Miracle-dz/UniFAR-FTUs huggingface dataset:
RuBLX: A RUBY-BASED BATCH LANGUAGE FOR XGRID

We present a Ruby-based batch language for Xgrid and its processor. Xgrid is an environment for distributed and parallel computing on the Mac OS X operating system, and Ruby is an objectoriented programming language for general purposes. In the standard Xgrid environment, jobs in batch files are statically defined by an XML-based language, and submitted jobs are managed by their ID numbers. It is not easy for human to read and write XML-based batch files and to manage jobs by ID numbers. In our approach, jobs in batch files can be dynamically defined by a Ruby-based language, and submitted jobs can be managed by their logical names. Semantic checks and consistency managements are also done at submission in our approach. Our approach syntactically and semantically makes it easy to use Xgrid. Keywords—Grid and Cluster Computing, Languages
System and method for sparse code multiple access

It is possible to realize the coding gain by directly encoding the binary data into multi-dimensional codeword. This is to avoid the QAM symbol mapping adopted in conventional CDMA coding technique. Furthermore, by assigning a different codebooks for different multiplex layer, it is possible to realize a plurality access. Further, since the sparse code word can be detected in the multiplexed code word in accordance with message passing algorithm (MPA), using a sparse code word, it is possible to reduce the computational complexity of the baseband processing of the receiver side of the network it can.
Simulation comparison on adaptability of econometrical models based on panel data

In recent years,there are more and more empirical papers using panel data in economic and management field.At the same time,theoretical results on how to process panel data are also increasing.However,one problem is that most theoretical methodologies on panel data are based on infinite sample,but we can only get limited samples in reality;another problem is that existing papers on using panel data don't do special treatment for panel data sample or don't explain why adopting their processing methodology.Therefore,the two contributions of this paper are as follows: 1) this paper compares the applicability of existing theoretical methodologies by simulating based on the three different data structures—that with individual effect,with time effect and with two effects simultaneously;2) considering that estimation results have significant differences for various processing methods in small sample,this paper provides a criterion for judging what effects exist in the actual data.
Endophysics Time Quantum And The Subjective

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A framework for an agent-based dynamic pricing for broadband wireless price rate plans

Abstract Dynamic pricing is the science of pricing a product in a time-varying way for optimising revenue. There is a slow but steady tendency over the last three decades for major businesses to move from fixed pricing to dynamic pricing. In this paper, we consider the problem of dynamic pricing for wireless broadband data. We propose a novel dynamic pricing approach, based on optimally adjusting premiums and discounts over the prevailing rate, where these adjustments are a function of traffic and demand conditions. Moreover, we propose an agent-based framework for designing and testing this or any other rate plan. In this framework, we have several offered broadband data rate plans, and we model subscribers as agents which react to the dynamic changes of the prices. The proposed framework can be used by wireless operators as a testbed to explore the success of novel dynamic rate plans, and optimise their revenue.
A Heuristic Approach for Shelf Space Allocation Problem

A shelf space allocation problem (SSAP) is a special form of multi constraint knapsack problem. The main difference between knapsack problem and SSAP is that a knapsack problem has only capacity constraints. Commercial space management systems use many different heuristic approaches for allocating shelf space due to NP-hard complexity of the SSAP. These heuristics are usually based on simple intuitive rules that could be easily used in practice to implement shelf space allocation decisions. In this paper, a new heuristic is developed to obtain good allocation of shelf space for different products in order to increase profitability under different constraints such as limited shelf space and elasticity factors.
Spatio-temporal Event Detection and Forecasting in Social Media

Semantics Layer a keyword Tweets of the user . Scial edia pace . Sulated orld } Simulated person Twitter user Figure 6.2: The illustration of the SimNest model. Liang Zhao Chapter 6. Deep learning based Epidemics modeling for Flu Forecasting 119 the mapping from abstract semantics layer to the user health status layer and s (·) is the sigmoid function. h = ∑m j=1W (2) j s(h (2) j ) +W (2) 0 . A common way to learn W is to define a loss function over the training data, and then obtain the best W by minimizing the loss of misclassification towards labels:
Assessment of student competency for personalised online learning using objective distance

Like traditional learning, online learning also requires effective personalised learning so that the appropriate feedback can be given individually for the students to achieve their goals. This paper proposes the objective distance which is the measurement representing the distance between current status of student's competency to the satisfied competency level required for accomplishing the entire course. This paper aims to study to what extent the proposed objective distance can be used for effective classification of student's competency comparing to raw score data and its combination. The experiments are conducted with two different online courses including computer skill and English language course having 55 and 111 students respectively. The students are classified with three classifiers including K-nearest neighbour, artificial neural network and decision tree into different classes accordingly to different competency levels. The classification results show that the proposed objective distance can be effectively used for competency classification of online students.
Screening of pgrs, L-Dopa precursor and polyploidy inducer treatments on protein content of Mucuna Pruriens callus.

