nils-herrmann/clean_scirepeval huggingface dataset:
The effects of praise on children's intrinsic motivation: a review and synthesis.

The authors argue against a purely behavioral definition of praise as verbal reinforcement in favor of the view that praise may serve to undermine, enhance, or have no effect on children's intrinsic motivation, depending on a set of conceptual variables. Provided that praise is perceived as sincere, it is particularly beneficial to motivation when it encourages performance attributions to controllable causes, promotes autonomy, enhances competence without an overreliance on social comparisons, and conveys attainable standards and expectations. The motivational consequences of praise also can be moderated by characteristics of the recipient, such as age, gender, and culture. Methodological considerations, such as including appropriate control groups and measuring postfailure outcomes, are stressed, and directions for future research are highlighted.
Multi-domain DDoS Mitigation Based on Blockchains

The exponential increase of the traffic volume makes Distributed Denial-of-Service (DDoS) attacks a top security threat to service providers. Existing DDoS defense mechanisms lack resources and flexibility to cope with attacks by themselves, and by utilizing other’s companies resources, the burden of the mitigation can be shared. Technologies as blockchain and smart contracts allow distributing attack information across multiple domains, while SDN (Software-Defined Networking) and NFV (Network Function Virtualization) enables to scale defense capabilities on demand for a single network domain. This proposal presents the design of a novel architecture combining these elements and introducing novel opportunities for flexible and efficient DDoS mitigation solutions across multiple domains.
Mergeable persistent data structures

Irmin is an OCaml library to design purely functional data structures that can be persisted on disk and be merged and synchronized efficiently. In this paper, we focus on the merge aspect of the library and present two data structures built on top of Irmin: (i) queues and (ii) ropes that extend the corresponding purely functional data structures with a 3-way merge operation. We provide early theoretical and practical complexity results for these new data structures. Irmin is available as open-source code as part of the MirageOS project.
An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation

This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.
Understanding social media marketing: a case study on topics, categories and sentiment on a Facebook brand page

Social networks have changed the way information is delivered to the customers, shifting from traditional one-to-many to one-to-one communication. Opinion mining and sentiment analysis offer the possibility to understand the user-generated comments and explain how a certain product or a brand is perceived. Classification of different types of content is the first step towards understanding the conversation on the social media platforms. Our study analyses the content shared on Facebook in terms of topics, categories and shared sentiment for the domain of a sponsored Facebook brand page. Our results indicate that Product, Sales and Brand are the three most discussed topics, while Requests and Suggestions, Expressing Affect and Sharing are the most common intentions for participation. We discuss the implications of our findings for social media marketing and opinion mining.
MRShare: Sharing Across Multiple Queries in MapReduce

Large-scale data analysis lies in the core of modern enterprises and scientific research. With the emergence of cloud computing, the use of an analytical query processing infrastructure (e.g., Amazon EC2) can be directly mapped to monetary value. MapReduce has been a popular framework in the context of cloud computing, designed to serve long running queries (jobs) which can be processed in batch mode. Taking into account that different jobs often perform similar work, there are many opportunities for sharing. In principle, sharing similar work reduces the overall amount of work, which can lead to reducing monetary charges incurred while utilizing the processing infrastructure. In this paper we propose a sharing framework tailored to MapReduce. Our framework, MRShare, transforms a batch of queries into a new batch that will be executed more efficiently, by merging jobs into groups and evaluating each group as a single query. Based on our cost model for MapReduce, we define an optimization problem and we provide a solution that derives the optimal grouping of queries. Experiments in our prototype, built on top of Hadoop, demonstrate the overall effectiveness of our approach and substantial savings.
How do we perceive the pain of others? A window into the neural processes involved in empathy

