Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of water infiltration on the mechanical behaviour of unsaturated soil

Natural slopes and embankments are generally unsaturated in nature with negative pore water pressure adding to the shear strength and hence the stability of the slopes. During the event of rainfall, pore water pressure becomes less negative or even positive as a result the shear strength of soil decreases and failure occur. Therefore, the strength and deformation characteristics for unsaturated soil become important when analysing the stability of these types of slopes. In this study, a triaxial test apparatus was used to study the effect of water infiltration on the mechanical behaviour of unsaturated soil. The test results show that water infiltration decreased with increase in net confining stress and the stress paths were independent of the matric suction, the net confining stress and the shearing conditions for the present experimental study.
A comparative study on combinations of forecasts and their individual forecasts by means of simulated series

Over the years, several studies that compare individual forecasts with the combination of forecasts were published. There is, however, no unanimity in the conclusions. Furthermore, methods of combination by regression are poorly explored. This paper presents a comparative study of three methods of combination and their individual forecasts. Based on simulated data, it is evaluated the accuracy of Artificial Neural Networks, ARIMA and exponential smoothing models; calculating the combined forecasts through simple average, minimum variance and regression methods. Four accuracy measurements, MAE, MAPE, RMSE and Theil’s U, were used for choosing the most accurate method. The main contribution is the accuracy of the combination by regression methods.
Influence of Adding Inulin as a Fat Replacer on the Characteristics of Yoghurt

The effects of substituting milk fat with different levels of inulin as a fat replacer on the chemical, microbiological and sensory characteristics of yoghurt were examined.Samples of yoghurt were kept in refrigerator at(5± 2Co), and examined when fresh, and after 2, 3 and 7 days of storage. Chemical composition, microbiological contents and sensory characteristics were examined. The obtained results indicated that addition of inulin resulted in a significant increase in the total solids, moisture contents, and the development of titratable acidity. On the other hand, less whey syneresis was observed during the storage of the yoghurt samples. On the other hand, replacing of milk fat by inulin was of no significant effect on the fat content.Addition of inulin stimulated the growth of lactic acid bacteria. The colony forming units of total bacterial counts, Lactobacillus delbrueckii ssp. bulgaricus and Streptococcus thermophilus increased up to the 3rd day of storage period, followed by decrease up to the end of the storage period. Sensory evaluations were almost stable during the first 7th days of storage, then decreased slightly until the end of storage the period. Yoghurt samples containing 1.2% of inulin showed minor difference in the sensory characteristics, compared with control, and of the most acceptable sample. In conclusion, inulin could be successfully used as a fat replacer in making of low fat yoghurt with additional nutritional benefits without affecting the physico-chemical properties of yoghurt.
Computational Modelling of Variance in Musical Harmony

European tonal art music, unlike other forms of art, enjoys rigorous formalization and a rich vocabulary of descriptions that have a relatively high precision and expressive power. With music theory, we aim to explain and offer generalizations about the concepts and processes of music. For example, the simultaneous sounding of multiple musical notes that form a coherent entity in the mind of a listener is commonly referred to as harmony. These entities, the basic components of harmony, are structures that are generally referred to as chords, which are sonorities of (commonly, three or more) notes. Notwithstanding the rigorous formalization of music, this dissertation argues that people often arrive at diverging harmonic analyses of the same musical piece, which results in harmonic variance: differing, yet useful harmonic analyses of the same musical piece. Reasons for diverging analyses are differences in application of music theory to ambiguous musical passages and fundamental and inherent human perceptual and cultural differences that stem from enculturation differences. Disagreement in harmony analyses is problematic for creating ground-truth datasets for current computational approaches to harmony analysis, such as automatic chord estimation. This dissertation proposes different approaches to investigate, incorporate, model, exploit and analyze variance in musical harmony found between human annotators and output of algorithms. In Chapter 2, this dissertation introduces a new dataset with multiple reference annotations and several statistical methods to investigate the degree of agreement between expert harmonic annotators. Having multiple reference annotations means that either the variance needs to be resolved in a smart way through a data fusion algorithm, as is proposed in Chapter 3, or that the paradigm of automatic chord estimation needs to be expanded to allow for multiple subjective reference annotations, as is proposed in Chapter 4. The inherent ambiguity of harmonic analysis and transcription is also problematic for evaluating automatic chord estimation systems. For example, it raises the problem of how to evaluate and compare the output of algorithms on the same piece of music. In the final chapter (Chapter 5) of this dissertation we investigate the algorithmic disagreement between automatic chord estimation systems evaluated on different musical performances of Schubert’s Winterreise. This dissertation demonstrates that the next leap forward for computational harmony analysis is to not stick to the conventional aim of describing immanent or idealized harmonic structures, but instead move towards modeling esthesic, or perceived musical structures. Since music is inseparable from the occasion and purpose for which it is produced, the computational modeling of harmony should also take into account occasion and purpose through the modelling of the inherent variance found in human harmonic transcriptions.
Scalable cloud-based time series analysis

