Miracle-dz/UniFAR-FTUs huggingface dataset:
A Codebook Based Simultaneous Beam Training for mmWave Multi-User MIMO Systems with Split Structures

The tradeoff between narrow beams and low training overhead is deemed to a bottleneck for millimeter-wave (mmWave) systems. Particularly, in multi-user (MU) scenarios, the training overhead grows linearly with the number of users. To address this issue, this paper proposes a codebook based simultaneous beam training scheme for mmWave MU systems with split&#x002F;partially- connected structures. In this scheme, the multi- resolution codebook based hierarchical beam searching is adopted to reduce the training overhead and acquire the angle of departure (AoD) for each user-group. Meanwhile, the concurrent beams, offered by the corresponding subarray-groups of split structures, enable simultaneous beam training for all served user- groups. Consequently, the overall training overhead is significantly reduced. Moreover, a two-phase training protocol is elegantly built, where the whole array is used to reap large array gain in the initial phase. Furthermore, in the subsequent parallel training phase, multiple subarray-groups conduct simultaneous beam training for the corresponding user-groups. Hence, advantages on both low training overhead and large array gain are achieved. Numerical results show the proposed scheme outperforms traditional counterparts, especially in moderate-high signal-to-noise ratio (SNR) regimes with limited transmission blocks.
On the Performance of Cell-Free Massive MIMO in Ricean Fading

We consider an uplink time-division duplex cell-free massive multiple-input multiple-output (MIMO) system in which many user equipments (UEs) are simultaneously served by many access points (APs) via simple matched filtering processing. The propagation channel is modeled via the Ricean distribution, which includes a dominant line-of-sight component on top of diffuse scattering. The Ricean $K$-factor of each link varies with the UE location (relative to the locations of the APs). The system performance in terms of the spectral efficiency is investigated taking into account imperfect channel knowledge. Power and AP-weighting control is exploited to maximize the lowest spectral efficiency across all UEs. This optimization problem can be efficiently solved via the bisection method by solving a sequence of linear feasibility problems together with the generalized eigen-value problem. We show that by optimally selecting the power control and AP-weighting coefficients, the per-UE throughput increases significantly. Furthermore, we propose an AP selection scheme to reduce the backhaul requirements in a cell-free massive MIMO system, with slight compromise in performance.
Energy-Efficient Transmission for Uplink NOMA in Wireless-Powered D2D Communications

We study energy-efficient resource allocation of overlaying device-to-device (D2D) communication in wireless cellular network. In this system, two D2D users first harvest wireless energy from a base station (BS) and then the far D2D user transmits information to the near D2D user. Besides, the near D2D user transmits information of both two users to the BS via NOMA scheme. Due to the interference of NOMA scheme, maximizing energy efficiency (bits/Joule) of the D2D system is a non-convex problem. We formula the problem taking into account the quality-of-service requirements of the D2D users. Exploiting the fractional programming theory and difference of convex (DC) programming, we obtain a sub-optimal resource allocation policy. Our solution reveals that the optimal energy-efficient D2D communication should occur if the far D2D user transmits information to the near D2D user with the minimal required rate. Simulation results are provided to demonstrate the advantage of energy efficiency of the proposed algorithm comparing with two baseline schemes.
A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on  $\epsilon$ -Dominance

Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-dominance has shown a proper balance between convergence and diversity. When using <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-dominance, diversity is ensured by partitioning the objective space into boxes of size <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>. In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> along the search without any need of a previous user’s knowledge. We include the proposed co-evolutionary scheme into an MOEA based on <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning state-of-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient.
A Synthesizable Digital Low-Dropout Regulator Based on Voltage-to-Time Conversion

