From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

Optimization of sodium sulfide treatment of rice straw to increase the enzymatic hydrolysis in bioethanol production

The efficiency of sodium sulfide-assisted alkaline pulping for cellulose preparation from Oryza sativa L. rice straw in Vietnam for enzymatic saccharification was investigated. The response surface methodology was used for the determination of optimal technological parameters of alkaline pulping such as active alkali dosage, temperature and time. The optimal technological parameters were established to be active alkali dosage of 7%, treatment temperature of 100 °C and treatment time of 120 min. At these regimes, a maximal sugar yield of 51.8% (over dry rice straw) was obtained. It meant that the saccharification efficiency up to 97.1% could be achieved by using sodium sulfide-assisted alkaline pretreatment method. Addition of sodium sulfide into alkaline pretreatment resulted in higher sugar yield, higher level of depolymerization of lignin and less loss of cellulose. Moreover, liquid hydrolyzate after enzymatic hydrolysis was analyzed by HPLC to determine the compositions of sugar mixture. The fiber morphology in pretreated biomass solid was also revealed by SEM.     

Monitoring the Ratio of Two Normal Variables Using EWMA Type Control Charts

In many fields, there is the need to monitor quality characteristics defined as the ratio of two random variables. The design and implementation of control charts directly monitoring the ratio stability is required for the continuous surveillance of these quality characteristics. In this paper, we propose two one‐sided exponentially weighted moving average (EWMA) charts with subgroups having sample size n > 1 to monitor the ratio of two normal random variables. The optimal EWMA smoothing constants, control limits, and ARLs have been computed for different values of the in‐control ratio and correlation between the variables and are shown in several figures and tables to discuss the statistical performance of the proposed one‐sided EWMA charts. Both deterministic and random shift sizes have been considered to test the two one‐sided EWMA charts' sensitivity. The obtained results show that the proposed one‐sided EWMA control charts are more sensitive to process shifts than other charts already proposed in the literature. The practical application of the proposed control schemes is discussed with an illustrative example. Copyright © 2015 John Wiley & Sons, Ltd.     

Integratable non-clogging microconcentrator based on counter-flow principle for continuous enrichment of CaSki cells sample

This study addresses the need to reduce the risk of clogging when preparing samples for cell concentration, i.e., the CaSki Cell-lines (epidermoid cervical carcinoma cells). Aiming to develop a non-clogging microconcentrator, we proposed a new counter-flow concentration unit characterized by the directions of penetrating flows being at an obtuse angle to the main flow, due to employment of streamlined turbine blade-like micropillars. Based on the optimization results of the counter-flow unit profile, a fractal arrangement for the counter-flow concentration unit was developed. A counter-flow microconcentrator chip was then designed and fabricated, with both the processing layer and collecting layer arranged in terms of the honeycomb structure. Visualized experiments using CaSki cell samples on the microconcentrator chip demonstrated that no cell-clogging phenomena occurred during the test and that no cells were found in the final filtrate. The test results show an excellent concentration performance for the microconcentrator chip, while a concentrating ratio of >4 with the flow rate being below 1.0 ml/min. As only geometrical structure is employed in the passive device, the counter-flow microconcentrator can be easily integrated into advanced microfluidic systems. Owing to the merit of non-clogging and continuous processing ability, the counter-flow microconcentrator is not only suitable for the sample preparation within biomedical field, but also applicable in water-particle separation.     

Visualization of diversity in large multivariate data sets

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.     

Integrated micro Pirani gauge based hermetical package monitoring for uncooled VO x bolometer FPAs

This paper presents the design and fabrication of a micro Pirani gauge using VO _x as the sensitive material for monitoring the pressure inside a hermetical package for micro bolometer focal plane arrays (FPAs). The designed Pirani gauge working in heat dissipating mode was intentionally fabricated using standard MEMS processing which is highly compatible with the FPAs fabrication. The functional layer of the micro Pirani gauge is a VO _x thin film designed as a 100 × 200 μm pixel, suspended 2 μm above the substrate. By modeling of rarefied gas heat conduction using the Extended Fourier’s law, finite element analysis is used to investigate the sensitivity of the pressure gauge. Also the thermal interactions between the micro Pirani gauge and bolometer FPAs are verified. From the fabricated prototype, the measured device TCR is about −0.8% K⁻¹ and the sensitivity about 1.84 × 10⁻³ W K⁻¹ mbar⁻¹.