basis for this multiplier. A simple source-coupled pair is used to study the MOS GSTC. Then, a technique is introduced for linearizing the source-coupled circuit. This scheme is extended to the MOS GSTC. The voltage ranges are further increased by introducing the folded CMOS GSTC.
Visualizing Association Rules : Introduction to the R-extension Package arulesViz

Association rule mining is a popular data mining method available in R as the extension package arules. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Visualization has a long history of making large data sets better accessible using techniques like selecting and zooming. In this paper we present the R-extension package arulesViz which implements several known and novel visualization techniques to explore association rules. With examples we show how these visualization techniques can be used to analyze a data set.
Spatial Memory Streaming

Prior research indicates that there is much spatial variation in applications' memory access patterns. Modern memory systems, however, use small fixed-size cache blocks and as such cannot exploit the variation. Increasing the block size would not only prohibitively increase pin and interconnect bandwidth demands, but also increase the likelihood of false sharing in shared-memory multiprocessors. In this paper, we show that memory accesses in commercial workloads often exhibit repetitive layouts that span large memory regions (e.g., several kB), and these accesses recur in patterns that are predictable through codebased correlation. We propose Spatial Memory Streaming, a practical on-chip hardware technique that identifies codecorrelated spatial access patterns and streams predicted blocks to the primary cache ahead of demand misses. Using cycle-accurate full-system multiprocessor simulation of commercial and scientific applications, we demonstrate that Spatial Memory Streaming can on average predict 58% of L1 and 65% of off-chip misses, for a mean performance improvement of 37% and at best 307%.
Pokémon Go: Impact on Yelp Restaurant Reviews

Pokémon Go, the popular Augmented Reality based mobile application, launched in July of 2016. The game's meteoric rise in usage since that time has had an impact on not just the mobile gaming industry, but also the physical activity of players, where they travel, where they spend their money, and possibly how they interact with other social media applications. In this paper, we studied the impact of Pokémon Go on Yelp reviews. For restaurants near PokéStops, we found a slight drop in the number of online reviews.
The many faces of data-centric workflow optimization: a survey

Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on data-centric workflows (or workflows for data analytics or data flows), where a key aspect is data passing through and getting manipulated by a sequence of steps. The large volume and variety of data, the complexity of operations performed, and the long time such workflows take to compute give rise to the need for optimization. In general, data-centric workflow optimization is a technology in evolution. This survey focuses on techniques applicable to workflows comprising arbitrary types of data manipulation steps and semantic inter-dependencies between such steps. Further, it serves a twofold purpose: firstly, to present the main dimensions of the relevant optimization problems and the types of optimizations that occur before flow execution and secondly, to provide a concise overview of the existing approaches with a view to highlighting key observations and areas deserving more attention from the community.
Orthogonal parallel MCMC methods for sampling and optimization

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some ”horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.
Political Bots and the Swedish General Election

In this paper we present a study of political bots on Twitter and their influence on the Swedish general election in September 2018. We have not restricted our study to bots that are a software program, instead we are interested in any type of bot-like automated behaviour. This includes a human being manually copying or retweeting content repeatedly in a robot-like way with the aim of influencing the interaction between a user and content or with other users. We examined how bots influence discussions about the Swedish general election on Twitter during a three month period starting half a year before the election. For this purpose we trained a machine learning model to recognize automatic behaviour and studied accounts that tweet about the Swedish election. Our results show, among other things, that bot activity is at a similar level to that found in other studies, and that distinctive clusters, for example among right-wing Twitter accounts, can be identified.
Mining significant graph patterns by leap search

With ever-increasing amounts of graph data from disparate sources, there has been a strong need for exploiting significant graph patterns with user-specified objective functions. Most objective functions are not antimonotonic, which could fail all of frequency-centric graph mining algorithms. In this paper, we give the first comprehensive study on general mining method aiming to find most significant patterns directly. Our new mining framework, called LEAP (Descending Leap Mine), is developed to exploit the correlation between structural similarity and significance similarity in a way that the most significant pattern could be identified quickly by searching dissimilar graph patterns. Two novel concepts, structural leap search and frequency descending mining, are proposed to support leap search in graph pattern space. Our new mining method revealed that the widely adopted branch-and-bound search in data mining literature is indeed not the best, thus sketching a new picture on scalable graph pattern discovery. Empirical results show that LEAP achieves orders of magnitude speedup in comparison with the state-of-the-art method. Furthermore, graph classifiers built on mined patterns outperform the up-to-date graph kernel method in terms of efficiency and accuracy, demonstrating the high promise of such patterns.
Reduced resting-state functional connectivity between amygdala and orbitofrontal cortex in social anxiety disorder

