Miracle-dz/UniFAR-FTUs huggingface dataset:
Nearest neighbour based task allocation with multi-agent path planning in dynamic environments

This paper presents an effective solution to the task assignment problem combined with path planning using a group of autonomous agents. In this approach, path planning is incorporated with the task assignment; where agents start to move once the overall task is given. The main contribution of the proposed method is that it can effectively solve the task allocation problem in a distributed manner (among the agents). In this line, the other contribution of the paper is that it can effectively plan an optimum path by avoiding any types of collisions. Moreover, this method is capable of dealing with dynamic environments. The total methodology is based on the Particle Swarm Optimization (PSO) algorithm and for task allocation purposes the nearest neighbour search, approach is implemented. Following this approach, autonomous agents can successfully complete their mission in complex environments. The simulation results validate the proposed solution in dynamic environments.
Personalization of interactive recommender systems for expert users

Interactive recommender systems involve users in the process of recommendations. Users with different skill levels have various preferences in their type of interactions with interactive recommender systems. Adapting users' type of interactions with recommender systems according to their experience levels seems to be a promising improvement to enhance recommendation process and user satisfaction with interactive recommender systems. In this paper, expert users are automatically recognized based on their interaction with an interactive recommender system. Shopr software, an interactive recommender application for smartphones, is used to track users' interactions. Users are grouped in two categories of expert and novice users based on their interactions with Shopr software in our user study. Task completion time is used to recognize users' skill level. The result of the study show that all users recognized as expert users are indeed expert but some expert users who spent more time in the recommender system to improve their choice of shopping cannot be detected by this algorithm. The result of this research can be used to offer personalized interaction for expert users in interactive recommender systems.
Low Complexity and Fast Processing Algorithms for V2I Massive MIMO Uplink Detection

The fast development of intelligent transport systems requires high-rate communications, high energy efficiency, and low latency. One promising solution to meet the requirements is to adopt the massive multiple-input multiple-output (MIMO) technique. The massive MIMO architecture is attractive to multiple vehicles on the road for vehicle-to-infrastructure access as large-scale antennas can be deployed at the roadside unit. Besides, massive MIMO systems can significantly improve the system spectrum efficiency and energy efficiency. However, the benefits are achieved at the cost of high computational complexity and long processing delay even with linear detection methods. In this paper, we propose low complexity and fast processing algorithms to address those issues. The proposed schemes transform the large-scale matrix inverse problems into solving linear equations. We then introduce iterative methods to solve linear equations. To speed up the updating process in iterative method, we utilize the properties of block matrix, and perform the updating process on a small size block independently. The independent processing progress can be paralleled, which greatly reduces the overall processing time. We also evaluate the performance of the proposed schemes in terms of the probability that the convergence conditions are met, and the system bit error rate. The results show that the proposed schemes achieve good system performance but at low complexity and latency.
Delay Skew Packet Flow Control in Wireless Systems With Dual Connectivity

The paper presents a new data flow controller for use in 4G and 5G wireless applications where the incoming internet data flow is split in support of multipoint downlink wireless transmission. The different paths of the data from the node of the split to the wireless transmission nodes and on to the mobile may then result in different travel times. The mobile can cope with small values of this delay skew by means of buffers and reordering protocols. The control objective is therefore to keep the delay skew within a prespecified interval of time, when the packets arrive in the mobile. The proposed algorithm solves the problem by cascade control. The packet dwell time of each wireless transmission node data queue is controlled by an inner loop, with the reference dwell times being determined by an outer delay skew control loop. The delay skew control loop exploits a deadzone to achieve interval control. In a further contribution the $\mathcal{L}_2$ -stability of the controller is analyzed for the dual connectivity functionality of the coming 5G cellular network deployment. A combined use of the input–output Nyquist- and Popov-criteria is required for the analysis. The performance is also evaluated by a laboratory testbed implementation.
An experimental study to investigate personality traits on pair programming efficiency in extreme programming

Human Factors should not be overlooked in software development project planning. While much emphasis is often laid on technical aspects of projects, attention is rarely given to the influence of team members' personality on project success. This study investigates the effects of personality traits on the efficiency of pair programming when programmers are at the same location compared to being at different locations. The personality test model used is the Myer-Briggs Indicator Type which comprises of four traits, namely: extraversion/ introversion, sensing/intuition, thinking/feeling and judging/perceiving. A series of experiments was conducted and preliminary findings indicate that depending on location, personality traits can have an effect on programmers' efficiency. Results presented in this paper are expected to inform project managers in planning for successful software development projects, which require pair programming.
Extraction of Intrinsic Electrical Parameters in Partially Depleted MoS2 Field-Effect Transistors