This paper proposes a synthesizable digital LDO that is implemented with standard-cell-based digital design flow. With inverter chains as voltage-controlled delay lines, the difference between output and reference voltages is converted into delay difference, then compared in time-domain. Since the time-domain difference is straightforwardly captured by a phase detector that consists of a D-FF, the proposed LDO does not need an analog voltage comparator, which requires careful manual design. The prototype of the proposed LDO is fabricated in 65 nm standard CMOS technology with 0.015 mm2 area occupation. The measurement results show that with 10.4 MHz internal clock the tracking response to 200 mV switching of the reference voltage is ~4.5 μs and the transient response to 5 mA change of the load current is ~6.6 μs. The quiescent current consumed by the LDO core is as low as 35.2 μA at 10 mA load current, which leads to 99.6 % current efficiency.
Block-based Comprehension: Exploring and Explaining Student Outcomes from a Read-only Block-based Exam

The success of block-based programming environments like Scratch and Alice has resulted in a growing presence of the block-based modality in classrooms. For example, in the United States, a new, nationally-administered computer science exam is evaluating students' understanding of programming concepts using both block-based and text-based presentations of short programs written in a custom pseudocode. The presence of the block-based modality on a written exam in an unimplemented pseudocode is a far cry from the informal, creative, and live coding contexts where block-based programming initially gained popularity. Further, the design of the block-based pseudocode used on the exam includes few of the features cited in the research as contributing to positive learner experiences. In this paper, we seek to understand the implications of the inclusion of an unimplemented block-based pseudocode on a written exam. To do so, we analyze responses from over 5,000 students to a 20 item assessment that included both block-based and text-based questions written in the same pseudocode as the national exam. Our analysis shows students performing better on questions presented in the block-based form compared to text-based questions. Further analysis shows that this difference is consistent across conceptual categories. This paper contributes to our understanding of the affordances of block-based programming and if and how the modality can help learners succeed in early computer science learning experiences.
Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions

The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener. In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities. Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart cities.
Realizing Multi-Gbps Vehicular Communication: Design, Implementation, and Validation

This paper proposes a vehicular communication system that can achieve multi-Gbps data rate transmission for train and car applications. Employing a millimeter-wave frequency band around 25 GHz, the proposed system provides mobile backhaul connectivity for vehicle user equipments (UEs). In order to support a very high data rate with such a high carrier frequency while guaranteeing sufficient robustness against high mobility-related behaviors such as fast channel variation and unstable handover, we employ a relaying network architecture consisting of a backhaul link to a vehicle UE and an in-vehicle access link. Based on that, we provide a set of fundamental design elements including numerology, frame structure, the reference signal, multi-antenna scheme, and handover. We then validate the proposed vehicular communication system by implementing an experimental testbed consisting of baseband modem, RF front end, an array antenna units, and by performing field trials in an actual subway tunnel and urban road environments. The experimental validation results reveal that providing multi-Gbps backhaul transmission is possible and worthwhile for both train and car scenarios.
Development of a Lean Computational Thinking Abilities Assessment for Middle Grades Students

The recognition of middle grades as a critical juncture in CS education has led to the widespread development of CS curricula and integration efforts. The goal of many of these interventions is to develop a set of underlying abilities that has been termed computational thinking (CT). This goal presents a key challenge for assessing student learning: we must identify assessment items associated with an emergent understanding of key cognitive abilities underlying CT that avoid specialized knowledge of specific programming languages. In this work we explore the psychometric properties of assessment items appropriate for use with middle grades (US grades 6-8; ages 11-13) students. We also investigate whether these items measure a single ability dimension. Finally, we strive to recommend a "lean" set of items that can be completed in a single 50-minute class period and have high face validity. The paper makes the following contributions: 1) adds to the literature related to the emerging construct of CT, and its relationship to the existing CTt and Bebras instruments, and 2) offers a research-based CT assessment instrument for use by both researchers and educators in the field.
A Survey on Simultaneous Wireless Information and Power Transfer With Cooperative Relay and Future Challenges

