Reviewerly/exHarmony huggingface dataset:
Solving Task Scheduling Problem in the Cloud Using a Hybrid Particle Swarm Optimization Approach

Synergistic confluence of pervasive sensing, computing, and networking is generating heterogeneous data at unprecedented scale and complexity. Cloud computing has emergered in the last two decades as a unique storage and computing resource to support a diverse assortment of applications. Numerous organizations are migrating to the cloud to store and process their information. When the cloud infrastructures and resources are insufficient to satisfy end-users requests, scheduling mechanisms are required. Task scheduling, especially in a distributed and heterogeneous system is an NP-hard problem since various task parameters must be considered for an appropriate scheduling. In this paper we propose a hybrid PSO and extremal optimization-based approach to resolve task scheduling in the cloud. The algorithm optimizes makespan which is an important criterion to schedule a number of tasks on different Virtual Machines. Experiments on synthetic and real-life workloads show the capability of the method to successfully schedule task and outperforms many known methods of the state of the art.
Identifying Dominant Nodes in Semantic Taxonomies

In this paper, we propose the use of the “Dominance Metric”, a scalable and modular methodology for measuring the importance of nodes in semantic taxonomies, under the assumption that dominance does not only derive from high volume, namely the number of publications a tag is related to, but also from a mixture of structural and topological properties. We apply the methodology on the vast multidisciplinary Microsoft Academic Graph Fields of Study taxonomy in order to produce a refined and enhanced version. Finally, we describe the cleansing process of the resulting taxonomy, whereby its representation quality is increased by deduplicating and merging noisy concepts within the original structure. Based on the subsequent evaluation procedure, which provided valuable insights, the results of our proposed approach are quite promising.
Targeted Universal Adversarial Perturbations for Automatic Speech Recognition

Automatic speech recognition (ASR) is an essential technology used in commercial products nowadays. However, the underlying deep learning models used in ASR systems are vulnerable to adversarial examples (AEs), which are generated by applying small or imperceptible perturbations to audio to fool these models. Recently, universal adversarial perturbations (UAPs) have attracted much research interest. UAPs used to generate audio AEs are not limited to a specific input audio signal. Instead, given a generic audio signal, audio AEs can be generated by directly applying UAPs. This paper presents a method of generating UAPs based on a targeted phrase. To the best of our knowledge, our proposed method of generating UAPs is the first to successfully attack ASR models with connectionist temporal classification (CTC) loss. In addition to generating UAPs, we empirically show that the UAPs can be considered as signals that are transcribed as the target phrase. We also show that the UAPs themselves preserve temporal dependency, such that the audio AEs generated using these UAPs also preserved temporal dependency.
Analysis of Feature Dimension Reduction Techniques Applied on the Prediction of Impact Force in Sports Climbing Based on IMU Data

Sports climbing has grown as a competitive sport over the last decades. This has leading to an increasing interest in guaranteeing the safety of the climber. In particular, operational errors, caused by the belayer, are one of the major issues leading to severe injuries. The objective of this study is to analyze and predict the severity of a pendulum fall based on the movement information from the belayer alone. Therefore, the impact force served as a reference. It was extracted using an Inertial Measurement Unit (IMU) on the climber. Additionally, another IMU was attached to the belayer, from which several hand-crafted features were explored. As this led to a high dimensional feature space, dimension reduction techniques were required to improve the performance. We were able to predict the impact force with a median error of about 4.96%. Pre-defined windows as well as the applied feature dimension reduction techniques allowed for a meaningful interpretation of the results. The belayer was able to reduce the impact force, which is acting onto the climber, by over 30%. So, a monitoring system in a training center could improve the skills of a belayer and hence alleviate the severity of the injuries.
Hard-Negatives or Non-Negatives? A Hard-Negative Selection Strategy for Cross-Modal Retrieval Using the Improved Marginal Ranking Loss

Cross-modal learning has gained a lot of interest recently, and many applications of it, such as image-text retrieval, cross-modal video search, or video captioning have been proposed. In this work, we deal with the cross-modal video retrieval problem. The state-of-the-art approaches are based on deep network architectures, and rely on mining hard-negative samples during training to optimize the selection of the network’s parameters. Starting from a state-of-the-art cross-modal architecture that uses the improved marginal ranking loss function, we propose a simple strategy for hard-negative mining to identify which training samples are hard-negatives and which, although presently treated as hard-negatives, are likely not negative samples at all and shouldn’t be treated as such. Additionally, to take full advantage of network models trained using different de-sign choices for hard-negative mining, we examine model combination strategies, and we design a hybrid one effectively combining large numbers of trained models.
Enhancing Detection Performance through Sensor Model-based Trajectory Optimization for UAVs

