nils-herrmann/clean_scirepeval huggingface dataset:
Conformity in virtual environments: a hybrid neurophysiological and psychosocial approach

The main aim of our study was to analyze the effects of a virtual environment on social conformity, with particular attention to the effects of different types of task and psychological variables on social influence, on one side, and to the neural correlates related to conformity, measured by means of an Emotiv EPOC device on the other. For our purpose, we replicated the famous Asch’s visual task and created two new tasks of increasing ambiguity, assessed through the calculation of the item’s entropy. We also administered five scales in order to assess different psychological traits. From the experiment, conducted on 181 university students, emerged that conformity grows according to the ambiguity of the task, but normative influence is significantly weaker in virtual environments, if compared to face-to-face experiments. The analyzed psychological traits, however, result not to be relatable to conformity, and they only affect the subjects’ response times. From the ERP (Event-related potentials) analysis, we detected N200 and P300 components comparing the plots of conformist and non-conformist subjects, alongside with the detection of their Late Positive Potential, Readiness Potential, and Error-Related Negativity, which appear consistently different for the two typologies.
THE CRIMINAL PSYCHOPATH: HISTORY, NEUROSCIENCE, TREATMENT, AND ECONOMICS.

The manuscript surveys the history of psychopathic personality, from its origins in psychiatric folklore to its modern assessment in the forensic arena. Individuals with psychopathic personality, or psychopaths, have a disproportionate impact on the criminal justice system. Psychopaths are twenty to twenty-five times more likely than non-psychopaths to be in prison, four to eight times more likely to violently recidivate compared to non-psychopaths, and are resistant to most forms of treatment. This article presents the most current clinical efforts and neuroscience research in the field of psychopathy. Given psychopathy's enormous impact on society in general and on the criminal justice system in particular, there are significant benefits to increasing awareness of the condition. This review also highlights a recent, compelling and cost-effective treatment program that has shown a significant reduction in violent recidivism in youth on a putative trajectory to psychopathic personality.
The Power Grid as a Complex Network: a Survey

The statistical tools of Complex Network Analysis are of great use to understand salient properties of complex systems, may these be natural or pertaining human engineered infrastructures. One of these that is receiving growing attention for its societal relevance is that of electricity distribution. In this paper, we present a survey of the most important scientific studies investigating the properties of several Power Grids infrastructures using Complex Network Analysis techniques and methodologies. We categorize and explore the most relevant literature works considering general topological properties, differences between the various graph-related indicators and reliability aspects.
Severely suppressed bone turnover: a potential complication of alendronate therapy.

Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.
Design of High-Gain Lens Antenna by Gradient-Index Metamaterials Using Transformation Optics

Transformation of space coordinates can be used as a convenient tool for producing a controlled electromagnetic field pattern. In this paper, using a conformal transformation, the design procedure of a lens antenna with high gain and low sidelobes is presented which can be realized by isotropic graded refractive index (GRIN) materials. Applying proper simplifying techniques, the designed lens can be made by non-resonant metamaterials or non-magnetic dielectrics having wide frequency band and low loss. A graded photonic crystal (GPC) operating in metamaterial regime is used for this purpose. By placing the lens in a horn antenna, a highly directive beam is achieved. It is also shown that by lateral displacement of the lens medium, the beam direction can be controlled to some extent. A multibeam antenna in which each beam can be controlled independently is also presented. Simulation results are used to justify the design approach.
The Multidimensional Knapsack Problem: Structure and Algorithms

Structure and Algorithms Jakob Puchinger NICTA Victoria Laboratory Department of Computer Science & Software Engineering University of Melbourne, Australia jakobp@csse.unimelb.edu.au Günther R. Raidl Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria raidl@ads.tuwien.ac.at Ulrich Pferschy Department of Statistics and Operations Research University of Graz, Austria pferschy@uni-graz.at
A novel dual-band beam-switching antenna based on active frequency selective surfaces

A novel design of a dual-band beam-switching antenna based on active frequency selective surfaces (FSSs) is presented. The antenna is composed of two layers of active cylindrical FSS and fed by a dual-band dipole at the center. The FSS layers are divided into on and off state sectors in both inner and outer layers. Each sector consists of columns of metallic strips with on or off state diodes. To achieve a dual-band matching, the inner FSS layer is transmissive at lower band in both on and off state. Also, it is reflective in the on state and transmissive in the off state at higher frequency band. The outer layer is a reflective surface in on state and transmissive in off state in both frequency bands. In each state of operation, the on state sectors operate as a reconfigurable reflector at both frequency bands and convert the omnidirectional radiation pattern of the dual-band dipole to a directive one. By switching the states of diodes, the antenna can scan the entire azimuth plane. The fullwave simulation results show the antenna operates at 0.75 GHz-1 GHz and 1.7 GHz-1.9 GHz simultaneously, with maximum gain of 6.4 dBi and 10 dBi respectively, and can be used in the base stations of wireless communication systems.
Social Brand Value and the Value Enhancing Role of Social Media Relationships for Brands

