Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributed Game Methodology for Crowdsensing in Uncertain Wireless Scenario

With the exponentially increasing number of mobile devices, crowdsensing has been a hot topic to use the available resource of neighbor mobile devices to perform sensing tasks cooperatively. However, there still remain three main obstacles to be solved in the practical system. First, since mobile devices are selfish and rational, it is natural to provide cooperation for sensing with a reasonable payment. Meanwhile, due to the arrival and departure of sensing tasks, resource should be allocated and released dynamically when sensing task comes or leaves. To this end, this paper designs a game theoretic approach based incentive mechanism to encourage the “best” neighbor mobile devices to share their own resource for sensing. Next, in order to adjust resource among mobile devices for the better crowdsensing response, an auction based task migration algorithm is proposed, which can guarantee the truthfulness of announced price of auctioneer, individual rationality, profitability, and computational efficiency. Moreover, taking into account the random movement of mobile devices resulting in the stochastic connection, we also use multi-stage stochastic decision to take posterior resource allocation to compensate for inaccurate prediction. The numerical results show the effectiveness and improvement of the proposed multi-stage stochastic programming based distributed game theoretic methodology (SPG) for crowdsensing.
Medium-Term Rolling Load Forecasting Based on Seasonal Decomposition and Long Short-Term Memory Neural Network

Accurate medium-term load forecasting plays an imperative role in power distribution systems. Since available forecasting models lack validity and applicability due to the irregularity of the original load series, this paper proposes a decomposition-ensemble approach to forecast daily electricity consumption for the next thirty days. First, seasonal decomposition is applied to decompose the original load data into the trend, seasonal and residual components for extracting and identifying the fluctuation characteristics of load data. Secondly, long short-term memory neural network (LSTM) is utilized to build the rolling forecasting model for each component severally. The maximal information coefficient method is adopted to select the input feature of the forecasting model. Thirdly, the forecasting results of three components are combined into the final forecasting result by an ensemble model. Finally, the empirical results indicate that the proposed method has a superior performance compared with the undecomposed forecasting model, which verifies the effectiveness and superiority in load forecasting accuracy.
Solution on the Power Flow Violation Caused by Provincial Spot Market

China is improving power spot market operation. The provincial market will take the inter-provincial transaction results as the boundary for market clearing. However, when the inter-provincial AC tie line power is directly regarded as the injection power on the boundary nodes in the provincial market, the power flow model of the provincial market will deviate from the actual situation, and even the power flow violation occurs. This paper first proposes the clearing models of inter-provincial market and provincial market. Then the essential reason for the power flow violation is analyzed. Finally, the solution based on the provincial clearing model with phase angle difference constraint of boundary nodes is proposed. Case study shows that the solution can give the correct power flow calculation in provincial market and avoids the power flow violation.
A 0.8-V 82.9- $\mu$ W In-Ear BCI Controller IC With 8.8 PEF EEG Instrumentation Amplifier and Wireless BAN Transceiver

In-ear brain–computer interface (BCI) controller system is implemented with a dedicated system-on-chip (SoC) including electroencephalography (EEG) readout and body channel communication (BCC) transceiver (TRX). The 8-mm2 chip is fabricated using 65-nm CMOS and contains three key features: 1) current reusing low-noise amplifier (CRLNA) for low power; 2) bootstrapping dc servo loop (BDSL) enabling low-noise measurement even on 350-mV electrode dc offset (EDO); and 3) dual-mode programmable gain amplifier (DMPGA) that reduces TRX power consumption by activating only when the intentional blink is present. EEG instrumentation amplifier (IA) shows the state-of-the-art 8.8 power efficiency factor (PEF) performance, and the entire integrated circuit (IC) consumes 82.9 $\mu \text{W}$ . From the measurement, with nine subjects, the proposed BCI system accomplished 84% average accuracy for the binary selection task.
Enhanced Voltage Control in Distribution Networks: A Data-driven Approach

Traditional voltage/var control(VVC) method assume the prerequisite of an exact distribution network(DN) model that keep unchanged during the whole control period, which may not hold in practice. In this paper, we proposed a data-driven enhanced voltage/var control algorithm(DaE-VVC) to regulate voltage profiles in DN with an incomplete model. The proposed method can estimate the DN topology and line parameter with satisfactory accuracy using the measurements of bus voltages and real/reactive power injections. The proposed algorithm can be integrated into the centralized control center without any additional hardware cost. Case studies on balanced modified IEEE-33 bus system demonstrate the performance of the proposed algorithm.
Life Design Career Counselling with an Abandoned Adolescent: A Case Study