To what extent do we share feelings with others? Neuroimaging investigations of the neural mechanisms involved in the perception of pain in others may cast light on one basic component of human empathy, the interpersonal sharing of affect. In this fMRI study, participants were shown a series of still photographs of hands and feet in situations that are likely to cause pain, and a matched set of control photographs without any painful events. They were asked to assess on-line the level of pain experienced by the person in the photographs. The results demonstrated that perceiving and assessing painful situations in others was associated with significant bilateral changes in activity in several regions notably, the anterior cingulate, the anterior insula, the cerebellum, and to a lesser extent the thalamus. These regions are known to play a significant role in pain processing. Finally, the activity in the anterior cingulate was strongly correlated with the participants' ratings of the others' pain, suggesting that the activity of this brain region is modulated according to subjects' reactivity to the pain of others. Our findings suggest that there is a partial cerebral commonality between perceiving pain in another individual and experiencing it oneself. This study adds to our understanding of the neurological mechanisms implicated in intersubjectivity and human empathy.
Indirect content privacy surveys: measuring privacy without asking about it

The strong emotional reaction elicited by privacy issues is well documented (e.g., [12, 8]). The emotional aspect of privacy makes it difficult to evaluate privacy concern, and directly asking about a privacy issue may result in an emotional reaction and a biased response. This effect may be partly responsible for the dramatic privacy concern ratings coming from recent surveys, ratings that often seem to be at odds with user behavior. In this paper we propose indirect techniques for measuring content privacy concerns through surveys, thus hopefully diminishing any emotional response. We present a design for indirect surveys and test the design's use as (1) a means to measure relative privacy concerns across content types, (2) a tool for predicting unwillingness to share content (a possible indicator of privacy concern), and (3) a gauge for two underlying dimensions of privacy - content importance and the willingness to share content. Our evaluation consists of 3 surveys, taken by 200 users each, in which privacy is never asked about directly, but privacy warnings are issued with increasing escalation in the instructions and individual question-wording. We demonstrate that this escalation results in statistically and practically significant differences in responses to individual questions. In addition, we compare results against a direct privacy survey and show that rankings of privacy concerns are increasingly preserved as privacy language increases in the indirect surveys, thus indicating our mapping of the indirect questions to privacy ratings is accurately reflecting privacy concerns.
Does language dominance affect cognitive performance in bilinguals? Lifespan evidence from preschoolers through older adults on card sorting, Simon, and metalinguistic tasks

This study explores the extent to which a bilingual advantage can be observed for three tasks in an established population of fully fluent bilinguals from childhood through adulthood. Welsh-English simultaneous and early sequential bilinguals, as well as English monolinguals, aged 3 years through older adults, were tested on three sets of cognitive and executive function tasks. Bilinguals were Welsh-dominant, balanced, or English-dominant, with only Welsh, Welsh and English, or only English at home. Card sorting, Simon, and a metalinguistic judgment task (650, 557, and 354 participants, respectively) reveal little support for a bilingual advantage, either in relation to control or globally. Primarily there is no difference in performance across groups, but there is occasionally better performance by monolinguals or persons dominant in the language being tested, and in one case-in one condition and in one age group-lower performance by the monolinguals. The lack of evidence for a bilingual advantage in these simultaneous and early sequential bilinguals suggests the need for much closer scrutiny of what type of bilingual might demonstrate the reported effects, under what conditions, and why.
Automated tracking and grasping of a moving object with a robotic hand-eye system

Most robotic grasping tasks assume a stationary or fixed object. In this paper, we explore the requirements for tracking and grasping a moving object. The focus of our work is to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object. There is an interest in exploring the interplay of hand-eye coordination for dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system. Coordination between an organism's sensing modalities and motor control system is a hallmark of intelligent behavior, and we are pursuing the goal of building an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system we have built addresses three distinct problems in robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system is able to operate at approximately human arm movement rates, and experimental results in which a moving model train is tracked is presented, stably grasped, and picked up by the system. The algorithms we have developed that relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks that require visual feedback for arm and hand control.
Object Reading: Text Recognition for Object Recognition

We propose to use text recognition to aid in visual object class recognition. To this end we first propose a new algorithm for text detection in natural images. The proposed text detection is based on saliency cues and a context fusion step. The algorithm does not need any parameter tuning and can deal with varying imaging conditions. We evaluate three different tasks: 1. Scene text recognition, where we increase the state-of-the-art by 0.17 on the ICDAR 2003 dataset. 2. Saliency based object recognition, where we outperform other state-of-the-art saliency methods for object recognition on the PASCAL VOC 2011 dataset. 3. Object recognition with the aid of recognized text, where we are the first to report multi-modal results on the IMET set. Results show that text helps for object class recognition if the text is not uniquely coupled to individual object instances.
Circulating presepsin (soluble CD14 subtype) as a marker of host response in patients with severe sepsis or septic shock: data from the multicenter, randomized ALBIOS trial