Social anxiety disorder patients suffer from excessive anxious responses in social interaction leading to avoidance behavior and social impairment. Although the amygdala has a central role in perception and processing of threatening cues, little is known about the involved networks and corresponding dysfunctions in social anxiety. Therefore, this study aims to investigate the functional connectivity network of the amygdala in patients with social anxiety disorder and to identify regions that might influence amygdalar reactivity via modulatory pathways. Ten patients with anxiety disorders (social and/or panic) and 27 healthy controls underwent a facial emotion processing task as well as 6-min functional MRI at resting state. Individual voxel-wise functional connectivity maps were calculated using the amygdala as seed region. Group comparisons were done by random-effects analysis in SPM. Patients exhibited an amygdala hyperactivation during the emotional task and decreased functional coupling of the left amygdala with the medial orbitofrontal cortex and the posterior cingulate cortex/precuneus. The strength of this functional connectivity showed a negative association with the severity of state anxiety. In addition, an exploratory analysis revealed further reduced functional connectivity and a marked functional separation between the medial orbitofrontal and anterior cingulate cortices in the patient group. Our results suggest alterations within the amygdalar functional connectivity network in social anxiety disorder. Combined with the amygdalar hyperactivation our findings corroborate the proposed dysfunction of the fronto-amygdalar inhibition in anxiety disorders and indicate a modulatory influence of the anterior and posterior cingulate cortices on threat perception and processing.
Cognitive PHY and MAC layers for dynamic spectrum access and sharing of TV bands

Research in the physical (PHY) and medium access control (MAC) layers for dynamic spectrum access (DSA) and dynamic spectrum sharing (DSS) is still at its infancy. Aspects such as spectrum sensing, coexistence, measurement and spectrum management, network reliability and QoS support in face of the need to avoid causing harmful interference into incumbents, to name a few, are key to the success of future cognitive radio (CR) systems and have received little attention so far. In addition, it is critical to understand the interplay of these various cognitive radio concepts and how they impact the overall network performance. In this paper we address these questions by presenting the design and performance evaluation of a CR-based PHY and MAC for DSA and DSS of vacant television (TV) channels. This air interface described here forms the baseline of the current IEEE 802.22 draft standard, and features a number of key PHY and MAC CR-based components for use by license-exempt devices in the spectrum that is currently allocated primarily to TV services. Through simulations and prototyping, we analyze the performance of this first CR-based wireless network with respect to spectrum sensing, system capacity, QoS support, coexistence, and network reliability.
Understanding ride-on-demand service: Demand and dynamic pricing

Emerging ride-on-demand services (e.g., Uber or Uber-like) are vying to penetrate into the market of traditional taxi service, and they are ubiquitous in the nature, by using smart mobile devices like on-car GPS and mobile phone. These ubiquitous services are also beneficial for the environment by increasing the utilization of cars and improving travel efficiency. Through collaboration with a leading service provider in China, we are able to collect vast amount of accurate data and analyze the nature of the demand and dynamic pricing mechanisms that match the supply with demand. We consider the analysis as an important step towards making the ubiquitous service more efficient and beneficial to the sustainability of future smart cities. We collect datasets of passengers' orders and payment information, and focus on the analysis of demand and dynamic pricing. In demand analysis, we discuss its general characteristics, passenger grouping and demand clustering; in dynamic pricing analysis, we discuss the pattern and determination of dynamic pricing multipliers. Our findings pave the way for future study on system optimization, dynamic pricing and policy considerations.
UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem

In the stochastic multi-armed bandit problem we consider a modification of the UCB algorithm of Auer et al. . For this modified algorithm we give an improved bound on the regret with respect to the optimal reward. While for the original UCB algorithm the regret in Karmed bandits after T trials is bounded by const · K log(T ) ∆ , where ∆ measures the distance between a suboptimal arm and the optimal arm, for the modified UCB algorithm we show an upper bound on the regret of const · K log(T∆ 2) ∆ .
Consideration on successive interference canceller (SIC) receiver at cell-edge users for non-orthogonal multiple access (NOMA) with SU-MIMO

Non-orthogonal multiple access (NOMA) utilizing transmit power difference and advanced successive interference canceller (SIC) receivers has been considered as a candidate radio access technology for future wireless communication systems. Single-user multiple-input multiple-output (SU-MIMO) is one of the key technologies in Long-Term-Evolution (LTE) systems. Thus, it has been proved to combine with NOMA for further system performance improvement. In previous studies, SIC receiver is adopted at cell center users (UEs), while SIC processing is not taken into account at cell-edge UEs, considering the feasibility of UE receiver and control signaling overhead. However, it may put limitation on the performance of NOMA. To further enhance the performance of NOMA with SU-MIMO, advanced receiver can be considered at cell-edge UEs. In this paper, we investigate the impact of applying the SIC receiver at cell-edge UEs in downlink NOMA with SU-MIMO. The detail SIC detection schemes at cell-edge UE will be discussed, including ideal SIC processing and non-ideal SIC processing. The simulation results show that performance gains of NOMA can be improved with SIC processing at cell-edge UEs. It indicates that more advanced receiver can be considered for cell-edge UE to achieve upper-bound performance.
A random graph model of kidney exchanges: efficiency, individual-rationality and incentives