This article describes the value of life design counselling with an abandoned adolescent female. The participant was selected purposively from among a group of abandoned adolescent females who had expressed a need for career counselling. The life intervention comprised two 90-minute sessions per week over a period of a month. Life design data included collage, family constellation, life line, career style interview, asset map and life chapters. Following the intervention, the participant demonstrated career adaptability and an eagerness to take an active part in her evolving life story. Life design counselling has potential to meet the career counselling needs of children with neglect and abandonment experience.
Applying modeling and simulation to evaluate statistical accuracy and inventory control perfomance of lumpy demand forecasting methods

A number of time series methods – 13-month simple moving average (SMA13), single exponential smoothing (SES), Croston’s method, and the SyntetosBoylan approximation (SBA) – are well-referenced methods in the literature on intermittent or lumpy demand forecasting. We apply these four methods to an industrial dataset involving more than 1000 stockkeeping units (SKUs) in the central warehouse of a firm operating in the professional electronics sector. Earlier studies have argued that the negative binomial distribution (NBD) satisfies both theoretical and empirical criteria for modeling intermittent demand. We have found that the NBD often does not provide a good fit. We apply an alternative approach, using a two-stage distribution involving the uniform and negative binomial distributions, in modeling actual demand. We use modeling and simulation to evaluate the four methods in terms of statistical forecast accuracy and, more importantly, inventory system efficiency.
Random Drug Testing of Schoolchildren: A Shot in the Arm or a Shot in the Foot for Drug Prevention?

The idea of randomly drug-testing UK schoolchildren to see if they have used illegal drugs has received supportive comments recently from both the Prime Minister and the Leader of the Opposition. This review of current literature draws together information from a range of sources to review all aspects of the issue. It considers evidence in support of drug testing, including experience in the US, where random drug-testing is commonplace. It asks whether such testing is in accordance with human rights legislation and the rights of the child. As well as ethical issues, it reviews practical questions of the feasibility of testing pupils in UK schools, as well as concerns about how such testing programmes might fundamentally alter children's experience of school. The author finds that such programmes, whilst they might help some pupils, may harm others. He concludes that, before random drug-testing programmes are widely developed in UK schools, this initiative should be subjected to rigorous and independent evaluation.
Carbon Dioxide Emissions and Economic Growth: A Bivariate Co-integration Analysis for Two Emerging Markets of India and China

The article investigates the co-integrating relation between carbon dioxide (CO2) emissions and economic growth of India and China: the two emerging markets of Asia. The article also tries to test the functional form of EKC as applicable to India and China. The study takes annual data for the 55 year period from 1960–2014 and the variables included are GDP per capita (a proxy for economic growth) and CO2 emissions per capita (a proxy for the environmental degradation). The co-integrating relation has been tested using Johansen (1988) co-integration test which is supplemented with VAR-VECM Model at p-1 lags with error correction mechanism showing adjustment between short- and long-run equilibrium. The diagnostics include stationarity, parameter stability and structural breaks (using Chow breakeven test & CUSUM plots) and serial correlation (using VEC-LM test). The empirical results showed long-term co-integration between GDP and CO2 emissions for India but not for China with lagged ECM term for India with a value of 0.0466 showed that the 4 per cent of the dis-equilibrium be corrected in same year itself. The stationary test showed that both our variables were stationary either at I(1) or I(2) but none of these were at levels. The null of serial correlation showed no serial correlation. The Chow and Stability tests revealed that for CO2 emissions in India, there was a break in 2009. VEC Granger causality tests showed that there was uni-directional causality for India flowing from GDP to CO2 emissions. EKC functional form of Cubic representation was proved for China where the curve was found to be ‘N’ shaped but EKC could not be proved in case of India.
In Which Time Slots Can People Save Power? An Analysis Using a Japanese Survey on Time Use

