Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Pragmatics of Requirements Engineering Practices in a Startup Ecosystem

Software startups play an important role to foster innovation, economic growth, and job creation in a region. Startups are likely to increase their odds of thriving if they are embedded in a startup ecosystem. Actors of an ecosystem cocreate value, share resources, and support the prosperous evolution of peers. In recent years, software startups have been the focus of intensive research by the software engineering community. In this paper, we conducted a tertiary study to synthesise literature in the field of requirements engineering in startups. We concluded that there are limited empirical studies reporting an in-depth contextual analysis on how software startups perform requirements engineering. In parallel, we conducted a case study of startups based at Digital Port ecosystem. Our empirical results confirm what has been presented by literature that startups adopt requirements practices in a flexible and informal manner. In fact, as novel empirical evidence, we observed that even as startups evolve by acquiring the first paying customers and growing the customer base, requirements practices remain very pragmatic. Our results also reveal how actors of the studied ecosystem interact to support the co-evolution of startups.
SAT-Sweeping Enhanced for Logic Synthesis

SAT-sweeping is a powerful method for simplifying logic networks. It consists of merging gates that are proven equivalent (up to complementation) by running simulation and SAT solving in synergy. SAT-sweeping is used in both verification and synthesis applications within EDA. In this paper, we focus on the development of a highly efficient, synthesis-oriented, SAT-sweeping engine. We introduce a new algorithm to guide initial simulation, which strongly reduces the number of false candidates for merge, thus increasing the computational efficiency of the sweeper. We revisit the SAT-sweeping flow in light of practical considerations for synthesis, with the aim of proving all valid merges and ensuring fast execution. Experimental results confirm remarkable speedup deriving from our methodology, up to 10× for large combinational networks, and better QoR as compared to previous SAT-sweeping implementation. Embedded in a commercial synthesis flow, our proposes SAT-sweeper enables area and power savings of 1.98% and 1.81%, respectively, with neutral timing at negligible runtime overhead, over 36 testcases.
Low Power Low Phase Noise 60 GHz Multichannel Transceiver in 28 nm CMOS for Radar Applications

This paper presents a highly integrated 60 GHz transceiver for FMCW radar applications realized in a 28 nm bulk CMOS technology. The chip provides two transmit (TX) and three receive (RX) channels. The 60 GHz local oscillator (LO) distribution is fed with the frequency-multiplied signal of the integrated low phase noise 15 GHz voltage-controlled oscillator (VCO). Realized in 28 nm technology node, the proposed chip is able to generate chirp signals over a continuous 57–64 GHz frequency-tuning-range. The phase noise is <−93 dBc/Hz @ 1 MHz offset. The measured peak phase noise performance is −99 dBc/Hz @1 MHz offset for a 55.5 GHz carrier frequency. The single TX/RX DC power consumptions are 63 mW and 39 mW, respectively. The total DC power dissipation is 478 mW.
Numeric Multi-Dimensional (r, z, t) Analysis Method for Compact Yb³⁺:YAG End-Pumped, Passively Q-Switched Lasers

We have applied a new simplified combination of numerical methods for studying the time and three-dimensional space dependence of quasi-three-level Yb3+:Yttrium Aluminum Garnet (YAG) end-pumped lasers passively Q-switched by a Cr4+:YAG saturable absorber. We base our 3-D model on iterative, efficient, time- and space-dependent numerical propagation of the optical field through the laser cavity. The complex-valued laser field is coupled to the Yb3+:YAG and Cr4+:YAG media via complex optical permittivities, which are subsequently altered by gain/loss intensity saturation. The calculation is simplified using the radial symmetry of the system, with the cavity round-trip time as the smallest increment for updating the permittivities. We also include the effects of field diffraction in an intra-cavity air gap. For specified CW spatial pump conditions, self-consistent repetitively pulsed solutions for the laser field in a flat-flat or flat-convex mirror cavity are found with no ad hoc laser mode size or shape assumptions; these solutions are not Gaussian modes. We concentrate on compact lasers with multi-Watt average output power, operating at modest pulse energy (~1.0 mJ), high repetition rate (~5 kHz) and short pulse duration (~1.5 ns). Typical room-temperature pump-to-laser slope power efficiencies exceeding 50% are predicted, depending on laser pump and cavity loss parameters. Model results agree well with recently published experimental data.
Grasp Quality Evaluation Network for Surface-to-Surface Contacts in Point Clouds