Presepsin is a soluble fragment of the cluster-of-differentiation marker protein 14 (CD14) involved in pathogen recognition by innate immunity. We evaluated the relation between its circulating concentration, host response, appropriateness of antibiotic therapy, and mortality in patients with severe sepsis. Plasma presepsin was measured 1, 2, and 7 days after enrollment of 997 patients with severe sepsis or septic shock in the multicenter Albumin Italian Outcome Sepsis (ALBIOS) trial. They were randomized to albumin or crystalloids. We tested with univariate and adjusted models the association of single measurements of presepsin or changes over time with clinical events, organ dysfunctions, appropriateness of antibiotic therapy, and ICU or 90-day mortality. Presepsin concentration at baseline (946 [492–1,887] ng/L) increased with the SOFA score, the number of prevalent organ dysfunctions or failures, and the incidence of new failures of the respiratory, coagulation, liver, and kidney systems. The concentration decreased in ICU over 7 days in patients with negative blood cultures, and in those with positive blood cultures and appropriate antibiotic therapy; it increased with inappropriate antibiotic therapy (p = 0.0009). Baseline presepsin was independently associated with, and correctly reclassified, the risk of ICU and 90-day mortality. Increasing concentrations of presepsin from day 1 to day 2 predicted higher ICU and 90-day mortality (adjusted p < 0.0001 and 0.01, respectively). Albumin had no effect on presepsin concentration. Presepsin is an early predictor of host response and mortality in septic patients. Changes in concentrations over time seem to reflect the appropriateness of antibiotic therapy.
AppProfiler: a flexible method of exposing privacy-related behavior in android applications to end users

Although Android's permission system is intended to allow users to make informed decisions about their privacy, it is often ineffective at conveying meaningful, useful information on how a user's privacy might be impacted by using an application. We present an alternate approach to providing users the knowledge needed to make informed decisions about the applications they install. First, we create a knowledge base of mappings between API calls and fine-grained privacy-related behaviors. We then use this knowledge base to produce, through static analysis, high-level behavior profiles of application behavior. We have analyzed almost 80,000 applications to date and have made the resulting behavior profiles available both through an Android application and online. Nearly 1500 users have used this application to date. Based on 2782 pieces of application-specific feedback, we analyze users' opinions about how applications affect their privacy and demonstrate that these profiles have had a substantial impact on their understanding of those applications. We also show the benefit of these profiles in understanding large-scale trends in how applications behave and the implications for user privacy.
A novel low-power full-adder cell with new technique in designing logical gates based on static CMOS inverter

A new low-power full-adder based on CMOS inverter is presented. This full-adder is comprised of inverters. Universal gates such as NOR, NAND and MAJORITY-NOT gates are implemented with a set of inverters and non-conventional implementation of them. In the proposed design approach the time consuming XOR gates are eliminated. As full-adders are frequently employed in a tree-structured configuration for high-performance arithmetic circuits, a cascaded simulation structure is employed to evaluate the full-adders in a realistic application environment. The circuits being studied were optimized for energy efficiency using 0.18mm and 90 nm CMOS process technologies. The proposed fulladder shows full swing logic, balanced outputs and strong output drivability. It is also observed that the presented design can be utilized in many cases especially whenever the lowest possible power consumption is targeted. Circuits layout implementations and checking their functionality have been done using Cadence IC package and Synopsys HSpice, respectively. & 2009 Elsevier Ltd. All rights reserved.
SoK: Introspections on Trust and the Semantic Gap

