Miracle-dz/UniFAR-FTUs huggingface dataset:
Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.
Identification of the Students Learning Process During Education Robotics Activities

This paper presents the design of an assessment process and its outcomes to investigate the impact of Educational Robotics activities on students' learning. Through data analytics techniques, the authors will explore the activities' output from a pedagogical and quantitative point of view. Sensors are utilized in the context of an Educational Robotics activity to obtain a more effective robot–environment interaction. Pupils work on specific exercises to make their robot smarter and to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is crucial, and learners have to comprehend how to use them. In the presented study, the potential of Educational Data Mining is used to investigate how a group of primary and secondary school students, using visual programming (Lego Mindstorms EV3 Education software), design programming sequences while they are solving an exercise related to an ultrasonic sensor mounted on their robotic artifact. For this purpose, a tracking system has been designed so that every programming attempt performed by students' teams is registered on a log file and stored in an SD card installed in the Lego Mindstorms EV3 brick. These log files are then analyzed using machine learning techniques (k-means clustering) in order to extract different patterns in the creation of the sequences and extract various problem-solving pathways performed by students. The difference between problem-solving pathways with respect to an indicator of early achievement is studied.
Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California’s Almond Orchards

Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California’s almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.
Exercise, Chronotropic and Inotropic Capacity in Athletes with Different Training Programmes

Abstract Amateur sport has significant influence on peoples’ physical activity and affects the prevalence of non-communicable diseases. The population of competing amateur athletes has not been sufficiently studied, and there is a huge gap between functional results of untrained individuals and professional athletes. The aim of the study was to evaluate the exercise capacity and chrono-tropic, inotropic capacity in amateur athletes with different training programmes, as assessed by cardiopulmonary exercise testing. In a longitudinal prospective study, 600 amateur athletes who performed high dynamic load sports, according to the Mitchell Classification of Sports, were assessed. The individuals underwent standard maximum cardiopulmonary exercise testing on the ISO certified Master screen CPX system. The exercise capacity was dependent on the training programme organisation, regularity and duration, but it was not dependent on age. During maximum work-rate, the functional parameters of the cardiorespiratory system and inotropic capacity were significantly dependent on duration of the training programme. Chronotropic and inotropic capacity, as well as cardiorespiratory adaptation were significantly higher for competing amateur athletes of both genders than for untrained individuals. The study showed that amateur athletes with a training programme 300 minutes per week had higher exercise capacity, which was based on the individually suitable training programme, and higher aerobic and anaerobic capacity. Regular pre-competition medical assessment of amateur athletes can be used to objectively evaluate their health condition, adaptation, cardiac risk and make changes in the athletes’ training programme.
Exploring Music Collections: An Interactive, Dimensionality Reduction Approach to Visualizing Songbanks

This is an overview paper on an interactive music exploration interface for music collections. This interface is meant to help explore the cross-cultural similarities, interactions, and patterns of music excerpts from different regions and understand the similarities by employing computational audio analysis, machine learning, and visualization techniques. In our computational analysis, we used standard audio features that capture timbre information and projected them onto a lower-dimensional space for visualizing the (dis)similarity. There are two collections of non-Eurogenetic music under study. The 2-D and 3-D mappings are visualized through a dashboard application and also rendered in Virtual Reality space where users can interact and explore to get meaningful insights about the structural (dis)similarities of the music collections.
Pan: Conversational Agent for Criminal Investigations

We present an early prototype conversational agent (CA), called Pan, for retrieving information to support criminal investigations. Our approach tackles the issue of algorithmic transparency, which is critical in unpredictable, high risk, and high consequence domains. We present a novel method to flexibly model CA intentions and provide transparency of attributes that is underpinned with human recognition. We propose that Pan can be used for experimentation to probe analyst requirements and to evaluate the effectiveness of our explanation structure.
Multi-Method Evaluation: Leveraging Multiple Methods to Answer What You Were Looking For

