nils-herrmann/clean_scirepeval huggingface dataset:
Low-power 4-2 and 5-2 compressors

This paper explores various low power higher order compressors such as 4-2 and 5-2 compressor units. These compressors are building blocks for binary multipliers. Various circuit architectures for 4-2 compressors are compared with respect to their delay and power consumption. The different circuits are simulated using HSPICE. A new circuit for a 5-2 compressor is then presented which is 12% faster and consumes 37% less power.
ECOFEN: An End-to-end energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks

Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.
The psychometric properties and utility of the Short Sadistic Impulse Scale (SSIS).

Sadistic personality disorder (SPD) has been underresearched and often misunderstood in forensic settings. Furthermore, personality disorders in general are the subject of much controversy in terms of their classification (i.e., whether they should be categorical or dimensional). The Sadistic Attitudes and Behaviors Scale (SABS; Davies & Hand, 2003; O'Meara, Davies, & Barnes-Holmes, 2004) is a recently developed scale for measuring sadistic inclinations. Derived from this is the Short Sadistic Impulse Scale (SSIS), which has proved to be a strong unidimensional measure of sadistic inclination. Through cumulative scaling, it was investigated whether the SSIS could measure sadism on a continuum of interest, thus providing a dimensional view of the construct. Further, the SSIS was administered along with a number of other measures related to sadism in order to assess the validity of the scale. Results showed that the SSIS has strong construct and discriminant validity and may be useful as a screening measure for sadistic impulse.
Triple bands antenna and high efficiency rectifier design for RF energy harvesting at 900, 1900 and 2400 MHz

We present the design and development of a novel antenna that effectively radiates at three frequency bands to harvest RF energy from cellular network frequency bands (900 MHz and 1900 MHz) and Wi-Fi sources (2.4 GHz) available in ambience. The antenna is designed using the combination of three different design techniques including composite right/left hand transmission line (CRLH). In addition, a high efficiency, sensitive triple-band rectifier is also proposed in this work. The rectifier provides a maximum conversion efficiency of 80%, 46% and 42% at 940MHz, 1.95GHz and 2.44 GHz, respectively. The experimental results demonstrate that the proposed triple-band RF energy harvesting system can collect 6.6 times more power than the single 900MHz band one and 3.4 times more power than three individual bands combined.
Large-vocabulary audio-visual speech recognition by machines and humans

We compare automatic recognition with human perception of audio-visual speech, in the large-vocabulary, continuous speech recognition (LVCSR) domain. Specifically, we study the benefit of the visual modality for both machines and humans, when combined with audio degraded by speech-babble noise at various signal-to-noise ratios (SNRs). We first consider an automatic speechreading system with a pixel based visual front end that uses feature fusion for bimodal integration, and we compare its performance with an audio-only LVCSR system. We then describe results of human speech perception experiments, where subjects are asked to transcribe audio-only and audiovisual utterances at various SNRs. For both machines and humans, we observe approximately a 6 dB effective SNR gain compared to the audio-only performance at 10 dB, however such gains significantly diverge at other SNRs. Furthermore, automatic audio-visual recognition outperforms human audioonly speech perception at low SNRs.
Netprobe: a fast and scalable system for fraud detection in online auction networks

Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe is both efficient and effective for fraud detection. We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges, where NetProbe was able to spot fraudulent nodes with over 90% precision and recall, within a matter of seconds. We also report experiments on a real dataset crawled from eBay, with nearly 700,000 transactions between more than 66,000users, where NetProbe was highly effective at unearthing hidden networks of fraudsters, within a realistic response time of about 6 minutes. For scenarios where the underlying data is dynamic in nature, we propose IncrementalNetProbe, which is an approximate, but fast, variant of NetProbe. Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe, while retaining over 99% of its accuracy.
Reward, Motivation, and Reinforcement Learning

There is substantial evidence that dopamine is involved in reward learning and appetitive conditioning. However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concerning the impact of motivational manipulations, show that these models are unreasonable. We review the data and consider the involvement of a rich collection of different neural systems in various aspects of these forms of conditioning. Dopamine plays a pivotal, but complicated, role.
A framework for improved video text detection and recognition

