nils-herrmann/clean_scirepeval huggingface dataset:
Communicating Algorithmic Process in Online Behavioral Advertising

Advertisers develop algorithms to select the most relevant advertisements for users. However, the opacity of these algorithms, along with their potential for violating user privacy, has decreased user trust and preference in behavioral advertising. To mitigate this, advertisers have started to communicate algorithmic processes in behavioral advertising. However, how revealing parts of the algorithmic process affects users' perceptions towards ads and platforms is still an open question. To investigate this, we exposed 32 users to why an ad is shown to them, what advertising algorithms infer about them, and how advertisers use this information. Users preferred interpretable, non-creepy explanations about why an ad is presented, along with a recognizable link to their identity. We further found that exposing users to their algorithmically-derived attributes led to algorithm disillusionment---users found that advertising algorithms they thought were perfect were far from it. We propose design implications to effectively communicate information about advertising algorithms.
XPPE: cross-platform performance estimation of hardware accelerators using machine learning

The increasing heterogeneity in the applications to be processed ceased ASICs to exist as the most efficient processing platform. Hybrid processing platforms such as CPU+FPGA are emerging as powerful processing platforms to support an efficient processing for a diverse range of applications. Hardware/Software co-design enabled designers to take advantage of these new hybrid platforms such as Zynq. However, dividing an application into two parts that one part runs on CPU and the other part is converted to a hardware accelerator implemented on FPGA, is making the platform selection difficult for the developers as there is a significant variation in the application's performance achieved on different platforms. Developers are required to fully implement the design on each platform to have an estimation of the performance. This process is tedious when the number of available platforms is large. To address such challenge, in this work we propose XPPE, a neural network based cross-platform performance estimation. XPPE utilizes the resource utilization of an application on a specific FPGA to estimate the performance on other FPGAs. The proposed estimation is performed for a wide range of applications and evaluated against a vast set of platforms. Moreover, XPPE enables developers to explore the design space without requiring to fully implement and map the application. Our evaluation results show that the correlation between the estimated speed up using XPPE and actual speedup of applications on a Hybrid platform over an ARM processor is more than 0.98.
Chimera: Collaborative Preemption for Multitasking on a Shared GPU

The demand for multitasking on graphics processing units (GPUs) is constantly increasing as they have become one of the default components on modern computer systems along with traditional processors (CPUs). Preemptive multitasking on CPUs has been primarily supported through context switching. However, the same preemption strategy incurs substantial overhead due to the large context in GPUs. The overhead comes in two dimensions: a preempting kernel suffers from a long preemption latency, and the system throughput is wasted during the switch. Without precise control over the large preemption overhead, multitasking on GPUs has little use for applications with strict latency requirements.
 In this paper, we propose Chimera, a collaborative preemption approach that can precisely control the overhead for multitasking on GPUs. Chimera first introduces streaming multiprocessor (SM) flushing, which can instantly preempt an SM by detecting and exploiting idempotent execution. Chimera utilizes flushing collaboratively with two previously proposed preemption techniques for GPUs, namely context switching and draining to minimize throughput overhead while achieving a required preemption latency. Evaluations show that Chimera violates the deadline for only 0.2% of preemption requests when a 15us preemption latency constraint is used. For multi-programmed workloads, Chimera can improve the average normalized turnaround time by 5.5x, and system throughput by 12.2%.
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

Mobile sensing and computing applications usually require timeseries inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, we show that DeepSense is feasible to implement on smartphones and embedded devices thanks to its moderate energy consumption and low latency.
Fog Computing: A Platform for Internet of Things and Analytics

Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disruptions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.
Incremental View Maintenance over Array Data

