Reviewerly/exHarmony huggingface dataset:
Empirical research on requirements quality: a systematic mapping study

Research has repeatedly shown that high-quality requirements are essential for the success of development projects. While the term "quality" is pervasive in the field of requirements engineering and while the body of research on requirements quality is large, there is no meta-study of the field that overviews and compares the concrete quality attributes addressed by the community. To fill this knowledge gap, we conducted a systematic mapping study of the scientific literature. We retrieved 6905 articles from six academic databases, which we filtered down to 105 relevant primary studies. The primary studies use empirical research to explicitly define, improve, or evaluate requirements quality. We found that empirical research on requirements quality focuses on improvement techniques, with very few primary studies addressing evidence-based definitions and evaluations of quality attributes. Among the 12 quality attributes identified, the most prominent in the field are ambiguity, completeness, consistency, and correctness. We identified 111 sub-types of quality attributes such as "template conformance" for consistency or "passive voice" for ambiguity. Ambiguity has the largest share of these sub-types. The artefacts being studied are mostly referred to in the broadest sense as "requirements", while little research targets quality attributes in specific types of requirements such as use cases or user stories. Our findings highlight the need to conduct more empirically grounded research defining requirements quality, using more varied research methods, and addressing a more diverse set of requirements types.
A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition

Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.
Towards a Blockchain-Based Peer-to-Peer Energy Marketplace

Blockchain technology is used as a distributed ledger to store and secure data and perform transactions between entities in smart grids. This paper proposes a platform based on blockchain technology and the multi-agent system paradigm to allow for the creation of an automated peer-to-peer electricity market in micro-grids. The use of a permissioned blockchain network has multiple benefits as it reduces transaction costs and enables micro-transactions. Moreover, an improvement in security is obtained, eliminating the single point of failure in the control and management of the platform along with creating the possibility to trace back the actions of the participants and a mechanism of identification. Furthermore, it provides the opportunity to create a decentralized and democratic energy market while complying with the current legislation and regulations on user privacy and data protection by incorporating Zero-Knowledge Proof protocols and ring signatures.
The Potential of Augmented Reality for Digital Twins: A Literature Review

As the boundary between real and virtual life is becoming increasingly blurred, researchers and practitioners are looking for ways to integrate the two intending to improve human lives in a plethora of domains. A cutting-edge concept is the design of Digital Twins (DT), having a broad range of implications and applications, spanning from education, training, as well as safety and productivity in the workplace. An emergent approach for implementing DTs is the usage of mixed reality (MR) and augmented reality (AR), which are well aligned with merging real and virtual objects to enhance the human&#x2019;s ability to interact with and manage DTs. Yet, this is still a novel area of research and, as such, a grounded understanding of the current state, challenges, and open questions is still lacking. Towards this, we conducted a PRISMA-based literature review of scientific articles and book chapters dealing with the use of MR and AR for digital twins. After a thorough screening phase and eligibility check, 25 papers were analyzed, sorted and compared by different categories like research topic (e.g., visualization, guidance), domain (e.g., manufacturing, education), paper type (e.g., design study, evaluation), evaluation type (user study, case study or none), used hardware (e.g., Microsoft HoloLens, mobile devices) as well as the different outcomes (result type and topic, problems, outlook). The major finding of this research survey is the predominant focus of the reviewed papers on the technology itself and the neglect of factors regarding the users. We, therefore, encourage researchers in this area to keep the importance of ease and joy of use in mind and include users in multiple stages of their work.
Development of QoS optimized routing using Artificial bee colony and TABU-GA with a mobile base station in Wireless Sensor Network

