nils-herrmann/clean_scirepeval huggingface dataset:
Software fault prediction metrics: A systematic literature review

Software metrics may be used in fault prediction models to improve software quality by predicting fault location.  This paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics’ selection and performance.  This systematic literature review includes 106 papers published between 1991–2011. The selected papers are classified according to metrics and context properties.  Object-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer’s (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics.  More studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.
Epidemic Routing for Partially-Connected Ad Hoc Networks

Mobile ad hoc routing protocols allow nodes with wireless adaptors to communicate with one another without any pre-existing network infrastructure. Existing ad hoc routing protocols, while robust to rapidly changing network topology, assume the presence of a connected path from source to destination. Given power limitations, the advent of short-range wireless networks, and the wide physical conditions over which ad hoc networks must be deployed, in some scenarios it is likely that this assumption is invalid. In this work, we develop techniques to deliver messages in the case where there is never a connected path from source to destination or when a network partition exists at the time a message is originated. To this end, we introduce Epidemic Routing, where random pair-wise exchanges of messages among mobile hosts ensure eventual message delivery. The goals of Epidemic Routing are to: i) maximize message delivery rate, ii) minimize message latency, and iii) minimize the total resources consumed in message delivery. Through an implementation in the Monarch simulator, we show that Epidemic Routing achieves eventual delivery of 100% of messages with reasonable aggregate resource consumption in a number of interesting scenarios.
Building Customer-Based Brand Equity : A Blueprint for Creating Strong Brands

and others at both organizations for their support and valuable input. Special thanks to Grey Advertising's Ben Arno who suggested the term brand resonance. Additional thanks to workshop participants at Duke University and Dartmouth College. MSI was established in 1961 as a not-for profit institute with the goal of bringing together business leaders and academics to create knowledge that will improve business performance. The primary mission was to provide intellectual leadership in marketing and its allied fields. Over the years, MSI's global network of scholars from leading graduate schools of management and thought leaders from sponsoring corporations has expanded to encompass multiple business functions and disciplines. Issues of key importance to business performance are identified by the Board of Trustees, which represents MSI corporations and the academic community. MSI supports studies by academics on these issues and disseminates the results through conferences and workshops, as well as through its publications series. This report, prepared with the support of MSI, is being sent to you for your information and review. It is not to be reproduced or published, in any form or by any means, electronic or mechanical, without written permission from the Institute and the author. Building a strong brand has been shown to provide numerous financial rewards to firms, and has become a top priority for many organizations. In this report, author Keller outlines the Customer-Based Brand Equity (CBBE) model to assist management in their brand-building efforts. According to the model, building a strong brand involves four steps: (1) establishing the proper brand identity, that is, establishing breadth and depth of brand awareness, (2) creating the appropriate brand meaning through strong, favorable, and unique brand associations, (3) eliciting positive, accessible brand responses, and (4) forging brand relationships with customers that are characterized by intense, active loyalty. Achieving these four steps, in turn, involves establishing six brand-building blocks—brand salience, brand performance, brand imagery, brand judgments, brand feelings, and brand resonance. The most valuable brand-building block, brand resonance, occurs when all the other brand-building blocks are established. With true brand resonance, customers express a high degree of loyalty to the brand such that they actively seek means to interact with the brand and share their experiences with others. Firms that are able to achieve brand resonance should reap a host of benefits, for example, greater price premiums and more efficient and effective marketing programs. The CBBE model provides a yardstick by …
Semantic Single-Image Dehazing

Single-image haze-removal is challenging due to limited information contained in one single image. Previous solutions largely rely on handcrafted priors to compensate for this deficiency. Recent convolutional neural network (CNN) models have been used to learn haze-related priors but they ultimately work as advanced image filters. In this paper we propose a novel semantic approach towards single image haze removal. Unlike existing methods, we infer color priors based on extracted semantic features. We argue that semantic context can be exploited to give informative cues for (a) learning color prior on clean image and (b) estimating ambient illumination. This design allowed our model to recover clean images from challenging cases with strong ambiguity, e.g. saturated illumination color and sky regions in image. In experiments, we validate our approach upon synthetic and real hazy images, where our method showed superior performance over state-of-the-art approaches, suggesting semantic information facilitates the haze removal task.
15 Ultra Wideband Antennas for High Pulsed Power Applications