Due to the social media revolution and the emergence of communities, social networks, and user generated content portals, prevalent branding concepts need to catch up with this reality. Given the importance of social ties, social interactions and social identity in the new media environment, there is a need to account for a relationship measure in marketing and branding. Based on the concept of social capital we introduce the concept of social brand value, defined as the perceived value derived by exchange and interactions with other users of the brand within a `community. Within a qualitative study marketing experts were interviewed and highlighted the importance towards social media activities, but also indicated that they do not have a clear picture on how strategies should look like and how their success can be measured. A second quantitative study was conducted which demonstrates the influence the social brand value construct has for consumers brand evangelism and willingness to pay a price premium and hence the value contribution of the social brand value for consumers.
Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks

This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture . The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology—fMRI data collected from subjects when reading affirmative or negative sentences and another one—on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy when compared with traditional AI and statistical methods. The created NeuCube-based models of fMRI data are directly and efficiently implementable on high performance and low energy consumption neuromorphic platforms for real-time applications.
Phoolproof Phishing Prevention

Phishing, or web spoofing, is a growing problem: the Anti-Phishing Working Group (APWG) received almost 14,000 unique phishing reports in A ugust 2005, a 56% jump over the number of reports in December 2004 . For finan cial institutions, phishing is a particularly insidious problem, since trust forms the fou ndation for customer relationships, and phishing attacks undermine confidence in an institution. Phishing attacks succeed by exploiting a user’s inability to distinguish legitimate s ites from spoofed sites. Most prior research focuses on assisting the use r in making this distinction; however, users must make the right security decision every time. Unfortunately, humans are ill-suited for performing the security checks necess ary for secure site identification, and a single mistake may result in a total compromise of the use r’s online account. Fundamentally, users should be authenticated using informa tion that they cannot readily reveal to malicious parties. Placing less reliance on the use r d ring the authentication process will enhance security and eliminate many forms of f raud. We propose using a trusted device to perform mutual authentication that elim inates reliance on perfect user behavior, thwarts Man-in-the-Middle attacks a fter setup, and protects a user’s account even in the presence of keyloggers and mo st forms of spyware. We demonstrate the practicality of our system with a prototype implementation.
Ernst Ferdinand Sauerbruch: Rise and Fall of the Pioneer of Thoracic Surgery

Ferdinand Sauerbruch (1875–1951) was a pioneer of thoracic and cardiac surgery and is undoubtedly one of the twentieth century's most outstanding surgeons. Before 1904 operations on the thorax met with fatal complications due to pneumothorax. Sauerbruch developed a pressure-differential chamber that maintained normal respiration and enabled safe operations to be undertaken on the thorax. Together with von Mikulicz, he initiated intrathoracic operations and later developed various surgical procedures on the mediastinum, lungs, pericardium, heart, and esophagus. The simple yet effective techniques of positive-pressure ventilation replaced the expensive, cumbersome negative-pressure chamber. Sauerbruch's latter years were marred by dementia that adversely affected his personality, intellect, and capacity as a surgeon. The unjustifiable toll of increasing patient morbidity and mortality forced authorities to dismiss him in 1949. He died at the age of 76 in Berlin. After almost a century since the advent of the first safe thoracic surgery, the advances in technique and technology have been enormous. A great deal is owed to the inspiration and contributions of Ferdinand Sauerbruch.
An Empirical Study on Android-Related Vulnerabilities

Mobile devices are used more and more in everyday life. They are our cameras, wallets, and keys. Basically, they embed most of our private information in our pocket. For this and other reasons, mobile devices, and in particular the software that runs on them, are considered first-class citizens in the software-vulnerabilities landscape. Several studies investigated the software-vulnerabilities phenomenon in the context of mobile apps and, more in general, mobile devices. Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS). Also, these studies have been run on a very limited set of vulnerabilities. In this paper we present the largest study at date investigating Android-related vulnerabilities, with a specific focus on the ones affecting the Android OS. In particular, we (i) define a detailed taxonomy of the types of Android-related vulnerability, (ii) investigate the layers and subsystems from the Android OS affected by vulnerabilities, and (iii) study the survivability of vulnerabilities (i.e., the number of days between the vulnerability introduction and its fixing). Our findings could help OS and apps developers in focusing their verification & validation activities, and researchers in building vulnerability detection tools tailored for the mobile world.
3D point cloud downsampling for 2D indoor scene modelling in mobile robotics