Routing in Wireless Sensor Networks (WSNs) have been playing an important and progressive role in different scenarios like the environment and traffic monitoring. In this research paper, extensive contributions have been explored concerning the routing mechanisms involving WSN. Enhancing the critical attributes like network lifetime and reliability are prime areas of research in this field. To obtain the desired results, optimizations have been performed using Swarm Intelligence (SI) based routing mechanism like the Artificial-Bee Colony (ABC) optimization and TABU-GA routing technique. Although, since the past decade, the Ant-Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been introduced and applied in this field, but the ABC and TABU-GA approach is relatively novel and considered a prominent choice as compared to other methods. However, a lot of scope for further enhancement of the ABC optimization and TABU-GA problem. This paper highlights and explains the comparison between RZLEACH (Rendezvous LEACH), ABC and TABU-GA algorithm based routing mechanisms. The simulation results obtained show that the TABU-GA have significant result over the ABC and RZLEACH and found 6% improvement when compared with the RZLEACH approach in terms of Remaining Energy and 9% lesser in case of First node dead (FND) with comparison to RZLEACH.
The Advanced Encryption Standard Process

The main subject of this book would probably have remained an esoteric topic of cryptographic research — with a name unpronounceable to most of the world — without the Advanced Encryption Standard (AES) process. Therefore, we thought it proper to include a short overview of the AES process.
Optimization of rooftop photovoltaic installations to maximize revenue in Finland based on customer class load profiles and simulated generation

The profitability of a rooftop solar photovoltaic (PV) system depends on several factors, such as the retail price of electricity, the load profile of the building, and orientation of the solar PV modules. If there are consumption peaks in the morning, evening, or both, the solar PV modules can be oriented eastward, westward, or east–west, respectively, in order to increase self-consumption. However, the market price of electricity varies, which makes it challenging to evaluate the revenue of a solar PV system in advance. In this study, solar PV module orientation is considered based on profitability. The study uses hourly data of 13 different statistical customer class load profiles, historical electricity market price data for the years 2016–2020, and simulated solar PV yield from southern Finland. A comparison is made between single-azimuth and dual-azimuth systems, the latter of which having the orientation of half of the modules mirrored respective to the north–south axis. A single-azimuth system is usually more profitable when compensation is paid for surplus electricity. The optimal orientations are at azimuth angles of −15°–5° and at tilt angles of 35°–45° with both fixed and dynamic electricity purchase prices. However, if no compensation is paid for surplus electricity, it is usually more profitable to minimize the electricity purchase by orienting the solar PV modules in two azimuths between east and west with tilt angles of 10°–55° depending on the capacity of the solar PV system and the load profile. The revenue is not very sensitive to moderate changes in orientation.
A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight

Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to be pushed to their physical limits. Recently, learning-based control policies have been proposed for quadrotors, as they would potentially allow learning direct mappings from high-dimensional raw sensory observations to actions. Due to sample inefficiency, training such learned controllers on the real platform is impractical or even impossible. Training in simulation is attractive but requires to transfer policies between domains, which demands trained policies to be robust to such domain gap. In this work, we make two contributions: (i) we perform the first benchmark comparison of existing learned control policies for agile quadrotor flight and show that training a control policy that commands body-rates and thrust results in more robust sim-to-real transfer compared to a policy that directly specifies individual rotor thrusts, (ii) we demonstrate for the first time that such a control policy trained via deep reinforcement learning can control a quadrotor in real-world experiments at speeds over 45 km/h.
Quantum error correction with molecular spin qudits

Molecular multi-level spin qudits are very promising for quantum computing, embedding quantum error correction within single objects. We compare the performance of electronic/nuclear molecular qudits in the implementation of quantum error correction.
Value-Based Protest Slogans