We propose an approach to enhance the overall detection performance in unmanned aerial reconnaissance with imaging sensors. This is achieved by generating near global optimal UAV trajectories utilizing previously developed sensor performance models. Here, we are taking into account UAV, sensor and environment characteristics as well as mission objectives. The approach can be briefly summarized as follows: First, we apply coverage path planning to determine the sensor footprint path within a predefined recon area. Second, the sensor performance model is utilized to calculate discretized two-dimensional perception maps for each footprint, which is a state-dependent representation of the detection performance. For each perception map, we identify suitable sensor positions of high detection performance. From here, we systematically connect these positions of consecutive perception maps to create a transition graph. With use of optimal control, we assign costs (e.g. recon duration) to these connections while taking several requirements and constraints into account. Dynamic programming is utilized to determine the optimal path in the graph with the lowest cost. Finally, we smooth the path by cubic spline motion primitives, which leads to the reference flight trajectory for the UAV. We evaluate our approach through a series of simulated vehicle detection reconnaissance tasks with a fixed wing UAV. Our approach improves the detection performance by 3% compared to a trajectory generated with a local optimal greedy optimization and the reconnaissance time is reduced by 20%. In comparison with a primitive trajectory, the improvement in detection performance is even up to 10% with an associated increase in reconnaissance time of at least 15%.
Generating visual explanations with natural language

Abstract We generate natural language explanations for a fine‐grained visual recognition task. Our explanations fulfill two criteria. First, explanations are class discriminative , meaning they mention attributes in an image which are important to identify a class. Second, explanations are image relevant , meaning they reflect the actual content of an image. Our system, composed of an explanation sampler and phrase‐critic model, generates class discriminative and image relevant explanations. In addition, we demonstrate that our explanations can help humans decide whether to accept or reject an AI decision.
Production of six-degrees-of-freedom (6DoF) navigable audio using 30 Ambisonic microphones

This paper describes a method for planning, recording, and post-production of six-degrees-of-freedom audio recorded with multiple 3$^{\mathrm{rd}}$ order Ambisonic microphone arrays. The description is based on the example of recordings conducted in August 2020 with the Poznan Philharmonic Orchestra using 30 units of Zylia ZM-1S. A convenient way to prepare and organize such a big project is proposed – this involves details of stage planning, collecting needed equipment, management, and running the recording. Additionally, the placement of 2D and 360 cameras as well as the design of moving camera paths are described. Two use cases of the collected Ambisonic audio are proposed. The first one is a production of a virtual reality application using Unreal Engine 4 and Wwise. The second one uses a combination of the virtual scene from the previously mentioned application and a footage from a mobile, moving camera to create a so-called “walkthrough movie”. The proposed approach for six-degrees-of-freedom Ambisonic interpolation was evaluated on listening tests. Those tests demonstrate that it is possible to recreate a navigable 3D audio taking into account Ambisonic only input and given assumptions for microphone placement.
On the constructions of resilient Boolean functions with five-valued Walsh spectra and resilient semi-bent functions

Boolean functions with five-valued Walsh spectra (shortened as five-valued functions) and semi-bent functions are two classes of Boolean functions with high nonlinearity. They have useful applications in cryptography and communications. In this paper, we first present the construction of resilient five-valued functions by modifying the support of Rothaus's bent function. And then, we give the method of constructing resilient semi-bent functions. At the same time, the number of the newly constructed resilient semi-bent functions is determined. Lastly, we give the construction of resilient semi-bent functions with maximal algebraic degree.
The role of organizational and social factors for information security in a nuclear power industry

Purpose The aim of this study was to explore the organizational and social prerequisites for employees' participative and rule-compliant information security behaviour in Swedish nuclear power production and its related industry. These industries are high-risk activities that must be meticulously secured. Protecting the information security in the related organizations is an essential aspect of this. Design/methodology/approach Individual in-depth interviews were conducted with 24 employees in two organizations within the nuclear power industry in Sweden. Findings We found that prerequisites for employees' participative and rule-compliant information security behaviour could be categorized into structural, social and individual aspects. Structural aspects included well-adapted rules, knowledge support and resources. Social aspects included a supportive organizational culture, collaboration and adequate resources, and individual aspects included individual responsibility. Originality/value The qualitative approach of the study provided comprehensive descriptions of the identified preconditions. The results may thus enable organizations to better promote conditions important for information security in a high-risk industry.
Autonomous exploration and navigation of mine countermeasures USV in complex unknown environment