Sensory perception and environment modelling are important for autonomous navigation in mobile robotics. 2D discrete grid representations such as the classic 2D occupancy grid maps are a widely used technique in scene representation because of the inherent simplicity and compact representation. In recent years, many 2.5D and 3D grid-based methods have been proposed however, as for the 2D case, a compromise between keeping a low computational bound and reliable sensor interpretation must be kept in order to perform real-world tasks. Assuming the input data in the form of a 3D point-cloud, in this paper we propose a 2D scene modelling approach which converts the 3D data to a 2.5D representation and then to a 2D grid map in an efficient and meaningful manner. The proposed approach incorporates a new rapidly exploring random tree inspired ground-plane detection (RRT-GPD), and an inverse sensor model (ISM) to correctly map 3D to 2.5D and then to 2D grid cells. Experiments were conducted in indoor scenarios with a robotic walker platform equipped with a Microsoft's Kinect One and a LeddarTech's Leddar IS16 sensor. Reported results show an improvement on the representation of non-trivial obstacles (stairs, floor outlets) over a ROS package solution, when applied to a 3D point cloud input.
A 77GHz automotive radar receiver in a wafer level package

In this paper, a 77-GHz radar receiver is presented, which comes in a wafer level package and thus eliminates the need for wire bonding yielding significant cost reduction. The high integration level available in the productive Silicon-Germanium (SiGe) technology used in this paper allows for implementation of in-system monitoring of the receiver conversion parameters. This facilitates the realization of ISO 26262 compliant radar sensors for automotive safety applications.
Petri Net Modeling of Cyber-Physical Attacks on Smart Grid

This paper investigates the use of Petri nets for modeling coordinated cyber-physical attacks on the smart grid. Petri nets offer more flexibility and expressiveness than traditional attack trees to represent the actions of simultaneous attackers. However, Petri net models for attacks on very large critical infrastructures such as the smart grid require a great amount of manual effort and detailed expertise in cyber-physical threats. To overcome these obstacles, we propose a novel hierarchical method to construct large Petri nets from a number of smaller Petri nets that can be created separately by different domain experts. The construction method is facilitated by a model description language that enables identical places in different Petri nets to be matched. The new modeling approach is described for an example attack on smart meters, and its efficacy is demonstrated by a proof-of-concept Python program.
Novelty and Reinforcement Learning in the Value System of Developmental Robots

The value system of a developmental robot signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. In the work reported here, a low level value system is modeled and implemented. It simulates the non-associative animal learning mechanism known as habituation e ect. Reinforcement learning is also integrated with novelty. Experimental results show that the proposed value system works as designed in a study of robot viewing angle selection.
Step Climbing Omnidirectional Mobile Robot with Passive Linkages

In this paper, we develop a holonomic omni-directional mobile vehicle with step-climbing ability. This system realizes omni-directional motion on flat floor using special wheels and passes over the step in forward or backward direction using the passive linkage mechanism. Our key ideas are the following topics. First topic is new omnidirectional mobile mechanism which consists of special wheels and passive linkage mechanism. Second topic is new passive linkage mechanism which can change its body configuration easily when the vehicle passes over the step. Third topic is wheel control reference derivation based on the body configuration, which changes passively during step climbing for reducing wheel slippage. Last topic is wheel control method which keeps the rotation velocity coordination among the wheels for reducing wheel slippage and increasing the step-climbing performance. We verified the effectiveness of our proposed method by the computer simulations and experiments. Utilizing our proposed mechanism and control systems, the vehicle has both omnidirectional mobility and step overcoming function. Furthermore, our developing vehicle can pass over the 128[mm] height step using the wheel which radius is 66[mm]. Its performance is three times larger than one of general wheeled vehicle.
Design of ultra high-speed CMOS CML buffers and latches

A comprehensive study of ultra high-speed current-mode logic (CML) buffers and regenerative CML latches will be illustrated. A new design procedure to systematically design a chain of tapered CML buffers is proposed. Next, a new 20GHz regenerative latch circuit will be introduced. Experimental results show a higher performance for the new latch architecture compared to a conventional CML latch circuit at ultra high-frequencies. It is also shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed low-voltage applications.
Language Expansion In Text-Based Games

Text-based games are suitable test-beds for designing agents that can learn by interaction with the environment in the form of natural language text. Very recently, deep reinforcement learning based agents have been successfully applied for playing text-based games. In this paper, we explore the possibility of designing a single agent to play several text-based games and of expanding the agent’s vocabulary using the vocabulary of agents trained for multiple games. To this extent, we explore the application of recently proposed policy distillation method for video games to the text-based game setting. We also use text-based games as a test-bed to analyze and hence understand policy distillation approach in detail.
Universal Planning Networks-Long Version + Supplementary

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities.
When does Consumer Empowerment Lead to Satisfied Customers? Some Mediating and Moderating Effects of the Empowerment-Satisfaction Link

Technological advances increasingly provide marketers with the opportunity to empower consumers. Consumer empowerment is a positive subjective state evoked by consumer perceptions of increasing control. As a positive state, increasing consumer empowerment should be associated with increasing consumer satisfaction. If such a relationship exists, it may be influenced by a number of contextual variables. Knowing in what contexts empowerment has a greater impact on satisfaction would help marketers decide when they could more effectively use such a strategy. This study has two purposes: 1) to investigate the relationship between consumer empowerment and satisfaction and 2) to investigate a set of potential influences on that relationship. Marketers will be in a better position to decide when to empower consumers if they have guidance on the relationships between these variables.