Reviewerly/exHarmony huggingface dataset:
<i>Otogarden</i>

In this article we present ludomusicological research associated with the development of the video game Otogarden. Players of Otogarden are able to repeat short musical phrases through the use of a loop mechanic, juxtaposing sounds extemporaneously. By using the methodology of research through design, Otogarden addresses aesthetic and design issues related to musical participation in video games. Specifically, this article argues that video games, a contemporary venue for technologically augmented musicking, can allow access to novel forms of musical improvisation. In fact, while video games afford a remarkable variety of musicking, examples related to musical improvisation remain underexplored, with popular games favoring score-based interactions, as established by titles such as Guitar Hero or Rock Band. In similar examples, music is presented as a task to be completed, mediated by prerecorded compositions and simplified notations. Notable exceptions, such as the experimental game Electroplankton, have been criticized specifically for their lack of composition-oriented functionalities, seemingly neglecting the inherent value of improvisatory musical practices in video games. Otogarden challenges this understanding of a “music game” by focusing on the largely untapped potential of musical improvisation, “an activity of enormous complexity and sophistication, or the simplest and most direct expression.”1 In order to gain feedback on Otogarden’s special characteristics, we held a playtesting session with a sample of university students (N=21) with a special interest in music and music education. We collected research data from this session in the form of a survey. Analysis reveals different manifested perspectives, offering players novel creative opportunities. In addition, the game has surprising potential as a music-education tool. We conclude that it is possible to deliberately stimulate players’ perspective on the game in an improvisatory musical direction, making evident the extemporaneous musical possibilities connected with digital game engagement.
A Quantum Evolutionary Algorithm and Its Application to Optimal Dynamic Investment in Market Microstructure Model

Falling into local minimum and slow convergence are the drawbacks of traditional evolutionary algorithm in the process of function optimization. Combined with Quantum mechanism and intelligent optimization algorithm organically, the quantum evolution method can be introduced into the differential evolution algorithm in this paper. In this method, Probability amplitude of qubit can represent the real coding of chromosome and the updating of the chromosome position can be achieved by quantum mutation, quantum crossover and quantum selection operation. The two probability amplitudes of qubit are exchanged by quantum not-gate and the self-adaptive operator is introduced to improve the diversity of the population, which can prevent the premature convergence of the algorithm and improve the ability of the optimization algorithm to solve problems. Taking the function extremum problem as an example, the simulation results verify the effectiveness of the algorithm through this method. Finally, quantum differential evolution algorithm is used to estimate the market microstructure model and the asset allocation control problems caused by the model, So we can obtain the optimal structure and the implied excess demand and market liquidity. And furthermore we can use the estimated excess demand information rather than the forecast price to guide asset investment. The results of empirical analysis in financial investment practice show the effectiveness of the proposed method, and it achieves good results in the optimization of the model and obtain higher return on asset investment.
An Effective Movie Recommendation System Using Collaborative Filtering and User Review Sentimental Analysis

In the present advanced world, a suggestion/recommendation engine is quite possibly the most incredible asset for showcasing. A recommender system is only a data separating system made out of machine learning algorithms that foresee a given client's evaluations or inclinations for a thing. A proposal engine assists with tending to the test of data over-burden in the internet business space. Accordingly, it can help in saving a ton of perusing time for clients, as the recommendation system guides the client to results of the things which he is destined to like. Its customized highlights improve client commitment and maintenance. The possibility of proposal engine is likewise something you are as of now acquainted with; whether it is item suggestions on Amazon, film suggestions on Netflix, or music ideas on YouTube, recommender frameworks are now supporting numerous parts of your experience on the web.
Decentralized Predictive Enterprise Resource Planning Framework on Private Blockchain Networks Using Neural Networks

Theoretically, cross-department predictive modeling can improve the operational efficiency of an enterprise, particularly on enterprise resource planning. For example, a model that predicts the volume of purchase goods will be more generalizable if the predication is based on the data from multiple departments. Most existing cross-department predictive models rely on a centralized technology, in which security and robustness are ignored, including unreliable single-point or malicious modification of records. Therefore, our works propose a decentralized framework to combine Blockchain technology with exited model so as to apply in predictive enterprise resource planning. In detail, model parameter estimation will be trained by without revealing any other information, which means only model-related data are exchanged across departments. In order to apply transaction metadata to disseminate models, we introduce neural networks combine with a private Blockchain network. In addition, we design an algorithm to train the neural networks that combine the loss function from each local model to achieve the smallest global level validation loss. Finally, we implement the experiments to prove the effectiveness of our framework by applying it to multi typical tasks in enterprise resource planning. Experimental results reveal the advantages of this framework on both tasks.
Optimal Storage Cloud Data Recoverability Audit Method Based on Regenerative Code