Research in the field of information retrieval and recommendation mostly focuses on one single evaluation method and one single quality objective. On the one hand, many research endeavors focus on system-centric evaluation from an algorithmic perspective and consider the context of use only to a minor extent. On the other hand, there are research endeavors focusing on user-centric approaches to the design and evaluation of systems. However, algorithmic quality and perceived quality of user experience do not necessarily match. Thus, it is essential for system evaluation to substantially integrate multiple evaluation methods that cover a variety of relevant aspects and perspectives. Only such an integrated combination of methods may lead to a deep understanding of users, their behavior, and experience in their interaction with a system. This half-day tutorial follows the objective to raise awareness in the CHIIR community concerning the significance of using multiple methods in the evaluation of information retrieval and recommender systems. The tutorial illustrates the "blind spots'' when using single methods. It introduces the concept of "multi-method evaluation'' and discusses its benefits and challenges. While multi-method evaluations may be designed very flexibly, the tutorial presents broadly-defined basic options of how multiple methods may be integrated in an evaluation design. In group work, participants are encouraged to select and fine-tune a specific design that best matches their research endeavor's purpose.
Southern Pines Are Resistant to Mortality From an Exceptional Drought in East Texas

In 2011, the state of Texas experienced its worst drought ever recorded, breaking statewide temperature, and precipitation records. With climate predictions suggesting increases in the severity and extent of future droughts in this region, forest managers will need to plan for such events to minimize tree mortality. In east Texas, pine species are economically and ecologically important and are often managed, providing an opportunity to examine silvicultural strategies for mitigating exceptional drought mortality. We used U.S. Forest Service Forest Inventory and Analysis data and Bayesian, logistic, mixed effects regression to model individual tree mortality and the effect of stand structure (i.e., tree size, relative density, and species dominance) on three major pine groups, planted (PL) and naturally-regenerated (NL) loblolly pine (Pinus taeda L.) and all shortleaf pine (SL, Pinus echinata Mill.), under pre-drought and drought periods in east Texas. These groups represent a spectrum of management intensity with PL generally intensively managed and NL and SL relatively unmanaged. Moreover, loblolly pine tends to be production-oriented while shortleaf pine has perceived drought tolerance. Surprisingly, pine mortality did not increase significantly from pre-drought to drought periods in spite of the record drought conditions. However, mortality differed between pine groups and in response to stand structure for loblolly pine. Planted loblolly was least affected as mortality rate increased 9.8%. In contrast, NL and SL pine mortality rates were significantly higher than PL and increased 26.3 and 20.0%, respectively. The smallest and largest stems experienced elevated mortality under both periods, notably PL under exceptional drought. As expected, higher densities of loblolly pine exacerbated exceptional drought mortality. Surprisingly, greater overstory diversity for NL reduced mortality under exceptional drought. Despite the unprecedented hot and dry conditions of the 2011 drought, our results suggest that current practices in PL that manage relative density and tree size for non-drought conditions confers mortality resistance under exceptional drought. In NL stands, mortality resistance could be increased through active thinning and promoting greater overstory diversity. These results offer critical knowledge for managers tasked with providing continued forest resources in the face of future exceptional droughts.
Optimal Arrangement of Surveillance Cameras Using Space Division and a Genetic Algorithm

This paper presents an optimal camera arrangement method for surveillance camera systems that evaluates the arrangement of cameras in 3D space. It makes use of multiple factors and optimizes this arrangement using a genetic algorithm (GA). We implemented a prototype in Unity that demonstrates our proposed method, and experiments show the performance of our method when used for placement in a virtual environment.
Syn: Synthetic Dataset for Training UI Element Detector From Lo-Fi Sketches