The problem of grasping is essential in robotics, in which robotic hands grasp target objects by deploying contact force. The contact between robotic hand and target object is always reduced to point-to-point while generally the actual type should be surface-to-surface. In this paper, we propose a novel surface contact model to parameterize the contact area. It computes the grasp quality of the contacts between the robotic hand and target object, then determines whether or not the surface-contact-based end effectors can resist an external wrench on target object. The key idea of the proposed approach is that the contact modeling is based on surface-to-surface contact rather than equivalent contact points which is more in line with the actual situations. Then we propose a grasp quality evaluation network based on surface-to-surface contact to evaluate grasp motion, which can capture the geometric feature of the actual contact area and classify the quality level of input grasp. Experimental results and comparisons with state-of-the-art methods (e.g. GPD and PointNetGPD) demonstrate that our approach could achieve superior performance.
Object Detection based on Fusing Monocular Camera and Lidar Data in Decision Level Using D-S Evidence Theory

Visual sensors such as optical camera possess fatal shortcomings like vulnerable to the weather condition and light intensity, which may cause catastrophic disasters. Fortunately, the prevalent 3D sensors like Light Detection and Ranging (Lidar) can overcome these drawbacks. Fusing both monocular camera and 3D Lidar data can often achieve a better performance than solely using one of them. In this paper, we propose an object detection approach by fusing monocular camera data and Lidar data in decision level based on Dempster-Shafer evidence theory. First, the 3D point cloud collected from Lidar is projected onto the image plane and the upsampling method is adopted to receive dense depth maps. Then, the RGB images and dense depth maps are separately fed into YOLOv3 detection framework to extract the object features. Next, the final detection attributes, the class confidence and bounding box, are generated by using Dempster’s combination rule and extracting intersection of the associated bounding boxes. Finally, the mean Average Precision (mAP) performance of the proposed method is improved by 0.5% and 3.1% compared with sigle camera method and Lidar method, respectively.
Survivable Hyper-Redundant Robotic Arm with Bayesian Policy Morphing

In this paper we present a Bayesian reinforcement learning framework that allows robotic manipulators to adaptively recover from random mechanical failures autonomously, hence being survivable. To this end, we formulate the framework of Bayesian Policy Morphing (BPM) that enables a robot agent to self-modify its learned policy after the diminution of its maneuvering dimensionality. We build upon existing actorcritic framework, and extend it to perform policy gradient updates as posterior learning, taking past policy updates as prior distributions. We show that policy search, in the direction biased by prior experience, significantly improves learning efficiency in terms of sampling requirements. We demonstrate our results on an 8-DOF robotic arm with our algorithm of BPM, while intentionally disabling random joints with different damage types like unresponsive joints, constant offset errors and angular imprecision. Our results have shown that, even with physical damages, the robotic arm can still successfully maintain its functionality to accurately locate and grasp a given target object.
Scalable Multi-FPGA Acceleration for Large RNNs with Full Parallelism Levels

The increasing size of recurrent neural networks (RNNs) makes it hard to meet the growing demand for real-time AI services. For low-latency RNN serving, FPGA-based accelerators can leverage specialized architectures with optimized dataflow. However, they also suffer from severe HW under-utilization when partitioning RNNs, and thus fail to obtain the scalable performance.In this paper, we identify the performance bottlenecks of existing RNN partitioning strategies. Then, we propose a novel RNN partitioning strategy to achieve the scalable multi-FPGA acceleration for large RNNs. First, we introduce three parallelism levels and exploit them by partitioning weight matrices, matrix/vector operations, and layers. Second, we examine the performance impact of collective communications and software pipelining to derive more accurate and optimal distribution results. We prototyped an FPGA-based acceleration system using multiple Intel high-end FPGAs, and our partitioning scheme allows up to 2.4x faster inference of modern RNN workloads than conventional partitioning methods.
Balanced to Unbalanced: An Overview of Multifunctional Wideband Balanced-to-Unbalanced Four- and Five-Port Filtering Power Dividers