With the development of autonomous control technology for unmanned surface vehicle (USV), its role in the military field has become more and more prominent. The autonomous control technology of mine countermeasures USV (MCM USV) is part of the current research hotspots. Aiming at the application scenario of the MCM USV performing minesweeping tasks in unfamiliar seas, an MCM USV path planner with autonomous exploration, real-time obstacle avoidance, and autonomous mine elimination capabilities is designed, and the training is carried out by layered reinforcement learning. Use the Unity physics engine to build a simulation environment, abstract and model mines, reefs, dynamic obstacles, etc., and build an unmanned boat model with environmental awareness and autonomous decision-making capabilities. Experimental verification shows the effectiveness of the path planner in dealing with dynamic and complex water exploration problems.
Music Royalty Payment Scheme Using Blockchain Technology

This paper briefly discusses the modern music industry setting and blockchain technology and how blockchain can be used to create a better music industry for the benefit of artist. The music industry is big and complex which generated a lot of problems for both the artist side and the industry as corporations and also put the artists and music labels and management at odds which has participated in a making the music industry a very bad environment and full of mistrust. Blockchain is classified by Gartner as one of the top ten strategic technologies in 2019, blockchain is based in distributed set or records linked together forming a chain, these records are held by different entities in a peer-to-peer network these entities work together to secure the information in the chain. by analyzing how the industry works and also present some use cases of the use of blockchain within the music industry. the paper gives a proposal to a new royalty payments system based on the environment of the blockchain technology. The paper also gives a brief about the challenges and road blocks that may face the implementation of the technology within the music industry.
Research on Helmet Wearing Detection in Multiple Scenarios Based on YOLOv5

Wearing a helmet can effectively protect the human head in industrial production and traffic activities. In order to monitor whether relevant people are wearing helmets in real time, YOLOv5 target detection algorithm is combined with helmet wearing detection in this paper, and the head and helmet dataset are pre-trained through its own yolov51.pt weight file to obtain the characteristics of the head and helmet. Then the test pictures of different scenarios are sent to the network for performance verification of the algorithm, and compared with YOLOv4 in the quantitative analysis. Finally, the score of mAP equals to 0.924 is obtained. The results show that the reference value for auxiliary real-time detection of helmet wearing can be provided by this algorithm.
Waste Classification Using EfficientNet-B0

Waste management has become one of the emerging problems. A way to speed up the whole process is by doing waste sorting, which could be done by computer using image recognition. EfficientNet-B0 could be utilized in this scenario due to the more efficient architecture and comparable performance with others deep convolutional neural network. For this experimentation, we did transfer learning and fine-tuning on it, and then do hyperparameter exploration. We also did the same process on few other models, and EfficientNet-B0 achieves the best accuracy at 96% accuracy on training with one of the smallest models. While we got 91% accuracy on validation, we also discover that our model has noticeable difficulty in classifying recyclables waste.
A survey on data integrity verification schemes using blockchain technology in Cloud Computing Environment

Abstract Cloud computing is the delivery of different services for business or any organization to store their data and utilize the various resources over the internet. The resources of cloud computing is storage, software, computing hardware and services. Usefulness of the cloud computing infrastructure is to grant services based on demand to the user. The security is a major concern in the cloud computing that are many modes of securities including accessibility, authentication, data integrity, data recovery and confidentiality. The data integrity is major role in the cloud computing security and storage service. Hence, the data integrity scheme provides the proof the truthfulness of the stored data in the cloud. In this paper, we are conducting a brief survey on data integrity verification schemes for cloud computing based on blockchain technology. The aim of this survey is to enhance the understanding the security issues and discuss the importance of data integrity scheme for outsourced data.
A Bluetooth-Based Contact-Tracing Mobile App for Airborne-Based Epidemic Control