Timestamped data can be read in parallel by multiple grid-computing devices. The timestamped data, which can be partitioned into groups based on time series criteria, can be deterministically distributed across the multiple grid-computing devices based on the time series criteria. Each grid-computing device can sort and accumulate the timestamped data into a time series for each group it receives and then process the resultant time series based on a previously distributed script, which can be compiled at each grid-computing device, to generate output data. The grid-computing devices can write their output data in parallel. As a result, vast amounts of timestamped data can be easily analyzed across an easily expandable number of grid-computing devices with reduced computational expense.
Performance-Cost Tradeoff of Using Mobile Roadside Units for V2X Communication

Roadside unit (RSU) is a communication device for vehicular networks that provides vehicle-to-infrastructure (V2I) connectivity to nearby vehicles. It allows broadcasting of traffic and safety information, Internet connectivity, shared storage, advertisement, as well as supporting and enhancing vehicle-to-vehicle (V2V) communication for future autonomous vehicles. However, high cost of its deployment and management has so far prevented RSUs from being used widely in practice despite its utility and importance. In this paper, we investigate the performance-cost tradeoff and viability of using buses as mobile RSUs (mRSUs). Although there have been some prior works that suggest the use of cars, public transportation, and controlled vehicles as mRSUs to address the cost problem of static RSUs (sRSUs), their assumptions were mostly rather idealistic. We aim to provide a more realistic view of the problem. Through real-world measurements, experiments, analysis, and simulation, we show how mRSUs can replace sRSUs while maintaining the same level of throughput, contact time, and inter-contact time. Our results provide a basis for judging whether it is beneficial to complement sRSUs with mRSUs depending on the deployment environment and cost-performance tradeoff.
Methods, New Software Tools, and Best Practices for Developing High-quality Training Data for Machine Learning-based Image Analysis in Biodiversity Research

Recent progress in using deep learning techniques to automate the analysis of complex image data is opening up exciting new avenues for research in biodiversity science. However, potential applications of machine learning methods in biodiversity research are often limited by the relative scarcity of data suitable for training machine learning models. Development of high-quality training data sets can be a surprisingly challenging task that can easily consume hundreds of person-hours of time. In this talk, we present the results of our recent work implementing and comparing several different methods for generating annotated, biodiversity-oriented image data for training machine learning models, including collaborative expert scoring, local volunteer image annotators with on-site training, and distributed, remote image annotation via citizen science platforms. We discuss error rates, among-annotator variance, and depth of coverage required to ensure highly reliable image annotations. We also discuss time considerations and efficiency of the various methods. Finally, we present new software, called ImageAnt (currently under development), that supports efficient, highly flexible image annotation workflows. ImageAnt was created primarily in response to the challenges we discovered in our own efforts to generate image-based training data for machine learning models. ImageAnt features a simple user interface and can be used to implement sophisticated, adaptive scripting of image annotation tasks.
Self-Organized Behaviour in a Modified Multi-Agent Simulation Model Based on Physical Force Approach

The multi-agent simulation models are very useful in predicting crowd behaviours, where experiments with human are too dangerous. However, works that include intelligence in the physical force-based model are very limited. Moreover, most works based on the physical force-based model are only restricted to producing behaviours of crowds in emergency situations. Utilizing a simpler mathematical representation, this paper proposes a modification to an existing crowd simulation model based on the physical force approach. The proposed method incorporates the concept of magnetic force interaction into the existing social force model of an agent movement. It simulates the agent’s interaction within their boundaries, preventing any collisions from occurring and to follow others when the agents move in a same direction. The proposed method incorporated agents’ intelligence to choose the shortest path in their movements towards their destinations. When the number of agents increases in the simulation environment, the model is able to produce a self-organized behaviour, such as the lane formation behaviour pattern when the agents are in a bi-directional movement as well as in a counter flow movement at intersections.
Prospective Statistical Power: Sample Size Recommendations for the Investigation of the Main Parapsychological Phenomena

The aim of this paper is to offer a practical guideline for researchers investigating parapsychological phenomena to choose appropriate sample sizes to achieve a statistical power equal or above 0.80. The availability of different meta-analyses related to different parapsychological phenomena, allow a sufficient estimation of the expected effect sizes, that usually range from small to very small.With these measures, it is possible to estimate the numerosity of sample sizes necessary to achieve a level of statistical power that can facilitate the replication of different parapsychological phenomena.I discuss ways to deal with the investigation of phenomena with very small effect sizes requiring very large sample sizes.
AR information for FMIPv6