In cloud data security storage, when data holders store 1ocally controllable data resources in the cloud, they lose control of data integrity and availability. For data holders, the cloud service provider CSP is untrustworthy, and it may damage the data stored in the cloud or forge and deceive the integrity of the damaged data. Aiming at the data recoverability problem after detecting data integrity damage in cloud data storage, an audit method of optimal storage cloud data recoverability based on regeneration code is proposed. This solution not only supports the third-party audit agency to verify whether the data block is damaged and locate the exact location of the damaged data block, but also realizes the data integrity recovery function. In the process of data processing and auditing, the privacy and safety of data are guaranteed. Experiments have proved that the program has a certain degree of safety and effectiveness.
Predictive Trajectory Planning for On-Road Autonomous Vehicles Based on a Spatiotemporal Risk Field

Trajectory planning plays a crucial role in autonomous driving. It generates feasible geometric paths within a certain time horizon to guide autonomous vehicles. However, typical planning methods give little consideration to the motion uncertainty of traffic objects. Therefore, this article proposes a predictive trajectory planning method based on a spatiotemporal risk potential field. And the key notion of this method is to separate the coupled executions, i.e., trajectory generation and risk assessment, into two parallel and independent modules in the curvilinear coordinate. First, the current spatial risks are quantified by artificial potential-like functions. Then, we measure the probabilities of surrounding vehicles driving toward each lane using an interacting multiple model structure. The temporal evolution of the spatial field is computed by incorporating both the probabilities and predicted trajectories. Meanwhile, the generation module focuses on providing sufficient trajectory candidates for the ego vehicle. To allocate computing resources reasonably, feasibility probabilities are calculated with a relational grid, and then candidates are biased sampled in lateral and longitudinal directions. Finally, taking into account driving safety, comfort, and efficiency, the planner selects an optimal trajectory by minimizing a well-designed cost function. In this way, the feasibility of planned trajectories can be greatly improved with limited time consumption. The performance of this proposed method is evaluated in various scenarios using a high-fidelity Unity3D environment.
Learning End-User Customized Mid-Air Hand Gestures Using a Depth Image Sensor

Interacting with computer applications using actions that are designed by end users themselves instead of pre-defined ones has advantages such as better memorability in some Human-Computer Interaction (HCI) scenarios. In this paper we propose a method for allowing users to use self-defined mid-air hand gestures as commands for HCI after they provide a few training samples for each gesture in front of a depth image sensor. The gesture detection and recognition algorithm is mainly based on pattern matching using 3 separate sets of features, which carry both finger-action and hand-motion information. An experiment in which each subject designed their own set of 8 gestures, provided about 5 samples for each, and then used them to play a game is conducted all in one sitting. During the experiment a recognition rate of 66.7% is achieved with a false positive ratio of 22.2%. Further analyses on the collected dataset shows that a higher recognition rate of up to about 85% can be achieved if more wrong detections were allowed.
A Framework for Big Earth Observation Data Using Horizontal Scaling Strategy

The rapid growth of earth observation (EO) data poses a challenge to the way of data management. An efficient framework based on big data technology can bring new solutions. Some excellent frameworks have been proposed, which provide efficient organization and management of EO data. However, they are not optimized for data distribution in the storage environment. In this paper, an optimized EO data management strategy is proposed. Different horizontal scaling strategies are designed to explore the optimal scheme of EO data distribution. The MapReduce parallel computing model was used to test the performance of data retrieval in the experiment. The results show that the proposed strategy contributes to the efficient organization and arrangement of data. Remote sensing (RS) data blocks can be evenly distributed to different shards according to the time characteristics and hash characteristics of the strategy, and the logical index of the data reduces the time consumed by the routing process. This distributed management mode that achieves load balancing provides a framework foundation for parallel computing. Therefore, the framework with an efficient strategy can improve the performance of data management.
Cybersecurity Knowledge Requirements for a Water Sector Employee