1.1 UWB antennas in the field of high pulsed power For the last few years, the generation of high-power electromagnetic waves has been one of the major applications of high pulsed power (HPP). It has aroused great interest in the scientific community since it is at the origin of several technological advances. Several kinds of high power radiation sources have been created. There currently appears to be a strong inclination towards compact and autonomous sources of high power microwaves (HPM) (Cadilhon et al., 2010; Pécastaing et al., 2009). The systems discussed here always consist of an electrical high pulsed power generator combined with an antenna. The HPP generator consists of a primary energy source, a power-amplification system and a pulse forming stage. It sends the energy to a suitable antenna. When this radiating element has good electromagnetic characteristics over a wide band of frequency and high dielectric strength, it is possible to generate high power electromagnetic waves in the form of pulses. The frequency band of the wave that is radiated can cover a very broad spectrum of over one decade in frequency. In this case, the technique is of undoubted interest for a wide variety of civil and military applications. Such applications can include, for example, ultra-wideband (UWB) pulse radars to detect buried mines or to rescue buried people, the production of nuclear electromagnetic pulse (NEMP) simulators for electromagnetic compatibility and vulnerability tests on electronic and IT equipment, and UWB communications systems and electromagnetic jamming, the principle of which consists of focusing high-power electromagnetic waves on an identified target to compromise the target’s mission by disrupting or destroying its electronic components. Over the years, the evolution of the R&D program for the development of HPM sources has evidenced the technological difficulties intrinsic to each elementary unit and to each of the physical parameters considered. Depending on the wave form chosen, there is in fact a very wide range of possibilities for the generation of microwave power. The only real question is
Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks

Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and parameter-free unsupervised AD technique, k-nearest neighbor (kNN) algorithm has attracted a lot of attention for its applications in computer networks and WSNs. However, the nature of lazy-learning makes the kNN-based AD schemes difficult to be used in an online manner, especially when communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure into a positive coordinate space in order to retain the redundancy for online update and tailor for bit operation. In addition, distributed computing is taken into account, and position of the hypercube is encoded by a few bits only using the bit operation. As a result, the new scheme is able to work successfully in any environment without human interventions. Finally, the experiments with a real WSN data set demonstrate that the proposed scheme is effective and robust.
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold

The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. The data, assumed to be sampled from an underlying manifold, are mapped into a single global coordinate system of lower dimensionality. The mapping is derived from the symmetries of locally linear reconstructions, and the actual computation of the embedding reduces to a sparse eigenvalue problem. Notably, the optimizations in LLE—though capable of generating highly nonlinear embeddings—are simple to implement, and they do not involve local minima. In this paper, we describe the implementation of the algorithm in detail and discuss several extensions that enhance its performance. We present results of the algorithm applied to data sampled from known manifolds, as well as to collections of images of faces, lips, and handwritten digits. These examples are used to provide extensive illustrations of the algorithm’s performance—both successes and failures—and to relate the algorithm to previous and ongoing work in nonlinear dimensionality reduction.
Proposal and experimental validation of a design strategy for a UAV with a passive rotating spherical shell

Unmanned aerial vehicles (UAVs) such as multi-copters are expected to be used for inspection of aged infrastructure or for searching damaged buildings in the event of a disaster. However, in a confined space in such environments, UAVs suffer a high risk of falling as a result of contact with an obstacle. To ensure an aerial inspection in the confined space, we have proposed a UAV with a passive rotating spherical shell (PRSS UAV); The UAV and the spherical shell are connected by a 3DOF gimbal mechanism to allow them to rotate in all directions independently, so that the UAV can maintain its flight stability during a collision with an obstacle because only the shell is disturbed and rotated. To apply the PRSS UAV into real-world missions, we have to carefully choose many design parameters such as weight, structure, diameter, strength of the spherical shell, axis configuration of the gimbal, and model of the UAV. In this paper, we propose a design strategy for applying the concept of the PRSS mechanism, focusing on disaster response and infrastructure inspection. We also demonstrate the validity of this approach by the successful result of quantitative experiments and practical field tests.
German NER with a Multilingual Rule Based Information Extraction System: Analysis and Issues

This paper presents a rule-based approach to Named Entity Recognition for the German language. The approach rests upon deep linguistic parsing and has already been applied to English and Russian. In this paper we present the first results of our system, ABBYY InfoExtractor, on GermEval 2014 Shared Task corpus. We focus on the main challenges of German NER that we have encountered when adapting our system to German and possible solutions for them.
Factors influencing the use of mobile payments — A conceptual model

The hype around mobile payments has been growing in Sri Lanka with the exponential growth of the mobile adoption and increasing connectivity to the Internet. Mobile payments offer advantages in comparison to other payment modes, benefiting both the consumer and the society at large. Drawing upon the traditional technology adoption theories, this research develops a conceptual framework to uncover the influential factors fundamental to the mobile payment usage. The phenomenon discussed in this research is the factors influencing the use of mobile payments. In relation to the topic, nine independent factors were selected and their influence is to be tested onto behavioral intention to use mobile payments. The questionnaires need to be handed out for data collection for correlation analyses to track the relationship between the nine independent variables and the dependent variable — behavioral intention to use mobile payments. The second correlation analysis between behavioral intention to mobile payments and mobile payment usage is also to be checked together with the two moderating variables — age and level of education.
Generality in Artificial Intelligence