The document [FMIP] describes a protocol to reduce the handover latency of MIPv6. In order to reduce the handover latency, MN in FMIPv6 protocol should first send request message to PAR for NAR's information, which is (AP-ID, AR-Info). If PAR can not provide (AP-ID, AR-Info) to MN, MN cannot use FMIPv6 protocol to perform fast handover. So it is important for PAR to have the information of NAR. But there is no mechanism to build the (AP-ID, AR-Info) in FMIPv6 protocol. This draft describes a mechanism used to build (AP-ID, AR- Info) information.
Exploring Undergraduates’ Digital Citizenship Levels: Adaptation of the Digital Citizenship Scale to Turkish

This study reports the results of two successive studies on digital citizenship. ‘Study I’ aims to adapt the Digital Citizenship Scale, developed by Choi, Glassman and Cristol (2017), into Turkish context, and ‘Study II’ examines undergraduates’ levels of digital citizenship with respect to the variables of age, faculty, gender, amount of daily computer use, amount of daily smartphone use, and self-evaluations as to their skills levels of digital technology use. In Study I, the adaptation process of the scale, and validity and reliability analyses are provided based on data obtained from 272 undergraduates from 8 different universities in Turkey. A valid and reliable 18-item adapted form of Digital Citizenship Scale is offered in this part. In Study II, a survey design study, data were obtained from 220 undergraduates from a state university in Turkey and their digital citizenship levels were examined with respect to various variables. It was found out that the participating undergraduates had a medium level of digital citizenship, and they had the lowest mean in Internet Political Activism and the highest mean in Technical Skills. A significant difference between the undergraduates’ levels of digital citizenship was found only for self-evaluations as to their skills levels of digital technology use but not for the variables of age, faculty, gender, amount of daily computer use, and amount of daily smartphone use. Results are discussed, and implications are suggested.
Comparative study of Page Ranking Algorithms for Web Mining

The World Wide Web is growing rapidly so there should be some means to provide information relevant to user. This need results in number of search engines that aims to provide information as per users need. Web search engines relies on various Page Ranking algorithms for finding suitable answers for user queries. In this review paper various Page Rank algorithms like PageRank, Distance PageRank and HITS used for Information Retrieval are discussed and compared. We compare the algorithm on the simulation interface.
Optimizing parameter sensitivity analysis of large‐scale microscopy image analysis workflows with multilevel computation reuse

Parameter sensitivity analysis (SA) is an effective tool to gain knowledge about complex analysis applications and assess the variability in their analysis results. However, it is an expensive process as it requires the execution of the target application multiple times with a large number of different input parameter values. In this work, we propose optimizations to reduce the overall computation cost of SA in the context of analysis applications that segment high‐resolution slide tissue images, ie, images with resolutions of 100k × 100k pixels. Two cost‐cutting techniques are combined to efficiently execute SA: use of distributed hybrid systems for parallel execution and computation reuse at multiple levels of an analysis pipeline to reduce the amount of computation. These techniques were evaluated using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. Our parallel execution method attained an efficiency of over 90% on 256 nodes. The hybrid execution on the CPU and Intel Phi improved the performance by 2×. Multilevel computation reuse led to performance gains of over 2.9×.
Human Tracking using Edge and Camera Extracted Pattern Feature for Denser Crowded Videos

Human tracking is a major issue in computer vision, with numerous time-critical applications, related to day-to-day activity, have a direct impact on the quality of life. Instantaneous detection of individuals in a real-time scenario is an emerging area of study and many methodologies have been suggested, based on a selection of topographies and algorithms. This paper proposes an innovative solution for tracking humans. The system utilizes a new algorithm, which is formed on the recognition of classical pattern extraction from camera surveillance probes. These extracted patterns can be used to focus on both still and walking person’s moves. Furthermore, the information is fused using a sequential implementation. Quite a lot of experiments illustrated the efficiency of our approach, showing that tracking humans can also be performed within multifaceted enclosed environments.
Conditional DCGAN's Challenge: Generating Handwritten Character Digit, Alphabet and Katakana

Developing deep learning models has a great potential in assisting human tasks involving design and creativity. This study deals with generating handwritten characters using deep learning techniques. The task is not simply generating images randomly, but generating them conditionally, making a distinction according to the UI designates. To solve this task, we constructed the Conditional DCGAN model which includes the techniques from DCGAN and Conditional GAN. We tried training the models to be able to generate conditional images by adding label information as input to the Generator. Deep learning experiments were performed using 141319 training data consisting of 96 kinds of characters including digits, Roman alphabets and Katakana. The Generator trained by inputting random noise concatenated with the 96 kinds of characters, could generate each kind of character by just adding the appropriate label information
Predicting Patent Value: A Data Mining Approach