Residential demand-side management (DSM) of electricity has been gaining attention as a way to reduce energy consumption at home and as a way of maximizing the utilization of fluctuating solar power generation. To promote the smooth introduction of DSM into homes, power usage trends according to the time of the day should be examined for individuals in relation to their lifestyles. The analyses of power usage trends can identify the types of home appliances that should be utilized differently in order to increase energy efficiency. Such analyses can also predict the individual behavioral changes that should result in home appliances being used in the time slots in which solar power is more conveniently available. The purpose of this research was to estimate and observe the amount of power saving potential for each daily time slot with respect to an individual’s particular attributes, and to derive the power saving potential of the whole country by accumulating these data on individuals. This was achieved by using the Survey on Time Use and Leisure Activities (STULA) and Energy-Saving Performance Catalog (ESPC) in Japan. According to the results of our estimation, a meaningful power saving potential is sufficient to address a power supply shortage after a disaster such as an earthquake. It is possible to save power by replacing existing home appliances with more energy efficient ones, by making environmentally conscious choices when using home appliances, and by discontinuing the use of home appliances during electricity shortages within the community as a whole. Using the estimated power saving potentials, we examined the effects of two DSMs: (1) adjusting the time for which home appliances are used; and (2) aggregating the power demand of households with different attributes. The results showed that these DSMs would contribute to a more stable power system operation. Future research might address the rapid penetration of community energy management systems and demand response systems.
Measuring individual plate waste in school lunches. Visual estimation and children's ratings vs. actual weighing of plate waste.

: Weighing individual plate waste is time consuming and often impractical. As alternatives, two indirect measures were evaluated-visual estimation and children's ratings of plate waste. Visual estimations were highly correlated with percent waste (r = 0.93) and were quite accurate. Ratings by children were less accurate but were still considered a useful indirect measure that should be further developed.
Technological spillovers of transferred inventors from the perspective of Social Network Analysis (SNA)

Personnel involved in high-tech R&D commonly move between enterprises, bringing with them technology obtained elsewhere. This leads to an imperceptible circulation of analogous technology among different companies. Unfortunately, this so-called technological spillover is difficult to detect. This study combined social network analysis with patent data covering nearly 30 years to construct the networks that involve the mobility of inventors and technological overlap in the Hsinchu semiconductor industry. Regression analysis using quadratic assignment procedures reveals that the network within which inventors migrate has a positive impact on the network technological overlap. Further analysis clarified the positive relationship between the mobility of inventors and technological overlap in terms of the organizational network characteristics. This confirms a process of co-evolution between technological overlap and the mobility of inventors, which may have a highly likely spillover. Key words: Technological spillover, transferred inventor, social network analysis, technological overlap.
The Folk Rule for Minimum Cost Spanning Tree Problems with Multiple Sources

In this paper, we introduce minimum cost spanning tree problems with multiple sources. This new setting is an extension of the classical model where there is a single source. We extend several definitions of the folk rule, the most prominent rule in the classical model, to this new context: first as the Shapley value of the irreducible game; second as an obligation rule; third as a partition rule and finally through a cone-wise decomposition. We prove that all the definitions provide the same cost allocation and present two axiomatic characterizations.
The Role of Computer Games in the Learning of Programming among Tertiary Students in Ghana

Computer Games may be an innovative and fun way to improve learning among students. For first year computer programming students, an introductory course in programming is challenging as there is difficulty in grasping the fundamental concepts in the course. This paper investigates how computer games can improve first year students’ learning of programming in the University of Education, Winneba. The study sought to investigate the type of computer game and game elements that will improve understanding of programming concepts. It also aimed at developing of a conceptual game model for programming skills development. A case study was conducted with questionnaires administered to 123 first year programming students on their gaming behaviour. A conceptual strategic game model is proposed for learning programming. The results show that strategic games have the key elements that can help learners understand programming. Programming students are encouraged to play more strategic games and should be included as part of the curricula for learning programming.
A Review of Researching on Dynamic Taint Analysis Technique

Taint analysis technique is the key technique means for analyzing the robustness of programs and vulnerability mining. By marking the data which are sensitive or untrusted, one can observe the flow of these tainted data during program execution, then determine whether the marked data affects the key nodes of the program. According to the implementation mechanism, the taint analysis can be divided into static taint analysis and dynamic taint analysis. As an auxiliary technique, it can be combined with mainstream vulnerability mining techniques such as fuzzing test and symbol execution, playing a great role in test case construction and path feasibility analysis. This article firstly introduces the basic concepts of dynamic taint analysis technique. Second, it focuses on the process of dynamic taint marking, propagation and detection. Then it summarizes the main defects in taint analysis and the application status of dynamic taint analysis technique. Finally, it is compared with the current mainstream taint analysis tools and explores the future trends of taint analysis technique. Keywords—taint analysis; vulnerability detection; information flow tracking; taint propagation
Moving and adapting with a learning exoskeleton

