TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.Comment: Accepted at IEEE BigData 202

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page

Learnable Path in Neural Controlled Differential Equations

Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i) the adoption of the continuous path created by an interpolation algorithm from each raw discrete time-series sample and ii) the adoption of the Riemann--Stieltjes integral. It is the continuous path which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing interpolation algorithms to create the path, which is unclear whether they can create an optimal path. To this end, we present a method to generate another latent path (rather than relying on existing interpolation algorithms), which is identical to learning an appropriate interpolation method. We design an encoder-decoder module based on NCDEs and NODEs, and a special training method for it. Our method shows the best performance in both time-series classification and forecasting

Digital Forensic Analysis to Improve User Privacy on Android

The Android platform accounts for 85% of the global smartphone operating-system market share, and recently, it has also been installed on Internet-of-Things (IoT) devices such as wearable devices and vehicles. These Android-based devices store various personal information such as user IDs, addresses, and payment information and device usage data when providing convenient functions to users. Insufficient security for the management and deletion of data stored in the device can lead to various cyber security threats such as personal information leakage and identity theft. Therefore, research on the protection of personal information stored in the device is very important. However, there is a limitation that the current research for protection of personal information on the existing Android platform was only conducted on Android platform 6 or lower. In this paper, we analyze the deleted data remaining on the device and the possibility of recovery to improve user privacy for smartphones using Android platforms 9 and 10. The deleted data analysis is performed based on three data deletion scenarios: data deletion using the app’s own function, data deletion using the system app’s data and cache deletion function, and uninstallation of installed apps. It demonstrates the potential user privacy problems that can occur when using Android platforms 9 and 10 due to the leakage of recovered data. It also highlights the need for improving the security of personal user information by erasing the traces of deleted data that remain in the journal area and directory entry area of the filesystem used in Android platforms 9 and 10

Evaluation of Paraspinal Muscle Degeneration on Pain Relief after Percutaneous Epidural Adhesiolysis in Patients with Degenerative Lumbar Spinal Disease

Background and Objectives: The analgesic effectiveness of epidural adhesiolysis may be influenced by morphological changes in the paraspinal muscles, particularly in elderly patients. The objective of this study was to assess whether the cross-sectional area or fatty infiltration of the paraspinal muscles impacts the treatment outcomes of epidural adhesiolysis. Materials and Methods: The analysis included a total of 183 patients with degenerative lumbar disease who underwent epidural adhesiolysis. Good analgesia was defined as a reduction in pain score of ≥30% at the 6-month follow up. We measured the cross-sectional area and fatty infiltration rate of the paraspinal muscles and divided the study population into age groups (≥65 years and Results: The results revealed that elderly patients experienced poorer analgesic outcomes as the rate of fatty infiltration in the paraspinal muscles increased (p = 0.029), predominantly in female patients. However, there was no correlation between the cross-sectional area and the analgesic outcome in patients younger than or older than 65 years (p = 0.397 and p = 0.349, respectively). Multivariable logistic regression analysis indicated that baseline pain scores p = 0.003), spondylolisthesis (OR = 4.074, 95% CI = 1.144–14.511, p = 0.030), and ≥ 50% fatty infiltration of the paraspinal muscles (OR = 6.576, 95% CI = 1.300–33.268, p = 0.023) were significantly associated with poor outcomes after adhesiolysis in elderly patients. Conclusions: Fatty degeneration of paraspinal muscles is correlated with inferior analgesic outcomes following epidural adhesiolysis in elderly patients, but not in young and middle-aged patients. The cross-sectional area of the paraspinal muscles is not associated with pain relief after the procedure

Evaluation of high nutrient diets on litter performance of heat-stressed lactating sows