When bringing philosophical attention to bear on social movement slogans in general, philosophers have often focused on their communicative nature—particularly the hermeneutical failures that arise in discourse. Some of the most popular of these failures are illustrated in “All lives matter” retorts to “Black lives matter” pronouncements. Although highlighting and criticizing these failures provide much needed insight into social movement slogans as a communicative practice, in doing so, philosophers and slogan users risk placing too much importance on outgroup understandings. This emphasis is misguided because gaining such uptake is not required of particular slogans to perform their functions; indeed, it is an inherent risk of them. I show how such an emphasis can also be distracting to users. Since social movement slogans that express values are first and foremost for <italic>users</italic>, I argue for a shift in focus in what these slogans (such as “Black is beautiful” and the more recent “Black lives matter”) do for users, as well as what they demand from users and enable them to express. When slogans have done these things, regardless of uptake, we can say they have performed one of their key functions.
Blockchain in sustainable supply chain management: an application of the analytical hierarchical process (AHP) methodology

Purpose The analytical hierarchical process (AHP)’s main purpose is to assess higher hierarchy levels based on the cooperation of its various levels. It results in a well-designed model-based method in which the weights for the selected attribute are calculated using dimensions, criteria, and indicators. This paper aims to highlight the benefits of blockchain in supply chain management with the help of a literature review along with opinions of experts from various sectors. Design/methodology/approach With the goal of enhancing the use of blockchain technology in supply chain management, particularly when comparing within the same industry, the AHP methodology has been used. In order to develop the AHP model, a total of eight elements are examined in this study, which are decentralization, resiliency, security, smart contracts, sustainability, traceability, transparency and trust. A calculation of a Desirability Index for conventional supply chain and blockchain-enabled supply chains has been also developed. Findings Findings where that in a blockchain-enabled supply chain, the global weights of individual benefit variables are considerably larger than in conventional supply chains. When the score of the Desirability Index for conventional supply chain and blockchain-enabled supply chain is compared, the blockchain-enabled supply chain significantly surpasses the conventional supply chain in terms of increasing sustainable development in today’s supply networks. Originality/value This study takes into account the AHP methodology applying it on blockchain. This has not been done before in the academic world, at least as far as the authors may be aware of. The originality of combining such process with a recent technology such as blockchain highlights the value of this research.
Phase flip code with semiconductor spin qubits

Abstract The fault-tolerant operation of logical qubits is an important requirement for realizing a universal quantum computer. Spin qubits based on quantum dots have great potential to be scaled to large numbers because of their compatibility with standard semiconductor manufacturing. Here, we show that a quantum error correction code can be implemented using a four-qubit array in germanium. We demonstrate a resonant SWAP gate and by combining controlled-Z and controlled-S −1 gates we construct a Toffoli-like three-qubit gate. We execute a two-qubit phase flip code and find that we can preserve the state of the data qubit by applying a refocusing pulse to the ancilla qubit. In addition, we implement a phase flip code on three qubits, making use of a Toffoli-like gate for the final correction step. Both the quality and quantity of the qubits will require significant improvement to achieve fault-tolerance. However, the capability to implement quantum error correction codes enables co-design development of quantum hardware and software, where codes tailored to the properties of spin qubits and advances in fabrication and operation can now come together to advance semiconductor quantum technology.
A Secure Blockchain Enabled V2V Communication System Using Smart Contracts

In recent years, the corporate and industrial sectors have been experiencing significant transformations in vehicle-to-vehicle (V2V) communication. It can improve vehicle safety by giving signals to other vehicles wirelessly. The latest software, hardware, and technologies are applied to develop trusted applications that make V2V communication more believable. Today, various technologies are incorporated into vehicles to remove the barrier to existing challenges. The connected vehicles in V2V communication use sensors, data storage, and communication devices. Vehicles can communicate using the latest secure and trusted Cellular Vehicle-to-Everything (C-V2X) technology using direct and network communication modes. Even connected vehicles suffer from data security, user privacy, reliable environment, and vehicle security. Blockchain can help to eliminate those issues in V2V communication systems. Herein, a secure blockchain-enabled V2V communication system (BVCS) is proposed to enhance the security of vehicles and secure data sharing and communication among vehicles. The developed smart contracts in this paper can authenticate users and their vehicles automatically. In this paper, the proposed algorithms can authenticate users, detect unauthorized access, and establish secure communication between vehicles. The proposed system can enhance data security, user privacy, and vehicle security and provide a trusted environment in V2V communication systems.
Robust Region Feature Synthesizer for Zero-Shot Object Detection

Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. The former is used to realize the one-to-more mapping to obtain diverse visual features from each class semantic vector, preventing miss-classifying the real unseen objects as image backgrounds. While the latter is used to avoid the synthesized features too scattered to mix up the inter-class and foreground-background relationship. To demonstrate the effectiveness of the proposed approach, comprehensive experiments on PASCAL VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the new state-of-the-art performance on PASCAL VOC and COCO and it is the first study to carry out zero-shot object detection in remote sensing imagery.
Eye tracking in virtual reality: Vive pro eye spatial accuracy, precision, and calibration reliability

A growing number of virtual reality devices now include eye tracking technology, which can facilitate oculomotor and cognitive research in VR and enable use cases like foveated rendering. These applications require different tracking performance, often measured as spatial accuracy and precision. While manufacturers report data quality estimates for their devices, these typically represent ideal performance and may not reflect real-world data quality. Additionally, it is unclear how accuracy and precision change across sessions within the same participant or between devices, and how performance is influenced by vision correction. Here, we measured spatial accuracy and precision of the Vive Pro Eye built-in eye tracker across a range of 30 visual degrees horizontally and vertically. Participants completed ten measurement sessions over multiple days, allowing to evaluate calibration reliability. Accuracy and precision were highest for central gaze and decreased with greater eccentricity in both axes. Calibration was successful in all participants, including those wearing contacts or glasses, but glasses yielded significantly lower performance. We further found differences in accuracy (but not precision) between two Vive Pro Eye headsets, and estimated participants' inter-pupillary distance. Our metrics suggest high calibration reliability and can serve as a baseline for expected eye tracking performance in VR experiments.
Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature

Abstract Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances—especially Artificial Intelligence—regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter.
A privacy-aware framework for detecting cyber attacks on internet of medical things systems using data fusion and quantum deep learning

Internet of Medical Things (IoMT) devices and systems are often designed without adequate security, leaving them highly susceptible to cyber threats. Unlike other IoT applications and devices, cyber attacks against IoMT devices will pose significant risks to patient safety. As a result of the progress made in attack detection through machine learning, numerous applications of machine learning are now involved in IoMT. However, how to securely collect and fuse the data from heterogeneous IoMT devices and systems and ensure efficient cyber-attack detection while avoiding the privacy disclosure of the dataset remains challenging for IoMT systems. To fill this gap, a new privacy-aware framework is advised for storing the collected IoMT data in distributed cloud nodes and providing data fusion of heterogeneous IoMT data using differential privacy and deep learning and efficient attack detection using quantum deep learning with maintaining data privacy. Specifically, the proposed privacy-aware framework provides sensitive data privacy protection at multi-levels starting from storing data in distributed cloud nodes with privacy-preserving and then fusing IoMT heterogeneous data using deep contractive autoencoder with differential privacy for protecting the sensitive data through the learning process and then using the output of data fusion which is reduced data dimension as input for a quantum neural network for identifying cyber-attacks. Our experimental results indicate that the proposed framework is highly effective in detecting cyber-attacks.
How Does the Metaverse Shape Education? A Systematic Literature Review

In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of papers have been found to address the adoption of the metaverse, a clear overview of the solutions in place and their impact on education has been largely neglected so far. In the context of increasing challenges found with the metaverse, this review aims to investigate the role of the metaverse as tool in education. This contribution aims to address this research gap by offering a state-of-the-art analysis of the role the metaverse plays in education in relation to the future of work. The study is based on a systematic review approach performed by means of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. The findings of this research help us to better understand the benefits, potential and risks of the metaverse as a tool for immersive and innovative learning experiences. Implications are discussed and streams for future investigation are identified.