Exploiting Feature Selection in Human Activity Recognition: Methodological Insights and Empirical Results Using Mobile Sensor Data

Human Activity Recognition (HAR) using mobile sensor data has gained increasing attention over the last few years, with a fast-growing number of reported applications. The central role of machine learning in this field has been discussed by a vast amount of research works, with several strategies proposed for processing raw data, extracting suitable features, and inducing predictive models capable of recognizing multiple types of daily activities. Since many HAR systems are implemented in resource-constrained mobile devices, the efficiency of the induced models is a crucial aspect to consider. This paper highlights the importance of exploiting dimensionality reduction techniques that can simplify the model and increase efficiency by identifying and retaining only the most informative and predictive features for activity recognition. More in detail, a large experimental study is presented that encompasses different feature selection algorithms as well as multiple HAR benchmarks containing mobile sensor data. Such a comparative evaluation relies on a methodological framework that is meant to assess not only the extent to which each selection method is effective in identifying the most predictive features but also the overall stability of the selection process, i.e., its robustness to changes in the input data. Although often neglected, in fact, the stability of the selected feature sets is important for a wider exploitability of the induced models. Our experimental results give an interesting insight into which selection algorithms may be most suited in the HAR domain, complementing and significantly extending the studies currently available in this field

Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes

The past decade has seen an explosion of the amount of digital information generated within the healthcare domain. Digital data exist in the form of images, video, speech, transcripts, electronic health records, clinical records, and free-text. Analysis and interpretation of healthcare data is a daunting task, and it demands a great deal of time, resources, and human effort. In this paper, we focus on the problem of co-morbidity recognition from patient’s clinical records. To this aim, we employ both classical machine learning and deep learning approaches.We use word embeddings and bag-of-words representations, coupled with feature selection techniques. The goal of our work is to develop a classification system to identify whether a certain health condition occurs for a patient by studying his/her past clinical records. In more detail, we have used pre-trained word2vec, domain-trained, GloVe, fastText, and universal sentence encoder embeddings to tackle the classification of sixteen morbidity conditions within clinical records. We have compared the outcomes of classical machine learning and deep learning approaches with the employed feature representation methods and feature selection methods. We present a comprehensive discussion of the performances and behaviour of the employed classical machine learning and deep learning approaches. Finally, we have also used ensemble learning techniques over a large number of combinations of classifiers to improve the single model performance. For our experiments, we used the n2c2 natural language processing research dataset, released by Harvard Medical School. The dataset is in the form of clinical notes that contain patient discharge summaries. Given the unbalancedness of the data and their small size, the experimental results indicate the advantage of the ensemble learning technique with respect to single classifier models. In particular, the ensemble learning technique has slightly improved the performances of single classification models but has greatly reduced the variance of predictions stabilizing the accuracies (i.e., the lower standard deviation in comparison with single classifiers). In real-life scenarios, our work can be employed to identify with high accuracy morbidity conditions of patients by feeding our tool with their current clinical notes. Moreover, other domains where classification is a common problem might benefit from our approach as well

TF-IDF vs word embeddings for morbidity identification in clinical notes: An initial study

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperform the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches

FootApp: An AI-powered system for football match annotation

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players’ and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario

Better postpartal performance in dairy cows supplemented with rumen-protected methionine compared with choline during the peripartal period