Fifth-generation (5G) communication techniques are growing in popularity, and 5G will become the commercial operation standard for mainland China in 2020 [1]. As the next-generation mobile communication network, 5G will not only greatly improve communication efficiency; it will also change the way people live and work. The 5G communication system contains the millimeter-wave and sub-6-GHz bands. For the sub-6-GHz band, circuits are becoming ever more compact and multifunction; thus, electromagnetic interference and multicoupling problems will greatly affect the entire system's performance [2]-[4]. In response, balanced circuits with higher immunity to environmental noises [5]-[8], lower electromagnetic interference [9]-[11], and better dynamic range [12]-[15] have attracted increasing attention in the past few years, and a large number of balanced circuits are being conceived [16], [17]. Currently, all feature differential-mode input and output terminals, so they cannot be connected directly to single-ended circuits. Balanced filters, antennas, and power dividers that can be cointegrated with balanced active circuits are required for the development of fully balanced transmitter and receiver RF chains, such as the balanced power dividers that can be connected to a Doherty power amplifier in a balanced system. In this case, balancing is only necessary at the input and output stages. It is expected that the transistor stage is still a single-ended structure, where only two amplifier devices are needed [18].
Cooperative Cognitive Network Slicing Virtualization for Smart IoT Applications

This paper proposes the cooperative cognitive net-work slicing virtualization solution for smart Internet of things (IoT) applications. To this end, we deploy virtualized small base stations (vSBSs) in SDR devices that offer network-slicing virtualization option. The proposed virtualized solution relies on Fed4Fire wireless experimental platform. In particular, we assume that multiple IoT devices can have access to different vSBSs, which coordinate their resources in a cooperative manner using machine learning (ML). To this end, a proactive resource management is deployed in the unlicensed band, where a cooperative solution is facilitated using the licensed band. The cooperative network slicing is managed and orchestrated using small cell virtualization offered by the Fed4Fire . Experimental trials are carried out for certain number of users and results are obtained that highlight the benefit of employing cooperative cognitive network slicing in future virtualized wireless networks.
Fingerprinting Localization Method Based on Clustering and Gaussian Process Regression in Distributed Massive MIMO Systems

Fingerprinting (FP) localization methods are used in massive multiple-input multiple-output (MIMO) systems due to their high reliability and accuracy. The Gaussian process regression (GPR) method could potentially be used, as an FP-based localization method, in a massive MIMO system to provide high accuracy. However, it is limited by high complexity, especially in a large-scale environment. In this paper, we propose an FP-based localization method, using affinity propagation (AP) clustering and Gaussian process regression (GPR) to estimate user’s location in a distributed massive MIMO system based on the uplink received signal strength (RSS) vectors. First, the training RSS vectors are clustered using the AP algorithm to reduce the computational complexity. Then, the data distribution within each cluster is accurately modeled using GPR to provide excellent support for further positioning. Simulation studies reveal that the proposed method improves root-mean-squared estimation error (RMSE) performance significantly by reducing the location estimation error compared to using only GPR for all training RSS data. Also, it reduces the computational complexity of using GPR.
A Wideband Variable-Gain Amplifier with a Negative Exponential Generation in 40-nm CMOS Technology

A wideband variable-gain amplifier (VGA) with a negative exponential generation using 40 nm CMOS technology is reported. By compensating a single-branch negative exponential generator (NEG) which features a composite of dual Taylor series, the proposed negative exponential generation further extends the dB-linear range. The measurement results show the overall VGA achieves a dB-linear range of 51 dB (-34 ~ 17dB) with a gain error less than ± 1 dB. In addition, the bandwidth is around 7 GHz under different gain settings. The core circuit draws 24.6 mA current from a 1.1 V power supply (excluding the output buffer) and occupies an active area of 0.038 mm2.
Efficient Scheduling of FPGAs for Cloud Data Center Infrastructures