Objective The present study investigated the litter performance of multiparous sows fed 3% and 6% densified diets at farrowing to weaning during summer with mean maximum room temperature of 30.5°C. Methods A total of 60 crossbred multiparous sows were allotted to one of three treatments based on body weight according to a completely randomized design. Three different nutrient levels based on NRC were applied as standard diet (ST; metabolizable energy, 3,300 kcal/kg), high nutrient level 1 (HE1; ST+3% higher energy and 16.59% protein) and high nutrient level 2 (HE2; ST+6% higher energy and 17.04% protein). Results There was no variation in the body weight change. However, backfat thickness change tended to reduce in HE1 in comparison to ST treatment. Dietary treatments had no effects on feed intake, daily energy intake and weaning-to-estrus interval in lactating sows. Litter size, litter weight at weaning and average daily gain of piglets were significantly greater in sows in HE1 compared with ST, however, no difference was observed between HE2 and ST. Increasing the nutrient levels had no effects on the blood urea nitrogen, glucose, triglyceride, and creatinine at post-farrowing and weaning time. The concentration of follicle stimulating hormone, cortisol and insulin were not affected by dietary treatments either in post-farrowing or weaning time. The concentration of blood luteinizing hormone of sows in ST treatment was numerically less than sows in HE2 treatment at weaning. Milk and colostrum compositions such as protein, fat and lactose were not affected by the treatments. Conclusion An energy level of 3,400 kcal/kg (14.23 MJ/kg) with 166 g/kg crude protein is suggested as the optimal level of dietary nutrients for heat stressed lactating sows with significant beneficial effects on litter size

An overview of hourly rhythm of demand-feeding pattern by a controlled feeding system on productive performance of lactating sows during summer

The present study investigated the impacts of the conventional feeder and free feeding time (FFT), and backfat thickness (<20 mm and ≥20 mm) on sows at farrowing to weaning during the summer season. A total of 56 crossbred sows were allotted to one of four treatments according to a 2 × 2 factorial arrangement. Feeder type affected body weight changes (p < .01) and backfat losses (p < .05), and the body weight changes (p < .05) and backfat losses (p < .01) of sows were lower for those with <20 mm backfat thickness compared with those with ≥20 mm backfat thickness during the lactation period. Daily feed intake was greater in sows with lower backfat thickness (5.47 kg; p < .01) and sows in the FFT group (5.46 kg; p < .05). A greater average daily gain was observed in sows in the FFT group (p < .05). There were no effects of feeder type or backfat thickness on weaning-to-oestrus interval, blood urea nitrogen, glucose, triglyceride, creatinine, FSH and LH, or colostrum and milk composition for sows during lactation. There was a linear increase in the count of Lactobacillus spp. and coliforms in conventional feeders over time (p < .01). A linear increase was detected for acetic acid production in the conventional feeders over time (p < .01). Hence, it was concluded that controlling sows’ feeding leads to improved feed intake for sows housed in hot ambient temperatures during the summer period

Fabrication and Characterizations of Hot-Melt Extruded Nanocomposites Based on Zinc Sulfate Monohydrate and Soluplus

Zinc sulfate monohydrate (ZnSO4)-loaded nanocomposites (NCs) were fabricated by using a hot-melt extruder (HME) system. Soluplus (SP) was adopted as an amphiphilic polymer matrix for HME processing. The micro-size of ZnSO4 dispersion was reduced to nano-size by HME processing with the use of SP. ZnSO4 could be homogeneously dispersed in SP through HME processing. ZnSO4/SP NCs with a 75 nm mean diameter, a 0.1 polydispersity index, and a −1 mV zeta potential value were prepared. The physicochemical properties of ZnSO4/SP NCs and the existence of SP in ZnSO4/SP NCs were further investigated by solid-state studies. Nano-size range of ZnSO4/SP NC dispersion was maintained in the simulated gastrointestinal environments (pH 1.2 and 6.8 media). No severe toxicity in intestinal epithelium after oral administration of ZnSO4/SP NCs (at 100 mg/kg dose of ZnSO4, single dosing) was observed in rats. These results imply that developed ZnSO4/SP NC can be used as a promising nano-sized zinc supplement formulation. In addition, developed HME technology can be widely applied to fabricate nanoformulations of inorganic materials