Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategic Planning for Integrated Mobility-on-Demand and Urban Public Bus Networks

Traditional planning for urban transportation usually assumes a given or calculated split between motorized individual transport and public transportation. However, app-based services and ridesharing in the field of mobility on demand (MoD) create an intermediate mode of transport, whose long-term role in the urban mobility landscape and within public transport systems is not fully understood as of today. If the public transport industry wants to capture the opportunities these new services offer and mitigate the risks that come with them, planning tools for integrated intermodal networks are indispensable. In this work, we develop a strategic network planning optimization model for bus lines that allows for intermodal trips with MoD as a first or last leg. For an existing public transport network, we decide simultaneously on the use of existing lines and segments in the future fixed-route network, on areas of the city where an integrated MoD service should be offered, on how MoD interacts with the fixed route network via transfer points, and on passenger routes. The objective is the optimization of the financial performance of the resulting network, including the additional demand and revenues induced by enhanced network coverage. The model provides several options for modeling MoD costs, which allows for linking with operational models such as dial-a-ride. Moreover, our model considers a range of additional important strategic decisions like subsidies for a potential private sector operator of MoD services and the interplay between the demand for public transport and the role of MoD. We develop a path-based formulation of the problem and a branch-and-price algorithm as well as an enhanced enumeration-based approach to solve real-world instances to proven optimality. The model is tested on instances generated with the help of real-world data from a medium-sized German city that currently operates around 20 bus lines.
High-Isolation Eight-Element MIMO Array for 5G Smartphone Applications

In this paper, an eight-element MIMO array for 5G smartphone applications in the 3.45-GHz band (3.3–3.6 GHz) is presented. The array consists of two types of four-antenna arrays (U-shaped and L-shaped coupled-fed loop elements), which are symmetrically distributed in the inner of the smartphone frame. The dimension of the system circuit board is <inline-formula> <tex-math notation="LaTeX">$124\,\,\text {mm}\times 74$ </tex-math></inline-formula> mm and the size of two elements is <inline-formula> <tex-math notation="LaTeX">$4.8\,\,\text {mm}\times 9.8$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$0.055\lambda \times 0.11\lambda $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> represents the free-space wavelength at 3.45 GHz) and 4.9 <inline-formula> <tex-math notation="LaTeX">$\text {mm}\times 12.5$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$0.056\lambda \times 0.14\lambda$ </tex-math></inline-formula>), respectively. The proposed MIMO array is simulated, and a prototype is fabricated and tested. The results show that all the elements can cover the desired band of 3.3–3.6 GHz under the condition of −6-dB impendence bandwidth. The isolations are enhanced to 15 dB by combining the inverted-I ground slots with neutralization line (NL) structure. In addition, the envelope correlation coefficient (ECC) via any two elements is below 0.15 that shows good independence in far-field radiation characteristic. The measured efficiencies of the elements in the operating band are higher than 40%. Moreover, the array ergodic channel capacity is also calculated based on the correlation matrix method to be about 35 bps/Hz with a 20-dB signal-to-noise ratio. In addition, the effects of the user’s hand and the head has been analyzed as well. Based on the above, the proposed eight-element MIMO array is a prospective candidate for future 5G smartphone applications.
DCT based Multi Exposure Image Fusion

We propose a novel algorithm for multi-exposure fusion (MEF). This algorithm decomposes image patches with the DCT transform. Coefficients from patches with different exposure are combined. The luminance and chrominance of the different images are fused separately. Details of the fused image are finally enhanced as a post-processing. Experiments with several data sets show that the proposed algorithm performs better than state-of-the-art.
Parametric Control to a Type of Descriptor Quasi-Linear Systems via Output Feedback

This paper investigates the parametric design method to a type of descriptor quasi-linear systems whose coefficient matrices contain system variables and time-varying parameters. Based on the solution to a class of generalized Sylvester matrix equation, the generally parametrized expression of output feedback controller is established; meanwhile, the completely parametric forms of left and right eigenvectors are obtained, giving a group of arbitrary parameters. With the parametric technique, the closed-loop system can be converted into a linear constant one by utilizing a group of canonical matrix pair. Finally, three examples are presented to prove the effectiveness of the proposed approach.
Today Was a Good Day: The Daily Life of Software Developers