Science applications are producing an ever-increasing volume of multi-dimensional data that are mainly processed with distributed array databases. These raw arrays are ``cooked'' into derived data products using complex pipelines that are time-consuming. As a result, derived data products are released infrequently and become stale soon thereafter. In this paper, we introduce materialized array views as a database construct for scientific data products. We model the ``cooking'' process as incremental view maintenance with batch updates and give a three-stage heuristic that finds effective update plans. Moreover, the heuristic repartitions the array and the view continuously based on a window of past updates as a side-effect of view maintenance without overhead. We design an analytical cost model for integrating materialized array views in queries. A thorough experimental evaluation confirms that the proposed techniques are able to incrementally maintain a real astronomical data product in a production environment.
Series Clutch Actuators for safe physical human-robot interaction

This paper presents the design, implementation and control of a device intented to mechanically improve the safety of serial robots interacting with humans. The device consists of an electronically adjustable torque limiter placed in series with each actuator, referred to as a Series Clutch Actuator (SCA). By appropriately adjusting the limit torques according to the robot's configuration, the maximum static force that the robot can apply to its environment at the Tool Centre Point (TCP) can be limited to a prescribed safe level. If a limit torque is exceeded, the SCA slips and an emergency stop is triggered while the inertia located upstream from the SCA in the kinematic chain is mechanically disconnected. A method is presented to determine the optimal limit torques that maximize the isotropically achievable force (which can be applied in all directions without triggering any SCA) while satisfying the safe force limit. An approach to optimize the pose of a redundant robot in order to maximize the isotropically achievable force while preserving a safe maximum force threshold is also proposed. The design and fabrication of a torque limiter using a large number of friction discs is presented. Finally, the mechanisms are implemented into a 4-DOF redundant serial arm and preliminary experimental results are presented.
Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia.

The study of human anxiety disorders has benefited greatly from functional neuroimaging approaches. Individual studies, however, vary greatly in their findings. The authors searched for common and disorder-specific functional neurobiological deficits in several anxiety disorders. The authors also compared these deficits to the neural systems engaged during anticipatory anxiety in healthy subjects.



Functional magnetic resonance imaging and positron emission tomography studies of posttraumatic stress disorder (PTSD), social anxiety disorder, specific phobia, and fear conditioning in healthy individuals were compared by quantitative meta-analysis. Included studies compared negative emotional processing to baseline, neutral, or positive emotion conditions.



Patients with any of the three disorders consistently showed greater activity than matched comparison subjects in the amygdala and insula, structures linked to negative emotional responses. A similar pattern was observed during fear conditioning in healthy subjects. Hyperactivation in the amygdala and insula were, of interest, more frequently observed in social anxiety disorder and specific phobia than in PTSD. By contrast, only patients with PTSD showed hypoactivation in the dorsal and rostral anterior cingulate cortices and the ventromedial prefrontal cortex-structures linked to the experience and regulation of emotion.



This meta-analysis allowed us to synthesize often disparate findings from individual studies and thereby provide neuroimaging evidence for common brain mechanisms in anxiety disorders and normal fear. Effects unique to PTSD furthermore suggested a mechanism for the emotional dysregulation symptoms in PTSD that extend beyond an exaggerated fear response. Therefore, these findings help refine our understanding of anxiety disorders and their interrelationships.
An Experiment of Influences of Facebook Posts in Other Users

People usually present similar behavior of their friends due to the social influence that is produced by group pressure. This factor is also present in online social networks such as Facebook. In this paper, we make a study on social influence produced by the contents published on Facebook. First, we have executed a systematic literature review of the previous works to understand better this field of research. Then, by executing the Asch experiment, we have illustrated how such social influence can change the behavior of users in Facebook. Through this experiment, we could identify how the social influence type called “conformity” is present in online social network platforms and how this influence can make changes in the behavior of people.
Hand rehabilitation based on augmented reality

This paper presents an augmented reality based system for hand rehabilitation. In this system, digital data gloves are used to detect the movements of the patients' hands and collect the physical information from the patients. Using augmented reality technology, a highly controllable environment with tasks of different levels of difficulty is provided to the patients for them to perform the rehabilitation exercises gradually. The targets of the exercises can be adjusted dynamically with respect to the physical conditions and progress of the patients. Multimodal feedbacks are provided to facilitate and encourage the patients during the rehabilitation sessions.
Controlled flight of a biologically inspired, insect-scale robot.

