nils-herrmann/clean_scirepeval huggingface dataset:
Analyzing software development as a noncooperative game

A significant number of failures of software projects are widely attributed to poor requirements gathering and making various errors in specifications, choosing an incorrect architecture, following a wrong design and development model, and incurring significant cost in the maintenance stage. While these and other reasons are correct, they are based on an assumption that everyone involved in a software project is driven to make it successful and agrees on the goals and the methods of how to achieve that success. However, each team participant views the ultimate success of the project differently in terms of his/her personal goals. These different views may result in conflicting decisions by team participants that affect the overall success of the project. In this paper we analyze software projects as noncooperative games and show how to use the tools and techniques of game theory to uncover some hidden causes of failures of software projects and we suggest ways to fix them.
PRISM: probabilistic model checking for performance and reliability analysis

Probabilistic model checking is a formal verification technique for the modelling and analysis of stochastic systems. It has proved to be useful for studying a wide range of quantitative properties of models taken from many diffierent application domains. This includes, for example, performance and reliability properties of computer and communication systems. In this paper, we give an overview of the probabilistic model checking tool PRISM, focusing in particular on its support for continuous-time Markov chains and Markov reward models, and how these can be used to analyse performability properties.
Sparsely sampled Fourier ptychography.

Fourier ptychography (FP) is an imaging technique that applies angular diversity functions for high-resolution complex image recovery. The FP recovery routine switches between two working domains: the spectral and spatial domains. In this paper, we investigate the spectral-spatial data redundancy requirement of the FP recovery process. We report a sparsely sampled FP scheme by exploring the sampling interplay between these two domains. We demonstrate the use of the reported scheme for bypassing the high-dynamic-range combination step in the original FP recovery routine. As such, it is able to shorten the acquisition time of the FP platform by ~50%. As a special case of the sparsely sample FP, we also discuss a sub-sampled scheme and demonstrate its application in solving the pixel aliasing problem plagued in the original FP algorithm. We validate the reported schemes with both simulations and experiments. This paper provides insights for the development of the FP approach.
The Utilibot Project : An Autonomous Mobile Robot Based on Utilitarianism

As autonomous mobile robots (AMRs) begin living in the home, performing service tasks and assisting with daily activities, their actions will have profound ethical implications. Consequently, AMRs need to be outfitted with the ability to act morally with regard to human life and safety. Yet, in the area of robotics where morality is a relevant field of endeavor (i.e. human-robot interaction) the sub-discipline of morality does not exist. In response, the Utilibot project seeks to provide a point of initiation for the implementation of ethics in an AMR. The Utilibot is a decision-theoretic AMR guided by the utilitarian notion of the maximization of human well-being. The core ethical decision-making capacity of the Utilibot consists of two dynamic Bayesian networks that model human and environmental health, a dynamic decision network that accounts for decisions and utilities, and a Markov decision process (MDP) that decomposes the planning problem to solve for the optimal course of action to maximize human safety and well-being.
MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection

A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence. With the present contribution, a large-scale fully-labeled image dataset is provided, and made publicly and freely available to the research community. The current dataset entitled MCIndoor20000 includes more than 20,000 digital images from three different indoor object categories, including doors, stairs, and hospital signs. To make a comprehensive dataset addressing current challenges that exist in indoor objects modeling, we cover a multiple set of variations in images, such as rotation, intra-class variation plus various noise models. The current dataset is freely and publicly available at https://github.com/bircatmcri/MCIndoor20000.
Intracranial EEG Correlates of Expectancy and Memory Formation in the Human Hippocampus and Nucleus Accumbens

The human brain is adept at anticipating upcoming events, but in a rapidly changing world, it is essential to detect and encode events that violate these expectancies. Unexpected events are more likely to be remembered than predictable events, but the underlying neural mechanisms for these effects remain unclear. We report intracranial EEG recordings from the hippocampus of epilepsy patients, and from the nucleus accumbens of depression patients. We found that unexpected stimuli enhance an early (187 ms) and a late (482 ms) hippocampal potential, and that the late potential is associated with successful memory encoding for these stimuli. Recordings from the nucleus accumbens revealed a late potential (peak at 475 ms), which increases in magnitude during unexpected items, but no subsequent memory effect and no early component. These results are consistent with the hypothesis that activity in a loop involving the hippocampus and the nucleus accumbens promotes encoding of unexpected events.
Five-Level Inverter for Renewable Power Generation System