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.
Color Portraits: From Color Picking to Interacting with Color

Although ubiquitous, color pickers have remained largely unchanged for 25 years. Based on contextual interviews with artists and designers, we created the Color Portraits design space to characterize five key color manipulation activities: sampling and tweaking individual colors, manipulating color relationships, combining colors with other elements, revisiting previous color choices, and revealing a design process through color. We found similar color manipulation requirements with scientists and engineers. We designed novel color interaction tools inspired by the design space, and used them as probes to identify specific design requirements, including: interactive palettes for sampling colors and exploring relationships; color composites for blending and decomposing colors with other elements; interactive histories to enable reuse of previous color choices; and providing color as a way to reveal underlying processes. We argue that color tools should allow users to interact with colors, not just pick or sample them.
Dynamic MRI reconstruction using low rank plus sparse tensor decomposition

In this paper, we introduce a multi-dimensional approach to the problem of reconstruction of MR image sequences that are highly undersampled in k-space. By formulating the reconstruction as a high-order low-rank plus sparse tensor decomposition problem, we propose an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) to solve the optimization. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the state-of-the-art reconstruction methods.
Alpha-structural recursion and induction

The nominal approach to abstract syntax deals with the issues of bound names and α-equivalence by considering constructions and properties that are invariant with respect to permuting names. The use of permutations gives rise to an attractively simple formalization of common, but often technically incorrect uses of structural recursion and induction for abstract syntax modulo α-equivalence. At the heart of this approach is the notion of finitely supported mathematical objects. This article explains the idea in as concrete a way as possible and gives a new derivation within higher-order classical logic of principles of α-structural recursion and induction for α-equivalence classes from the ordinary versions of these principles for abstract syntax trees.
The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of “tricks”. The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
Interactions Between Philosophy and Artificial Intelligence: The Role of Intuition and Non-Logical Reasoning in Intelligence

This paper echoes, from a philosophical standpoint, the claim of McCarthy and Hayes that Philosophy and Artificial Intelligence have important relations. Philosophical problems about the use of ’intuition’ in reasoning are related, via a concept of analogical representation, to problems in the simulation of perception, problem-solving and the generation of useful sets of possibilities in considering how to act. The requirements for intelligent decision-making proposed by McCarthy and Hayes are criticised as too narrow, and more general requirements are suggested instead.
A COMPREHENSIVE REVIEW OF THE ENTERPRISE SYSTEMS RESEARCH

Enterprise systems (ES) can be considered as a novel phenomenon for the information system research and other academic fields (e.g. operations and supply chain), which has opened an imm ense potential and opportunities for research. Although the interest of the scholars on ES is recent, the number of publications is continuously growing since 2000. The aim of this paper is to review a sample of important contributions of the ES works published to date. To do this, the selected works have been classified in four key topics: business implications, technical issues, managerial issues, and implementation issues.
German S2k guidelines for the therapy of pathological scars (hypertrophic scars and keloids).

Preamble The present guideline was primarily prepared by representatives of the field dermatology and dermatosurgery and is based on the S1 guideline of the German Society of Dermatology from 2004 . Besides the dermatologists many other disciplines are involved in the treatment of keloids. The German Society of Plastic, Reconstructive and Aesthetic Surgeons participated in the review process of this guideline and fundamentally agrees with the content. For further update inclusion of all relevant medical societies already in the initial stage as well as an even more detailed depiction of surgical therapy options are planned.
A 3D object detection and pose estimation pipeline using RGB-D images

3D object detection and pose estimation has been studied extensively in recent decades for its potential applications in robotics. However, there still remains challenges when we aim at detecting multiple objects while retaining low false positive rate in cluttered environments. This paper proposes a robust 3D object detection and pose estimation pipeline based on RGB-D images, which can detect multiple objects simultaneously while reducing false positives. Detection begins with template matching and yields a set of template matches. A clustering algorithm then groups templates of similar spatial location and produces multiple-object hypotheses. A scoring function evaluates the hypotheses using their associated templates and non-maximum suppression is adopted to remove duplicate results based on the scores. Finally, a combination of point cloud processing algorithms are used to compute objects' 3D poses. Existing object hypotheses are verified by computing the overlap between model and scene points. Experiments demonstrate that our approach provides competitive results comparable to the state-of-the-arts and can be applied to robot random bin-picking.
Distribution expansion planning considering reliability and security of energy using modified PSO ( Particle Swarm Optimization ) algorithm

Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented.
Learning from positive and unlabeled examples

In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications.
A Neural Schema Architecture for Autonomous Robots

As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema  software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots , cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cogniti ve Psychology, with applications in Distributed Artificial Intell igence (DAI), Autonomous Agents and Robotics.  Robotics, Agent-oriented programming, Neural
Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex.

To make economic choices between goods, the brain needs to compute representations of their values. A great deal of research has been performed to determine the neural correlates of value representations in the human brain. However, it is still unknown whether there exists a region of the brain that commonly encodes decision values for different types of goods, or if, in contrast, the values of different types of goods are represented in distinct brain regions. We addressed this question by scanning subjects with functional magnetic resonance imaging while they made real purchasing decisions among different categories of goods (food, nonfood consumables, and monetary gambles). We found activity in a key brain region previously implicated in encoding goal-values: the ventromedial prefrontal cortex (vmPFC) was correlated with the subjects' value for each category of good. Moreover, we found a single area in vmPFC to be correlated with the subjects' valuations for all categories of goods. Our results provide evidence that the brain encodes a "common currency" that allows for a shared valuation for different categories of goods.
Facial emotion recognition using deep convolutional networks

Facial emotion recognition is an emerging field which use in many nowadays application including social robots, neuromarketing and games. Non-verbal communication methods like facial expressions, eye movement and gestures are used in many application of human computer interaction, which among them facial emotion is widely used because it convey the emotional states and feelings of persons. The emotion recognition is not an easy task because there is no landmark distinction between the emotions on the face and also there are a lot of complexity and variability. In the traditional machine learning algorithm some important extracted features used for modeling the face, so, it can not achieve high accuracy rate for recognition of emotion because the features are hand-engineered and depend on prior knowledge. Convolutional neural networks (CNN) have developed in this work for recognition facial emotion expression and classify them into seven basic categories. Instead of calculating hand-engineered features, CNN calculates features by learning automatically. The novelty of the proposed method is using facial action units (AUs) of the face which first these units are recognized by C NN and incorporate to recognizing the seven basic emotion states. To evaluated the proposed model, Cohn-Kanade database is used so that the model achieves the best accuracy rate 97.01 by incorporating AU while other works in the literature used a direct CNN and achieve accuracy rate 95.75.
Predictors of Computer Use in Community-Dwelling, Ethnically Diverse Older Adults

In this study, we analyzed self-reported computer use, demographic variables, psychosocial variables, and health and well-being variables collected from 460 ethnically diverse, community-dwelling elders to investigate the relationship computer use has with demographics, well-being, and other key psychosocial variables in older adults.



Although younger elders with more education, those who employ active coping strategies, or those who are low in anxiety levels are thought to use computers at higher rates than do others, previous research has produced mixed or inconclusive results regarding ethnic, gender, and psychological factors or has concentrated on computer-specific psychological factors only (e.g., computer anxiety). Few such studies have employed large sample sizes or have focused on ethnically diverse populations of community-dwelling elders.



With a large number of overlapping predictors, zero-order analysis alone is poorly equipped to identify variables that are independently associated with computer use. Accordingly, both zero-order and stepwise logistic regression analyses were conducted to determine the correlates of two types of computer use: e-mail and general computer use.



Results indicate that younger age, greater level of education, non-Hispanic ethnicity, behaviorally active coping style, general physical health, and role-related emotional health each independently predicted computer usage.



Study findings highlight differences in computer usage, especially in regard to Hispanic ethnicity and specific health and well-being factors.



Potential applications of this research include future intervention studies, individualized computer-based activity programming, or customizable software and user interface design for older adults responsive to a variety of personal characteristics and capabilities.