nils-herrmann/clean_scirepeval huggingface dataset:
An analysis of learned helplessness: II. The processing of success.

Helpless children attribute their failures to lack of ability and view them as insurmountable. Mastery-oriented children, in contrast, tend to emphasize motivational factors and to view failure as surmountable. Although the performance of the two groups is usually identical during success of prior to failure, past research suggests that these groups may well differ in the degree to which they perceive that their successes are replicable and hence that their failures are avoidable. The present study was concerned with the nature of such differences. Children performed a task on which they encountered success and then failure. Half were asked a series of questions about their performance after success and half after failure. Striking differences emerged: Compared to mastery-oriented children, helpless children underestimated the number of success (and overestimated the number of failures), did not view successes as indicative of ability, and did not expect the successes to continue. subsequent failure led them to devalue ;their performance but left the mastery-oriented children undaunted. Thus, for helpless children, successes are less salient, less predictive, and less enduring--less successful.
A component analysis of cognitive-behavioral therapy for generalized anxiety disorder and the role of interpersonal problems.

Clients with generalized anxiety disorder (GAD) received either (a) applied relaxation and self-control desensitization, (b) cognitive therapy, or (c) a combination of these methods. Treatment resulted in significant improvement in anxiety and depression that was maintained for 2 years. The large majority no longer met diagnostic criteria; a minority sought further treatment during follow-up. No differences in outcome were found between conditions; review of the GAD therapy literature suggested that this may have been due to strong effects generated by each component condition. Finally, interpersonal difficulties remaining at posttherapy, measured by the Inventory of Interpersonal Problems Circumplex Scales (L. E. Alden, J. S. Wiggins, & A. L. Pincus, 1990) in a subset of clients, were negatively associated with posttherapy and follow-up improvement, suggesting the possible utility of adding interpersonal treatment to cognitive-behavioral therapy to increase therapeutic effectiveness.
Development of a compact wrist mechanism with high torque density

This paper proposes a new wrist mechanism for robot manipulation. To develop multi-dof wrist mechanisms that can emulate human wrists, compactness and high torque density are the major challenges. Traditional wrist mechanisms consist of series of rotary motors that require gearing to amplify the output torque. This often results in a bulky wrist mechanism. Instead, large linear force can be easily realized in a compact space by using lead screw motors. Inspired by the muscle-tendon actuation pattern, the proposed mechanism consists of two parallel placed linear motors. Their linear motions are transmitted to two perpendicular rotations through a spherical mechanism and two slider crank mechanisms. High torque density can be achieved. Static and dynamic models are developed to design the wrist mechanism. A wrist prototype and its position control experiments will be presented with results discussed. The novel mechanism is expected to serve as an alternative for robot manipulators in applications that require human-friendly interactions.
A Rail-To-Rail Class-AB Amplifier With an Offset Cancellation for LCD Drivers

A rail-to-rail amplifier with an offset cancellation, which is suitable for high color depth and high-resolution liquid crystal display (LCD) drivers, is proposed. The amplifier incorporates dual complementary differential pairs, which are classified as main and auxiliary transconductance amplifiers, to obtain a full input voltage swing and an offset canceling capability. Both offset voltage and injection-induced error, due to the device mismatch and charge injection, respectively, are greatly reduced. The offset cancellation and charge conservation, which is used to reduce the dynamic power consumption, are operated during the same time slot so that the driving period does not need to increase. An experimental prototype amplifier is implemented with 0.35m CMOS technology. The circuit draws 7.5 A static current and exhibits the settling time of 3 s, for a voltage swing of 5 V under a 3.4 k resistance, and a 140 pF capacitance load with a power supply of 5 V. The offset voltage of the amplifier with offset cancellation is 0.48 mV.
Symbol emergence in robotics: a survey

College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577, Japan Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kubogi, Soja-shi, Okayama 719-1197, Japan Department of Intermedia Art and Science, School of Fundamental Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo 169-8555, Japan Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 1, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8560, Japan
A Self-Biased Current Source Based Power-On Reset Circuit for On-Chip Applications