Critical infrastructure in South Africa remains highly vulnerable to cybercrime threats due to a poor cyber-crime fighting capacity and a lack of a strong cybersecurity policy. South Africa appears to have fallen behind in securing and protecting cyberspace, considering the country’s dependability as well as the interconnectedness to the internet. Globally, the water and wastewater sector were ranked number four in the global security incidents. This study presents the findings of a systematic literature review conducted to assess the cybersecurity knowledge necessary for a general employee in the water sector. The study proposes a framework for determining the minimum knowledge that a general employee in the water sector should have. The frameworks start by defining the eight different types of cybersecurity challenges, then move on to mitigation strategies for dealing with such attacks. Several approaches and strategies were provided for mitigating various cybersecurity challenges. To deal with such risks, mitigations such as cybersecurity knowledge and skills, cybersecurity awareness, and cybersecurity training were proposed. The strategies for developing knowledge to deal with various sorts of dangers were provided at both the individual and organizational levels.
Futuristic Technologies for Supply Chain Management: A Survey

As the needs of the modern supply chain industry grows bigger, the technology to deal the complexities demands a simple but an resilient solutions that meets both profitability and yet environmental needs for the organizations, Kirchner (Five ways to weather supply-chain disruptions). Supply Chain Industry have already started their digital transformation journey by adopting futuristic technologies like Machine Learning to sense a specific event across the supply chain and solve problems such as delivery delays and weather-related disruptions in unexpected ways (Kirchner in Five ways to weather supply-chain disruptions). In addition to AI and Machine learning, supply chain industry also warrants new technology like Internet of Things (IoT) and Blockchain to improve the operations excellence by bringing more automation in product monitoring and control along with the application of Smart contract to establish the statement of trust between supply chain stakeholders. This work has been designed to provide readers an comprehensive and yet with critical insights in the context of current advancements and the future vision around the technologies like Internet of Things (IoT), Artificial Intelligence/Machine Learning, Blockchain and Quantum Computing within supply chain domain.
Delegatable Order-Revealing Encryption for Reliable Cross-Database Query

Cloud service providers adopt pay-per-query pricing models to charge data owners based on the amount of data scanned by each query. In such models, the trustworthiness of the underlying billing system is as important as the privacy preservation for the data and queries. In this paper, we revisit delegatable order-revealing encryption (DORE), a range query algorithm allowing authorized users to retrieve data of specific ranges across multiple databases encrypted under different secret keys. We first investigate which factor in the authorization mechanism of DORE can lead to overprivileged users and let them allow any unauthorized user to query over the database of the victim without risking their credits, such as leaking the secret keys. Unfortunately, such unauthorized queries would incur unexpected financial damage to the victim in practical pay-per-query models. We then propose SEDORE, a secure order-revealing encryption scheme with resilience to unauthorized queries across databases. SEDORE features a novel user authorization mechanism limiting user privileges carefully. Consequently, the authorized users cannot illegally invite any unauthorized user to query unless they entirely leak their credits. We demonstrate that the performance of SEDORE is comparable to that of DORE while achieving a higher security level.
An Exploratory Factor Analysis of Personality Factors: An Insider Threat Perspective

This study used an exploratory factor analysis to examine the factors underlying personality traits that influence the constructs of information security compliance. Studies of this nature could be germane to organisations grappling with the insider threat problem. The current study, which is situated within the socio-technical realm and considers the human element within the information security domain, concludes by providing a conceptual model that could be useful to both researchers and practitioners.
COLTRANE – Towards a Methodology and Platform Supported Educational Basis for Cybersecurity Education

Based on an analysis of current cybersecurity education in Europe and findings from a series of workshops conducted with selected groups of educators and learners in several European HEIs, this paper describes a methodology that is aimed at integrating the teaching of applied skills with the prevailing teaching, which is more focused on theoretical knowledge. The resulting COLTRANE Methodology aims at achieving this goal through providing a scenario-based and problem-oriented learning environment. A first case study is described and analyzed.
Solar Driven Self-learning Duty Cycle Control Algorithm for Maintenance-free Environmental Data Acquisition Nodes

Environmental wireless sensor network (EWSN) nodes are ordinarily implemented as embedded devices which use low-power and low-cost microcontrollers whose duty cycle is controlled by a machine learning method. This article updates a previously designed Q-learning (QL) algorithm to reduce the number of failed operational duty cycles in solar-powered EWSN nodes. The innovation is based on modification of the QL learning process and was tested and verified on an EWSN node model using a 5-year dataset of solar irradiance values. By designing a new learning algorithm, the number of failures in operational cycles was significantly reduced, specifically by a factor of ten compared to the reference algorithm.
Exploiting User Preferences for Multiscenarios in Query-Less Search