In modern cloud data centers, reconfigurable devices can be directly connected to the network of a data center. This configuration enables FPGAs to be rented for acceleration of data-intensive workloads. In this context, novel scheduling solutions are needed to maximize the utilization (profitability) of FPGAs, e.g., reduce latency and resource fragmentation. Algorithms that schedule groups of tasks (clusters, packs), rather than individual tasks (list scheduling), well match the functioning of FPGAs. Here, groups of tasks that execute together are interposed by hardware reconfigurations. In this paper, we propose a heuristic based on a novel method for grouping tasks. These are gathered around a high-latency task that hides the latency of remaining tasks within the same group. We evaluated our solution on a benchmark of almost 30000 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 71.3% of the cases. It produces solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 88.1% of the cases.
Hawkware: Network Intrusion Detection based on Behavior Analysis with ANNs on an IoT Device

The network-based Intrusion detection system (NIDS) plays a key role in Internet of Things (IoT) as most IoT services are network-driven. However, the existing NIDSes for IoT systems are either too costly to scale or vulnerable against advanced attacks such as traffic mimicry. In this paper, we propose a novel IDS named Hawkware, a lightweight ANN-based distributed NIDS that runs on an IoT device and analyzes the device’s runtime behavior in tandem with its network traffic. By analyzing device behavior, Hawkware is able to replace expensive, deep data analysis that has traditionally been used to detect advanced attacks. Our evaluations show that Hawkware is lightweight enough to be distributed and deployed on a Raspberry PI, and yet capable of detecting such attacks at a satisfactory level.
MaxDelivery: A New Approach to a DTN Buffer Management

In the recent years, the Delay Tolerant Networks (DTNs) have been increasingly researched, offering alternative solutions in real-life contexts where the Internet cannot be used. Even if most of the proposed routing algorithms do not provide a buffer management policy, it was later demonstrated that the implementation of such a policy can considerably improve the performance metrics of the network. This paper presents an intermediate phase of an algorithm that optimizes the message transfer rate and resource consumption in a DTN network, in the context of a natural disaster situation. The proposed algorithm is called MaxDelivery and it has a specific policy for buffer management implemented.
A Lightweight Blockchain-based Technique for Anti-Tampering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are nowadays used in a variety of applications, to monitor relevant quantities often in large and harsh environments, thanks to their flexibility and versatility. Unfortunately, the nature of the wireless communication channel exposes WSN nodes to security risks. Conventional security countermeasures cannot be applied to the WSN nodes due their resource limitations. In this paper, we propose a lightweight blockchain approach to contrast the physical or logical tampering of WSN nodes. We validate our approach through experiments showing the performance impact and the storage footprint due to the use of the proposed blockchain anti-tampering technique. The proposed approach appears to be suitable in the context of WSNs, although the overhead generated by the blockchain depends on the number of nodes in the WSN.
Connectivity Performance Evaluation for Grant-Free Narrowband IoT With Widely Linear Receivers

Future wireless cellular communication networks are expected to provide connectivity for massive machine-type communication (mMTC) devices. The main challenge of supporting mMTC traffic for a cellular network lies in the high density of these devices, which individually have relatively little data to transmit. This suggests the use of low overhead, grant-free access scheme for uplink data transmission, which, however, suffers from packet collisions when devices attempt to access the channel. In this article, we suggest the use of real-valued transmission together with widely linear (WL) reception for improving resource access and thus data throughput in the uplink of mMTC traffic scenarios. We show that not surprisingly, the WL scheme can virtually double the number of receive antennas at the base station (BS). We analyze the effect of this on the supported user density and data throughput for grant-free uplink transmission. As a specific example, we consider the narrowband Internet-of-Things (NB-IoT) cellular communication system, which already includes real-valued modulation modes. Our numerical results show that the supported user density and data throughput of grant-free NB-IoT systems can be significantly improved due to the use of WL receivers. For example, considering an NB-IoT system with 1% packet drop probability, we obtain a tenfold (for single-antenna BS) and a sixfold (for dual-antenna BS) increase in the supported user density by using WL receivers instead of their conventional linear counterparts.
DNNZip: Selective Layers Compression Technique in Deep Neural Network Accelerators