The operation of a powered exoskeleton is a type of human-robot interaction with extremely tight human-robot coupling. As exoskeletons become increasingly intelligent, it is increasingly appropriate to think of them not simply as tools, but rather as semi-autonomous teammates. This thesis explores the implementation, operation, and consequences of intelligent exoskeletons - teammates that move and adapt to the human to which they are physically coupled. Exoskeletons have potential applications in several domains, including strength augmentation, injury reduction, and rehabilitation. Appropriately mapping human intent to exoskeleton action is crucial. Generating this mapping can be difficult, as operator movements are constrained by the exoskeletons they are trying to control. This problem is particularly significant with upper-body exoskeletons, where high degrees of freedom allow for much less predictable motion than in the lower body. Surface electromyography (sEMG) - reading electrical signals from muscles - is one way to estimate human intent. sEMG contains anticipatory information that precedes the associated limb movement, allowing for better human-exoskeleton coordination than reactive control methods. However, sEMG is very sensitive to individual physiologies and sensor placement. We use machine learning from demonstration (LfD) to create personalized, robust sEMG mappings for exoskeleton control. We demonstrate classification of transient dynamic grasping gestures with data where sEMG sensors on the forearm have been shifted from a nominal configuration. Next, sEMG-based gesture recognition is applied to exoskeleton control, where sEMG mappings are learned as the exoskeleton is controlled with a pressure-based inputs. Finally, we analyze the human-exoskeleton team performance, fluency, and adaptation using a pressure-based controller, a static sEMG mapping, and a dynamic sEMG mapping. We show that LfD allows us to use anticipatory signaling to reduce human-exoskeleton interaction pressure. Subjects were able to adapt to all three controllers, but team performance and fluency were affected by the controller type and order of exposure. These results have implications for future exoskeleton controller design, and for exoskeleton operator training. They also open up new avenues of research in relation to adaptation to exoskeletons, intent classification algorithms, and the application of metrics from the human-robot interaction literature to the field of human exoskeleton research.
Experimental Results For the Propagation of Outdoor IEEE802.11 Links

WiFi-based Long Distance (WiLD) networks have emerged as a promising alternative technology approach for providing Internet in rural areas. An important factor in network planning of these wireless networks is estimating the path loss. In this work, we present various propagation models we found suitable for point-to-point (P2P) operation in the WiFi frequency bands. We conducted outdoor experiments with commercial offthe- shelf (COTS) hardware in our testbed made of 7 different long-distance links ranging from 450 m to 10.3 km and a mobile measurement station. We found that for short links with omni-directional antennas ground-reflection is a measurable phenomenon. For longer links, we show that either FSPL or the Longley-Rice model provides accurate results for certain links. We conclude that a good site survey is needed to exclude influences not included in the propagation models.
An Investigation of the Medical Ultrasound Image Sparse Spaces used for the Model-Based Imaging

In this thesis, we investigate a sparse basis for ultrasound images, so that we can use sparse regularization in imaging. Actually, there are few previous researches explicitly demonstrating that medical ultrasound images can be sparsified for some dictionary. We consider various orthogonal transforms such as wavelet transforms, cosine transforms and wave atom transforms. Then, we perform those transforms on various ultrasound images and analyzes their sparsity. These ultrasound images include the images of two computer ultrasound phantoms and beamformed ultrasound images with good quality from real people. We looked at sparsity of the true pre-beamformed images, as well as beamformed images. We also consider constructing a specific ultrasound image dictionary using the K-SVD algorithm. We observed that, the pre-beamformed images hardly haVe no sparse basis, and the sparsity of beamformed images will only increase slightly if we use different 1D-DWT in each direction. We also found that the wide overdetermined dictionary generated by K-SVD significantly increases sparsity. After this, we simulate the ultrasound image reconstruction from the ultrasound RF measurements, and we analyze the effects of the different sparse spaces on the reconstruction performance. We observed that, the L1-regularization can work for ultrasound imaging better than L2-regularization, but the orthogonal transforms as well as the dictionary do not improve the reconstruction image quality much.
The “Sixth” Factor—A Social Media Factor Derived Directly from Tweet Sentiments

Institutional investors may have an unclear understanding of the role of social media in asset price determination. Although some of the top quant hedge funds use crowd-based information gleaned from tweets, this relationship may be opaque to the rest of our community. In this article, the authors attempt to clarify the confusion regarding social media sentiment and security return behavior. They show that, surprisingly, Tweet sentiments have significant power in explaining the time-series contemporaneous variation in daily stock returns, even in the presence of well-known equity factors. By examining direct tweet sentiments as provided by StockTwits, the authors claim to have identified a Social Media Factor, the “sixth” factor, and they highlight the distinctions vis-à-vis Fama–French’s factors.