nils-herrmann/clean_scirepeval huggingface dataset:
A Review of Ensemble Methods in Bioinformatics

Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.
The Rise of Bots: A Survey of Conversational Interfaces, Patterns, and Paradigms

This work documents the recent rise in popularity of messaging bots: chatterbot-like agents with simple, textual interfaces that allow users to access information, make use of services, or provide entertainment through online messaging platforms. Conversational interfaces have been often studied in their many facets, including natural language processing, artificial intelligence, human-computer interaction, and usability. In this work we analyze the recent trends in chatterbots and provide a survey of major messaging platforms, reviewing their support for bots and their distinguishing features. We then argue for what we call "Botplication", a bot interface paradigm that makes use of context, history, and structured conversation elements for input and output in order to provide a conversational user experience while overcoming the limitations of text-only interfaces.
Current Mode Control Integrated Circuit with High Accuracy Current Sensing Circuit for Buck Converter

A current mode control integrated circuit with accuracy current sensing circuit ( CSC ) for buck converter is presented in this the proposed accurate integrated current sensed inductor with the internal ramp be used for DC-DC converter feedback proposed CSC doesn't need an op amp to implement, and has been fabricated with a standard 0.35 mum CMOS process. Simulation result show that the switching converter can be operated up to 1 MHZ. The supply is suitable for signal cell lithium-ion battery supply power efficiency is over 85% for supply voltage from 2.5 V to 5 V and output current is 200 mA. The performance of the proposed circuit is the good compared to the other circuits.
Some Theoretical Considerations in Off-the-Shelf Text Analysis Software

This paper is concerned with theoretical considerations of commercial content analysis software, namely Linguistic Inquiry and Word Count (LIWC), developed by social psychologists at the University of Texas. LIWC is widely cited and forms the basis of many research papers from a range of disciplines. Here, LIWC is taken as an example of a context-independent, word-counting approach to text analysis, and the strengths and potential pitfalls of such a methodology are discussed. It is shown that text analysis software is constrained not only by its functions, but also by its underlying theoretical assumptions. The paper offers recommendations for good practice in software commercialisation and application, stressing the importance of transparency and acknowledgement of biases.
Learning to Detect Roads in High-Resolution Aerial Images

Reliably extracting information from aerial imagery is a di fficult problem with many practical applications. One specific case of th is problem is the task of automatically detecting roads. This task is a difficult vi sion problem because of occlusions, shadows, and a wide variety of non-road objec ts. Despite 30 years of work on automatic road detection, no automatic or semi-au tomatic road detection system is currently on the market and no published metho d has been shown to work reliably on large datasets of urban imagery. We propo se detecting roads using a neural network with millions of trainable weights wh ich looks at a much larger context than was used in previous attempts at learnin g the task. The network is trained on massive amounts of data using a consumer GP U. We demonstrate that predictive performance can be substantially im proved by initializing the feature detectors using recently developed unsupervis ed learning methods as well as by taking advantage of the local spatial coherence of the output labels. We show that our method works reliably on two challenging urban d tasets that are an order of magnitude larger than what was used to evaluate pr evious approaches.
Prediction of heart disease using data mining techniques

The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.
Design and Realization of a GA-Optimized VHF/UHF Antenna With "On-Body" Matching Network

This letter presents a VHF/UHF loaded wire antenna with an on-body matching network for broadband or multiband wireless applications. A genetic algorithm (GA) is used to optimize antenna structure and the on-body matching network. The designed 1.47-m antenna with a lossless LC parallel circuit load and a 0.32-m sleeve pedestal can work from 30 to 520 MHz. In the working band, the measured voltage standing-wave ratio (VSWR) and the gain of the loaded antenna with the on-body matching network are less than 2 and greater than 0 dBi, respectively. The efficiency of the on-body matching network is greater than 35% over the frequency range of 30 ˜ 520 MHz, greater than 38% over 60 ˜ 200 MHz, and greater than 42% over 210 ˜ 520 MHz.
Top 10 algorithms in data mining

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.
Fuzzy c-Means Algorithms for Very Large Data