Online travel platforms (OTPs), for example, booking.com, Ctrip.com, and Fliggy, deliver travel experiences to online users by providing travel-related products. Hotel recommendation is significantly important for OTPs since hotel bookings account for almost half of the travel expenses and hotel products generate more than half of OTP’s revenues. More than 58% Fliggy users may choose to use query-less hotel searches to find candidate hotels, where no additional keywords are given except the expected check-in date and travel destination city. Thus, how to recommend hotels to traveler users is important and challenging. In this article, we explore the unique characteristics of query-less hotel users and propose a novel multiscenario query-less search network (MSQS). According to their searching date, expected check-in date, current city, and expected hotel city, MSQS groups users’ behaviors (e.g., click, purchase, search) into four scenario groups, namely today-local, today-nonlocal, future-local, and future-nonlocal. The key components of MSQS are the global expert, the scenario expert, and the feedback expert. The global expert learns common features among different scenarios and extracts the feature interactions between context, users, and hotels. The scenario expert utilizes multilayer perception to learn the differentiating features between scenarios. The feedback expert learns users’ preferences for hotels in different scenarios through their historical behaviors, and a scenario interest extractor is carefully designed to enhance attention across scenarios and behaviors. An offline experiment on the Fliggy production dataset with over 8 million users and 0.49 million travel items and an online A/B test both show that MSQS effectively predicts users’ hotel booking intentions.
Benchmarking of Symmetric Cryptographic Algorithms on a Deeply Embedded System

In this paper, we study the runtime performance of symmetric cryptographic algorithms on an embedded ARM Cortex-M4 platform. Symmetric cryptographic algorithms can serve to protect the integrity and optionally, if supported by the algorithm, the confidentiality of data. A broad range of well-established algorithms exists, where the different algorithms typically have different properties and come with different computational complexity. On deeply embedded systems, the overhead imposed by cryptographic operations may be significant. We execute the algorithms AES-GCM, ChaCha20-Poly1305, HMAC-SHA256, KMAC, and SipHash on an STM32 embedded microcontroller and benchmark the execution times of the algorithms as a function of the input lengths.
The Need of a Digital Typeface for Assamese Script

India is a land of vast diversity, a home to hundreds of communities and languages. In the northeast of India lies Assam. Even with the existence of multiple tribes and languages in Assam, Assamese is considered as the official language with more than 15 million speakers and serves as lingua franca of the region. Though there are differences in spoken medium, the script for Assamese language is very similar to that of Bengali script, historically there were certain differences in the former scripts but the ones which are being used now are the same except the character ‘ ’ (ra) and ‘ ’ (wa), which is missing in Bengali script. However, there are not a lot of Assamese Unicode typefaces available on the internet. Even among the available ones most of them have been designed for print mediums like newspapers or magazines. This paper is aimed to understand different guidelines important for designing a digital typeface by reviewing existing literature and to design a new Assamese typeface suitable for screen based applications.
Perception and Continuous Intention of Wearable Fitness Trackers Among Different Age Groups: En Route Towards Health and Fitness

There is a significant societal change in the world with the growth in the ageing population. Older adults are likely to accumulate some health sternness with age, often leading to the functional disability with time. Assistive devices are an excellent resource in such cases, but independence is compromised to some extent. However, wearable fitness trackers (WFT) play a significant role in motivating people to indulge in a healthy lifestyle avoiding vulnerabilities in the old age. Despite the sales of WFT being large-scaled, it faces challenges in its sustainable usage. This paper discusses the various challenges faced by older adults through the ageing process. Correspondingly, it addresses the effects of health and fitness intervention in the enhancement of their living standards. It highlights the methods and results of the study conducted on people aged between 18 and 75 + years towards the Behavioural and Continuous Intention of WFT. As a solution, the paper highlights the positive prospects of WFT as an essential tool towards health and fitness and methodically addresses the sustainability issue concerning its acceptance and Continuance.
BEHRTDAY: Dynamic Mortality Risk Prediction using Time-Variant COVID-19 Patient Specific Trajectories.

Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study, demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York, with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory measurements given the entire patients' hospital stay. The study population includes 3699 COVID-19 positive (57% male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver operator curve (0.92) for next-day mortality prediction given entire patients' trajectories, and through masking, it learnt each variable's context.