Power-on reset circuits are available as discrete devices as well as on-chip solutions and are indispensable to initialize some critical nodes of analog and digital designs during power-on. In this paper, we present a power-on reset circuit specifically designed for on-chip applications. The mentioned POR circuit should meet certain design requirements necessary to be integrated on-chip, some of them being area-efficiency, power-efficiency, supply rise-time insensitivity and ambient temperature insensitivity. The circuit is implemented within a small area (60mum times 35mum) using the 2.5V tolerant MOSFETs of a 0.28mum CMOS technology. It has a maximum quiescent current consumption of 40muA and works over infinite range of supply rise-times and ambient temperature range of -40degC to 150degC
Empathy: Its ultimate and proximate bases.

There is disagreement in the literature about the exact nature of the phenomenon of empathy. There are emotional, cognitive, and conditioning views, applying in varying degrees across species. An adequate description of the ultimate and proximate mechanism can integrate these views. Proximately, the perception of an object's state activates the subject's corresponding representations, which in turn activate somatic and autonomic responses. This mechanism supports basic behaviors (e.g., alarm, social facilitation, vicariousness of emotions, mother-infant responsiveness, and the modeling of competitors and predators) that are crucial for the reproductive success of animals living in groups. The Perception-Action Model (PAM), together with an understanding of how representations change with experience, can explain the major empirical effects in the literature (similarity, familiarity, past experience, explicit teaching, and salience). It can also predict a variety of empathy disorders. The interaction between the PAM and prefrontal functioning can also explain different levels of empathy across species and age groups. This view can advance our evolutionary understanding of empathy beyond inclusive fitness and reciprocal altruism and can explain different levels of empathy across individuals, species, stages of development, and situations.
What type of content are smartphone users addicted to?: SNS vs. games

Based on a sample of 944 respondents who were recruited from 20 elementary schools in South Korea, this research surveyed the factors that lead to smartphone addiction. This research examined the user characteristics and media content types that can lead to addiction. With regard to user characteristics, results showed that those who have lower self-control and those who have greater stress were more likely to be addicted to smartphones. For media content types, those who use smartphones for SNS, games, and entertainment were more likely to be addicted to smartphones, whereas those who use smartphones for study-related purposes were not. Although both SNS use and game use were positive predictors of smartphone addiction, SNS use was a stronger predictor of smartphone addiction than
Characterization of micromachined spiked biopotential electrodes

We present the characterization of dry spiked biopotential electrodes and test their suitability to be used in anesthesia monitoring systems based on the measurement of electroencephalographic signals. The spiked electrode consists of an array of microneedles penetrating the outer skin layers. We found a significant dependency of the electrode-skin-electrode impedance (ESEI) on the electrode size (i.e., the number of spikes) and the coating material of the spikes. Electrodes larger than 3/spl times/3 mm/sup 2/ coated with Ag-AgCl have sufficiently low ESEI to be well suited for electroencephalograph (EEG) recordings. The maximum measured ESEI was 4.24 k/spl Omega/ and 87 k/spl Omega/, at 1 kHz and 0.6 Hz, respectively. The minimum ESEI was 0.65 k/spl Omega/ an 16 k/spl Omega/, at the same frequencies. The ESEI of spiked electrodes is stable over an extended period of time. The arithmetic mean of the generated DC offset voltage is 11.8 mV immediately after application on the skin and 9.8 mV after 20-30 min. A spectral study of the generated potential difference revealed that the AC part was unstable at frequencies below approximately 0.8 Hz. Thus, the signal does not interfere with a number of clinical applications using real-time EEG. Comparing raw EEG recordings of the spiked electrode with commercial Zipprep electrodes showed that both signals were similar. Due to the mechanical strength of the silicon microneedles and the fact that neither skin preparation nor electrolytic gel is required, use of the spiked electrode is convenient. The spiked electrode is very comfortable for the patient.
Sub-Channel Assignment, Power Allocation, and User Scheduling for Non-Orthogonal Multiple Access Networks