In this paper, a five-level inverter is developed and applied for injecting the real power of the renewable power into the grid to reduce the switching power loss, harmonic distortion, and electromagnetic interference caused by the switching operation of power electronic devices. Two dc capacitors, a dual-buck converter, a full-bridge inverter, and a filter configure the five-level inverter. The input of the dual-buck converter is two dc capacitor voltage sources. The dual-buck converter converts two dc capacitor voltage sources to a dc output voltage with three levels and balances these two dc capacitor voltages. The output voltage of the dual-buck converter supplies to the full-bridge inverter. The power electronic switches of the full-bridge inverter are switched in low frequency synchronous with the utility voltage to convert the output voltage of the dual-buck converter to a five-level ac voltage. The output current of the five-level inverter is controlled to generate a sinusoidal current in phase with the utility voltage to inject into the grid. A hardware prototype is developed to verify the performance of the developed renewable power generation system. The experimental results show that the developed renewable power generation system reaches the expected performance.
Feature selection using multiple auto-encoders

Real-world data such as medical images and sensor measurements is usually high-dimensional and limited. Using such datasets directly in machine learning tasks can lead to poor generalization. Feature learning is a general approach for transforming high-dimensional data points to a representational space with lower dimensionality. Machine learning models can be trained efficiently with such representations. In this paper, a novel feature selection method based on multiple trained sparse auto-encoders (SAEs) is described. It works by selecting diverse, non-redundant features from multiple pinched SAEs with very narrow hidden layers, and then using these features in a more appropriately sized classifier without further feature tuning. The feature learning ability of the method is evaluated in a handwritten digits recognition task. Results show that this type of feature selection provides improved representations for a softmax classifier, and that using pinched SAEs produces results equal to or better than regular SAEs.
57.5GHz bandwidth 4.8Vpp swing linear modulator driver for 64GBaud m-PAM systems

A novel series-stacked large swing push-pull MOS-HBT driver was implemented in 55nm SiGe BiCMOS technology. The circuit achieves 4.8Vpp differential swing, 57.5GHz band-width and has an output compression point of 12 dBm per side. 4-PAM and 8-PAM eye diagrams were measured at 56 GBaud for a record data rate of 168 Gb/s. 4-PAM 64GBaud eye diagrams were also demonstrated. The circuit consumes 820/600 mW with/without the predriver, for an energy efficiency of 4.88/3.57 pJ/b.
EdgeChain: An Edge-IoT Framework and Prototype Based on Blockchain and Smart Contracts

The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On the one hand, IoT devices are “weak” and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively “vulnerable” facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named “EdgeChain” based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or “coin” system to link the edge cloud resource pool with each IoT device’ account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on pre-defined rules on priority, application types and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a non-deniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.
Mortality salience and the uncanny valley

It seems natural to assume that the more closely robots come to resemble people, the more likely they are to elicit the kinds of responses people direct toward each other. However, subtle flaws in appearance and movement only seem eerie in very human-like robots. This uncanny phenomenon may be symptomatic of entities that elicit a model of a human other but do not measure up to it. If so, a very human-like robot may provide the best means of finding out what kinds of behavior are perceived as human, since deviations from a human other are more obvious. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that an uncanny robot elicits an innate fear of death and culturally-supported defenses for coping with death's inevitability. An experiment, which borrows from the methods of terror management research, was performed to test this hypothesis. Across all questions subjects who were exposed to a still image of an uncanny humanlike robot had on average a heightened preference for worldview supporters and a diminished preference for worldview threats relative to the control group
Human emotion and the uncanny valley: A GLM, MDS, and Isomap analysis of robot video ratings