What is a good workday for a software developer? What is a typical workday? We seek to answer these two questions to learn how to make good days typical. Concretely, answering these questions will help to optimize development processes and select tools that increase job satisfaction and productivity. Our work adds to a large body of research on how software developers spend their time. We report the results from 5,971 responses of professional developers at Microsoft, who reflected about what made their workdays good and typical, and self-reported about how they spent their time on various activities at work. We developed conceptual frameworks to help define and characterize developer workdays from two new perspectives: good and typical. Our analysis confirms some findings in previous work, including the fact that developers actually spend little time on development and developers’ aversion for meetings and interruptions. It also discovered new findings, such as that only 1.7 percent of survey responses mentioned emails as a reason for a bad workday, and that meetings and interruptions are only unproductive during development phases; during phases of planning, specification and release, they are common and constructive. One key finding is the importance of agency, developers’ control over their workday and whether it goes as planned or is disrupted by external factors. We present actionable recommendations for researchers and managers to prioritize process and tool improvements that make good workdays typical. For instance, in light of our finding on the importance of agency, we recommend that, where possible, managers empower developers to choose their tools and tasks.
Exploiting YouTube's Video ASR scripts to Extend Educational Videos Textual Representative Tags Based on Gibb's Sampling Technique

Given the importance of the textual information in content retrieval, it is desirable that the textual representation of educational videos contents in social media platforms like YouTube capture the semantics of what is really in content they represent. Such coherent textual representations are important in objective video content retrieval, repurposing, reuse and sense-making of the content. In this study,the Automatic Speech Recognition (ASR) in the video tracks was leveraged to supplement the insufficient video content representations done through video title alone. The Latent Dirichlet allocation (LDA) implementation of Gibb’s sampling topic modeling approach was used to evaluate the suitability of various textual representations for YouTube educational videos and extract the candidate topic that extends well the original YouTube keywords. The results show that in topics space, YouTube ASR script performs well as a representative textual source in dominant topic than the combined textual representations. The automatic keywords extension obtained using our method add value to applications that use tags for content discovery or retrieval
Mutual Fund Selection for Realistically Short Samples

Performance of mutual fund selection methods is typically assessed using long samples (long time series). We investigate how well the methods perform in shorter samples. We carry out an extensive simulation study based on empirically motivated skill distributions. For both short and long samples, we present evidence of large differences in performance between popular fund selection methods. In an empirical analysis, we show that the differences documented by the simulations are empirically relevant.
Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.
Localization and Tracking for AUVs in Marine Information Networks: Research Directions, Recent Advances, and Challenges

In marine information networks, localization information is crucial for AUVs to label their collected data as well as to support intelligent auto-navigation or timely remote control. However, the extremely hostile underwater environments impose a number of challenges on designing AUV localization and tracking algorithms that can strike flexible balances among energy, accuracy, and practicability. To deal with these challenges, this article provides a systematic research roadmap for AUVs' localization and tracking, spreading from anchor deployment to time synchronization and range estimation and to trajectory prediction. Specifically, we first review the existing research on anchor deployment schemes, based on which we propose a spatially circular deployment configuration that achieves higher localization accuracy and discuss the anchors' number determination problem for balancing the energy-accuracy tradeoff. We then describe the principles for time synchronization and range estimation and summarize their state-of-the-art research advances. Finally, after introducing some widespread trajectory tracking methods, we develop a Kalman filtering based trajectory prediction algorithm to compromise tracking accuracy and signaling overheads. In particular, we also present and analyze the related open issues and challenges in each direction for future investigations.
Zero-Velocity Detection—A Bayesian Approach to Adaptive Thresholding

A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior information about the two hypotheses—the sensors being stationary or in motion—to be incorporated into the test. It is also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider a hypothesis prior based on the velocity estimates provided by the navigation system and an exponential model for how the cost of a missed detection increases with the time since the last zero-velocity update. Thereby, we obtain a detection threshold that adapts to the motion characteristics of the user. Thus, the proposed detection framework efficiently solves one of the key challenges in current zero-velocity-aided inertial navigation systems: the tuning of the zero-velocity detection threshold. A performance evaluation on data with normal and fast gait demonstrates that the proposed detection framework outperforms any detector that chooses two separate fixed thresholds for the two gait speeds.
A Flight Simulator-Based Active Learning Environment

The U.S. K-12 education continues to face two major challenges, the poor performance of students in math and science in comparison to other industrialized nations of the world and paucity of professional development on integrating technology in teaching for rural school teachers. While there certainly are structural reasons for this situation, pedagogical elements contrib-ute as well. Motivation and engagement in the classroom lead to deeper learning and academic success. The learning environment therefore should engage students affectively, behaviorally and cognitively. However, the design and implementation of a learning environment to engage and motivate students is a major aspect of the pedagogical challenge. Appropriate use of technology can support the design of such a learning environment. This paper provides details of an innovative technology-based learning environment to teach certain math and physics concepts to middle school students. Learning modules using an active-learning approach through the incorporation of flight simulation software were developed. The pedagogical approach was modeled in a teacher professional development workshop. An increase was observed in teaching efficacy and outcomes expectancy of the teachers who participated in the professional development. Post workshop data indicated the teachers’ acceptance of the effectiveness of the pedagogy and self-efficacy in using the approach.
Machine Learning Based Attack Against Artificial Noise-Aided Secure Communication