User Interface design is an iterative process that progresses through low-, medium-, and high-fidelity prototypes. A few research projects use deep learning to automate this process by transforming low fidelity (lo-fi) sketches into front-end code. However, these research projects lack a large scale dataset of lo-fi sketches to train detection models. As a solution, we created Syn, a synthetic dataset containing 125,000 lo-fi sketches. These lo-fi sketches were synthetically generated using our UISketch dataset containing 5,917 sketches of 19 UI elements drawn by 350 participants. To realize the usage of Syn, we used it to train a UI element detector, Meta-Morph. It detects UI elements from a lo-fi sketch with 84.9% mAP and 72.7% AR. This work aims to support future research on UI element sketch detection and automating prototype fidelity transformation.
Social Network Analysis as a Tool for Data Analysis and Visualization in Information Behaviour and Interactive Information Retrieval Research

Social network analysis (SNA) is an empirical approach and a set of techniques that investigates actors, their dyadic links and the network they form. In this half-day tutorial, participants will learn about social network analysis as a tool for data analysis and visualization and how it can be applied in studies of information behaviour and interactive information retrieval. Participants will learn about its theoretical substantiation and gain practical experience by applying these theoretical concepts in a hands-on session using the open-source software Gephi.
Differences in the Search Behavior of Cancer Detection Dogs Trained to Have Either a Sit or Stand-Stare Final Response

Recent literature has demonstrated that dogs have the potential to detect, and communicate the presence of, various human diseases. However, there is a lack of investigation into whether commonplace training differences within the field could influence a dog's behavior during a biomedical detection task. Here we report on the behavior of four dogs trained to alert to blood plasma samples taken from individuals with ovarian cancer. One hundred trials per dog were selected from routine video recordings collected over a period of 13 months. Videos were coded frame by frame to quantify sample checking, alerting behavior, and durations of alert. Dogs had previously been trained to elicit a final response behavior once they had located the target odor. Two dogs had a “sit” response while the other two had a “stand-stare” response. Alert behavior was categorized as true positive (a correct alert to a cancer sample) or false positive (an incorrect alert to biological and non-biological controls and distractors). Hesitations were also recorded, where the dog either checks the sample twice or, spends a longer duration of time sniffing the sample than a true pass without carrying out their final response. Results show individual variation in the total frequency of false alerts elicited. However, the rate of hesitations appears to be influenced by alert style, with stand-stare dogs carrying out 40 and 32, respectively (total = 72) and sit dogs carrying out 7 and 8, respectively (total = 15). The stand-stare dogs had a non-significant difference in the duration of their true and false positive alerts. In contrast, the sit dogs showed a significant difference (p < 0.001), maintaining their false alerts for, on average, two times the duration of their true alerts. Stand-stare dogs increased the duration of time spent in contact with the port when plasma samples were present, whereas sit dogs spent on average 0.3 s in contact with the port regardless of what sample type it contained. These findings suggest that the type of operant response a biomedical detection dog has been trained may influence their sample checking and response behavior.
Gaussian Sketching yields a J-L Lemma in RKHS

The main contribution of the paper is to show that Gaussian sketching of a kernel-Gram matrix $\boldsymbol K$ yields an operator whose counterpart in an RKHS $\mathcal H$, is a \emph{random projection} operator---in the spirit of Johnson-Lindenstrauss (J-L) lemma. To be precise, given a random matrix $Z$ with i.i. d. Gaussian entries, we show that a sketch $Z\boldsymbol{K}$ corresponds to a particular random operator in (infinite-dimensional) Hilbert space $\mathcal H$ that maps functions $f \in \mathcal H$ to a low-dimensional space $\mathbb R^d$, while preserving a weighted RKHS inner-product of the form $\langle f, g \rangle_{\Sigma} \doteq \langle f, \Sigma^3 g \rangle_{\mathcal H}$, where $\Sigma$ is the \emph{covariance} operator induced by the data distribution. In particular, under similar assumptions as in kernel PCA (KPCA), or kernel $k$-means (K-$k$-means), well-separated subsets of feature-space $\{K(\cdot, x): x \in \cal X\}$ remain well-separated after such operation, which suggests similar benefits as in KPCA and/or K-$k$-means, albeit at the much cheaper cost of a random projection. In particular, our convergence rates suggest that, given a large dataset $\{X_i\}_{i=1}^N$ of size $N$, we can build the Gram matrix $\boldsymbol K$ on a much smaller subsample of size $n\ll N$, so that the sketch $Z\boldsymbol K$ is very cheap to obtain and subsequently apply as a projection operator on the original data $\{X_i\}_{i=1}^N$. We verify these insights empirically on synthetic data, and on real-world clustering applications.
Enhancing Travel Experience Leveraging on-line and off-line Users' Behaviour Data