Flies are among the most agile flying creatures on Earth. To mimic this aerial prowess in a similarly sized robot requires tiny, high-efficiency mechanical components that pose miniaturization challenges governed by force-scaling laws, suggesting unconventional solutions for propulsion, actuation, and manufacturing. To this end, we developed high-power-density piezoelectric flight muscles and a manufacturing methodology capable of rapidly prototyping articulated, flexure-based sub-millimeter mechanisms. We built an 80-milligram, insect-scale, flapping-wing robot modeled loosely on the morphology of flies. Using a modular approach to flight control that relies on limited information about the robot's dynamics, we demonstrated tethered but unconstrained stable hovering and basic controlled flight maneuvers. The result validates a sufficient suite of innovations for achieving artificial, insect-like flight.
A Descriptive Content Analysis of Trust-Building Measures in B2B Electronic Marketplaces

Because business-to-business (B2B) electronic marketplaces (e-marketplaces) facilitate transactions between buyers and sellers, they strive to foster a trustworthy trading environment with a variety of trust-building measures. However, little research has been undertaken to explore trust-building measures used in B2B e-marketplaces, or to determine to what extent these measures are applied in B2B e-marketplaces and how they are applied. Based on reviews of the scholarly, trade, and professional literature on trust in electronic commerce, we identified 11 trustbuilding measures used to create trust in B2B e-marketplaces. Zucker’s trust production theory [1986] was applied to understand how these trust-building measures will enhance participants’ trust in buyers and sellers in B2B e-marketplaces or in B2B e-marketplace providers. A descriptive content analysis of 100 B2B e-marketplaces was conducted to survey the current usage of the 11 trust-building measures. Many of the trust-building measures were found to be widely used in the B2B e-marketplaces. However, although they were proven to be effective in building trust-related beliefs in online business environments, several institutional-based trustbuilding measures, such as escrow services, insurance and third-party assurance seals, are not widely used in B2B e-marketplaces.
Mining Stack Exchange : Expertise is Evident From Earliest Interactions

Stack Exchange is a very popular Question & Answer internet community. Users can post questions on a wide variety of topics; other users provide answers, usually within minutes. Participants are not compensated for their services and anyone can freely gain value from the efforts of the users; Stack Exchange is therefore a gift economy. Users, however, do gain reputation points when other users “upvote” their questions and/or answers. Stack Exchange thus functions as a learning community with a strong reputation-seeking element that creates a valuable public good, viz., the Q&A archive. The incentive structure of the community suggests that over time, the quality of the product (viz.., delivered answers) steadily improves, and furthermore, that any individual who durably participates in this community for an extended period also would enjoy an increase in the quality of their output (viz., the answers they provide). We investigate the validity of these widely held beliefs in greater detail, using data downloaded from Stack Exchange. Our analysis indicates that these intuitions are actually not supported by the data; indeed the data suggests that overall answer scores decrease, and that people’s tenure with the community is unrelated to the quality of their answers. Most interestingly, we show that answering skill, i.e. getting high average answer scores, which is different than reputation, is evident from the start and persists during one’s tenure with the community. Conversely, people providing low rated answers are likely to have done so from the start.
Simple task-specific bilingual word embeddings

We introduce a simple wrapper method that uses off-the-shelf word embedding algorithms to learn task-specific bilingual word embeddings. We use a small dictionary of easily-obtainable task-specific word equivalence classes to produce mixed context-target pairs that we use to train off-the-shelf embedding models. Our model has the advantage that it (a) is independent of the choice of embedding algorithm, (b) does not require parallel data, and (c) can be adapted to specific tasks by re-defining the equivalence classes. We show how our method outperforms off-the-shelf bilingual embeddings on the task of unsupervised cross-language partof-speech (POS) tagging, as well as on the task of semi-supervised cross-language super sense (SuS) tagging.
Impact of supply chain integration and selective disintegration on supply chain efficiency and organization performance