My 1971 Turing Award Lecture was entitled "Generality in Artificial Intelligence." The topic turned out to have been overambitious in that I discovered I was unable to put my thoughts on the subject in a satisfactory written form at that time. It would have been better to have reviewed my previous work rather than attempt something new, but such was not my custom at that time.
I am grateful to ACM for the opportunity to try again. Unfortunately for our science, although perhaps fortunately for this project, the problem of generality in artificial intelligence (AI) is almost as unsolved as ever, although we now have many ideas not available in 1971. This paper relies heavily on such ideas, but it is far from a full 1987 survey of approaches for achieving generality. Ideas are therefore discussed at a length proportional to my familiarity with them rather than according to some objective criterion.
It was obvious in 1971 and even in 1958 that AI programs suffered from a lack of generality. It is still obvious; there are many more details. The first gross symptom is that a small addition to the idea of a program often involves a complete rewrite beginning with the data structures. Some progress has been made in modularizing data structures, but small modifications of the search strategies are even less likely to be accomplished without rewriting.
Another symptom is no one knows how to make a general database of commonsense knowledge that could be used by any program that needed the knowledge. Along with other information, such a database would contain what a robot would need to know about the effects of moving objects around, what a person can be expected to know about his family, and the facts about buying and selling. This does not depend on whether the knowledge is to be expressed in a logical language or in some other formalism. When we take the logic approach to AI, lack of generality shows up in that the axioms we devise to express commonsense knowledge are too restricted in their applicability for a general commonsense database. In my opinion, getting a language for expressing general commonsense knowledge for inclusion in a general database is the key problem of generality in AI.
Here are some ideas for achieving generality proposed both before and after 1971. I repeat my disclaimer of comprehensiveness.
Ward's Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward's Criterion?

The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. Two algorithms are found in the literature and software, both announcing that they implement the Ward clustering method. When applied to the same distance matrix, they produce different results. One algorithm preserves Ward’s criterion, the other does not. Our survey work and case studies will be useful for all those involved in developing software for data analysis using Ward’s hierarchical clustering method.
Evaluation of News-Based Trading Strategies

The marvel of markets lies in the fact that dispersed information is instantaneously processed by adjusting the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we attempt to design trading strategies that are built on textual news in order to obtain higher profits than benchmark strategies achieve. Essentially, we succeed by showing evidence that a news-based trading strategy indeed outperforms our benchmarks by a 9.06-fold performance.
Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges

In recent years, deep neural networks (DNNs) have received increased attention, have been applied to different applications, and achieved dramatic accuracy improvements in many tasks. These works rely on deep networks with millions or even billions of parameters, and the availability of graphics processing units (GPUs) with very high computation capability plays a key role in their success. For example, Krizhevsky et al.  achieved breakthrough results in the 2012 ImageNet Challenge using a network containing 60 million parameters with five convolutional layers and three fully connected layers. Usually, it takes two to three days to train the whole model on the ImagetNet data set with an NVIDIA K40 machine. In another example, the top face-verification results from the Labeled Faces in the Wild (LFW) data set were obtained with networks containing hundreds of millions of parameters, using a mix of convolutional, locally connected, and fully connected layers , . It is also very time-consuming to train such a model to obtain a reasonable performance. In architectures that only rely on fully connected layers, the number of parameters can grow to billions [4].
Recurrent Attention Network on Memory for Aspect Sentiment Analysis

We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications. The weightedmemory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence. We examine the merit of our model on four datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity. The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.
Seismic Performance Evaluation for Steel Moment Frames

A performance prediction and evaluation procedure based on nonlinear dynamics and reliability theory is prese features full integration over the three key stochastic models: ground motion hazard curve, nonlinear dynamic displacement dem displacement capacity. Further, both epistemic and aleatory uncertainties are evaluated and carried through the analysis. uncertainty analyses are input to the procedure such as period, live load, material properties, damping, analysis procedure, and of the structure. Two limit states are defined instead of the traditional single state. The procedure provides a simple method for e the confidence level for satisfying the performance level for a given hazard. The confidence level of a postand a pre-No nine-story building for a given hazard level is calculated using the procedure described in the paper. New steel moment frame are expected to perform much better during major earthquakes than existing buildings designed and built with older technolog DOI: 10.1061/ ~ASCE!0733-9445~2002!128:4~534! CE Database keywords: Seismic response; Earthquakes; Steel frames; Nonlinear analysis; Reliability analysis; Perfo evaluation. s o orthe em acy mi ced eithe no but on, ysicise ility erthe ate ds ed rth-
SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words’ sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure.
The female bunch bears about 2500-3000 fruits borne on THE OIL PALM AND ITS SUSTAINABILITY

As the palm oil industry progresses, its many aspects, such as economic, environmental and social benefits, from its production are reviewed. More recently, sustainability has received great attention with efforts to integrate it into the palm oil business strategy. In the sustainability framework, the economic (financial), environmental and social aspects are reviewed for their impacts in both the shortand long-terms. The threepronged strategy of high income, value addition and zero waste is scrutinized as part of the journey towards corporate sustainability. Doing so has once again demonstrated the benefits of the crop in supplying oil to the