nils-herrmann/clean_scirepeval huggingface dataset:
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Evolutionary Algorithms (EAs) are a fascinating branch of computational intelligence with much potential for use in many application areas. The fundamental principle of EAs is to use ideas inspired by the biological mechanisms observed in nature, such as selection and genetic changes, to find the best solution for a given optimization problem. Generally, EAs use iterative processes, by growing a population of solutions selected in a guided random search and using parallel processing, in order to achieve a desired result. Such population based approaches, for example particle swarm and ant colony optimization (inspired from biology), are among the most popular metaheuristic methods being used in machine learning, along with others such as the simulated annealing (inspired from thermodynamics). In this paper, we provide a short survey on the state-of-the-art of EAs, beginning with some background on the theory of evolution and contrasting the original ideas of Darwin and Lamarck; we then continue with a discussion on the analogy between biological and computational sciences, and briefly describe some fundamentals of EAs, including the Genetic Algorithms, Genetic Programming, Evolution Strategies, Swarm Intelligence Algorithms (i.e., Particle Swarm Optimization, Ant Colony Optimization, Bacteria Foraging Algorithms, Bees Algorithm, Invasive Weed Optimization), Memetic Search, Differential Evolution Search, Artificial Immune Systems, Gravitational Search Algorithm, Intelligent Water Drops Algorithm. We conclude with a short description of the usefulness of EAs for Knowledge Discovery and Data Mining tasks and present some open problems and challenges to further stimulate research.
Reducing the Dimensionality of Data with Neural

http://www.sciencemag.org/cgi/content/full/313/5786/504 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/cgi/content/full/313/5786/504/DC1 can be found at: Supporting Online Material found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/313/5786/504#related-content http://www.sciencemag.org/cgi/content/full/313/5786/504#otherarticles , 6 of which can be accessed for free: cites 8 articles This article 15 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/313/5786/504#otherarticles 4 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining
Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage

While applications for mobile devices have become extremely important in the last few years, little public information exists on mobile application usage behavior. We describe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descriptive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an application lasts less than a minute, even though users spend almost an hour a day using their phones. Our contextual findings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data.
Hi YouTube!: personality impressions and verbal content in social video

Despite the evidence that social video conveys rich human personality information, research investigating the automatic prediction of personality impressions in vlogging has shown that, amongst the Big-Five traits, automatic nonverbal behavioral cues are useful to predict mainly the Extraversion trait. This finding, also reported in other conversational settings, indicates that personality information may be coded in other behavioral dimensions like the verbal channel, which has been less studied in multimodal interaction research. In this paper, we address the task of predicting personality impressions from vloggers based on what they say in their YouTube videos. First, we use manual transcripts of vlogs and verbal content analysis techniques to understand the ability of verbal content for the prediction of crowdsourced Big-Five personality impressions. Second, we explore the feasibility of a fully-automatic framework in which transcripts are obtained using automatic speech recognition (ASR). Our results show that the analysis of error-free verbal content is useful to predict four of the Big-Five traits, three of them better than using nonverbal cues, and that the errors caused by the ASR system decrease the performance significantly.
Parallel Hough Transform-Based Straight Line Detection and Its FPGA Implementation in Embedded Vision

Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware acceleration to reap tremendous computing performance. In this paper, we propose a novel parallel Hough Transform (PHT) and FPGA architecture-associated framework for real-time straight line detection in high-definition videos. A resource-optimized Canny edge detection method with enhanced non-maximum suppression conditions is presented to suppress most possible false edges and obtain more accurate candidate edge pixels for subsequent accelerated computation. Then, a novel PHT algorithm exploiting spatial angle-level parallelism is proposed to upgrade computational accuracy by improving the minimum computational step. Moreover, the FPGA based multi-level pipelined PHT architecture optimized by spatial parallelism ensures real-time computation for 1,024 × 768 resolution videos without any off-chip memory consumption. This framework is evaluated on ALTERA DE2-115 FPGA evaluation platform at a maximum frequency of 200 MHz, and it can calculate straight line parameters in 15.59 ms on the average for one frame. Qualitative and quantitative evaluation results have validated the system performance regarding data throughput, memory bandwidth, resource, speed and robustness.
Scheduling Processes with Release Times, Deadlines, Precedence, and Exclusion Relations