In Deep Neural Network (DNN) accelerators, the on-chip traffic and memory traffic accounts for a relevant fraction of the inference latency and energy consumption. A major component of such traffic is due to the moving of the DNN model parameters from the main memory to the memory interface and from the latter to the processing elements (PEs) of the accelerator. In this paper, we present DNNZip, a technique aimed at compressing the model parameters of a DNN, thus resulting in significant energy and performance improvement. DNNZip implements a lossy compression whose compression ratio is tuned based on the maximum tolerated error on the model parameters provided by the user. DNNZip is assessed on several convolutional NNs and the trade-off inference energy saving vs. inference latency reduction vs. network accuracy degradation is discussed. We found that up to 64% energy saving, and up to 67% latency reduction can be obtained with a limited impact on the accuracy of the network.
UWB NLOS/LOS Classification Using Deep Learning Method

Ultra-Wide-Band (UWB) was recognized as its great potential in constructing accurate indoor position system (IPS). However, indoor environments were full of complex objects, the signals might be reflected by the obstacles. Compared with the Line-Of-Sight (LOS) signal, the signal transmitting path delay contained in None-Line-Of-Sight (NLOS) signal would induce positive distance errors and position errors. Before employing ranging information from the channels to calculate the position, LOS/NLOS classification or identification was necessary for selecting the “clean” channels. In conventional method, features extracted from the UWB channel impulse response (CIR) or some other signal properties were employed as the input vector of the machine learning methods, e.g. Support Vector Machine (SVM), Multi-layer Perception (MLP). Deep learning methods represented by Convolutional neural network (CNN) and Long Short-Term Memory (LSTM) had performed superior performance in dealing with time series data classification. In this pap er, deep learning method CNN-LSTM was employed in the UWB NLOS/LOS signal classification. UWB CIR data was directly input to the CNN-LSTM. CNN was employed for exploring and extracting the features automatically, and then, the CNN outputs were fed into the LSTM for classification. Open source datasets collected from seven different sites were employed in the experiments. Classification accuracy of CNN-LSTM with different settings was compared for analyzing the performance. The results showed that CNN-LSTM obtained stat e-of-art classification performance.
Machine Learning Assisted Modulation-Format Transparent and Nonlinearity Tolerant Carrier Recovery Scheme for Intelligent Receiver

A modulation-format transparent and nonlinearity tolerant carrier recovery scheme, based on machine learning and kurtosis, is proposed and demonstrated experimentally. In our scheme, kurtosis-based blind phase search (BPS) and machine learning assisted maximum likelihood (ML) estimator are deployed together to achieve precise phase noise estimation. In the first stage, kurtosis-based BPS is used to estimate phase noise coarsely in a decision-free and modulation-format transparent manner. Then, mean-shift clustering, the well-known machine learning algorithm, is employed to find the centroid points of clusters automatically. Finally, the centroid points are taken as reference in ML estimator for precise residual phase noise estimation. Compared with BPS and universal carrier phase estimation scheme (UCPE), higher nonlinearity tolerance in our scheme is demonstrated via numerical simulation. Meanwhile, the linewidth tolerance of the proposed scheme is six times than that of UCPE for 64QAM. Our scheme is also verified experimentally for both 28GS/s QPSK and 16-QAM systems in the back-to-back (BTB) and transmission case. The results indicate that our scheme obtains comparable OSNR sensitivity compared with UCPE and BPS in the BTB case. Furthermore, the proposed scheme also achieves 0.4 dB and 0.2 dB Q-factor improvement than UCPE and BPS for 16QAM after 915 km fiber link transmission, respectively.