An investigation has been conducted to analyse the effect of different concentrations of phytohormones (2, 4-D and kinetin) precursor (Tyrosine, 1.0, 1.5, and 2.0mM) and polyploidy inducer (colchicine 313 μM) treatments on protein content in Mucuna pruriens four weeks old calli. Highest amount of protein accumulated in callus raised on MS+ 3.0mg/l 2, 4-D+0.1 mg/l Kn+1.5mM tyrosine as compared to all other treatments of colchicine and tyrosine individually. It was also noticed that the calli sub cultured after 12 hr treatment of colchicine in suspension media on MS+ 3.0mg/l 2, 4-D showed higher concentrations of protein content as compared to all other treatments of colchicine. It indicates that treatment of tyrosine being a precursor of L-DOPA, led to increased level of protein while colchicine being an anti-mitotic, leads to accumulation of all those proteins, within the cell, which take part in the cell division. The study indicates supplementation of polyploidy inducer for shorter duration (313 μM for 12hr) and combination of phytohormone (3.0mg/l 2, 4-D+0.1 mg/l Kn) with 1.5mM tyrosine may be preferred for increased accumulation of protein content in Mucuna pruriens calli.
The Role of Interdependent Self-Construal in Increasing Donation Behavioral Intention: Underlying Processing Mechanism of Impression Motives

Abstract This study examines how individuals may engage with online donation behaviors by comparing two donation conditions: a public context and a private context. The study findings suggest that interdependent self-construal is likely to play a role in promoting donation behavior when individuals consider making a donation on a Facebook page. Furthermore, this study showed that impression outcome expectancy is the underlying mechanism mediating the effect of interdependent self-construal on willingness to donate, moderated by the donation contexts (public context: Facebook vs. private context: e-mail). This outcome suggests that high levels of interdependent self-construal alone do not prompt individuals to give, but that understanding the advertising medium and its interaction with underlying impression-outcome motives does so prompt individuals.
The Challenge of On Demand Routing Protocols Improvement in Mobility Context

Mobile Ad hoc NETworks are characterized by rapid changes in their topology and the lossy nature of wireless links. In this context, achieving respect the QoS constraints of multimedia communications is a real challenge. Routing protocols play an important role in achieving the required performance. They should be smart enough to select better paths for data transmissions. Several routing approaches are used. Most are derived from adapting those used in wired networks. They are not suited to the context of wireless networks. We focus on the well-known Ad-hoc On-demand Distance Vector (AODV) protocol, commonly used protocol in ad hoc network. Standard route discovery process used in AODV is expected to obtain the best path in term of delay. However, in lossy-links context, multimedia data packet transmission success, on path established thanks to control packets, may require several attempts. These retransmissions increase delay and overhead. Many QoS-based methods failed to make a meaningful improvement due to added complexity and additional delay and overhead. In this paper, we use a convenient and practical way to evaluate quality of links in mobile context. With these measures relating to the number of retransmissions, we produce a new metric. Thereafter, we use this metric to highlight the limitations of QoS approaches used to improve the performance of AODV. We show that improving performance of on-demand routing protocols, in the mobility context, lies on effective control of node neighborhood.
Towards Brain Decoding for Real-World Drowsiness Detection