An efficient business system integration (BSI) is the key determinant factor for the organization to stay competitive. The goal of this study is to develop and execute a research project to study BSI in the oil & gas industry and how an efficient BSI in supply chain (SCI) can be used to gain advantage. A mixed (qualitative & quantitative) survey method was employed and structural equation modeling (SEM) is applied to test the theoretical framework and hypotheses. Findings reveal that, total integration do not offer optimum performance and selective disintegration (DIS) is necessary for supply chain efficiency (SCE) and organization performance (PER). This study helps to better understand the degree of integration required for optimum supply chain performance and came up with new construct DIS. Specifically, this study investigated the effect of SCI and DIS on SCE and PER. The compelled sample size precludes evaluating and testing of additional broad models of the relationship amongst the constructs. The main managerial lesson is that, in contrast to what has been written in many books and other popular publications, selective disintegration is necessary for optimum supply chain efficiency.
Flexible Learning on the Sphere Via Adaptive Needlet Shrinkage and Selection

This paper introduces an approach for flexible, robust Bayesian modeling of structure in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding for selection, and the horseshoe prior for shrinkage. We study the performance of the proposed methodology both on simulated data and on a real data set involving the cosmic microwave background radiation. Horseshoe shrinkage of needlet coefficients is shown to yield the best overall performance against some common benchmarks.
Teleoperation of humanoid baxter robot using haptic feedback

This paper presents a teleoperation strategy, which is featured by haptic feedback. The teleoperation system is composed of a SensAble® Omni haptic device, set as the master and providing haptic feedback, and an anthropomorphic robot slave, which is embodied by the 7-DOF (degrees of freedom) robotic arm of the Baxter® robot. The haptic feedback enables a bilateral manipulation of the teleoperation system. The joint angles and Cartesian position of the stylus of the Omni device are sampled and transferred to the slave, determining its motion. Meanwhile a force, proportional to the amplitude of position error of the slave manipulator, is sent back to the master and applied to the stylus. Hereby, the operator can sense the motion of the Baxter robot and adjust its manipulator accordingly. The kinematics of the master and slave have been analysed and a workspace mapping has been realized. Two methods, direct angle mapping and CLIK (closed-loop inverse kinematics), are used to implement the manipulation of the slave in position-position mode. Two experiments have been designed and tested to verify the validity of the methods provided by this paper. The results of the experiments illustrate that the designed teleoperation system is feasible and effective.
On Quaternions and Octonions : Their Geometry , Arithmetic , and Symmetry

Conway and Smith’s book is a wonderful introduction to the normed division algebras: the real numbers (R), the complex numbers (C), the quaternions (H), and the octonions (O). The first two are well-known to every mathematician. In contrast, the quaternions and especially the octonions are sadly neglected, so the authors rightly concentrate on these. They develop these number systems from scratch, explore their connections to geometry, and even study number theory in quaternionic and octonionic versions of the integers. Conway and Smith warm up by studying two famous subrings of C: the Gaussian integers and Eisenstein integers. The Gaussian integers are the complex numbers x + iy for which x and y are integers. They form a square lattice:
Context-Aware Captions from Context-Agnostic Supervision

We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of siamese cat and tiger cat, we generate language that describes the siamese cat in a way that distinguishes it from tiger cat. Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.
Rectifying the Bound Document Image Captured by the Camera: A Model Based Approach

A model based approach for rectifying the camera image of the bound document has been developed, i.e., the surface of the document is represented by a general cylindrical surface. The principle of using the model to unwrap the image is discussed. Practically, the skeleton of each horizontal text line is extracted to help estimate the parameter of the model, and rectify the images. To use the model, only a few priori is required, and no more auxiliary device is necessary. Experiment results are given to demonstrate the feasibility and the stability of the method.