Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Requirements Traceability with Domain-Specific Taxonomies - A Pilot Experiment

Background: Establishing traceability from requirements documents to downstream artifacts early can be beneficial as it allows engineers to reason about requirements quality (e.g. completeness, consistency, redundancy). However, creating such early traces is difficult if downstream artifacts do not exist yet. Objective: We propose to use domain-specific taxonomies to establish early traceability, raising the value and perceived benefits of trace links so that they are also available at later development phases, e.g. in design, testing or maintenance. Method: We developed a recommender system that suggests trace links from requirements to a domain-specific taxonomy based on a series of heuristics. We designed a controlled experiment to compare industry practitioners’ efficiency, accuracy, consistency and confidence with and without support from the recommender. Results: We have piloted the experimental material with seven practitioners. The analysis of self-reported confidence suggests that the trace task itself is very challenging as both control and treatment group report low confidence on correctness and completeness. Conclusions: As a pilot, the experiment was successful since it provided initial feedback on the performance of the recommender, insight on the experimental material and illustrated that the collected data can be meaningfully analysed.
Physical Security of Ring-based PUF

The design of a secure, efficient, lightweight silicon physical unclonable function (PUF) is a serious challenge for the hardware security community. In this context, this paper presents a survey of physical attacks targeting ring-based PUFs because such PUFs are known as some of the more efficient. The paper discuss the threats and try to propose solution to design efficient and secure ring-based silicon PUFs.
Energy Aware Opportunistic Routing for Energy Harvesting Wireless Sensor Networks

In this paper, the joint optimization of information transmission and energy utilization in energy harvesting wireless sensor networks (EH-WSNs) is studied. In EH-WSNs, the imbalance of energy harvesting and consumption affects the sensing and communication ability of the network. To solve this problem, a novel energy aware opportunistic routing protocol with long-short term memory (LSTM) based solar energy prediction is proposed. The protocol innovatively takes account of the nodes’ current residual energy and the harvesting solar energy in a short term which is predicted by a LSTM neural network as key factors in forwarding candidates election process of the opportunistic routing. Additionally, in order to jointly optimize the energy consumption and information transmission, a new metric with combined energy factor and relay efficiency for each node is proposed to assist candidate node selection where the relay priority considers the residual energy and working history of nodes. Simulation results show that the proposed protocol increases the network throughput by 12% and reduces retransmission rate by 15% compared to opportunistic routing based on geography. Simultaneously, it has stronger ability to balance energy consumption among sensor nodes.
ReTriple: Reduction of Redundant Rendering on Android Devices for Performance and Energy Optimizations

Graphics rendering is a compute-intensive work and a major source of energy consumption on battery-driven mobile devices. Unlike the existing works that degrade user experience or reuse rendering results coarsely, we propose ReTriple, a fine-grained scheme to reduce rendering workload by reusing the past rendering results at the UI element level. This fine-grained reuse mechanism can explore more opportunities to reduce the workload of the rendering process and save energy. The experiments tested with popular apps show that ReTriple achieves an average speedup of 2.6x and per-frame energy saving of 32.3% for the rendering process while improving user experience.
Automatic State Space Analysis for Modeling Untrusted Embedded Device Drivers

This paper presents a semi-automatic methodology to create abstract driver models to be used as formal reference when developing the firmware for embedded device drivers. Our methodology extracts the behavior of driver software automatically as an abstract finite state machine. This replaces manually crafting these models from informal specifications, which is error-prone, laborious, and does not account for undocumented behavior. Our approach specifically targets untrusted driver software that is only available as binary code, for example as third-party IP, and for which the source code is unknown. Our extracted model is formally sound with respect to the implementation, while still being understandable by a human developer. Our experiments for industry-size driver software demonstrate that human-readable, sound, abstract driver models can be extracted from binary code in affordable run times and with small manual effort.
A Comparison of Application Layer Communication Protocols in IoT-enabled Smart Grid

The modern smart grid consists of the classical power grid augmented with a large-scale ICT and renewable energy integration. An indispensable part of the implementation of such grid is IoT, a system built by integrating internet-connectivity into all kinds of plant, equipment and devices in smart grid. It provides a reliable, real-time communication among applications running on different IoT devices and on cloud or edge infrastructures. To this end, it establishes communication channels by using application protocols for IoT devices. However, to provide effective communication between its devices, it is necessary that an IoT-enabled smart grid uses suitable application protocol regards specific terms, such as minimal traffic load and delivery latency. In order to build an IoT-enabled smart grid which will support the most efficient data flow in the above-mentioned terms, we made an experimental analysis of the communication based on the three most commonly used application protocols: HTTP, MQTT and AMQP. The experiment has been conducted using IoT devices, which are a part of a real Microgrid-IoT laboratory.
Analysis and optimization of perovskite-silicon tandem solar cells by full opto-electronic simulation