Author(s): Wei, Chun-Shu | Advisor(s): Jung, Tzyy-Ping | Abstract: A brain-computer interface (BCI) allows human to communicate with a computer by thoughts. Recent advances in brain decoding have shown the capability of BCIs in monitoring physiological and cognitive state of the brain, including drowsiness. Since drowsy driving has been an urgent issue in vehicle safety that causes numerous deaths and injuries, BCIs based on non-invasive electroencephalogram (EEG) are developed to monitor drivers’ drowsiness continuously and instantaneously. Nonetheless, on the pathway of transitioning laboratory-oriented BCI into real-world applications, there are major challenges that limit the usability and convenience for drowsiness detection (DD). To completely understand the association between human EEG and drowsiness, this study employed a large-scale dataset collected from simulated driving experiments with a lane-keeping task and EEG recordings. A DD-BCI that acquires EEG from only non-hair-bearing (NHB) areas was proposed to maximize the comfort and convenience. The performance of the NHB DD-BCI was validated and compared with that using whole-scalp EEG, showing no significant difference in the accuracy of alert/drowsy classification. In addition, a subject-transfer framework that leverages large-scale existing data from other subjects was proposed to reduce the calibration time of a DD-BCI. Alert baseline data were involved to enhance the efficiency of subject-to-subject model transfer. The subject-transfer approach significantly reduced the calibration time of the DD-BCI, exhibiting the potential in facilitating plug-and-play brain decoding for real-world BCI applications. Overall, this thesis presents the contributions to developing a DD-BCI for real-world use with maximal usability and convenience. The methodologies and findings could further catalyze the exploration of real-world BCIs in more applications.
National Child and Adolescent Health Policies as Indicators of Adolescent Mental Health: A Multilevel Analysis of 30 European Countries

There is little evidence on the association between child and adolescent mental health (CAMH) policies and adolescent mental health. This study examined this association using data on indicators of adolescent mental health—aggressive behavior, life satisfaction, and psychosomatic symptoms—in 172,829 eleven- to fifteen-year-olds from 30 European countries in the 2013-2014 Health Behaviour in School-Aged Children (HBSC) study. Individual records were linked to national-level policies for CAMH, controlling for national-level adult violence, adult well-being, and income inequality. Multilevel analyses revealed lower adolescent aggressive behavior in countries with more CAMH policies, even after controlling for other national-level indicators. Adolescent life satisfaction and psychosomatic symptoms were not associated with CAMH policies. Results may inform policy recommendations regarding investments in adolescent mental health.
Single Machine Scheduling Model with Total Tardiness Problem

Objectives: In this paper, Five Dispatching Rules and a Branch & Bound algorithm is introduced for Single Machine Total Tardiness Scheduling Problem (SMTTSP) to minimize the total (average) tardiness and number of tardy jobs. Methods/Statistical Analysis: We proposed five dispatching (priority) rules as Shortest Processing Time, Earliest Due Dates, Longest Processing Time, Minimum Slack Time and First Come First Serve for SMTTSP and compared the performance of all the proposed priority rules. Furthermore, a numerical illustrations is also provided to select the best dispatched rule of SMTTSP. Next, we developed a Branch & Bound Algorithm for SMTTSP using best selected dispatching rule. We also developed Branch Tree to understand the Lower bound process of the B& B Algorithm. Findings: The main aim to proposed these dispatching rules and a Branch and Bound Algorithm is to obtain the optimal sequence to optimize the total (average) tardiness and number of total tardy jobs. The comparative analysis of the dispatching rules shows that EDD rule is better than other dispatching rules for minimization of total tardy jobs and tardiness of the jobs while SPT rule is better for minimization of make span. But Dispatching rules do not have the guarantee to give an optimal solution. So in this study, an exact algorithm (Branch & Bound) was developed with EDD rule for finding the optimal solution for SMTTSP. The comparative study between dispatching rules and an exact (B&B) algorithm is being justified by numerical illustrations and we found that the EDD rule and B&B Algorithm give the same results. Hence it was concluded that EDD rule works as an Exact algorithm and gives the optimal solution for SMTTSP. Application/Improvements: The computational results of the proposed (B&B) algorithm and Dispatching rules show that our methodology is more useful than other optimal approach for SMTTSP and it provides an important tool for decision makers.
The Loss-Averse Retailer’s Order Decisions Under Risk Management

