The Nobel Prize has been a prominent research topic in Scientometrics. This study aims to explore Nobel citation effects, which refer to changes in the cumulative citation counts of publications after authors have won the Nobel Prize. ...

Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect to several criteria. Every comparison framework is confronted with (at ...

Quantum computing has been attracting the attention of both applied research and companies. Continuous progress on fundamental hardware technology promises to bring us more reliable and large-scale quantum computers on which to run the next generation ...

In order to avoid missing representative features, we should select a lot of features as far as possible when using machine learning algorithms in stock trading. Meanwhile, these high dimensional features can lead to redundancy of information and ...

One typical way of building test collections for offline measurement of information retrieval systems is to pool the ranked outputs of different systems down to some chosen depth d and then form relevance judgments for those documents only. Non-pooled ...

Evolutionary algorithms are cost-effective for solving real-world optimization problems, such as NP-hard and black-box problems. Before an evolutionary algorithm can be put into real-world applications, it is desirable that the algorithm was tested on a ...

Fault diagnosis of closed‐loop systems is extremely relevant for high‐precision equipment and safety critical systems. Fault diagnosis is usually divided into 2 schemes: active and passive fault diagnosis. Recent studies have highlighted some ...

Automatic traffic sign detection and recognition plays a very significant role in advance driver assistance system and intelligent transportation system. In this paper, approach for circular traffic sign detection and recognition is proposed. The entire ...

Elicitation studies, where users supply proposals meant to effect system commands, have become a popular method for system designers. But the method to date has assumed a within-subjects procedure and statistics. Despite the benefits of examining the ...

Anomalies are subsequences that exhibit departures from normal state of operation. In this paper, to solve the problems of unknown data distribution, control limit determination, multiple parameters, training data and fuzziness of ‘anomaly’, a self-...

The statistical test rule induction method (STRIM) has been proposed as a method for effectively inducing if-then rules from a decision table. Its usefulness has been confirmed by a simulation experiment and comparison with conventional methods. However,...

We address in this work the process of agreement rate analysis for characterizing the level of consensus between participants' proposals elicited during guessability studies. Two new measures, i.e., disagreement rate for referents and coagreement rate ...

Model-based statistical testing with Markov chain usage models (MCUMs) represents a highly automated test approach. However, specifying the usage model by hand is not a trivial task, especially if the system under test (SUT) allows concurrent streams of ...

NIST SP800-22 is a statistical test suite for determining whether given sequences are random or not for each statistical test. The statistical test suite has been used widely. However, it was not mentioned in the statistical test suite how many the ...

Existing co-location mining algorithms require a user provided distance threshold at which prevalent patterns are searched. Since spatial interactions, in reality, may happen at different distances, finding the right distance threshold to mine all true ...

Sensor networks mainly deployed to monitor and report real events, and thus it is very difficult and expensive to achieve event source anonymity for it, as sensor networks are very limited in resources. Data obscurity i.e. The source anonymity problem ...

Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been ...

Random sequences are widely used in the field of computer science. In practice, random sequences are generated by pseudorandom number generators which are in fact deterministic algorithms. Statistical tests are used to check whether there are ...

With the growing demand of categorical data clustering, a new hybrid clustering algorithm, namely Rough set based Fuzzy K-Modes, is proposed in this paper. The principles of rough and fuzzy sets are used in integrated form. It gives the better ...

In order to utilize multi-core CPUs more effectively, a new Concurrent Differential Evolution (CDE) is proposed. Then the proposed CDE (CDE/G) is compared with a conventional CDE (CDE/S). CDE/S uses only one population because it is based on the ...