We present a comprehensive opto-electronic simulation framework for the computational analysis and optimization of perovskite-silicon tandem solar cells, consisting of a combination of a multiscale optical model for the simultaneous consideration of interference in thin coatings and scattering at textured interfaces with a mixed electronic-ionic drift-diffusion transport model that captures the peculiarities of the geometries and materials used in the tandem architecture.
Robotic Harvesting of Asparagus using Machine Learning and Time-of-Flight Imaging – Overview of Development and Field Trials

Asparagus is a problematic crop because it grows so quickly that it requires harvesting every one or two days. Asparagus farms require typically 8 workers per hectare for harvesting during peak season. The subsequent labor issues this causes, means it is an ideal crop for robotic harvesting. Several prototype robotic machines have been trialed with limited success. This work investigates the combination of Machine Learning and Time-of-Flight cameras to locate the cutting point of the asparagus spear when travelling at a constant speed of 0. 33m/s. A ‘proof of concept’ machine was developed to validate the detection system and demonstrate harvesting with a rudimentary robotic arm and end effector. Field trials showed the arm harvested 92.3– of the targeted spears. However, it was found that multiple arms will be required to be commercially viable.
Energy-Efficient Power Allocation in URLLC Enabled Wireless Control for Factory Automation Applications

The coming fifth-generation (5G) cellular networks encourage to support several innovations and services, some of which will demand Ultra-reliable and Low-latency Communications (URLLC). For instance, URLLC can support real-time control to facilitate several emerging applications, such as robotic arms in industrial applications, and remote surgery for healthcare applications. However, URLLC is expected to be supported without considering the resources usage efficiency in wireless control systems due to the challenging to satisfy Quality of Service (QoS) requirements at the expense of diminishing energy efficiency. In this paper, we analyze uplink energy efficiency in URLLC utilizing multiple antennas in the transmitter and the receiver as well (MIMO) in real-time wireless control systems. We firstly formulate an optimization problem to maximize energy efficiency concerning the effect of the control convergence rate constraint. Then, we develop an exhaustive search method to obtain the maximum energy efficiency. Finally, simulation results are provided to demonstrate the performance of our proposed method.
Quantitative and Qualitative Evaluation Methods of Automotive Time of Flight Based Sensors

Time of Flight (ToF) based three-dimensional (3D) imaging sensors, such as Light Detection and Ranging (LiDAR) sensors or ToF cameras, can be used to depict their surroundings in form of a point cloud. The ToF method measures the time an emitted light signal takes to be reflected by a point in space and derives the distance from the known travel time of light. More and more ToF based 3D sensors are developed for automotive use as their target application. Automotive sensors are part of a safety critical application. Therefore, it is important to ensure that the provided sensor data is accurate with a known probability. In this paper, we describe and evaluate test procedures for quantitative and qualitative performance evaluation of ToF/LiDAR sensors with focus on automotive use. We propose a LiDAR error and influence model from which we derived eight test areas. We described and conducted tests for six of the eight test areas. The described test cases were evaluated on three LiDAR sensors and one ToF camera. The results show that targets and test procedures need to be adapted to the specific tested ToF/LiDAR sensor. Especially noticeable where influences on test procedures due sparse sensor resolution. Furthermore, the test results show that target application specific tests can provide additional information on the behaviour of the sensor.
Transformation of the Theta-band Functional Connectivity During Motor Initiation Under Healthy Aging

Human brain functioning is strongly affected by the factor of age, which crucially reduces a performance of the motor system. It causes a decline in reaction time, degraded ability to execute and control complex motor actions, low flexibility in learning new skills. A reduced daily activity old people combined with a cognitive and motor decline, usually result in a developed ambidexterity, i.e., the loss of a dominant limb advances. We suggest that such weakening of a dominant limb abilities and functioning on the neuronal level should be observed not only during the exact motor execution phase, but also in the motor initiation. In this paper we apply functional connectivity analysis to reveal the age-related changes in the integrative brain dynamic during the motor initiation before the dominant hand movements accompanied. Analyzing the whole-scalp electroencephalography (EEG) signals on the sensor level, we find higher theta-band coupling in the ipsilateral hemisphere.
A 10.56 Gbit/s, -27.8 dB EVM Polar Transmitter at 60 GHz in 28nm CMOS