This paper characterizes the retailer’s loss aversion by introducing a loss aversion coefficient and proposes a new loss aversion utility function for the retailer. To hedge against the risk arising from the uncertain market demand, we use the Conditional Value-at-Risk (CVaR) measure to quantify the potential risks and obtain the optimal order quantity for the retailer to maximize the CVaR objective of loss aversion utility. It is shown that that the optimal order quantity for a retailer to maximize the expected loss aversion utility is smaller than expected profit maximizing (EPM) order quantity in the classical newsvendor model, which can help to explain decision bias in the newsvendor model. This study shows that the optimal order quantity with the CVaR objective can decrease in retail price under certain conditions, which has never occurred in the newsvendor literature. With the optimal order quantity under the CVaR objective, it is proved that the retailer’s expected loss aversion utility is decreasing in the confidence level. This confirms the fact that high return means high risk, while low risk comes with low return. Based on the results, several management insights are suggested for the loss-averse newsvendor model.
Nonlinear, Adaptive and Fault-tolerant Control for Electro-hydraulic Servo Systems

the thesis combines the techniques developed in fault detection and nonlinear control in order to develop an active fault-tolerant controller for electro hydraulic servo systems. In order to maintain overall service and performances as high as possible when a potential fault occurs, the fault-tolerant controlled system prognoses the fault and changes its controller parameters or structure. The consequences of an unexpected fault are avoided, high availability is ensured and the overall safety in electro hydraulic servo systems is increased.
Action recognition in depth video from RGB perspective: A knowledge transfer manner

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.
Investigating the Decision-Making Approach to Risk Assessment in Police Custody

When a person is booked into police custody in England and Wales they are assessed for risk of harm to themselves or to others. This risk assessment informs the decision as to what observation level they are placed on, ranging from hourly visits to constant observation for the highest risk detainees. In comparison to the international standard for risk management, there are gaps in the risk assessment process in police custody. Currently, the analysis and evaluation of identified risk is down to the experience and judgement of the Custody Officer, rather than a more structured method. This paper questions whether the process should be more formalized, using a statistical tool rather than relying on expert judgement. This paper uses a mixed methods approach investigating custody record data from three English police forces to identify key risk factors that lead to variances in observation levels, and interviewing sixteen Custody Officers from a further two forces investigating their perspective of the risk assessment process The findings suggest that whilst there are key factors affecting observation level, an entirely statistically based risk assessment process would lack the flexibility to account for the individual and would need to include additional information custody officers consider. It is concluded that further investigation should be conducted into a process which combines an actuarial approach with the intuitive insights gained from expert decision-making.
The Added Value of Latinx and Black Teachers for Latinx and Black Students: Implications for the Reauthorization of the Higher Education Act

A research synthesis points to the added-value—benefits to social and emotional development, as well as learning outcomes—for students of color taught by teachers of color. Given ongoing education debates, policymakers can use this evidence base to craft legislation aimed at increasing the ethnoracial diversity of the U.S. educator workforce. To begin, historical research shows how Latinx and Black teachers have supported their Latinx and Black students’ social and emotional development during state-sanctioned school segregation. Contemporary qualitative and quantitative research highlights how teachers of color improve social and emotional development, as well as learning, for their students of color. Implications for the reauthorization of the Higher Education Act are discussed.
Start-ups, job creation, and founder characteristics

Analyzing data on all US employers in a cohort of entering firms, we document a highly skewed size distribution, such that the largest 5% account for over half of cohort employment at firm birth and more than two-thirds at firm age 7. Analyzing linked survey-administrative data, we find that female, African–American, and younger founders are initially less likely to start large firms. The gender gap persists through firm age 7, while racial and age gaps do not. Education is positively associated with start-up size, except for graduate degrees. Prior entrepreneurship and founding team size are positively associated, but team diversity is not. Specifications with capital and industry controls illuminate roles of financial constraints and sectoral choice.
Automated Navigation of Guided Vehicle for Warehouses

: For the reduction of pollution and shortage of oils, nowadays there is a high demand on the part of manufacturers for their shipping warehouses in an effective manner without any havoc. Human error has been a negative effect on various factors such as safety, efficiency, and quality. These difficulties can be eliminated or reduced with the introduction of a Microcontroller Based Guided Vehicle (MBGV) – a driverless, intelligent forklift which uses an optical path to traverse a warehouse in a quick and safe manner. This vehicle employs a microcontroller and a rechargeable battery for doing the shipping operation in an automated manner without any need for fuel. Since there is going to be a fuel depletion in the world in the coming days, this automated vehicle can be an alternative solution for the above purpose. This paper deals with the existing different types of automated vehicles available, their selection, principle of operation of the proposed model, merits and demerits of this Automated Guided Vehicle and its plan of future work.