Miracle-dz/UniFAR-FTUs huggingface dataset:
PRINS: Processing-in-Storage Acceleration of Machine Learning

Machine learning algorithms have become a major tool in various applications. The high-performance requirements on large-scale datasets pose a challenge for traditional von Neumann architectures. We present two machine learning implementations and evaluations on PRINS, a novel processing-in-storage system based on resistive content addressable memory (ReCAM). PRINS functions simultaneously as a storage and a massively parallel associative processor. PRINS processing-in-storage resolves the bandwidth wall faced by near-data von Neumann architectures, such as three-dimensional DRAM and CPU stack or SSD with embedded CPU, by keeping the computing inside the storage arrays, thus implementing in-data, rather than near-data, processing. We show that PRINS-based processing-in-storage architecture may outperform existing in-storage designs and accelerator-based designs. Multiple performance comparisons for the ReCAM processing-in-storage implementations of $K$ -means and K-nearest neighbors are performed. Compared platforms include CPU, GPU, FPGA, and Automata Processor. We show that PRINS may achieve an order-of-magnitude speedup and improved power efficiency relative to all compared platforms.
A Critical Analysis of the V-Model of Big Data

In order to provide a solid and stable platform for Big Data (BD) management and application development, a generic model of BD needs to be agreed upon. There is no firm consensus on what defines BD, although a broad spectrum of competing, sometimes conflicting, ideas in terms of "V"s have been put forth. In this paper, we provide an exhaustive survey of the V-model of Big Data (BD) characteristics proposed by several academic and industry researchers, and motivate a formal approach to a selection of independent Vs that can model BD sufficiently well in any application sector.
Multi-Scan Generalized Labeled Multi-Bernoulli Filter

This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagates the labeled multi-target posterior density. The GLMB filter is an analytic solution to the labeled multi-target filtering recursion. In this work, we show that the GLMB filter can be extended to an analytic multi-object posterior recursion.
Preserving Order of Data When Validating Defect Prediction Models

[Context] The use of defect prediction models, such as classifiers, can support testing resource allocations by using data of the previous releases of the same project for predicting which software components are likely to be defective. A validation technique, hereinafter technique defines a specific way to split available data in training and test sets to measure a classifier accuracy. Time-series techniques have the unique ability to preserve the temporal order of data; i.e., preventing the testing set to have data antecedent to the training set. [Aim] The aim of this paper is twofold: first we check if there is a difference in the classifiers accuracy measured by time-series versus non-time-series techniques. Afterward, we check for a possible reason for this difference, i.e., if defect rates change across releases of a project. [Method] Our method consists of measuring the accuracy, i.e., AUC, of 10 classifiers on 13 open and two closed projects by using three validation techniques, namely cross validation, bootstrap, and walk-forward, where only the latter is a time-series technique. [Results] We find that the AUC of the same classifier used on the same project and measured by 10-fold varies compared to when measured by walk-forward in the range [-0.20, 0.22], and it is statistically different in 45% of the cases. Similarly, the AUC measured by bootstrap varies compared to when measured by walk-forward in the range [-0.17, 0.43], and it is statistically different in 56% of the cases. [Conclusions] We recommend choosing the technique to be used by carefully considering the conclusions to draw, the property of the available datasets, and the level of realism with the classifier usage scenario.
Unobtrusive Sleep Monitoring Using Cardiac, Breathing and Movements Activities: An Exhaustive Review

At least 50% of the world’s elderly population, whose range is fast growing, experience disturbed sleep. Sleep studies have become an extensive approach serving as a diagnostic tool for health-care professionals. Currently, the gold standard is polysomnography (PSG) recorded in a sleep laboratory. However, it is obtrusive, requires qualified technicians, and is time and cost expensive. With the introduction of commercial off-the-shelf technologies in the medical field, alternatives to the conventional methods have been conceived to ensure sleep stages and sleep quality detection, which may be now used at home on several nights. Cardio respiratory and physical activities abide the most promising physiological measurements to detect sleep stages without complete PSG. The statistically proven impacts and budgets related to sleep disorders are phenomenal, showing that the field needs more research. This paper aims at providing the reader with a multidimensional research perspective by presenting a review of research literature on developments made in unobtrusive sleep assessment. Additionally, a categorization of current approaches is presented based on methodological considerations, from data acquisition frameworks and physiological measurements, to information processing. Subsequently, limitations and challenges facing current solutions are discussed, and open research areas are highlighted, which we hope would pave the way for future research endeavors addressing the question: how to assess sleep stages and sleep quality less intrusively, and reliably?
Large‐scale purification of ovalbumin using polyethylene glycol precipitation and isoelectric precipitation