We present an algorithm that finds an optimal schedule on a single processor for a given set of processes such that each process starts executing after its release time and completes its computation before its deadline, and a given set of precedence relations and a given set of exclusion relations defined on ordered pairs of process segments are satisfied. This algorithm can be applied to the important and previously unsolved problem of automated pre-run-time scheduling of processes with arbitrary precedence and exclusion relations in hard-
Deeper, Broader and Artier Domain Generalization

The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there are target domains with distinct characteristics, yet sparse data for training. For example recognition in sketch images, which are distinctly more abstract and rarer than photos. Nevertheless, DG methods have primarily been evaluated on photo-only benchmarks focusing on alleviating the dataset bias where both problems of domain distinctiveness and data sparsity can be minimal. We argue that these benchmarks are overly straightforward, and show that simple deep learning baselines perform surprisingly well on them. In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG benchmark dataset covering photo, sketch, cartoon and painting domains. This is both more practically relevant, and harder (bigger domain shift) than existing benchmarks. The results show that our method outperforms existing DG alternatives, and our dataset provides a more significant DG challenge to drive future research.
Visual Tracking of High DOF Articulated Structures: an Application to Human Hand Tracking

Passive sensing of human hand and limb motion is important for a wide range of applications from human-computer interaction to athletic performance measurement. High degree of freedom articulated mechanisms like the human hand are di cult to track because of their large state space and complex image appearance. This article describes a model-based hand tracking system, called DigitEyes, that can recover the state of a 27 DOF hand model from ordinary gray scale images at speeds of up to 10 Hz.
Risk perception and risk management in cloud computing: Results from a case study of Swiss companies

In today’s economic turmoil, the pay-per-use pricing model of cloud computing, its flexibility and scalability and the potential for better security and availability levels are alluring to both SMEs and large enterprises. However, cloud computing is fraught with security risks which need to be carefully evaluated before any engagement in this area. This article elaborates on the most important risks inherent to the cloud such as information security, regulatory compliance, data location, investigative support, provider lock-in and disaster recovery. We focus on risk and control analysis in relation to a sample of Swiss companies with regard to their prospective adoption of public cloud services. We observe a sufficient degree of risk awareness with a focus on those risks that are relevant to the IT function to be migrated to the cloud. Moreover, the recommendations as to the adoption of cloud services depend on the company’s size with larger and more technologically advanced companies being better prepared for the cloud. As an exploratory first step, the results of this study would allow us to design and implement broader research into cloud computing risk management in Switzerland.
Increases in Drug and Opioid Overdose Deaths--United States, 2000-2014.

The United States is experiencing an epidemic of drug overdose (poisoning) deaths. Since 2000, the rate of deaths from drug overdoses has increased 137%, including a 200% increase in the rate of overdose deaths involving opioids (opioid pain relievers and heroin). CDC analyzed recent multiple cause-of-death mortality data to examine current trends and characteristics of drug overdose deaths, including the types of opioids associated with drug overdose deaths. During 2014, a total of 47,055 drug overdose deaths occurred in the United States, representing a 1-year increase of 6.5%, from 13.8 per 100,000 persons in 2013 to 14.7 per 100,000 persons in 2014. The rate of drug overdose deaths increased significantly for both sexes, persons aged 25-44 years and ≥55 years, non-Hispanic whites and non-Hispanic blacks, and in the Northeastern, Midwestern, and Southern regions of the United States. Rates of opioid overdose deaths also increased significantly, from 7.9 per 100,000 in 2013 to 9.0 per 100,000 in 2014, a 14% increase. Historically, CDC has programmatically characterized all opioid pain reliever deaths (natural and semisynthetic opioids, methadone, and other synthetic opioids) as "prescription" opioid overdoses (1). Between 2013 and 2014, the age-adjusted rate of death involving methadone remained unchanged; however, the age-adjusted rate of death involving natural and semisynthetic opioid pain relievers, heroin, and synthetic opioids, other than methadone (e.g., fentanyl) increased 9%, 26%, and 80%, respectively. The sharp increase in deaths involving synthetic opioids, other than methadone, in 2014 coincided with law enforcement reports of increased availability of illicitly manufactured fentanyl, a synthetic opioid; however, illicitly manufactured fentanyl cannot be distinguished from prescription fentanyl in death certificate data. These findings indicate that the opioid overdose epidemic is worsening. There is a need for continued action to prevent opioid abuse, dependence, and death, improve treatment capacity for opioid use disorders, and reduce the supply of illicit opioids, particularly heroin and illicit fentanyl.
HOT BUTTONS AND TIME SINKS: THE EFFECTS OF ELECTRONIC COMMUNICATION DURING NONWORK TIME ON EMOTIONS AND WORK-NONWORK CONFLICT