Electrical performance and transport mechanisms in 2-D transition-metal dichalcogenide materials should be investigated under a range of electrical parameters for practical application. In this paper, partially depleted (PD) molybdenum disulfide (MoS<sub>2</sub>) transistors were fabricated with a thick flake mechanically exfoliated from bulk crystals, and their operating mechanism is discussed considering the gate-uncontrollable conduction channel, the maximum depletion width (<inline-formula> <tex-math notation="LaTeX">${D}_{\mathrm {\text {max}}}$ </tex-math></inline-formula>), and the impact of series resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\mathrm {\text {sd}}}$ </tex-math></inline-formula>). In addition, the intrinsic mobility of a neutral bulk channel in PD-MoS<sub>2</sub> transistors was extracted from the simply separated gate-controllable drain current with a depletion approximation.
Prevalence and determinants of undernutrition among children under 5-year-old in rural areas: A cross-sectional survey in North Sudan

Background: Child malnutrition is a major public health problem in developing countries. Therefore, the aim of this study was to estimate the prevalence of undernutrition among children <5 years in River Nile state (RNS) in North Sudan. Subjects and Methods: A cross-sectional household survey was done in four localities in RNS. Using Multistage Cluster sampling, 1635 under 5 years' children had participated. Pretested questionnaire and anthropometric measures were used during data collection. The analysis was done using SPSS software program version 21 and World Health Organization (WHO) Anthro 2005 software. Indices were reported in z-scores and compared with the WHO 2005 reference population to determine the nutritional status of children. Results: Among 1,447 surveyed children, the prevalence of stunting, underweight, and wasting were 42.5%, 32.7%, and 21%, respectively. Stunting was highest among the 48–60 months of age group (82.5%). Boys had poorer indicators of undernutrition in comparison to girls. Geographically stunting was more prevalent in Berber locality. Infectious diseases (gastroenteritis and respiratory symptoms) and incomplete vaccination were significantly associated with wasting (P = 0.007, P = 0.013, and P = 0.008). Poor socioeconomic status (P = 0.043), poorer household sanitation (P = 0.022), large family size, lack of family spacing, and infants weaned suddenly were regarded as risk factors for undernutrition. Conclusion: There was a high prevalence of undernutrition in the 4th and 5th year of life in RNS population, with significant gender imbalance. Our survey highlighted the importance of urgent need to improve child health in this region.
Partially-Supervised Image Captioning

Image captioning models are becoming increasingly successful at describing the content of images in restricted domains. However, if these models are to function in the wild - for example, as assistants for people with impaired vision - a much larger number and variety of visual concepts must be understood. To address this problem, we teach image captioning models new visual concepts from labeled images and object detection datasets. Since image labels and object classes can be interpreted as partial captions, we formulate this problem as learning from partially-specified sequence data. We then propose a novel algorithm for training sequence models, such as recurrent neural networks, on partially-specified sequences which we represent using finite state automata. In the context of image captioning, our method lifts the restriction that previously required image captioning models to be trained on paired image-sentence corpora only, or otherwise required specialized model architectures to take advantage of alternative data modalities. Applying our approach to an existing neural captioning model, we achieve state of the art results on the novel object captioning task using the COCO dataset. We further show that we can train a captioning model to describe new visual concepts from the Open Images dataset while maintaining competitive COCO evaluation scores.
CIA-Towards a Unified Marketing Optimization Framework for e-Commerce Sponsored Search

As the largest e-commerce platform, Taobao helps advertisers reach billions of search requests each day with its sponsored search service, which has also contributed considerable revenue to the platform. How to design a suit of marketing optimization tool to cater various advertiser demands while balancing platform revenue and consumer experience is significant to a healthy and sustainable marketing ecosystem, among which bidding strategy plays a critical role. Traditional keyword-level bidding optimization only provides a coarse-grained match between advertisement and impression. Meanwhile impression-level expected value bidder is not applicable to various demand optimization of massive advertisers, not to mention its lack of mechanism to balance benefits of three parties. In this paper we propose \emph{Customer Intelligent Agent}, a bidding optimization framework which designs an impression-level bidding strategy to reflect advertiser's conversion willingness and budget control. In this way, with a simplified control ability for advertisers, CIA is capable of fulfilling various e-commerce advertiser demands in different levels, such as GMV optimization, style comparison etc. Additionally, a replay based simulation system is designed to predict the performance of different take-rate. CIA unifies the benefits of three parties in the marketing ecosystem without changing the classic expected Cost Per Mille mechanism. Our extensive offline simulations and large-scale online experiments on \emph{Taobao Search Advertising} platform verify the high effectiveness of the CIA framework. Moreover, CIA has been deployed online as a major bidding tool for advertisers in TSA.
Nested Precoding in MU-MIMO Channels: The Superposition Transmission of Space-Time Coding and Spatial-Division Multiplexing