The eerie feeling attributed to human-looking robots and animated characters may be a key factor in our perceptual and cognitive discrimination of the human and humanlike. This study applies regression, the generalized linear model (GLM), factor analysis, multidimensional scaling (MDS), and kernel isometric mapping (Isomap) to analyze ratings of 27 emotions of 18 moving figures whose appearance varies along a human likeness continuum. The results indicate (1) Attributions of eerie and creepy better capture our visceral reaction to an uncanny robot than strange. (2) Eerie and creepy are mainly associated with fear but also shocked, disgusted, and nervous. Strange is less strongly associated with emotion. (3) Thus, strange may be more cognitive, while eerie and creepy are more perceptual/emotional. (4) Human features increase ratings of human likeness. (5) Women are slightly more sensitive to eerie and creepy than men; and older people may be more willing to attribute human likeness to a robot despite its eeriness.
Emerging Principles of Gene Expression Programs and Their Regulation.

Many mechanisms contribute to regulation of gene expression to ensure coordinated cellular behaviors and fate decisions. Transcriptional responses to external signals can consist of many hundreds of genes that can be parsed into different categories based on kinetics of induction, cell-type and signal specificity, and duration of the response. Here we discuss the structure of transcription programs and suggest a basic framework to categorize gene expression programs based on characteristics related to their control mechanisms. We also discuss possible evolutionary implications of this framework.
Compassion: an evolutionary analysis and empirical review.

What is compassion? And how did it evolve? In this review, we integrate 3 evolutionary arguments that converge on the hypothesis that compassion evolved as a distinct affective experience whose primary function is to facilitate cooperation and protection of the weak and those who suffer. Our empirical review reveals compassion to have distinct appraisal processes attuned to undeserved suffering; distinct signaling behavior related to caregiving patterns of touch, posture, and vocalization; and a phenomenological experience and physiological response that orients the individual to social approach. This response profile of compassion differs from those of distress, sadness, and love, suggesting that compassion is indeed a distinct emotion. We conclude by considering how compassion shapes moral judgment and action, how it varies across different cultures, and how it may engage specific patterns of neural activation, as well as emerging directions of research.
An in-pipe robot with underactuated parallelogram crawler modules

In this paper, we present a new in-pipe robot with independent underactuated parallelogram crawler modules, which can automatically overcome inner obstacles in the pipes. The parallelogram crawler modules are adopted to maintain the anterior-posterior symmetry of forward and backward movements, and a simple differential mechanism based on a pair of spur gears is installed to provide underactuated mechanisms. A central base unit connects each crawler module through foldable pantograph mechanisms. To verify the basic behavior of this robot, primary experiments in pipes with different diameters and at partial steps were conducted.
Analysis of YouTube’s traffic adaptation to dynamic environments

The popular Internet service, YouTube, has adopted by default the HyperText Markup Language version 5 (HTML5). With this adoption, YouTube has moved to Dynamic Adaptive Streaming over HTTP (DASH) as Adaptive BitRate (ABR) video streaming technology. Furthermore, rate adaptation in DASH is solely receiver-driven. This issue motivates this work to make a deep analysis of YouTube’s particular DASH implementation. Firstly, this article provides a state of the art about DASH and adaptive streaming technology, and also YouTube traffic characterization related work. Secondly, this paper describes a new methodology and test-bed for YouTube’s DASH implementation traffic characterization and performance measurement. This methodology and test-bed do not make use of proxies and, moreover, they are able to cope with YouTube traffic redirections. Finally, a set of experimental results are provided, involving a dataset of 310 YouTube’s videos. The depicted results show a YouTube’s traffic pattern characterization and a discussion about allowed download bandwidth, YouTube’s consumed bitrate and quality of the video. Moreover, the obtained results are cross-validated with the analysis of HTTP requests performed by YouTube’s video player. The outcomes of this article are applicable in the field of Quality of Service (QoS) and Quality of Experience (QoE) management. This is valuable information for Internet Service Providers (ISPs), because QoS management based on assured download bandwidth can be used in order to provide a target end-user’s QoE when YouTube service is being consumed.
An ef fi cient and effective convolutional auto-encoder extreme learning machine network for 3 d feature learning