Patents have long been recognized as a rich source of data for studying innovation, technical changes, and value creation. Patent data includes citations to previous patents, and patent citations allow one to create an indicator of patent value. Identifying valuable patents in a timely manner is essential for effectively harnessing the business value of inventions in the increasingly competitive global market. However, the existing methods of evaluating patent value suffer the issues of timeliness and accuracy. In this paper, we propose a data mining approach that utilizes the structural properties of patent citations networks to predict the value of patents while aiming to improve timeliness and accuracy
Prediction-Based Elastic Load Balancing Mechanism in Cloud Environment

An elastic load balancing mechanism in cloud computing environment is studied in this paper. The mechanism that uses kNN (k-Nearest Neighbors) and Naive Bayes classification algorithms in machine learning can effectively solve the problem of resource allocation lag by predicting the future load trend on the basis of analysis and study of historical data. And taking into account the cross regional nature of the cloud computing environment, applications will be deployed to the computing nodes closer to the user to reduce user access time. Finally, we verify the feasibility and effectiveness of the proposed mechanism through some experiments.
Dihydrogen Bonds in Salts of Boron Cluster Anions [BnHn]2− with Protonated Heterocyclic Organic Bases

Dihydrogen bonds attract much attention as unconventional hydrogen bonds between strong donors of H-bonding and polyhedral (car)borane cages with delocalized charge density. Salts of closo-borate anions [B10H10]2− and [B12H12]2− with protonated organic ligands 2,2’-dipyridylamine (BPA), 1,10-phenanthroline (Phen), and rhodamine 6G (Rh6G) were selectively synthesized to investigate N−H.H−B intermolecular bonding. It was found that the salts contain monoprotonated and/or diprotonated N-containing cations at different ratios. Protonation of the ligands can be implemented in an acidic medium or in water because of hydrolysis of metal cations resulting in the release of H3O+ cations into the reaction solution. Six novel compounds were characterized by X-ray diffraction and FT-IR spectroscopy. It was found that strong dihydrogen bonds manifest themselves in FT-IR spectra that allows one to use this technique even in the absence of crystallographic data.
Efficient Estimation of Tighter Bounds for Worst Case Execution Time of Programs

In this paper, we will present a framework for the statistical analysis of the execution time of program units. We will show alternative methods for computing the distribution of the execution times and provide justifica tion for the use of each of the methods presented. We will estimate the worst-case execution time (WCET) of the program units using several methods and compare the results of these methods. We will also present a new method for estimating the WCET, based on the theory of extreme value distributions.
IMPROVED SPECTRUM CORRECTION ALGORITHM OF ESTIMATING POWER ELECTRIC PARAMETERS AND ITS APPLICATIONS

DFT is one of the most common measuring algorithms of estimating power electric parameters. However, because of leakage and picket-fence effect caused by asynchronous sampling, the error occurs as the traditional estimating algorithm is applied. According to the characteristics of power electric signals, a novel algorithm, which is based on the improved DFT spectrum correction technology and needs no synchronous sampling, is present. By choosing different length of data window, sampling frequency, and corresponding windows, the algorithm can meet the different demands of computing speed and precision at various applying situation, such as measurement, control, and protection in power system. The algorithm can be easily employed; both the simulation results and the practices show its feasibility and effectivity.
Inductive Zero-Shot Image Annotation via Embedding Graph

Conventional image annotation systems can only handle those images having labels within the exist library, but cannot recognize those novel labels. In order to learn new concepts, one has to gather large amount of labeled images and train the model from scratch. More importantly, it can come with a high price to collect those labeled images. For these reasons, we put forward a zero-shot image annotation model, to reduce the demand for the images with novel labels. In this paper, we focus on the two big challenges of zero-shot image annotation: polysemous words and a strong bias in the generalized zero-shot setting. For the first problem, instead of training on large corpus datasets as previous methods, we propose to adopt Node2Vec to obtain contextualized word embeddings, which can easily produce word vectors of the polysemous words. For the second problem, we alleviate the strong bias in two ways: on one hand, we utilize a model based on graph convolutional network (GCN) to make target images involved in the training process; on the other hand, we put forward a novel semantic coherent (SC) loss to capture the semantic relations of the source and target labels. The extensive experiments on NUSWIDE, COCO, IAPR TC-12, and Corel5k datasets show the superiority of the proposed model and the annotation performance get improved by 4%–6% comparing with state-of-the-art methods.