Very large (VL) data or big data are any data that you cannot load into your computer's working memory. This is not an objective definition, but a definition that is easy to understand and one that is practical, because there is a dataset too big for any computer you might use; hence, this is VL data for you. Clustering is one of the primary tasks used in the pattern recognition and data mining communities to search VL databases (including VL images) in various applications, and so, clustering algorithms that scale well to VL data are important and useful. This paper compares the efficacy of three different implementations of techniques aimed to extend fuzzy c-means (FCM) clustering to VL data. Specifically, we compare methods that are based on 1) sampling followed by noniterative extension; 2) incremental techniques that make one sequential pass through subsets of the data; and 3) kernelized versions of FCM that provide approximations based on sampling, including three proposed algorithms. We use both loadable and VL datasets to conduct the numerical experiments that facilitate comparisons based on time and space complexity, speed, quality of approximations to batch FCM (for loadable data), and assessment of matches between partitions and ground truth. Empirical results show that random sampling plus extension FCM, bit-reduced FCM, and approximate kernel FCM are good choices to approximate FCM for VL data. We conclude by demonstrating the VL algorithms on a dataset with 5 billion objects and presenting a set of recommendations regarding the use of different VL FCM clustering schemes.
Security subcultures in an organization - exploring value conflicts

Security culture is considered as an important factor in overcoming the problem with employees’ lack of compliance with Information Security (IS) policies. Within one organization different subcultures might transcribe to different and sometimes even conflicting, values. In this paper we study such value conflicts and their implications on IS management and practice. Shein’s (1999) model of organizational culture is used as a tool supporting analysis of our empirical data. We found that value conflicts exists between different security cultures within the same organization and that users anchor their values related to IS in their professional values. Thus our empirical results highlight value conflicts as an important factor to take into account when security culture is developed in an organization. Moreover, we found Shein’s model as a useful tool for analysis of value conflicts between different subcultures in an organization.
A thin electroadhesive inchworm climbing robot driven by an electrostatic film actuator for inspection in a narrow gap

To inspect in a narrow gap of a critical construction or a large machine, such as a generator in a power plant, a thin inchworm climbing robot is proposed. It achieves the unique feature of low height by employing electrostatic adhesion and an electrostatic film actuator, both of which are composed of flexible printed circuit films. A prototype was developed in this research, which has only 2.4 mm of thickness and weighs 21.7 g. The experiments demonstrated that it can climb up a slope of 80°, and carry a 0.5 N payload on a horizontal surface with applied voltage of 600 V.
Metagenes and molecular pattern discovery using matrix factorization.

We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.
Hybrid context-sensitivity for points-to analysis

Context-sensitive points-to analysis is valuable for achieving high precision with good performance. The standard flavors of context-sensitivity are call-site-sensitivity (kCFA) and object-sensitivity. Combining both flavors of context-sensitivity increases precision but at an infeasibly high cost. We show that a selective combination of call-site- and object-sensitivity for Java points-to analysis is highly profitable. Namely, by keeping a combined context only when analyzing selected language features, we can closely approximate the precision of an analysis that keeps both contexts at all times. In terms of speed, the selective combination of both kinds of context not only vastly outperforms non-selective combinations but is also faster than a mere object-sensitive analysis. This result holds for a large array of analyses (e.g., 1-object-sensitive, 2-object-sensitive with a context-sensitive heap, type-sensitive) establishing a new set of performance/precision sweet spots.
Autonomous UAV Surveillance in Complex Urban Environments

We address the problem of multi-UAV-based surveillance in complex urban environments with occlusions. The problem consists of controlling the flight of UAVs with on-board cameras so that the coverage and recency of the information about the designated area is maximized. In contrast to the existing work, sensing constraints due to occlusions and UAV motion constraints are modeled realistically and taken into account. We propose a novel \emph{occlusion-aware} surveillance algorithm based on a decomposition of the surveillance problem into a variant of the 3D art gallery problem and an instance of traveling salesman problem for Dubins vehicles. The algorithm is evaluated on the high-fidelity \textsc{AgentFly} UAV simulation testbed which accurately models all constraints and effects involved. The results confirm the importance of occlusion-aware flight path planning, in particular in the case of narrow-street areas and low UAV flight altitudes.
Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis

In DNN-based TTS synthesis, DNNs hidden layers can be viewed as deep transformation for linguistic features and the output layers as representation of acoustic space to regress the transformed linguistic features to acoustic parameters. The deep-layered architectures of DNN can not only represent highly-complex transformation compactly, but also take advantage of huge amount of training data. In this paper, we propose an approach to model multiple speakers TTS with a general DNN, where the same hidden layers are shared among different speakers while the output layers are composed of speaker-dependent nodes explaining the target of each speaker. The experimental results show that our approach can significantly improve the quality of synthesized speech objectively and subjectively, comparing with speech synthesized from the individual, speaker-dependent DNN-based TTS. We further transfer the hidden layers for a new speaker with limited training data and the resultant synthesized speech of the new speaker can also achieve a good quality in term of naturalness and speaker similarity.
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity

The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves. Disparate streams of research have approached nonstationarity from several angles, which make a variety of implicit assumptions that make it hard to keep an overview of the state of the art and to validate the innovation and significance of new works. This survey presents a coherent overview of work that addresses opponent-induced non-stationarity with tools from game theory, reinforcement learning and multi-armed bandits. Further, we reflect on the principle approaches how algorithms model and cope with this non-stationarity, arriving at a new framework and five categories (in increasing order of sophistication): ignore, forget, respond to target models, learn models, and theory of mind. A wide range of state-of-the-art algorithms is classified into a taxonomy, using these categories and key characteristics of the environment (e.g., observability) and adaptation behaviour of the opponents (e.g., smooth, abrupt). To clarify even further we present illustrative variations of one domain, contrasting the strengths and limitations of each category. Finally, we discuss in which environments the different approaches yield most merit, and point to promising avenues of future research.
Towards Modeling False Memory With Computational Knowledge Bases

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling.
Facebook use, envy, and depression among college students: Is facebooking depressing?

It is not—unless it triggers feelings of envy. This study uses the framework of social rank theory of depression and conceptualizes Facebook envy as a possible link between Facebook surveillance use and depression among college students. Using a survey of 736 college students, we found that the effect of surveillance use of Facebook on depression is mediated by Facebook envy. However, when Facebook envy is controlled for, Facebook use actually lessens depression.
Data Series Management: Fulfilling the Need for Big Sequence Analytics

Massive data sequence collections exist in virtually every scientific and social domain, and have to be analyzed to extract useful knowledge. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics. We argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analysis of very large sequence collections. To this effect, we need to develop novel techniques to efficiently support a wide range of sequence queries and mining operations, while leveraging modern hardware. The overall goal is to allow analysts across domains to tap in the goldmine of the massive and ever-growing sequence collections they (already) have.
Do you care if a computer says sorry?: user experience design through affective messages

While traditional HCI research emphasizes usability based on models of cognition, user experience (UX) focuses on affect and emotion through the provision of positive interactive experiences. Providing affective cues, such as apologetic on-screen display messages, appears to be a way to influence users' affective states as well as their perceptions toward an information retrieval system. A study was designed to determine whether users' affect and perceptions differ between three types of systems: neutral, apologetic, and non-apologetic. Our results revealed that the users perceived the apologetic system as more aesthetically appealing and usable than the neutral or non-apologetic system. The result also showed that users' frustration was the lowest when using the apologetic system. We discuss the implications of these results in designing a more experience-centered system.
Transparent, conductive graphene electrodes for dye-sensitized solar cells.

Transparent, conductive, and ultrathin graphene films, as an alternative to the ubiquitously employed metal oxides window electrodes for solid-state dye-sensitized solar cells, are demonstrated. These graphene films are fabricated from exfoliated graphite oxide, followed by thermal reduction. The obtained films exhibit a high conductivity of 550 S/cm and a transparency of more than 70% over 1000-3000 nm. Furthermore, they show high chemical and thermal stabilities as well as an ultrasmooth surface with tunable wettability.
Heart rate variability biofeedback: how and why does it work?

In recent years there has been substantial support for heart rate variability biofeedback (HRVB) as a treatment for a variety of disorders and for performance enhancement (Gevirtz, 2013). Since conditions as widely varied as asthma and depression seem to respond to this form of cardiorespiratory feedback training, the issue of possible mechanisms becomes more salient. The most supported possible mechanism is the strengthening of homeostasis in the baroreceptor (Vaschillo et al., 2002; Lehrer et al., 2003). Recently, the effect on the vagal afferent pathway to the frontal cortical areas has been proposed. In this article, we review these and other possible mechanisms that might explain the positive effects of HRVB.
Communication in reactive multiagent robotic systems

Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and e ectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three di erent types of communication for three di erent tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can signi cantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as e ective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.