In kidney exchanges, hospitals share patient lists and receive transplantations. A kidney-paired donation (KPD) mechanism needs to promote full sharing of information about donor-patient pairs, and identify a Pareto efficient outcome that also satisfies participation constraints of hospitals. We introduce a random graph model of the KPD exchange and then fully characterize the structure of the efficient outcome and the expected number of transplantations that can be performed. Random graph theory allows early experimental results to be explained analytically, and enables the study of participation incentives in a methodological way. We derive a square-root law between the welfare gains from sharing patient-donor pairs in a central pool and the individual sizes of hospitals, illustrating the urgent need for the nationwide expansion of such programs. Finally, we establish through theoretical and computational analysis that enforcing simple individual rationality constraints on the outcome can mitigate the negative impact of strategic behavior by hospitals.
Are GSM Phones THE Solution for Localization?

In this paper, we argue that localization solution based on cellular phone technology, specifically GSM phones, is a sufficient and attractive option in terms of coverage and accuracy for a wide range of indoor, outdoor, and place-based location-aware applications. We present preliminary results that indicate that GSM-based localization systems have the potential to detect the places that people visit in their everyday lives, and can achieve median localization accuracies of 5 and 75 meters for indoor and outdoor environments, respectively
Fast and Flexible Successive-Cancellation List Decoders for Polar Codes

Polar codes have gained significant amount of attention during the past few years and have been selected as a coding scheme for the next generation of mobile broadband standard. Among decoding schemes, successive-cancellation list (SCL) decoding provides a reasonable tradeoff between the error-correction performance and hardware implementation complexity when used to decode polar codes, at the cost of limited throughput. The simplified SCL (SSCL) and its extension SSCL-SPC increase the speed of decoding by removing redundant calculations when encountering particular information and frozen bit patterns (rate one and single parity check codes), while keeping the error-correction performance unaltered. In this paper, we improve SSCL and SSCL-SPC by proving that the list size imposes a specific number of path splitting required to decode rate one and single parity check codes. Thus, the number of splitting can be limited while guaranteeing exactly the same error-correction performance as if the paths were forked at each bit estimation. We call the new decoding algorithms Fast-SSCL and Fast-SSCL-SPC. Moreover, we show that the number of path forks in a practical application can be tuned to achieve desirable speed, while keeping the error-correction performance almost unchanged. Hardware architectures implementing both algorithms are then described and implemented: It is shown that our design can achieve $\mathbf {1.86}$  Gb/s throughput, higher than the best state-of-the-art decoders.
The Internet of Things: A survey

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.
The doormat effect: when forgiving erodes self-respect and self-concept clarity.

We build on principles from interdependence theory and evolutionary psychology to propose that forgiving bolsters one's self-respect and self-concept clarity if the perpetrator has acted in a manner that signals that the victim will be safe and valued in a continued relationship with the perpetrator but that forgiving diminishes one's self-respect and self-concept clarity if the perpetrator has not. Study 1 employed a longitudinal design to demonstrate that the association of marital forgiveness with trajectories of self-respect over the first 5 years of marriage depends on the spouse's dispositional tendency to indicate that the partner will be safe and valued (i.e., agreeableness). Studies 2 and 3 employed experimental procedures to demonstrate that the effects of forgiveness on self-respect and self-concept clarity depend on the perpetrator's event-specific indication that the victim will be safe and valued (i.e., amends). Study 4 employed a longitudinal design to demonstrate that the association of forgiveness with subsequent self-respect and self-concept clarity similarly depends on the extent to which the perpetrator has made amends. These studies reveal that, under some circumstances, forgiveness negatively impacts the self.
Can Computers Overcome Humans? Consciousness Interaction and its Implications

Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related to future generations of machines and the understanding of the brain. It will discuss to what extent computers might reach human capabilities, and how it would be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have similar limitations than humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.
Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme

Ball and plate system is the extension of traditional ball and beam system. In this paper, a trajectory planning and tracking problem of ball and plate system is put forward to proof-test diverse control schemes. Firstly, we derive the mathematical model of the ball and plate system in detail. Then a hierarchical fuzzy control scheme is proposed to deal with this problem. Our scheme is composed of three levels. The lowest level is a TS type fuzzy tacking controller; the middle level is a fuzzy supervision controller that takes actions in extreme situations; and the top level is a fuzzy planning controller that determines the desired trajectory. In order to optimize the ball’s moving trajectory, the genetic algorithm is introduced to adjust the output membership functions of the fuzzy planning controller. Simulation results have shown that the hierarchical fuzzy control scheme can control the ball from a point to another without hitting the obstacles and in the least time.