ABSTRACT In addition to small amounts of minerals and carbohydrates, most of the dry matter of chicken egg white is protein, making egg white an ideal resource for obtaining food proteins. Ovalbumin (OVA), which accounts for more than 50% of the total egg white protein, is one of the most widely studied food proteins due to its multiple functional properties, and it has also been used as a model protein molecule in many research fields. The objective of this study was to develop a simple and rapid method for the purification of OVA from egg whites on large scale. First, OVA was separated from ovomucin, ovotransferrin, and ovomucoid by polyethylene glycol (PEG) concentration, using the following optimal parameters: the PEG concentration was 15%, the pH was 6.5, the salt concentration was 100 mmol/L, and the operating temperature was 10°C. The OVA‐rich supernatant obtained from PEG precipitation was further purified by isoelectric precipitation at a pI of 4.5 and a temperature of 4°C, and the purified OVA was obtained with at a purity of 95.1% by HPLC, with a yield of 46.4%. After the extraction of OVA, the PEG solution was vacuum dried and then utilized cyclically in the PEG precipitation steps. The whole purification process could be finished within 2 to 3 h at a scale of several kilograms of egg white. This method has the advantages of rapidity, simplicity, low cost, and ease of scalability.
New modalities of allergen immunotherapy

ABSTRACT Allergen immunotherapy is a rapidly evolving field. Although subcutaneous immunotherapy has been practiced for over a hundred years, improved understanding of the underlying immunological mechanisms has led to the development of new, efficacious and better tolerated allergen-derivatives, adjuvants and encapsulated allergens. Diverse routes of allergen immunotherapy – oral, sublingual, epicutanoeus and intralymphatic – are enabling immunotherapy for anaphylactic food allergies and pollen-food allergy syndrome, while improving the tolerability and effectiveness of aeroallergen immunotherapy. The addition of Anti-IgE therapy decreases adverse effects of subcutaneous and oral immunotherapy.
Discussion on Applying Wireless Information and Power Transfer to Relay Networks

Recently, there have been numerous research works on applying wireless information and power transfer (WIPT) to relay networks. In this letter, we first include the path-loss effect in the performance evaluation of WIPT-based relay networks. Results reveal that the received signal quality of these networks is generally much worse than that of direct transmission due to the enlarged path loss, and then a large amount of energy must be harvested at the relay node before the actual information-forwarding process. Based on this observation, we also show that suitably employing multiple energy-harvested relays can help in overcoming the unfavorably large path loss induced, and therefore let WIPT-based relaying become rational for practical implementation.
Improving Service Recommendation by Alleviating the Sparsity with a Novel Ontology-Based Clustering

Web service recommendation in an efficient and accurate manner has become a significant tool with information overload and an increasingly urgent demand to provide appropriate recommendations to users. Among the service recommendation algorithms, Collaborative Filtering (CF) gives credence to user inputs by comparing user's correlations. Performance of the service recommendation approaches becomes deficient due to the data sparsity and cold-start issues, which make the incomplete and inadequate information to analyze a user predicament on Web services. This paper proposes a CF-based recommendation approach that first alleviates the sparsity problem using a novel ontology-based clustering approach that used domain specificity and service similarity for the ontology generation. Then, we propose a trustbased user rating prediction by determining the trust value between users by calculating the correlation of users. The experimental results indicate that the proposed approach can effectively alleviate the sparsity and cold-start problems by lower prediction error compared with existing sparsity managing mechanisms in service recommendations.
Assessment of Signal Jamming Efficiency Against LTE UAVs