Several tourism applications have been designed to support on-line information search and content browsing. However, often they neglect user's current visit experience, i.e., what the user already experienced off-line. In this demo we showcase a novel mobile app enhancing a traveller's visit experience by considering the visit context and the traveller's currently visited locations. The app has been designed as a tool for advancing the state of the art in decision support systems. The app can be used outside the lab, hence taking into account the true complexity of user decision making, while lab experiments tend to over-simplify that.
Structured Mesh Generation and Numerical Analysis of a Scroll Expander in an Open-Source Environment

The spread of the organic rankine cycle applications has driven researchers and companies to focus on the improvement of their performance. In small to medium-sized plants, the expander is the component that has typically attracted the most attention. One of the most used types of machine in this scenario is the scroll. Among the other methods, numerical analyses have been increasingly exploited for the investigation of the machine’s behaviour. Nonetheless, there are major challenges for the successful application of computational fluid dynamics (CFD) to scrolls. Specifically, the dynamic mesh treatment required to capture the movement of working chambers and the nature of the expanding fluids require special care. In this work, a mesh generator for scroll machines is presented. Given few inputs, the software described provides the mesh and the nodal positions required for the evolution of the motion in a predefined mesh motion approach. The mesh generator is developed ad hoc for the coupling with the open-source CFD suite OpenFOAM. A full analysis is then carried out on a reverse-engineered commercial machine, including the refrigerant properties calculations via CoolProp. It is demonstrated that the proposed methodology allows for a fast simulation and achieves a good agreement with respect to former analyses.
Subjective Search Intent Predictions using Customer Reviews

Query intent prediction is a component of information retrieval which improves result relevance through an understanding of latent user intents in addition to explicit query keywords. We target context-of-use intents, such as the activity for which a product is used and the target audience for a product, which are subjective and not usually indexed as product attributes in the catalog. We describe a method to predict latent query intents: we extract intents from product reviews on amazon.com and, using behavioral purchase signals that associate queries with the reviewed products, train query classifiers that label queries with the intents extracted from reviews. For example, we predict the activity "running" for the query "adidas mens pants. " We show that our method can predict latent intents not indexed directly in the product catalog.
Barriers facing people with obesity and type 2 diabetes in weight control: a systematic review

Type 2 diabetes has both non-modifiable and modifiable risk factors, such as heredity and obesity respectively. Obesity or overweight is a key modifiable risk factor for type 2 diabetes. Therefore, weight control through non-pharmacological interventions such as diet and physical exercise are some of the important measures used to reduce the potential complications and mortality associated with diabetes. Although, there are various policies and guidelines to tackle obesity in the UK, controlling weight gain in clinical practice remains a challenge. This systematic review sought to examine the evidence relating to the barriers preventing people with obesity or overweight and type 2 diabetes from making lifestyle changes, such as increased physical activity and changes to diet as a means of weight control.
Concentration Estimation in E-Learning Based on Learner's Facial Reaction to Teacher's Action

In video-based learning, estimating the level of concentration is important for increasing the efficiency of learning. Facial expressions during learning obtained with a Web camera are often used to estimate concentration because cameras are easy to install. In this work, we focus on how learners react to video contents and propose a new method which is based on the Jaccard coefficient calculated from learner's facial reactions to teacher's actions. We conduct experiments and collect data in a Japanese cram school. Analysis of our collected data shows a weighted-F1 score of 0.57 for four levels of concentration classification, which is higher than the accuracy obtained with the methods based on learner's facial expression alone. The results indicate that our method can be effective for concentration estimation in an actual learning environment.