Nonorthogonal multiple access (NOMA) can improve the spectrum efficiency through reusing the power-level space by different users. Although NOMA in single-input single-output channels has been thoroughly explored, it is still underexploited in multiple-input multiple-output (MIMO) channels. Precoding design in multiuser MIMO channels with nonorthogonal transmission is a multidimensional nesting problem, it is hard to compare two matrix channels if only their power levels are considered. In this paper, we first propose a method to compare the covariance matrices of two channel matrices, and then derive a channel condition under which the linear precoding scheme can achieve the sum capacity. Considering arbitrary realizations of multiuser channels, we provide a nested precoding algorithm that can work well in highly correlated multiuser channels. Based on this idea, a specialized superposition transmission scheme between signaling and data information is proposed, where plenty of resources beneath the signaling signal are utilized to transmit data information for dedicated users, and a large amount of extra rates are achieved. In addition, space-time coding is used for signaling information and spatial-division multiplexing is used for multiuser data information. Under the constraint that signaling information should be correctly decoded at any position in the cell, we seek to maximize the sum rate of the underneath multiuser data streams. To this end, the optimal and a low-complexity simplified precoding algorithms are developed. Finally, simulation results verify the throughput improvement and the correct decoding probability of the signaling information.
Optimal placement and sizing of multiple active power filters for radial distribution system using grey wolf optimizer

This paper presents, grey wolf optimizer (GWO) algorithm to solve a constrained nonlinear optimization problem of optimal placement and sizing of multiple active power filters (OPSMAPFs) for radial distribution system (RDS). It is a nature inspired stochastic algorithm. It has balanced exploration and exploitation characteristics. Minimization of the current of APF is taken as an object with three inequality constraints. To evaluate the performance of GWO algorithm, the simulation is performed on 33-bus RDS for twenty independent runs and compared with recently developed dragonfly algorithm (DA) and whale optimization algorithm (WOA). Simulation results demonstrate the effectiveness and stability of GWO algorithm.
Fair Coflow Scheduling without Prior Knowledge

Coflow scheduling improves the networking performance at the application level in datacenters. Ideally, a coflow scheduler should provide tenants with isolation guarantees to achieve predictable networking performance. Existing works in this regard (e.g., DRF [1] and HUG [2]) are limited to the clairvoyant scheduling, in that the complete knowledge of coflow sizes is assumed to be available before the communication starts. However, this assumption does not hold for many applications with pipelined computation, in which clairvoyant coflow schedulers become inapplicable. To bridge this gap, we develop a new non-clairvoyant coflow scheduler, called Non-Clairvoyant DRF (NC-DRF), which provides isolation guarantees between contending coflows without prior knowledge of coflow size. We show that NC-DRF achieves provable isolation guarantees in the long run. Cluster deployment and trace-driven simulations show that with NC-DRF, coflows are only delayed by 68% on average as compared with the clairvoyant, isolation-optimal DRF [1]. NC-DRF also outperforms existing alternatives (e.g., per-link fairness) by 1.7× in terms of the average coflow completion time.
Nanostructured Organosilicon Luminophores for Effective Light Conversion in Organic Light Emitting Diodes

Abstract Full characterization of nanostructured organosilicon luminophores NOL4 and NOL5 based on the donor 2,2’-bithiophene and acceptor 1,4-bis(2,2′- bithiophene-5-yl)benzene units in dilute solutions and thin films by UV-Vis spectroscopy, DSC, TGA and X-ray techniques was reported. It was found that usage of these molecules as dopants (10–20 wt%) to the electroactive polyfluorene host in organic light-emitting devices (OLEDs) leads to the efficient spectral long wavelength shifting of the electroluminescence and an increase of the OLED performance as compared to the devices based on pristine polyfluorene, NOL4 and NOL5.
An overview on feature-based classification algorithms for multivariate time series

The research on multivariate time series (MTS) has developed rapidly in the past two decades. As an important part of data mining, the classification task for MTS has gained increasing attention from experts of diverse fields. In this paper, 26 feature-based classification methods for MTS are analyzed and summarize. Since the extraction of temporal features are the core of feature-based MTS classification, these methods are mainly divided into two categories: methods with hand-crafted features and methods with learnt features. The principles and procedures of these methods are introduced, and the advantages and disadvantages are also analyzed. Besides, the recent research directions in MTS classification, such as: early classification, imbalanced classification and classification with missing value are also discussed.
Decentralized interchange scheduling in India