In this paper, we study the resource allocation and user scheduling problem for a downlink non-orthogonal multiple access network where the base station allocates spectrum and power resources to a set of users. We aim to jointly optimize the sub-channel assignment and power allocation to maximize the weighted total sum-rate while taking into account user fairness. We formulate the sub-channel allocation problem as equivalent to a many-to-many two-sided user-subchannel matching game in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. We then propose a matching algorithm, which converges to a two-side exchange stable matching after a limited number of iterations. A joint solution is thus provided to solve the sub-channel assignment and power allocation problems iteratively. Simulation results show that the proposed algorithm greatly outperforms the orthogonal multiple access scheme and a previous non-orthogonal multiple access scheme.
Deep Modeling of Group Preferences for Group-Based Recommendation

Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.
Fast Video Shot Boundary Detection Based on SVD and Pattern Matching

Video shot boundary detection (SBD) is the first and essential step for content-based video management and structural analysis. Great efforts have been paid to develop SBD algorithms for years. However, the high computational cost in the SBD becomes a block for further applications such as video indexing, browsing, retrieval, and representation. Motivated by the requirement of the real-time interactive applications, a unified fast SBD scheme is proposed in this paper. We adopted a candidate segment selection and singular value decomposition (SVD) to speed up the SBD. Initially, the positions of the shot boundaries and lengths of gradual transitions are predicted using adaptive thresholds and most non-boundary frames are discarded at the same time. Only the candidate segments that may contain the shot boundaries are preserved for further detection. Then, for all frames in each candidate segment, their color histograms in the hue-saturation-value) space are extracted, forming a frame-feature matrix. The SVD is then performed on the frame-feature matrices of all candidate segments to reduce the feature dimension. The refined feature vector of each frame in the candidate segments is obtained as a new metric for boundary detection. Finally, cut and gradual transitions are identified using our pattern matching method based on a new similarity measurement. Experiments on TRECVID 2001 test data and other video materials show that the proposed scheme can achieve a high detection speed and excellent accuracy compared with recent SBD schemes.
Energy-Aware Coverage Path Planning of UAVs

Coverage path planning is the operation of finding a path that covers all the points of a specific area. Thanks to the recent advances of hardware technology, Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. However, most of the research focused on finding the optimal path taking only geometrical constraints into account, without considering the peculiar features of the robot, like available energy, weight, maximum speed, sensor resolution, etc. This paper proposes an energy-aware path planning algorithm that minimizes energy consumption while satisfying a set of other requirements, such as coverage and resolution. The algorithm is based on an energy model derived from real measurements. Finally, the proposed approach is validated through a set of experiments.
A High Step-Up Three-Port DC–DC Converter for Stand-Alone PV/Battery Power Systems

A three-port dc–dc converter integrating photovoltaic (PV) and battery power for high step-up applications is proposed in this paper. The topology includes five power switches, two coupled inductors, and two active-clamp circuits. The coupled inductors are used to achieve high step-up voltage gain and to reduce the voltage stress of input side switches. Two sets of active-clamp circuits are used to recycle the energy stored in the leakage inductors and to improve the system efficiency. The operation mode does not need to be changed when a transition between charging and discharging occurs. Moreover, tracking maximum power point of the PV source and regulating the output voltage can be operated simultaneously during charging/discharging transitions. As long as the sun irradiation level is not too low, the maximum power point tracking (MPPT) algorithm will be disabled only when the battery charging voltage is too high. Therefore, the control scheme of the proposed converter provides maximum utilization of PV power most of the time. As a result, the proposed converter has merits of high boosting level, reduced number of devices, and simple control strategy. Experimental results of a 200-W laboratory prototype are presented to verify the performance of the proposed three-port converter.
"What is relevant in a text document?": An interpretable machine learning approach

Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text's category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications.
Design and evolution of modular neural network architectures