An essential goal of Virtual Machine Introspection (VMI) is assuring security policy enforcement and overall functionality in the presence of an untrustworthy OS. A fundamental obstacle to this goal is the difficulty in accurately extracting semantic meaning from the hypervisor's hardware level view of a guest OS, called the semantic gap. Over the twelve years since the semantic gap was identified, immense progress has been made in developing powerful VMI tools. Unfortunately, much of this progress has been made at the cost of reintroducing trust into the guest OS, often in direct contradiction to the underlying threat model motivating the introspection. Although this choice is reasonable in some contexts and has facilitated progress, the ultimate goal of reducing the trusted computing base of software systems is best served by a fresh look at the VMI design space. This paper organizes previous work based on the essential design considerations when building a VMI system, and then explains how these design choices dictate the trust model and security properties of the overall system. The paper then observes portions of the VMI design space which have been under-explored, as well as potential adaptations of existing techniques to bridge the semantic gap without trusting the guest OS. Overall, this paper aims to create an essential checkpoint in the broader quest for meaningful trust in virtualized environments through VM introspection.
DGA Botnet detection using Collaborative Filtering and Density-based Clustering

In recent years, the botnet phenomenon is one of the most dangerous threat to Internet security, which supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, etc. An increasing number of botnets use Domain Generation Algorithms (DGAs) to avoid detection and exclusion by the traditional methods. By dynamically and frequently generating a large number of random domain names for candidate command and control (C&C) server, botnet can be still survive even when a C&C server domain is identified and taken down. This paper presents a novel method to detect DGA botnets using Collaborative Filtering and Density-Based Clustering. We propose a combination of clustering and classification algorithm that relies on the similarity in characteristic distribution of domain names to remove noise and group similar domains. Collaborative Filtering (CF) technique is applied to find out bots in each botnet, help finding out offline malwares infected-machine. We implemented our prototype system, carried out the analysis of a huge amount of DNS traffic log of Viettel Group and obtain positive results.
Zero and data reuse-aware fast convolution for deep neural networks on GPU

Convolution operations dominate the total execution time of deep convolutional neural networks (CNNs). In this paper, we aim at enhancing the performance of the state-of-the-art convolution algorithm (called Winograd convolution) on the GPU. Our work is based on two observations: (1) CNNs often have abundant zero weights and (2) the performance benefit of Winograd convolution is limited mainly due to extra additions incurred during data transformation. In order to exploit abundant zero weights, we propose a low-overhead and efficient hardware mechanism that skips multiplications that will always give zero results regardless of input data (called ZeroSkip). In addition, to leverage the second observation, we present data reuse optimization for addition operations in Winograd convolution (called AddOpt), which improves the utilization of local registers, thereby reducing on-chip cache accesses. Our experiments with a real-world deep CNN, VGG-16, on GPGPU-Sim and Titan X show that the proposed methods, ZeroSkip and AddOpt, achieve 51.8% higher convolution performance than the baseline Winograd convolution. Moreover, even without any hardware modification, AddOpt alone gives 35.6% higher performance on a real hardware platform, Titan X.
A Dual Approach to Scalable Verification of Deep Networks

This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications relating their inputs and outputs (robustness to bounded norm adversarial perturbations, for example). Most previous work on this topic was limited in its applicability by the size of the network, network architecture and the complexity of properties to be verified. In contrast, our framework applies to a general class of activation functions and specifications on neural network inputs and outputs. We formulate verification as an optimization problem (seeking to find the largest violation of the specification) and solve a Lagrangian relaxation of the optimization problem to obtain an upper bound on the worst case violation of the specification being verified. Our approach is anytime i.e. it can be stopped at any time and a valid bound on the maximum violation can be obtained. We develop specialized verification algorithms with provable tightness guarantees under special assumptions and demonstrate the practical significance of our general verification approach on a variety of verification tasks.
Consumer Socialization of Children : A Retrospective Look at Twenty-Five Years of Research

Twenty-five years of consumer socialization research have yielded an impressive set of findings. The purpose of our article is to review these findings and assess what we know about children’s development as consumers. Our focus is on the developmental sequence characterizing the growth of consumer knowledge, skills, and values as children mature throughout childhood and adolescence. In doing so, we present a conceptual framework for understanding consumer socialization as a series of stages, with transitions between stages occurring as children grow older and mature in cognitive and social terms. We then review empirical findings illustrating these stages, including children’s knowledge of products, brands, advertising, shopping, pricing, decision-making strategies, parental influence strategies, and consumption motives and values. Based on the evidence reviewed, implications are drawn for future theoretical and empirical development in the field of consumer socialization.