The onset of lactation in dairy cows is characterized by high output of methylated compounds in milk when sources of methyl group are in short supply. Methionine and choline (CHOL) are key methyl donors and their availability during this time may be limiting for milk production, hepatic lipid metabolism, and immune function. Supplementing rumen-protected Met and CHOL may improve overall performance and health of transition cows. The objective of this study was to evaluate the effect of supplemental rumen-protected Met and CHOL on performance and health of transition cows. Eighty-one multiparous Holstein cows were used in a randomized, complete, unbalanced block design with 2×2 factorial arrangement of Met (Smartamine M, Adisseo NA, Alpharetta, GA) and CHOL (ReaShure, Balchem Inc., New Hampton, NY) inclusion (with or without). Treatments (20 to 21 cows each) were control (CON), CON+Met (SMA), CON+CHOL (REA), and CON+Met+CHOL (MIX). From -50 to -21d before expected calving, all cows received the same diet (1.40Mcal of NEL/kg of DM) with no Met or CHOL. From -21d to calving, cows received the same close-up diet (1.52Mcal of NEL/kg of DM) and were assigned randomly to treatments (CON, SMA, REA, or MIX) supplied as top dresses. From calving to 30 DIM, cows were fed the same postpartal diet (1.71Mcal of NEL/kg of DM) and continued to receive the same treatments through 30 DIM. The Met supplementation was adjusted daily at 0.08% DM of diet and REA was supplemented at 60g/d. Incidence of clinical ketosis and retained placenta tended to be lower in Met-supplemented cows. Supplementation of Met (SMA, MIX) led to greater DMI compared with other treatments (CON, REA) in both close-up (14.3 vs. 13.2kg/d, SEM 0.3) and first 30d postpartum (19.2 vs. 17.2kg/d, SEM 0.6). Cows supplemented with Met (SMA, MIX) had greater yields of milk (44.2 vs. 40.4kg/d, SEM 1.2), ECM (44.6 vs. 40.5kg/d, SEM 1.0), and FCM (44.6 vs. 40.8kg/d, SEM 1.0) compared with other (CON, REA) treatments. Milk fat content did not differ in response to Met or CHOL. However, milk protein content was greater in Met-supplemented (3.32% vs. 3.14%, SEM 0.04%) but not CHOL-supplemented (3.27 vs. 3.19%, SEM 0.04%) cows. Supplemental CHOL led to greater blood glucose and insulin concentrations with lower glucose:insulin ratio. No Met or CHOL effects were detected for blood fatty acids or BHB, but a Met × time effect was observed for fatty acids due to higher concentrations on d 20. Results from the present study indicate that peripartal supplementation of rumen-protected Met but not CHOL has positive effects on cow performance

Rumen-protected methionine compared with rumen-protected choline improves immunometabolic status in dairy cows during the peripartal period.

The immunometabolic status of peripartal cows is altered due to changes in liver function, inflammation, and oxidative stress. Nutritional management during this physiological state can affect the biological components of immunometabolism. The objectives of this study were to measure concentrations of biomarkers in plasma, liver tissue, and milk, and also polymorphonuclear leukocyte function to assess the immunometabolic status of cows supplemented with rumen-protected methionine (Met) or choline (CHOL). Forty-eight multiparous Holstein cows were used in a randomized complete block design with 2×2 factorial arrangement of Met (Smartamine M, Adisseo NA, Alpharetta, GA) and CHOL (ReaShure, Balchem Inc., New Hampton, NY) level (with or without). Treatments (12 cows each) were control (CON), no Met or CHOL; CON and Met (SMA); CON and CHOL (REA); and CON and Met and CHOL (MIX). From -50 to -21d before expected calving, all cows received the same diet [1.40Mcal of net energy for lactation (NEL)/kg of DM] with no Met or CHOL. From -21d to calving, cows received the same close-up diet (1.52Mcal of NEL/kg of DM) and were assigned randomly to each treatment. From calving to 30d, cows were on the same postpartal diet (1.71Mcal of NEL/kg of DM) and continued to receive the same treatments until 30d. The Met supplementation was adjusted daily at 0.08% DM of diet, and CHOL was supplemented at 60g/cow per day. Liver (-10, 7, 21, and 30d) and blood (-10, 4, 8, 20, and 30d) samples were harvested for biomarker analyses. Neutrophil and monocyte phagocytosis and oxidative burst were assessed at d 1, 4, 14, and 28d. The Met-supplemented cows tended to have greater plasma paraoxonase. Greater plasma albumin and IL-6 as well as a tendency for lower haptoglobin were detected in Met- but not CHOL-supplemented cows. Similarly, cows fed Met compared with CHOL had greater concentrations of total and reduced glutathione (a potent intracellular antioxidant) in liver tissue. Upon a pathogen challenge in vitro, blood polymorphonuclear leukocyte phagocytosis capacity and oxidative burst activity were greater in Met-supplemented cows. Overall, liver and blood biomarker analyses revealed favorable changes in liver function, inflammation status, and immune response in Met-supplemented cows