Physical layer security (PLS) technologies have attracted much attention in recent years for their potential to provide information-theoretically secure communications. Artificial Noise (AN)-aided transmission is considered as one of the most practicable PLS technologies, as it can realize secure transmission independent of the eavesdropper's channel status. In this paper, we reveal that AN transmission has the dependency of eavesdropper's channel condition by introducing our proposed attack method based on a supervised-learning algorithm which utilizes the modulation scheme, available from known packet preamble and/or header information, as supervisory signals of training data. Numerical simulation results with the comparison to conventional clustering methods show that our proposed method improves the success probability of attack from 4.8% to at most 95.8% for the QPSK modulation. It implies that the transmission to the receiver in the cell-edge with low order modulation will be cracked if the eavesdropper's channel is good enough by employing more antennas than the transmitter. This work brings new insights into the effectiveness of AN schemes and provides useful guidance for the design of robust PLS techniques for practical wireless systems.
Platform for the discovery of newsworthy events in Twitter

The new communication paradigm established by Social Media, along with its growing popularity in recent years, have contributed to attract an increasing interest by several research fields. One such research field is the field of event detection in Social Media, whose relevance stems from its potential applicability in many diverse applications. One such application is the detection of newsworthy events in Social Media. The purpose of this work is therefore to implement a system to detect newsworthy events in Twitter. A similar system proposed in the literature is used as the base of this implementation. For this purpose a segmentation algorithm was implemented using a dynamic programming approach in order to split the tweets into segments. A weighting scheme that takes into account the burstiness, user support and newsworthiness of the segments was then used to rank these segments. Wikipedia was leveraged in order to derive this newsworthiness. The top K segments in this ranking were further processed and clustered into candidate events according to their similarity. These candidate events were then filtered by an SVM model trained on manually annotated data in order to retain only those related to real-world newsworthy events. The support infrastructure required by the system, namely regarding the precomputed values considered necessary to its operation was also implemented. The implemented system was tested with three months of data, representing a total of 4,770,636 tweets created in Portugal and mostly written in the Portuguese language. The precision obtained by the system was 76.9 % with a recall of 41.6%.
Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive, which makes them unsuitable for mW-devices such as IoT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth, and system-level efficiency that are crucial for the deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for an arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)— $3.1\times $ higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness.
Software Engineering for Machine Learning: A Case Study

Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components - models may be "entangled" in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.
Network and Channel Selection for Heterogeneous M2M Communications

In this paper, we deal with the problem of M2M gateways' network selection for M2M traffic in heterogeneous wireless networks. Game theory is adopted to solve the problem of network-channel selection. Then, an iterative algorithm based on optimal reaction criterion is presented to obtain the NE of the proposed game. In simulations, the proposed iterative algorithm can achieve a near optimal sum utility of whole network with low complexity compared to the exhaustive search. In addition, the simulation results show that our proposed algorithms outperform existing methods in terms of sum utility and load balance. Introduction Machine-to-Machine (M2M) communications technology is a key technique of the Internet of things (IoT) to realize efficient, real-time transmission [1]. In M2M communications, machines communicate with each other without human intervention. The goal of M2M communications is to provide ubiquitous connectivity among all machine nodes. However, different types of M2M traffic require different degrees of QoS. Due to the fixed and limited resources of heterogeneous networks, the resources should be allocated to M2M nodes appropriately in order to satisfy different types of traffic's QoS requirements. How to provide efficient and reliable M2M communications in heterogeneous wireless networks has become an urgent problem. Network selection is an efficient solution to guarantee users' QoS by using various network resources [2]-[11]. Recently, many studies have proposed various network selection schemes. However, the experimental results in [12] showed that, compared to traditional human-to-human (H2H) traffic, M2M traffic exhibited different patterns in multiple aspects. Therefore, without considering the characteristics of M2M traffic during network selection procedure, these schemes mentioned in [2]-[11] can't be applied to M2M communications directly. In addition, a cost-effective and scalable M2M solution can be offered by hierarchical network architectures, in which various M2M applications will be supported. In this paper, a wireless access scheme in the M2M gateways is investigated for hierarchical heterogeneous wireless network. Game theory is adopted to solve the problem of the M2M gateways' network-channel selection. Then, an iterative algorithm based on optimal reaction criterion [13] are presented to obtain the NE of the proposed game. Finally, simulation results show that the proposed scheme can improve the efficiency of network resources, i.e., achieving better load balance. System Model We consider a particular service area in the coverage of the heterogeneous wireless network environment consisting of heterogeneous access networks (e.g., GSM/WCDMA/CDMA-2000/LTE, etc.), M2M gateways and many M2M nodes as shown in Figure 1. In this paper, we focus on the uplink communication of M2M nodes, i.e., the transmission from M2M nodes to M2M servers. Figure 1 captures a hierarchical M2M system architecture over heterogeneous wireless networks, wherein multiple M2M nodes and an M2M gateway forms a group. In a group, M2M nodes can't send