The use of robotic aircrafts (UAVs) has seen a significant growth in the recent years and, as regulations permit, continues to expand its uses in different areas: aerial photography, disaster management, shipping, geographic mapping, precision agriculture. LTE technology eliminated the line of sight control limitations for small drones. However, due to the possibility of flying unrestricted, countermeasures must be considered. One of them is jamming the drone's signal in order to stop its communication with the controlling party. This paper analyses the impact of jamming signals applied to the useful frequency spectrum used to pilot the UAVs.
Narrow-Band Through-Wall Imaging with Received Signal Strength Data

This paper solves the through-wall imaging (TWI) problem with a narrow-band system, and proposes an adaptive TWI method based on data fusion of multiple scan paths. First, we use a Wentzel-Kramers-Brillouin-based (WKB-based) approximation to model the interaction of the transmitted wave with the unknown area. Then we use Radon inverse transform to reconstruct the image from the received signal strength data of different paths. Furthermore, we evaluate the impact of scan paths on imaging. Finally, finite-difference time-domain (FDTD) simulation results demonstrate the validity of proposed method.
3D Extended Object Tracking Using Recursive Gaussian Processes

In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can jointly estimate the position, orientation, and the shape of the object. The inference is performed by an extended Kalman filter which is suitable for online realtime applications. Lastly, we demonstrate the initial results of a promising approach, which aims at reducing the computational complexity.
Improving the sustainability of hospital-based interventions: a study protocol for a systematic review

Introduction Sustaining effective interventions in hospital environments is essential to improving health outcomes, and reducing research waste. Current evidence suggests many interventions are not sustained beyond their initial delivery. The reason for this failure remains unclear. Increasingly research is employing theoretical frameworks and models to identify critical factors that influence the implementation of interventions. However, little is known about the value of these frameworks on sustainability. The aim of this review is to examine the evidence regarding the use of theoretical frameworks to maximise effective intervention sustainability in hospital-based settings in order to better understand their role in supporting long-term intervention use. Methods and analysis Systematic review. We will systematically search the following databases: Medline, AMED, CINAHL, Embase and Cochrane Library (CENTRAL, CDSR, DARE, HTA). We will also hand search relevant journals and will check the bibliographies of all included studies. Language and date limitations will be applied. We will include empirical studies that have used a theoretical framework (or model) and have explicitly reported the sustainability of an intervention (or programme). One reviewer will remove obviously irrelevant titles. The remaining abstracts and full-text articles will be screened by two independent reviewers to determine their eligibility for inclusion. Disagreements will be resolved by discussion, and may involve a third reviewer if required. Key study characteristics will be extracted (study design, population demographics, setting, evidence of sustained change, use of theoretical frameworks and any barriers or facilitators data reported) by one reviewer and cross-checked by another reviewer. Descriptive data will be tabulated within evidence tables, and key findings will be brought together within a narrative synthesis. Ethics and dissemination Formal ethical approval is not required as no primary data will be collected. Dissemination of results will be through peer-reviewed journal publications, presentation at an international conference and social media. PROSPERO registration number CRD42017081992.
Event-Triggered Consensus of Homogeneous and Heterogeneous Multiagent Systems With Jointly Connected Switching Topologies

This paper investigates the distributed event-based consensus problem of switching networks satisfying the jointly connected condition. Both the state consensus of homogeneous linear networks and the output consensus of heterogeneous networks are studied. Two kinds of event-based protocols based on local sampled information are designed, without the need to solve any matrix equation or inequality. Theoretical analysis indicates that the proposed event-based protocols guarantee the achievement of consensus and the exclusion of Zeno behaviors for jointly connected undirected switching graphs. These protocols, relying on no global knowledge of the network topology and independent of switching rules, can be devised and utilized in a completely distributed manner. They are able to avoid continuous information exchanges for either controllers’ updating or triggering functions’ monitoring, which ensures the feasibility of the presented protocols.
Sub-picotesla widely tunable atomic magnetometer operating at room-temperature in unshielded environments.