In the situation of an epidemic outbreak, a contact tracing tool is preferred to alert the infection status of daily encountered people. Since we are in the era that smartphone is carried everywhere and embedded with Bluetooth technology, a Bluetooth-based mobile app is proposed in this paper for advanced contact tracing. The proposed app can not only trace the infectious people contacted with the user but also label the danger level by scanning the proximity and lingering time for each case. It is simple yet efficient to apply as it does not employ any new Bluetooth protocol but only requires basic inputs that are acquirable from any smartphone with Bluetooth 2.0 and above. This application is built using service-oriented architecture which helps mobile devices to communicate with a data collection server as well as each other. The collected data will be shown in a web application and used to further study the propagation characteristics of new infectious viruses. It also comprises a daily survey that users answer, and which will be used by health officials for early prognosis. The app is currently tested campus-wide and showed salient features in terms of scalability, mobility, and sensing inaccuracy-proof, which has the potential to be applied in larger populations with more complicated scenarios.
A Performance Evaluation of Pairing-Based Broadcast Encryption Systems

In a broadcast encryption system, a sender can encrypt a message for any subset of users who are listening on a broadcast channel. The goal is to leverage the broadcasting structure to achieve better efficiency than individually encrypting to each user; in particular, reducing the ciphertext size required to transmit securely, although other factors such as public and private key size and the time to execute setup, encryption and decryption are also important. In this work, we conduct a detailed performance evaluation of eleven public-key, pairing-based broadcast encryption schemes offering different features and security guarantees, including public-key, identity-based, traitor-tracing, private linear and augmented systems. We implemented each system using the MCL Java pairings library, reworking some of the constructions to achieve better efficiency. We tested their performance on a variety of parameter choices, resulting in hundreds of data points to compare, with some interesting results from the classic Boneh-Gentry-Waters scheme (CRYPTO 2005) to Zhandry’s recent generalized scheme (CRYPTO 2020), and more. We combine this performance data with data we collected on practical usage scenarios to determine which schemes are likely to perform best for certain applications, such as video streaming services, online gaming, live sports betting and distributor-limited applications. This work can inform both practitioners and future cryptographic designers in this area.
Firefly Algorithm for Structural Optimization Using ANSYS

In the mid-1980s, several metaheuristic methods began to be developed for solving a very large class of computational problems with the aim of obtaining more robust and efficient procedures. Among them, many metaheuristic methods use bio-inspired intelligent algorithms. In recent years, these methods are becoming increasingly important and they can be used in various subject areas for solving complex problems. Firefly Algorithm is a nature-inspired optimization algorithm proposed by Yang to solve multimodal optimization problems. In particular, the method is inspired by the nature of fireflies to emit a light signal to attract other individuals of this species. In this work, a numerical study for solving a structural problem using the Firefly Algorithm as optimization method is conducted. In particular, the implementation of the Firefly Algorithm in several input files realized in the ANSYS Parametric Design Language has allowed the definition of the optimal stacking sequence and the laminate thickness of a composite gear housing used to enclose the components of a mechanical reducer.
Understanding Energy Efficiency of Databases on Single Board Computers for Edge Computing

With the rapid advancement in edge computing, a recent trend is to migrate data processing from data centers to the edge to avoid long data transmission latencies. Databases, as an indispensable component, play a crucial role in efficient data management on edge devices. However, a critical limitation of edge devices is the highly constrained energy resource. Databases often incur a heavy load of CPU and storage I/O activities, which raises a particular concern on power-constrained platforms. In this paper, we have conducted an experimental study on the energy consumption of three representative databases, namely SQLite, LevelDB, and MongoDB, on single board computers for edge computing. We find that by deploying an appropriate database according to specific scenarios, the energy consumption can be reduced by a factor of 58.3, and the bandwidth can be improved by a factor of 54.4. Based on our experimental results, we also present several important system implications associated with our findings. We hope our first-hand data and the obtained insight can provide useful guidance for database and edge system designers and practitioners to develop and deploy energy-efficient databases for edge computing.
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains

Medical applications have benefited greatly from the rapid advancement in computer vision. Considering patient monitoring in particular, in-bed human posture estimation offers important health-related metrics with potential value in medical condition assessments. Despite great progress in this domain, it remains challenging due to substantial ambiguity during occlusions, and the lack of large corpora of manually labeled data for model training, particularly with domains such as thermal infrared imaging which are privacy-preserving, and thus of great interest. Motivated by the effectiveness of self-supervised methods in learning features directly from data, we propose a multi-modal conditional variational autoencoder (MC-VAE) capable of reconstructing features from missing modalities seen during training. This approach is used with HRNet to enable single modality inference for in-bed pose estimation. Through extensive evaluations, we demonstrate that body positions can be effectively recognized from the available modality, achieving on par results with baseline models that are highly dependent on having access to multiple modes at inference time. The proposed framework supports future research towards self-supervised learning that generates a robust model from a single source, and expects it to generalize over many unknown distributions in clinical environments.