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Systems need to know the physical locations of objects and people to optimize user experience and solve logistical and security issues. Also, there is a growing demand for applications that need to locate individual assets for industrial automation. This work proposes an indoor positioning system (IPS) able to estimate the item-level location of stationary objects using off-the-shelf equipment. By...
Advances in protein three-dimensional structure prediction depend strongly on the ability to measure the quality of a protein model. One of the best single measures of the model quality is the area under the graph of the so-called “GDT function” that assigns to each distance cutoff θ the percentage of residues in the model structure that can be superimposed at distance ≤ θ from the corresponding residues...
Due to the challenges in automatically observing child behaviour in a social interaction, an automatic extraction of high-level features, such as head poses and hand gestures, is difficult and noisy, leading to an inaccurate model. Hence, the feasibility of using easily obtainable low-level optical flow based features is investigated in this work. A comparative study involving high-level features,...
This paper deals with new technologies being applied in Smart Asset Management field, namely in field of predictive maintenance. Multi-criteria and multi-parameter models are being assessed to leverage predictive abilities of predictive models. Ways of integration into distribution system operators' asset management infrastructure are being determined as well.
The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in...
With the rapid development of E-commerce, more online reviews for products and services are created, which form an important source of information for both sellers and customers. Research on sentiment and opinion mining for online review analysis has attracted increasingly more attention because such study helps leverage information from online reviews for potential economic impact. The paper discusses...
With the advancement of relational databases, the number of configuration parameters that control memory allocation, concurrency, cost of query plans, I/O optimization, logging, recovery or transaction consistency, increases. Users and even expert database administrators struggle to tune these parameters in order to ensure high availability and performance, and in many cases rely on their experience...
Host load prediction is one of the key research issues in Cloud computing. However, due to the drastic fluctuation of the host load in the Cloud, accurately predicting the host load remains a challenge. In this paper, a discriminative model (SVM) is employed to improve upon the accuracy of host load prediction in a Cloud data center. A rich set of features are generated by function based methods and...
The variable behavior of ReRAM memory cells is modeled with machine learning. Two types of prediction are investigated, reset in the next-cycle and cell fail in the long term. A new proposal, Proactive Bit Redundancy, introduces a ML-traied Prediction Engine into the SSD controller, to predict fail cells and replace them proactively - before actual failure- by redundancy. With the Invalid Masking...
Optimizing the weighting of features significantly improves the predictions in regression tasks. In this paper, we employ evolution strategies to evolve distance measures in a spatio-temporal regression approach for short-term wind prediction. The well-understood nearest neighbor regression method is the basis of our study. We compare a classic feature selection approach based on binary representations...
Blast furnace gas (BFG) is regarded as a very important secondary energy in steel industry, and an effective model to describe the status of BFG system is fairly significant to maintain the system balance and stability. However, the high level noises in industrial data and the disturbances in training samples could lead to the overfitting phenomenon. A fuzzy subset fusion combined with a rule reduction...
Electric load forecasting is one of the most important areas in electrical engineering, due to its main role for economic and reliable operation in power systems. In particular, accurate medium and long-term forecasts have significant effect on grid expansion planning and future generating capacity scheduling. This paper uses the Algerian electricity demand observations to evaluate methods for medium...
This paper presents a hybrid model for electricity price forecasting with focus on price spikes predictions. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive spot electricity markets. A two-layered model is introduced for forecasting 7-days ahead hourly electricity price values of electricity spot market. Due to the importance of improved analysis...
Forecasting can be used for helping the decision-makers to determine the next business strategy to improve the quality of Indonesia tourism such as the improvement of the accommodation facility like transportation and lodging, public services, and promotion to introduce Indonesia tourism objects. This research compared the forecasting performance between GM (1,1) and ARIMA models to determine the...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
For a significant number of questions at Stack Overflow, none of the posted answers were accepted as solutions. Acceptance of an answer indicates that the answer actually solves the discussed problem in the question, and the question is answered sufficiently. In this paper, we investigate 3,956 such unresolved questions using an exploratory study where we analyze four important aspects of those questions,...
Monitoring the boiling point of a diesel fuel is an important step to understand the characteristics of the diesel fuel. This study evaluated the feasibility of adaptive linear neuron (Adaline) as a predictive model to predict the boiling point of diesel fuel based on near infrared spectrum. The parameters of learning rate and training cycle that involved in the optimization process were examined...
Developers work on parallel tasks and switch between them due to interruptions and dependencies. For each task, developers interact with artifacts that constitute the task context. The more dissimilar tasks are, the more time is needed for switches to restore the contexts and adjust the mindset. Organizing tasks by their similarity can increase the efficiency of task switches. Moreover, knowing similar...
In this paper, we propose a linear dependent rate-quantization model for video enhancement layers encoding in H.264/AVC based scalable video coding (SVC). It is noted that the proposed model is applicable for different scalable structures, such as temporal, quality, spatial and combined scalability. Leveraging the base layer information (such as bitrate and quantization parameter), proposed model...
Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more...
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