Aluminum alloy production for the reinforcement of the CMS conductor

The Compact Muon Solenoid (CMS) is one of the general-purpose detectors to be provided for the Large Hadron Collider (LHC) project at CERN. The design field of the CMS superconducting magnet is 4 T, the magnetic length is 12.5 m and the free bore is 6 m. To reinforce the high-purity (99.998%) Al-stabilized conductor of the magnet against the magnetic loadings experienced during operation at 4.2 K, two continuous sections of Al-alloy (AA) reinforcement are Electron Beam (EB) welded to it. The reinforcements have a section of 24*18 mm and are produced in continuous 2.55 km lengths. The alloy EN AW-6082 has been selected for the reinforcement due to its excellent extrudability, high strength in the precipitation hardened states, high toughness and strength at cryogenic temperature and good EB weldability. Each of the continuous lengths of the reinforcement is extruded billet on billet and press quenched on-line from the extrusion temperature in an industrial extrusion plant. In order to insure the ready EB weldability of the reinforcement onto the pure aluminum of the insert, tight dimensional tolerances and proper surface finish of the reinforcement are required in the as-extruded state. As well, in order to facilitate the winding operation of the conductor, the uniformity of the mechanical properties of the extruded reinforcement, especially at the billet on billet joints, is critical. To achieve these requirements in an industrial environment, substantial effort was made to refine existing production techniques and to monitor critical extrusion parameters during production. This paper summarizes the main results obtained during the establishment of the extrusion line and of the production phase of the reinforcement. (10 refs)

Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising Recommendation

In this paper, we provide an overview of the approach we used as team Gabibboost for the ACM RecSys Challenge 2023, organized by ShareChat and Moj. The challenge focused on predicting user activity in the online advertising setting based on impression data, in particular, predicting whether a user would install an advertised application using a high-dimensional anonymized feature vector. Our proposed solution is based on an ensemble model that combines the strengths of several machine learning sub-models, including CatBoost, LightGBM, HistGradientBoosting, and two hybrid models. Our proposal is able to harness the strengths of our models through a distribution shift postprocessing and fine-Tune the final prediction via a custom build pessimistic rescaling function. The final ensemble model allowed us to rank 1st on the academic leaderboard and 9th overall

Continuous EB welding of the reinforcement of the CMS conductor

The Compact Muon Solenoid (CMS) is one of the general-purpose detectors to be provided for the LHC project at CERN. The design field of the CMS superconducting magnet is 4 T, the magnetic length is 12.5 m and the free bore is 6 m. In order to withstand the electro-mechanical forces during the operation of the CMS magnet, the superconducting cable embedded in a 99.998% pure aluminum matrix is reinforced with two sections of aluminum alloy EN AW-6082 assembled by continuous Electron Beam Welding (EBW). A dedicated production line has been designed by Techmeta, a leading company in the field of EBW. The production line has a total length of 70 m. Non-stop welding of each of the 20 lengths of 2.5 km, required to build the coil, will last 22 hours. EBW is the most critical process involved in the production line. The main advantage of the EBW process is to minimize the Heat Affected Zone; this is particularly important for avoiding damage to the superconducting cable located only 4.7 mm from the welded joints. Two welding guns of 20 kW each operate in parallel in a vacuum chamber fitted with dynamic airlocks. After welding, the conductor is continuously machined on the four faces and on each corner to obtain the required dimensions and surface finish. Special emphasis has been put on quality monitoring. All significant production parameters are recorded during operation and relevant samples are taken from each produced length for destructive testing purposes. In addition, a continuous phased array ultrasonic checking device is located immediately after the welding unit for the continuous welding quality control, along with a dimension laser measurement unit following the machining. (8 refs)

Early progression of the autonomic dysfunction observed in pediatric type 1 diabetes mellitus

To focus on early cardiac and vascular autonomic dysfunction that might complicate type 1 diabetes mellitus in children, we planned an observational, cross-sectional study in a population of 93 young patients, under insulin treatment, subdivided in 2 age subgroups (children: 11.5 +/- 0.4 years; adolescents: 19.3 +/- 0.2 years). Time and frequency domain analysis of RR interval and systolic arterial pressure variability provided quantitative indices of the sympatho-vagal balance regulating the heart period, of the gain of cardiac baroreflex, and of the sympathetic vasomotor control. Sixty-eight children of comparable age served as a reference group. At rest, systolic arterial pressure and the power of its low-frequency component were greater in patients than in controls, particularly in children (14.0 +/- 2.3 versus 3.1 +/- 0.3 mm Hg(2)). Moreover, baroreflex gain was significantly reduced in both subgroups of patients. Standing induced similar changes in the autonomic profiles of controls and patients. A repeat study after 1 year showed a progression in low- frequency oscillations of arterial pressure and a shift toward low frequency in RR variability. Data in young patients with type 1 diabetes mellitus show a significant increase in arterial pressure, a reduced gain of the baroreflex regulation of the heart period, and an increase of the low- frequency component of systolic arterial pressure variability, suggestive of simultaneous impairment of vagal cardiac control and increases of sympathetic vasomotor regulation. A repeat study after 1 year shows a further increase of sympathetic cardiac and vascular modulation, suggesting early progression of the autonomic dysfunction