A 28 nm CMOS 60 GHz polar transmitter (TX) is presented. It can handle modulations up to 64 QAM with a baud rate up to 1.76 Gbaud. Both amplitude and phase modulation are performed on chip at a sample rate up to 5.28 GS/s. The saturated output power is 12.6 dBm and the peak drain efficiency is 26.0 %. A TX data rate of 10.56 Gb/s is achieved with a DAC alias at -19.4 dBc. With a core area of 0.115 mm2 this transmitter consumes 95.4 mW (without on-chip memory) from a 0.9 V supply.
Coordinated Optimization of EVs and Multiple Buildings with Renewable Energy

This paper focuses on the jointly optimization of a micro-grid comprising electric vehicles (EVs) and buildings equipped with photovoltaic (PV) on the roof. The optimization problem of EVs and multiple buildings with renewable energy is formulated as a stochastic mixed integer linear programming (MILP) problem, in which bidirectional integration of EVs, building’s demand response (DR) flexibility caused by outside air temperature (OAT) and thermostat setpoint temperature adjustment, and the uncertainty in PV power generation are fully taking into account. Scenario based method is adopted to deal with the randomness of PV generation and stochastic capacity requirements of EVs. The performance of jointly optimization is discussed based on case studies. Numerical results show that the aggregated EVs modeled as virtual storage system could not only significantly reduce the system cost and add building DR flexibility, but also could compensate the intermittency of renewable energy generation through peak shaving and valley filling
CTC-CEM: Low-Latency Cross-Technology Channel Establishment with Multiple Nodes

Cross-Technology Communication (CTC) allows direct message exchange between devices with different (i.e., incompatible) wireless communication standards. CTC is particularly suitable to allow for coordination between heterogeneous devices sharing the same spectrum, as in the Internet of Things. Existing research on CTC has focused on enabling communications for diverse technologies with the goal of achieving a high throughput. However, it did not address how to establish a link suitable for CTC, which is necessary for successful data exchange. This article specifically addresses such a problem by introducing CTC-CEM (CTC Channel Establishment with Multiple nodes), a scheme to establish a CTC channel involving the use of multiple nodes in a network. CTC-CEM employs duty-cycling and leverages network density to reduce energy consumption, while keeping a low discovery latency. In particular, CTC-CEM defines different discovery protocols to reliably detect co-located networks. Moreover, it addresses the selection of multiple CTC nodes as a set cover problem, and includes an optimization technique based on dynamic programming to balance the energy consumption in the whole network. Extensive simulations show that CTC-CEM effectively distributes the energy consumption in the network, increasing fairness by 97% after optimization. Furthermore, the latency in establishing a channel with CTC-CEM is two orders of magnitude lower than that for device discovery in duty-cycled networks.
Uncertainty Measurement for a Hybrid Information System With Images: An Application in Attribute Reduction

A hybrid information system with images (HISI) is an information system (IS) where there exist many kinds of data or attributes (e.g., boolean, categorical, real-valued, set-valued, interval-valued, imaged, decision and missing data or attributes). Uncertainty measurement (UM) is an effective tool for evaluation. This article inquires into UM for a HISI with application to attribute reduction. The distance between attribute values in a HISI is first constructed. Then, the tolerance relations on an object set of a HISI are obtained by means of this distance. Next, information structures in a HISI are proposed. After that, UM for a HISI is considered by using its information structures. Moreover, effectiveness analysis for the proposed measures is conducted from the angle of statistics. Finally, an application of the proposed measures in attribute reduction for a HISI is given, and the corresponding algorithms are put forward.
AtLAS: An Activity-Based Indoor Localization and Semantic Labeling Mechanism for Residences

Currently, indoor localization technology and indoor location-based services are becoming increasingly important in the area of mobile and ubiquitous computing. However, the design of an indoor location-based system confronts two challenges: 1) achieving high-precision location recognition and 2) identifying what indoor objects actually are (which is called semantic labeling). In this article, we propose AtLAS, an activity-based indoor localization and semantic labeling mechanism. The key idea is that some objects in an indoor environment, such as doors and toilets, determine predictable human behaviors in small areas, which can be reflected in unique sensor readings. AtLAS leverages this idea to determine a user’s accurate location by identifying users’ activities. Furthermore, we leverage the topological structure of indoor objects to mine the semantic knowledge and label the objects through gained knowledge automatically. To the best of our knowledge, AtLAS is the first attempt to build a system that leverages users’ activities to conduct a high-precision indoor localization and semantic labeling system for the case of residences. The experimental results show that AtLAS can achieve a median localization accuracy of 0.57 m, and the system can localize the landmarks with a median accuracy of 0.43 m on average without 5% worst errors. AtLAS can label the objects semantically with a 5.7% false-positive rate and a 5.8% false-negative rate on average.
Large Database Compression Based on Perceived Information

Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the information loss perceived by the final user instead of the actual source quality loss, under compression rate constraints. As main contributions, we first introduce the concept of perceived information (PI), which reflects the information perceived by a given user experiencing a data collection, and which is evaluated as the volume spanned by the sources features in a personalized latent space. We then formalize the rate-PI optimization problem and propose an algorithm to solve this compression problem. Finally, we validate our algorithm against benchmark solutions with simulation results, showing the gain in taking into account users’ preferences while also maximizing the perceived information in the feature domain.
Classification-Based Unified Cache Replacement via Partitioned Victim Address History

In modern microprocessors, lower level cache memories are usually implemented as unified caches where different classes of cachelines such as data, instructions, and Page Table Entries (PTEs) coexist. Particularly, frequent PTE accesses following after TLB missies can happen on modern systems, which is driven by the increasing demands of applications for larger working set size, and this trend naturally leads to significant conflicts among these different kinds of cachelines.This paper targets the emerging conflict problem and provides a systematic mechanism using a partitioned victim address history. Prior studies have shown the effectiveness of history-based cache managements to predict the reuseness and thus to improve the hit rate. This work augments the following functionalities: (1) partitioning the history into multiple areas to separately keep track of the reuseness for all the different cacheline categories; and (2) setting different allocation priorities to the different cacheline categories when cache replacement. Furthermore, this paper proposes a control system to dynamically optimize the history partitions and the cache allocation priorities at the same time by using the statistics of the history structure. The experimental result indicates that the proposed technique improves performance considerably compared with the conventional LRU-based approach and others.
Hardware and Software Components towards the Integration of Network-Attached Accelerators into Data Centers

The usage of Field Programmable Gate Arrays(FPGAs) as application-specific accelerators in data centers has grown rapidly in recent years. The main reasons for this are the higher throughput of application-specific accelerators, an improved latency behavior as well as higher energy efficiency compared to conventional computing systems with CPUs and GPUs. In particular, network-attached FPGA accelerators exhibit excellent latency behavior and very high energy efficiency. The high efficiency not only decreases operating costs, but also results in a lower carbon footprint. A disadvantage compared to conventional CPUs and GPUs is the difficult programmability as well as the integration into data centers, which hinders a widespread adoption. To simplify the integration of FPGA accelerators in heterogeneous data centers, we have designed a flexible hardware and software framework to overcome these problems. We also show which components are necessary to fulfill the requirements within a data center.
SCAAT: Secure Cache Alternative Address Table for mitigating cache logical side-channel attacks

Interest in memory systems’ security has increased during the last decade due to their vulnerabilities to be exploited by logical side channels attacks. A promising approach for attack detection at run-time is to monitor the cache memory’s behavior. However, designing an environment capable of detecting and mitigating these attacks is very challenging. In current monitoring systems, attack mitigation has been largely neglected. To overcome these shortcomings, in this work, we present a secure cache called SCAAT. SCAAT is equipped with an attack mitigation system to handle attacks by remapping where data is stored in the cache to random locations. In addition, SCAAT uses an attack monitor that identifies suspicious behavior that indicates cache logical side-channel attacks. The effectiveness of SCAAT is analyzed and evaluated for several cache configurations in terms of area overhead and performance.
Backscatter-Based Cooperative NOMA

A backscatter cooperation (BC) mechanism is pro-posed for power-domain non-orthogonal multiple access (NOMA) downlink transmission. The key idea is to enable one user to split and then backscatter part of its received signals to improve the reception at another user. To evaluate its performance, three benchmark schemes are introduced. They are non-cooperation (NC)-NOMA, conventional relaying (CR)-NOMA, and incremental relaying (IR)-NOMA. For all these schemes, the analytical expressions of the minimum total power to avoid information outage are derived, based on which their respective outage performance, expected rates, and diversity-multiplexing trade-off (DMT) are investigated. Analytical results show that the proposed BC-NOMA strictly outperforms NC-NOMA in terms of all the three metrics. Furthermore, theoretical analyses are validated via Monte-Carlo simulations. It is shown that compared with CR/IR-NOMA, only the proposed BC-NOMA scheme can enhance the transmission reliability without impairing the transmission rate.