3D shape features play a crucial role in graphics applications, such as 3D shape matching, recognition, and retrieval. Various 3D shape descriptors have been developed over the last two decades; however, existing descriptors are handcrafted features that are labor-intensively designed and cannot extract discriminative information for a large set of data. In this paper, we propose a rapid 3D feature learning advantages of the convolutional neuron network, auto-encoder, and extreme learning machine (ELM). This method performs better and faster than other methods. In addition, we define a novel architecture based on CAE-ELM. The architecture accepts two types of 3D shape representation, namely, voxel data and signed distance field data (SDF), as inputs to extract the global and local features of 3D shapes. Voxel data describe structural information, whereas SDF data contain details on 3D shapes. Moreover, the proposed CAE-ELM can be used in practical graphics applications, such as 3D shape completion. Experiments show that the features extracted by CAE-ELM are superior to existing hand-crafted features and other deep learning methods or ELM models. Moreover, the classification accuracy of the proposed architecture is superior to that of other methods on ModelNet10 (91.4%) and ModelNet40 (84.35%). The training process also runs faster than existing deep learning methods by approximately two orders of magnitude. & 2015 Elsevier B.V. All rights reserved.
Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images

Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task. To this end, we present a novel automatic segmentation method, termed as Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly, considering that the traditional learningbased segmentation methods normally employ either whole images or squared patches as the training samples, we innovatively sample the orthogonal non-squared patches (namely crossbar patches), to fully cover the whole kidney tumors in either horizontal or vertical directions. These sampled crossbar patches could not only represent the detailed local information of kidney tumor as the traditional patches, but also describe the global appearance from either horizontal or vertical direction using contextual information. Secondly, with the obtained crossbar patches, we trained a convolutional neural network with two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate the segmentation results from two directions (i.e., horizontal and vertical). This cascaded training strategy could effectively guarantee the consistency between submodels, by feeding each other with the most difficult samples, for a better segmentation. In the experiment, we evaluate our method on a real CT kidney tumor dataset, collected from 94 different patients including 3,500 images. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior results of our method on dice ratio score, true positive fraction, centroid distance and Hausdorff distance. Moreover, we have extended our crossbar-net to a different task: cardiac segmentation, showing the promising results for the better generalization.
Explaining unplanned online media behaviors: Dual system theory models of impulsive use and swearing on social networking sites

Unplanned (i.e., spontaneous) online behaviors such as impulsive use of social networking sites (SNS) and swearing on SNS are prevalent and can adversely affect users and society. Drawing on dual system theory, this paper conceptualizes and empirically investigates the etiology of such behaviors. Results of two studies (n1=295 SNS users, focuses on impulsive use; n2=336 SNS users, focuses on swearing) show that both of these online behaviors are driven, in part, by cognitive-emotional preoccupation with the SNS and inhibited, in part, by cognitive-behavioral control over using the SNS. The inhibition effect is both direct and indirect, through the suppression of the cognitive-emotional preoccupation effects. The findings provide a theoretical lens of analysis through which impulsive media use behaviors can be explained.
A Dual Decomposition Approach to Feature Correspondence

In this paper, we present a new approach for establishing correspondences between sparse image features related by an unknown nonrigid mapping and corrupted by clutter and occlusion, such as points extracted from images of different instances of the same object category. We formulate this matching task as an energy minimization problem by defining an elaborate objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general an NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples, DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods.
Text Classification using the Concept of Association Rule of Data Mining

As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper we will discuss a procedure of classifying text using the concept of association rule of data mining. Association rule mining technique has been used to derive feature set from pre-classified text documents. Naïve Bayes classifier is then used on derived features for final classification.
DIP: the Database of Interacting Proteins

The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. This database is intended to provide the scientific community with a comprehensive and integrated tool for browsing and efficiently extracting information about protein interactions and interaction networks in biological processes. Beyond cataloging details of protein-protein interactions, the DIP is useful for understanding protein function and protein-protein relationships, studying the properties of networks of interacting proteins, benchmarking predictions of protein-protein interactions, and studying the evolution of protein-protein interactions.