The paper presents a review of the decentralized interchange scheduling and despatch mechanism for Inter-State transactions in electricity as implemented in India. It elaborates the mechanisms adopted to address the fundamental power system problems of Unit commitment, Economic Dispatch and Generation Control of shared generation resources within the federal governance structure in India. The authors derive learning from the experience at the inter-state level and recommend implementation of the similar mechanism within the intra-State system for realizing the objective of economic and efficient operation of the electricity market in India. The paper also discusses the emerging challenges in Interchange Scheduling with the increasing number and volume of short-term open access transactions, envisaged integration of large scale Renewable Energy Sources and growing aspiration of market players in India.
Internet addiction and psychological well-being among college students: A cross-sectional study from Central India

Background: Internet provides tremendous educational benefits for college students and also provided better opportunities for communication, information, and social interaction for young adults; however, excessive internet use can lead to negative psychological well-being (PWB). Objective: The present study was conducted with the objective to find out the relationship between internet addiction and PWB of college students. Materials and Methods: A multicenter cross-sectional study was carried out in college students of Jabalpur city of Madhya Pradesh, India. A total of 461 college students, using internet for at least past 6 months were included in this study. Young's Internet addiction scale, consisting of 20-item, based on five-point Likert scale was used to calculate internet addiction scores and 42-item version of the Ryff's PWB scale based on six-point scale was used in this study. Results: A total of 440 questionnaire forms were analyzed. The mean age of students was 19.11 (±1.540) years, and 62.3% were male. Internet addiction was significantly negatively correlated to PWB (r = –0.572, P < 0.01) and subdimensions of PWB. Students with higher levels of internet addiction are more likely to be low in PWB. Simple linear regression showed that internet addiction was a significant negative predictor of PWB. Conclusion: PWB of college students negatively affected by internet addiction. Hence, it is essential to develop strategies for prevention of internet addiction which is very important for promoting PWB of college students.
When the Party Decides: The Effects of Facial Competence and Dominance on Internal Nominations of Political Candidates

The facial traits and appearance of political candidates have been found to predict election outcomes across countries with different electoral systems and institutions. Research over the last decade has provided two different versions of this overall conclusion. First and most thoroughly studied, candidates who from their mere faces are evaluated as more competent get more votes on Election Day. Second, recent research finds that the ideological leanings of candidates and the voters they cater to also matter: Right-wing and conservative candidates receive more votes if they look more dominant, while liberal candidates lose votes when looking dominant and masculine. In this article, we investigate whether these patterns extend to candidate selection and support within parties as determined by party organizations. We test this through an original combination of naive respondents’ trait ratings of candidates in Danish local elections and these candidates’ positions on the ballot as decided by nomination processes within local party organizations. The results strongly support that the conclusions in previous studies extend to dynamics within the party among party members: Danish local party organizations tend to nominate facially competent candidates at the top of the ballot regardless of their ideological leaning. Moreover, liberal and conservative parties position dominant-looking candidates significantly different on the ballot with liberal parties being less likely to assign facially dominant candidates to top ballot positions. These results add important new insights about the underlying psychological processes causing appearance-based voting and relate to the ongoing discussion about the quality of public opinion formation.
Ontology alignment using inductive logic programming

Ontologies are one of the important and effective parts of semantic web which constitute the infrastructure and background knowledge of this realm of web science. Finding valid mappings as much as possible between the concepts or entities of ontologies, especially for the large ones, is a prominent task to align those concepts together and finally merge and integrate their ontologies to make a general and global ontology that is smaller and more flexible in many applications of semantic web. This paper describes a new learning-based ontology mapping method in which inductive logic programming (ILP) is used to learn ontology mapping using information gathered from instances of each entity in order to make some correct and valid alignments between concepts of different ontologies.
Control algorithm for G2V/V2G operation under unbalanced grid condition

This paper presents the critical side of the control of grid connected electric vehicles. Grid to vehicle (G2V) and vehicle to grid (V2G) are two operating modes that are possible with electric vehicles. In the conventional control algorithm with single reference frame controller when the grid voltages are unbalanced, double frequency oscillations are appeared in grid currents in dq synchronous reference frame and also having higher THD. For solving this problem, dual reference frame controller is developed with notch filters. It removes the double frequency oscillations from dq sequence current but is not accurately detected. In the proposed algorithm, a decoupled network is used to extract sequence components of grid voltage and current. Proposed algorithm works on the principle of double synchronous reference frame theory which accurately maintain the sequence components as dc quantity and THD as per IEEE International Grid Standards. The control algorithm is explained with detailed block diagram and its effectiveness is validated by simulation results.