We report on a single-channel rubidium radio-frequency atomic magnetometer operating in unshielded environments and near room temperature with a measured sensitivity of 130 fT/ Hz . We demonstrate consistent, narrow-bandwidth operation across the kHz-MHz band, corresponding to three orders of magnitude of the magnetic field amplitude. A compensation coil system controlled by a feedback loop actively and automatically stabilizes the magnetic field around the sensor. We measure a reduction in the 50 Hz noise contribution by an order of magnitude. The small effective sensor volume, 57 mm3, increases the spatial resolution of the measurements. Low temperature operation, without any magnetic shielding, coupled with the broad tunability, and low beam power, dramatically extends the range of potential field applications for our device.
Differential methylation values in differential methylation analysis

MOTIVATION Both β-value and M-value have been used as metrics to measure methylation levels. The M-value is more statistically valid for the differential analysis of methylation levels. However, the β-value is much more biologically interpretable and needs to be reported when M-value method is used for conducting differential methylation analysis. There is an urgent need to know how to interpret the degree of differential methylation from the M-value. In M-value linear regression model, differential methylation M-value ΔM can be easily obtained from the coefficient estimate, but it is not straightforward to get the differential methylation β-value, Δβ since it cannot be obtained from the coefficient alone. RESULTS To fill the gap, we have built a bridge to connect the statistically sound M-value linear regression model and the biologically interpretable Δβ. In this article, three methods were proposed to calculate differential methylation values, Δβ from M-value linear regression model and compared with the Δβ directly obtained from β-value linear regression model. We showed that under the condition that M-value linear regression model is correct, the method M-model-coef is the best among the four methods. M-model-M-mean method works very well too. If the coefficients α0, α2,…αp are not given (as 'MethLAB' package), the M-model-M-mean method should be used. The Δβ directly obtained from β-value linear regression model can give very biased results, especially when M-values are not in (-2, 2) or β-values are not in (0.2, 0.8). AVAILABILITY AND IMPLEMENTATION The dataset for example is available at the National Center for Biotechnology Information Gene Expression Omnibus repository, GSE104778. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
An Intelligent Analytics System for Real-Time Catchment Regulation and Water Management

Regulation procedures and water management that incorporate projected hydrological changes with related uncertainties become extremely important in order to prevent degradation of water ecosystems. Ensuring real time water management and optimization becomes mandatory for resolving the constraints of water supply/demand and to comply with biodiversity requirements. We focus our research on water optimization and catchment regulation and present our solution that has been developed as part of the Innovate UK Radical project.1 1Developing a Real Time Abstraction and Discharge Permitting Process for Catchment Regulation and Optimized Water Management. In our study, we use the Usk reservoir in South Wales with rich biodiversity and nationally significant fishery to optimize catchment flow and to conserve water with real-time catchment management information to support the decision makers. Our developed solution uses artificial intelligence techniques to deliver real-time decision support for water management and catchment regulation with reflection to biodiversity protection and reservation. We present an intelligent analytics system that uses real-time data from river stations enabling informed decisions and a more dynamic approach for managing water resources. The system utilizes a neuronal network engine to support river level prediction based on which a dependency modeling is developed for assessing the probability of risk in the Usk reservoir.
P-GENT: Privacy-Preserving Geocoding of Non-Geotagged Tweets

With the widespread proliferation of location-aware devices and social media applications, more and more people share information on location-based social networks such as Twitter. Such data can be beneficial to better plan and manage individual's activities and other social applications, e.g., location-based advertisement or recommendation. However, only a very small proportion of tweets are geotagged due to privacy concerns or lack of underlying positioning infrastructures. Hence it is meaningful to estimate the geographic information for non-geotagged tweets, i.e., geocoding, which can help to improve the applicability and utility of social media data. Contrary to existing geocoding approaches, this paper aims at the privacy risk and providing a fine-grained estimation. In this paper, we propose Privacy-preserving GEocoding of Non-geotagged Tweets (P-GENT) for geocoding non-geotagged tweets with fine-grained estimation whilst protecting privacy. Our approach estimates the geographic location of a non-geotagged tweet based on the similarities between the content of the tweet and the keyword lists of detected local events form the archived geo-tagged tweets during the same time period. This approach implements a spatio-temporal clustering algorithm to discover local events with a fine-grained granularity and an important keyword extraction mechanism to describe the detected local event. In addition, a density-seed discovery approach is used to reduce the sparseness of geo-tagged tweets and the time complexity of clustering approach. The experimental evaluation with real-world data demonstrates that our approach has at most 92% precision for one timeslot and 33-43% precision remained for all time slots after using privacy-preserving mechanisms.