The integration of simultaneous wireless information and power transfer (SWIPT) and cooperative relay (CoR) techniques has evolved as a new phenomenon for the next-generation wireless communication system. CoR is used to get energy and spectral efficient network and to solve the issues of fading, path loss, shadowing, and smaller coverage area. Relay nodes are battery-constrained or battery-less devices. They need some charging systems externally as replacing or recharging of their batteries sometimes which are not feasible and convenient. Energy harvesting (EH) is the most cost-effective, suitable, and safer solutions to power up these relays. Among various types of the EH, SWIPT is the most prominent technique as it provides spectral efficiency by delivering energy and information to the relays at the same time. This paper reviews the combination of CoR and SWIPT. From basic to advanced architectures, applications and taxonomies of CoR and SWIPT are presented, various forms of resource allocation and relay selection algorithms are covered. The usage of CoR and SWIPT in the fifth-generation wireless networks is discussed. This paper focuses on the integral aspects of the CoR and SWIPT to other next-generation wireless communication systems and techniques such as multiple-input-multiple-output, wireless sensor network, cognitive radio, vehicular ad hoc network, non-orthogonal multiple access, beamforming technique, and the Internet of Things. Some open issues and future directions and challenges are given in this paper.
An FPGA-based Hardware Accelerator for Scene Text Character Recognition

Scene text character recognition is a challenging task in Computer Vision since natural scene images usually have cluttered background and the character’s size, font, orientation, texture, brightness, and alignment in the picture are variable and non-predictable. Furthermore, most systems including scene text character recognition are usually embedded in a system on a chip (SoC), which has critical requirements, such as low latency, low area, mobility, and flexibility, at the same time that they require high accuracy. In this context, in this work we propose a heterogeneous system for embedded applications with time, area and power constraints, that combines hardware and software to accelerate a technique for scene text character recognition, based on Histogram of Oriented Gradients (HOG) for feature extraction and a neural network Extreme Learning Machine (ELM) as a classifier. The system was prototyped and experimented in the Terasic embedded platform DE2i-150 and the results showed that the system has accuracy of 65.5% in the Chars74k-15 dataset and is able to process up to 11 frames per second, having a good trade-off between processing time and accuracy in embedded environments. Moreover, it occupies only 11% logic elements of the Altera Cyclone IV FPGA, enabling its use in embedded systems.
Fuzzy Observer-Based Prescribed Performance Control of Vehicle Roll Behavior via Controllable Damper

This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform.
Software Testing in Introductory Programming Courses: A Systematic Mapping Study

Traditionally, students learn about software testing during intermediate or advanced computing courses. However, it is widely advocated that testing should be addressed beginning in introductory programming courses. In this context, testing practices can help students think more critically while working on programming assignments. At the same time, students can develop testing skills throughout the computing curriculum. Considering this scenario, we conducted a systematic mapping of the literature about software testing in introductory programming courses, resulting in 293 selected papers. We mapped the papers to categories with respect to their investigated topic (curriculum, teaching methods, programming assignments, programming process, tools, program/test quality, concept understanding, and students' perceptions and behaviors) and evaluation method (literature review, exploratory study, descriptive/persuasive study, survey, qualitative study, experimental and experience report). We also identified the benefits and drawbacks of this teaching approach, as pointed out in the selected papers. The goal is to provide an overview of research performed in the area, highlighting gaps that should be further investigated.
Ecommerce Product Title Classification

E-commerce catalogs include a continuously growing number of products that are constantly updated. Each item in a catalog is characterized by several attributes and identified by a taxonomy label. Categorizing products with their taxonomy labels is fundamental to effectively search and organize listings in a catalog. However, manual and/or rule based approaches to categorization are not scalable. In this paper, we explain our work for the SIGIR eCom’18 Rakuten Data Challenge [1] which focuses on the Topic of largescale taxonomy classification. We first start with data processing. Secondly we investigate a number of feature extraction techniques and observe that TF-IDF with both bigram and unigram work best for categorization than CNN and word embedding. Finally, we evaluate several models and find than Support Vector Machines yield the highest result. CCS CONCEPTS Computing methodologies → Machine learning
A Survey on Network Verification and Testing With Formal Methods: Approaches and Challenges