To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist out of several modules, standard subnetworks that serve as higher-order units with a distinct structure and function. The simulations rely on a particular network module called CALM (Murre, Phaf, and Wolters, 1989, 1992). This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behaviour of the network as a whole. Based on arguments derived from neuroscience, psychology, computational learning theory, and hardware implementation, a framework for the design of such modular networks is laid-out. A number of small-scale simulation studies shows how intermodule connectivity patterns implement ’neural assemblies’ (Hebb, 1949) that induce a particular category structure in the network. Learning and categorization improves as the induced categories are more compatible with the structure of the task domain. In addition to structural compatibility, two other principles of design are proposed that underlie information processing in interactive activation networks:replication and recurrence. Because a general theory for relating network architectures to specific neural functions does not exist, we extend the biological metaphor of neural networks, by applying ge etic algorithms(a biocomputing method for search and optimization based on natural selection and evolution) to search for optimal modular network architectures for learning a visual categorization task. The best performing network architectures seemed to have reproduced some of the overall characteristics of the natural visual system, such as the organization of coarse and fine processing of stimuli in separate pathways. A potentially important result is that a genetically defined initial architecture cannot only enhance learning and recognition performance, but it can also induce a system to better generalizeits learned behaviour to instances never encountered before. This may explain why for many vital learning tasks in organisms only a minimal exposure to relevant stimuli is necessary. 1 Leiden Connectionist Group, Unit of Experimental and Theoretical Psychology, P.O. Box 9555, 2300 RB Leiden, The Netherlands.  Happel@rulfsw.leidenuniv.nl. 2 The second author is presently at the MRC Applied Psychology Unit, 15 Chaucer Road, Cambridge CB2 2EF, United Kingdom.  Jaap.Murre@mrc-apu.cam.ac.uk. This work is sponsored in part by the Dutch Foundation for Neural Networks and by the Medical Research Council. Happel and Murre The design and evolution of modular neural networks2
Concepts for mars on-orbit robotic sample capture and transfer

A potential Mars Sample Return (MSR) mission would require robotic autonomous capture and manipulation of an Orbital Sample (OS) before returning the samples to Earth. An orbiter would capture the OS, manipulate to a preferential orientation, transition it through the steps required to break-the-chain with Mars, stowing it in a containment vessel or an Earth Entry Vehicle (EEV) and providing redundant containment to the OS (for example by closing and sealing the lid of the EEV). In this paper, we discuss the trade-space of concepts generated for both the individual aspects of capture and manipulation of the OS, as well as concepts for the end-to-end system. Notably, we discuss concepts for OS capture, manipulation of the OS to orient it to a preferred configuration, and steps for transitioning the OS between different stages of manipulation, ultimately securing it in a containment vessel or Earth Entry Vehicle.
Implementing Mobile Ad Hoc Networking (MANET) over Legacy Tactical Radio Links

Mobile Ad Hoc Networking (MANET) is a key technology enabler in the tactical communication domain for the Network Centric Warfare.[1] A self-forming, self-healing, infrastructure-less network consisting of mobile nodes is very attractive for beyond line of sight (BLOS) voice and data range extension as well as tactical networking applications in general. Current research and development mostly focus on implementing MANET over new wideband data waveforms. However, a large number of currently fielded tactical radios and the next generation software defined radios (SDR) support various legacy tactical radio waveforms. A mobile ad hoc network over such legacy tactical radio links not only provides war fighters mission critical networking applications such as Situational Awareness and short payload messaging, the MANET nodes can also support voice and legacy data interoperation with the existing fielded legacy radios. Furthermore, the small spectrum footprint of current narrowband tactical radio waveforms can be complementary to the new wideband data waveforms for providing networking access in a spectrum constrained environment. This paper first describes the networking usage requirements for MANET over legacy narrowband tactical waveforms. Next, the common characteristics of legacy tactical radio waveforms and the implications of such characteristics for the MANET implementation are discussed. Then an actual MANET implementation over a legacy tactical radio waveform on a SDR is presented with the results of actual field tests. Finally, several improvements to this implementation are proposed.