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Indoor localization has greatly leveraged applications regarding to location based service (LBS), which witnessed ever-increasing impact on human life. Among the existing localization solutions, WIFI-based received signal strength index (RSSI) fingerprinting is widely used due to desirable features such as universal availability, privacy protection, and low deployment cost. However, to build a robust,...
WiFi transceivers are available in most hand-held devices and thus are well suited for indoor localisation. However, their critical high-power consumption is challenging acceptable battery runtimes for frequent localisation (such as tracking). Alternative transceivers that operate at frequencies of 868 MHz can be expected to be included in upcoming smartphones and achieve similar transmission ranges...
Voice activity detection (VAD) plays a crucial role in speech processing, especially in automatic speech recognition (ASR). It identifies the boundaries of the speech to be recognized and the boundary accuracies may significantly affect the recognition performance. Conventional VAD evaluation criteria are mostly based on frame-level accuracy of speech/non-speech classification, which may result in...
The Polish State Fire Service gathers information about incidents which require their intervention. This information is stored to document the events. However, it can be very useful for new officers training, better identification of threats and planning of more effective procedures. The identification of key risk factors for casualties among firefighters, children or other involved people was a topic...
In this paper, we propose a method for matching biometric data from disparate domains. Specifically, we focus on the problem of comparing a low-resolution (LR) image with a high-resolution (HR) one. Existing coupled mapping methods do not fully exploit the HR information or they do not simultaneously use samples from both domains during training. To this end, we propose a method that learns coupled...
LRL technique for calibrating the automatic network analyzer is presented. LRL technique is a variation of TRL, it is especially useful for microwave, non-coaxial environments. This technique would enhance the accuracy extent of calibration. Detailed information about the choice of LRL calibration kits and LRL calibration steps are given. In addition, an measurement example using LRL calibration approves...
Development of software change prediction models, based on the change histories of a software, are valuable for early identification of change prone classes. Classification of these change prone classes is vital to yield competent use of limited resources in an organization. This paper validates Artificial Immune System (AIS) algorithms for development of change prediction models using six open source...
Refactorings are behavior-preserving source code transformations. While tool support exists for (semi) automatically identifying refactoring solutions, applying or not a recommended refactoring is usually up to the software developers, who have to assess the impact that the transformation will have on their system. Evaluating the pros (e.g., the bad smell removal) and cons (e.g., side effects of the...
Arc detection is difficulty for the processing, assembly and testing of industrial production because of limitations of the detection method, algorithm and the instrument. The least-squares algorithm usually is used to fit data in circle detection. The application of the conventional least-squares algorithm is limited, its roundness error is bigger, and precision is lower. For detecting arc with data...
Removal of defects is the key in ensuring long-term error free operation of a software system. Although improvements in the software testing process has resulted in better coverage, it is evident that some parts of a software system tend to be more defect prone than the other parts and identification of these parts can greatly benefit the software practitioners in order to deliver high quality maintainable...
Many clone detection tools and techniques have been introduced in the literature, and these tools have been used to manage clones and study their effects on software maintenance and evolution. However, the performance of these modern tools is not well known, especially recall. In this paper, we evaluate and compare the recall of eleven modern clone detection tools using four benchmark frameworks,...
Localizing bugs is important, difficult, and expensive, especially for large software projects. To address this problem, information retrieval (IR) based bug localization has increasingly been used to suggest potential buggy files given a bug report. To date, researchers have proposed a number of IR techniques for bug localization and empirically evaluated them to understand their effectiveness. However,...
In this article, a non-signature based statistical scanner for metamorphic malware detection, employing feature ranking methods like Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF), Galavotti-Sebastiani-Simi Coefficient (GSS), Term Significance (TS) and Odds Ratio (OR) is proposed. Malware and benign models for classification are created by considering top ranked features obtained...
The degree distribution is an important characteristic in complex networks. In many applications, quantification of degree distribution in the form of a fixed-length feature vector is a necessary step. Additionally, we often need to compare the degree distribution of two given networks and extract the amount of similarity between the two distributions. In this paper, we propose a novel method for...
When using electronic health record (EHR) data to build models for predicting adverse drug effects (ADEs), one is typically facing the problem of data sparsity, i.e., Drugs and diagnosis codes that could be used for predicting a certain ADE are absent for most observations. For such tasks, the ability to effectively handle sparsity by the employed machine learning technique is crucial. The state-of-the-art...
Recommendation systems are popular both commercially and in the research community. For example, Online Social Networks (OSNs), like Twitter, are of special attention since a lot of connection are established between users without any previous knowledge. This highlights one of the key features of a lot of OSNs: the creation of relationships between users. Therefore, it is important to find new ways...
Trust plays a very important role in people's real lives. As online social networking becomes more and more popular nowadays, it is urgent to use trust in online communities, like Amazon.com, Epinions.com, Facebook, Twitter and so on. Unfortunately, trust links in online communities are sparse compared with the number of pairs of users. To address this problem, computer scientists are now using trust...
In this paper, we apply learning techniques to predict link quality evolution in a WSN and take advantage of wireless links with the best possible quality to improve the packet delivery rate. We model this problem as a forecaster prediction game based on the advice of several experts. The forecaster learns on-line how to adjust its prediction to better fit the environment metric values. Simulations...
This preliminary study investigated the use of cardiac information or more specifically, heart rate variability (HRV), for automatic deep sleep detection throughout the night. The HRV data can be derived from cardiac signals, which were obtained from polysomnography (PSG) recordings. In total 42 features were extracted from the HRV data of 15 single-night PSG recordings (from 15 healthy subjects)...
This paper presents a highly parallel solution for cross-document co reference resolution, which can deal with billions of documents that exist in the current web. At the core of our solution lies a novel algorithm for community detection in large scale graphs. We operate on graphs which we construct by representing documents' keywords as nodes and the colocation of those keywords in a document as...
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