Networks have grown increasingly complicated. Violations of intended policies can compromise network availability and network reliability. Network operators need to ensure that their policies are correctly implemented. This has inspired a research field, network verification and testing, that enables users to automatically detect bugs and systematically reason their network. Furthermore, techniques ranging from formal modeling to verification and testing have been applied to help operators build reliable systems in electronic design automation and software. Inspired by its success, network verification has recently seen increased attention in the academic and industrial communities. As an area of current interest, it is an interdisciplinary subject (with fields including formal methods, mathematical logic, programming languages, and networks), making it daunting for a nonprofessional. We perform a comprehensive survey on well-developed methodologies and tools for data plane verification, control plane verification, data plane testing and control plane testing. This survey also provides lessons gained from existing solutions and a perspective of future research developments.
A Survey of Refactoring Detection Tools

Several tools for detecting refactorings in the code exist and have been evaluated in the literature. However, we found that the benchmarks used for the evaluation so far are incomplete and therefore, the validity of the previous evaluations is at stake. While our completed benchmark largely confirmed the previous results, in particular confirming that RefactoringMiner generally outperforms its competitors, we also identified a weak spot of RefactoringMiner that was not noted before: Refactorings of the type Move Class and Rename Package are frequently classified falsely. In this paper we discuss the reasons for this wrong classification and outline a possible fix, which potentially boosts the overall precision and recall of RefactoringMiner to over 95%.
DL-dashboard: user-friendly deep learning model development environment

In recent years, deep learning has contributed to a big step forward in artificial intelligence, so that deep learning models have been created extensively in a variety of areas. However, development of deep learning model requires high implementation skills as well as domain knowledge. Additionally, finding the best model is a process of a lot of trial-and-error for developers. To alleviate the developers' difficulties, we have developed a deep learning model development environment called DL-Dashboard that allows developers can create new models easily and quickly by drag-and-dropping built-in layer component and can train the models by selecting one of the suggested training options without much deep learning experience. We explain design principles and implementation of DL-Dashboard system and show how developers can create and train models user-friendly on it.
Using Runtime Monitoring to Enhance Offline Analysis

Offline log file analysis of unmanned aircraft systems is a challenging experts task. Due to more automation, the amount and complexity of logged data increases. Experts need support, for instance by automatically generating additional statistical information or by correlating data. Runtime Monitoring is a formal method for analyzing system executions. It allows users to express temporal properties in a formal language which then can be used for the generation of a corresponding monitor. In this paper, we propose to integrate Runtime Monitoring into our offline analysis process. We show how the results of the monitor can be used to enhance the log file analysis and, therefore, support the expert. Specifically, we use the stream-based specification language LOLA and apply it to the log files of an unmanned cargo aircraft.
Deep Learning on Large-Scale Muticore Clusters

Convolutional neural networks (CNNs) have achieved outstanding accuracy among conventional machine learning algorithms. Recent works have shown that large and complicated models, which take significant cost for training are needed to get higher accuracy. To train these models efficiently in high performance computers (HPCs), many parallelization techniques for CNNs have been developed. However, most techniques are mainly targeting GPUs and parallelizations for CPUs are not fully investigated. This paper explores CNN training performance on large-scale multicore clusters by optimizing intra-node processing and applying techniques of inter-node parallelization for multiple GPUs. Detailed experiments conducted on state-of-the-art multi-core processors using the openMP API and MPI framework demonstrated that Caffe-based CNNs can be accelerated by using well-designed multithreaded programs. We achieved at most 1.64 times speedup in convolution operations with devised lowering strategy compared to conventional lowering and acquired 772 times speedup with 864 nodes compared to one node.