As advances in communication technologies have made organizations more easily connected to their workforce outside of normal work hours, there is increased concern that employees may experience heightened work-nonwork conflict when away from the office. The current study investigates the effects of electronic communication received during nonwork time using an experience sampling methodology to examine withinperson relationships among elements of electronic communication (affective tone, time required), emotional responses (anger, happiness), and work-to-nonwork conflict in a sample of 341 working adults surveyed over a seven-day period. Hierarchical linear modeling results suggested that both affective tone and time required were associated with anger, but only affective tone was associated with happiness. Further, anger was associated with work-to-nonwork conflict and mediated the effects of affective tone and time required on work-to-nonwork conflict. Results also revealed cross-level moderating effects of abusive supervision and communication sender together, as well as segmentation preference. Implications of these findings for future theorizing and research on electronic communication during nonwork time are discussed.
Interactive Volume Rendering with Dynamic Ambient Occlusion and Color Bleeding

We propose a method for rendering volumetric data sets at interactive frame rates while supporting dynamic ambient occlusion as well as an approximation to color bleeding. In contrast to ambient occlusion approaches for polygonal data, techniques for volumetric data sets have to face additional challenges, since by changing rendering parameters, such as the transfer function or the thresholding, the structure of the data set and thus the light interactions may vary drastically. Therefore, during a preprocessing step which is independent of the rendering parameters we capture light interactions for all combinations of structures extractable from a volumetric data set. In order to compute the light interactions between the different structures, we combine this preprocessed information during rendering based on the rendering parameters defined interactively by the user. Thus our method supports interactive exploration of a volumetric data set but still gives the user control over the most important rendering parameters. For instance, if the user alters the transfer function to extract different structures from a volumetric data set the light interactions between the extracted structures are captured in the rendering while still allowing interactive frame rates. Compared to known local illumination models for volume rendering our method does not introduce any substantial rendering overhead and can be integrated easily into existing volume rendering applications. In this paper we will explain our approach, discuss the implications for interactive volume rendering and present the achieved results.
Steal my idea! Organizational adoption of user innovations from a user innovation community: A case study of Dell IdeaStorm

⁎ Corresponding author.  E-mail addresses: pdigangi@fsu.edu (P.M. Di Gangi), (M. Wasko). 1 Tel.: +1 850 644 0916. 2 Throughout this manuscript, we interchangeably r When examining a user innovation community, in conceptual ideas proposed by end users that, if adopte action to produce the physical innovation or service. Ou idea is based on its need for maintaining clarity in spe Specifically, innovation is used when discussing the theo adoption while idea is used in the operationalized stud
Strategy and Policy Learning for Non-Task-Oriented Conversational Systems

We propose a set of generic conversational strategies to handle possible system breakdowns in non-task-oriented dialog systems. We also design dialog policies to select among these strategies with respect to different dialog contexts. We combine expert knowledge and the statistical findings that derived from previous collected data in designing these policies. The dialog policy learned via reinforcement learning outperforms the random selection policy and the locally greedy policy in both the simulated and the realworld settings. In addition, we propose three metrics, which consider both the local and global quality of the conversation, to evaluate conversation quality.
An overview of emerging results in cooperative UAV control

Inexpensive fixed wing UAV are increasingly useful in remote sensing operations. They are a cheaper alternative to manned vehicles, and are ideally suited for dangerous or monotonous missions that would be inadvisable for a human pilot. Groups of UAV are of special interest for their abilities to coordinate simultaneous coverage of large areas, or cooperate to achieve goals such as mapping. Cooperation and coordination in UAV groups also allows increasingly large numbers of aircraft to be operated by a single user. Specific applications under consideration for groups of cooperating UAV are border patrol, search and rescue, surveillance, communications relaying, and mapping of hostile territory. The capabilities of small UAV continue to grow with advances in wireless communications and computing power. Accordingly, research topics in cooperative UAV control include efficient computer vision for real-time navigation and networked computing and communication strategies for distributed control, as well as traditional aircraft-related topics such as collision avoidance and formation flight. Emerging results in cooperative UAV control are presented via discussion of these topics, including particular requirements, challenges, and some promising strategies relating to each area. Case studies from a variety of programs highlight specific solutions and recent results, ranging from pure simulation to control of multiple UAV. This wide range of case studies serves as an overview of current problems of Interest, and does not present every relevant result.
Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs

The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent substantial improvement by others dates back 7 years (error rate 0.4%) . Recently we were able to significantly improve this result, using graphics cards to greatly speed up training of simple but deep MLPs, which achieved 0.35%, outperforming all the previous more complex methods. Here we report another substantial improvement: 0.31% obtained using a committee of MLPs.
The role of theory and theorising in Design Science research

The literature on Design Science (or Design Research) has been mixed on the inclusion, form, and role of theory and theorising in Design Science. Some authors have explicitly excluded theory development and testing from Design Science, leaving them to the Natural and Social/Behavioural Sciences. Others propose including theory development and testing as part of Design Science. Others propose some ideas for the content of IS Design Theories, although more detailed and clear concepts would be helpful. This paper discusses the need and role for theory in Design Science. It further proposes some ideas for standards for the form and level of detail needed for theories in Design Science. Finally it develops a framework of activities for the interaction of Design Science with research in other scientific paradigms.
A Centralized Monitoring Infrastructure for Improving DNS Security

Researchers have recently noted (14; 27) the potential of fast poisoning attacks against DNS servers, which allows attackers to easily manipulate records in open recursive DNS resolvers. A vendor-wide upgrade mitigated but did not eliminate this attack. Further, existing DNS protection systems, including bailiwick-checking (12) and IDS-style filtration, do not stop this type of DNS poisoning. We therefore propose Anax, a DNS protection system that detects poisoned records in cache. Our system can observe changes in cached DNS records, and applies machine learning to classify these updates as malicious or benign. We describe our classification features and machine learning model selection process while noting that the proposed approach is easily integrated into existing local network protection systems. To evaluate Anax, we studied cache changes in a geographically diverse set of 300,000 open recursive DNS servers (ORDNSs) over an eight month period. Using hand-verified data as ground truth, evaluation of Anax showed a very low false positive rate (0.6% of all new resource records) and a high detection
Glassbox: Dynamic Analysis Platform for Malware Android Applications on Real Devices

Android is the most widely used smartphone OS with 82.8% market share in 2015 (IDC, 2015). It is therefore the most widely targeted system by malware authors. Researchers rely on dynamic analysis to extract malware behaviors and often use emulators to do so. However, using emulators lead to new issues. Malware may detect emulation and as a result it does not execute the payload to prevent the analysis. Dealing with virtual device evasion is a never-ending war and comes with a non-negligible computation cost (Lindorfer et al., 2014). To overcome this state of affairs, we propose a system that does not use virtual devices for analysing malware behavior. Glassbox is a functional prototype for the dynamic analysis of malware applications. It executes applications on real devices in a monitored and controlled environment. It is a fully automated system that installs, tests and extracts features from the application for further analysis. We present the architecture of the platform and we compare it with existing Android dynamic analysis platforms. Lastly, we evaluate the capacity of Glassbox to trigger application behaviors by measuring the average coverage of basic blocks on the AndroCoverage dataset (AndroCoverage, 2016). We show that it executes on average 13.52% more basic blocks than the Monkey program.
Ensemble Methods for App Review Classification: An Approach for Software Evolution (N)

App marketplaces are distribution platforms for mobile applications that serve as a communication channel between users and developers. These platforms allow users to write reviews about downloaded apps. Recent studies found that such reviews include information that is useful for software evolution. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this work we propose a taxonomy for classifying app reviews into categories relevant for software evolution. Additionally, we describe an experiment that investigates the performance of individual machine learning algorithms and its ensembles for automatically classifying the app reviews. We evaluated the performance of the machine learning techniques on 4550 reviews that were systematically labeled using content analysis methods. Overall, the ensembles had a better performance than the individual classifiers, with an average precision of 0.74 and 0.59 recall.