Reviewerly/exHarmony huggingface dataset:
An Energy-Efficient Dynamic Scheduling Method of Deadline-Constrained Workflows in a Cloud Environment

With the rapid development of cloud applications, the computing requests of cloud data centers have increased significantly, consuming a lot of energy, making cloud data centers unsustainable, which is very unfavorable from both the cloud provider’s point of view and the environmental point of view. Therefore, it is crucial to minimize energy consumption and improve resource utilization while ensuring user service quality constraints. In this paper, we propose a hybrid workflow scheduling algorithm (Online Hybrid Dynamic Scheduling, OHDS), which aims to minimize the energy consumption of tasks and maximize service resource utilization while satisfying the sub-deadline and data dependency constraints of workflow tasks. Firstly, the data dependencies between workflow tasks are considered for multi-task merging, and sub-deadline constraints are assigned to workflow tasks based on task priority. Secondly, based on the independent nature of the tasks of different workflows, a hybrid scheduling of multiple workflows is performed to reduce service idle time. Then, the workflow task scheduling priority and its sub-deadlines are dynamically adjusted, and the service status is sensed by the CPU utilization of the service, and the workload on the overloaded/underloaded service is balanced by dynamic migration of virtual machines. Finally, the OHDS method is compared with three existing scheduling methods to verify its better performance in terms of scheduling energy consumption, scheduling success rate and service resource utilization.
Constructing and Deconstructing Intentional Weaknesses in Symmetric Ciphers

Deliberately weakened ciphers are of great interest in political discussion on law enforcement, as in the constantly recurring crypto wars, and have been put in the spotlight of academics by recent progress. A paper at Eurocrypt 2021 showed a strong indication that the security of the widely-deployed stream cipher GEA-1 was deliberately and secretly weakened to 40 bits in order to fulfill European export restrictions that have been in place in the late 1990s. However, no explanation of how this could have been constructed was given. On the other hand, we have seen the MALICIOUS design framework, published at CRYPTO 2020, that allows to construct tweakable block ciphers with a backdoor, where the difficulty of recovering the backdoor relies on well-understood cryptographic assumptions. The constructed tweakable block cipher however is rather unusual and very different from, say, general-purpose ciphers like the AES. In this paper, we pick up both topics. For GEA-1 we thoroughly explain how the weakness was constructed, solving the main open question of the work mentioned above. By generalizing MALICIOUS we – for the first time – construct backdoored tweakable block ciphers that follow modern design principles for general-purpose block ciphers, i.e., more natural-looking deliberately weakened tweakable block ciphers.
Design and Implementation of an Autonomous Driving Delivery Robot

We have developed a self-driving robot as a fully autonomous last-mile delivery solution for a variety of outdoor scenarios. The robot features a completely original design of the modern appearance, compactly and intelligently arranged sensor layout as well as a hardware platform with an extremely flexible four independent steering function. Our software framework has been established initially based on Autoware, a software platform for self-driving. To enable entirely autonomous, stable, and reliable navigation in the case of our delivery robot and taking specific characteristics of its operating scenarios into account, we propose a few crucial algorithmic enhancements and improvements. Incorporating a lidar (light detection and ranging) sensor, an IMU (Inertial Measurement Unit), a wheel odometer, and GPS (Global Positioning System) contributes to accurate mapping and localization in various critical environments such as high-tech parks with high-rise buildings or open areas. The fusion of sensing results of a 16-line lidar, a semi-solid-state lidar as well as an ultrasonic sensor allows a precise obstacle detection. With effective path planning, ground filtering, and object tracking algorithms employed, our robot is able to accurately perceive its driving environment in real time and navigates from the starting point to the target smoothly, safely, and reliably even in the presence of complicated and unexpected driving conditions. Experimental results show that the hardware platform and software algorithms of our robot meet the requirements of self-driving and delivery, significantly saving manpower and improving the distribution efficiency.
Implementing a Digital Microcredential Strategy at the University of Washington Continuum College

This chapter explores the emergence of digital microcredentials and describes how the University of Washington's Continuum College is participating in the iterative design of infrastructures and approaches to support these new forms of credentials. The authors explore the current landscape of digital credentials, including the possible benefits, nascent research, and offer a brief introduction to some of the coalitions and formative work underway in many settings. The chapter details a three-pronged strategic approach at the University of Washington's Continuum College. Each of the three efforts is intended to help both the local context served by Continuum College and a new digital credential ecosystem. The three project areas at Continuum College include using digital credentials for university employees, digitally badging the college's extensive portfolio of non-degree programs, and offering digital credentialing as a service to other university departments. The authors describe these ongoing projects, their current state, and implications for further work in digital credentials.
SpeechHide: A Hybrid Privacy-preserving Mechanism for Speech Content and Voiceprint in Speech Data Sharing

With the development of speech technology, huge amounts of speech data generated by users is collected by speech service providers and may be used for data sharing. However, speech data contains users’ private information. An attacker may use speaker recognition to identify the target user’s speech data and then analyze the speech content in it, thus causing harm to the user’s privacy. In this paper, we propose a privacy-preserving mechanism for speech data, aiming to protect users’ speech privacy from two dimensions: speech content and voiceprint. Specifically, we use named entity recognition and KeyBERT keyword extraction algorithms to identify words in speech content that may contain private information, and then replace them with secure words, thus protecting the privacy of users’ speech content. We design a voiceprint anonymization method based on differential privacy, which perturbs the voiceprint of the source speaker into the voiceprint of another speaker to prevent the leakage of the user’s voiceprint. Meanwhile, we are able to ensure that the sanitized and anonymized speech data has good sound quality and data utility, which is measured by the accuracy of the speech recognition performed on it. Experiments conducted on public datasets validate the effectiveness of our proposed scheme, and the experimental results show that our scheme reduces the accuracy of speaker recognition by 100% and the accuracy of speech recognition by 14.3%.
THE POTENTIAL OF VARIOUS ARTIFICIAL INTELLIGENCE METHODS APPLICATION IN THE PROBLEM OF DRAWING RECOGNITION AND 2D 3D TRANSFORMATION

The article analyzes the main methods of artificial intelligence in the task of recognizing drawings and transforming a 2D model into a 3D model. With the rapid development of information technologies, and especially in the pursuit of the most realistic reproduction of the project of the future product/house and other objects in digital form, the question of recognizing drawings and transforming a 2D model into a 3D model is very acute. As the number and complexity of tasks arising from the digitization of existing paper-based drawing and technical documentation grows, and the parallel need to transform two-dimensional models into three-dimensional models for visualization in three-dimensional space of complex objects, researchers have drawn attention to the possibilities of applying technologies and systems of artificial intelligence in the processes of drawing recognition and transformation of two-dimensional models into three-dimensional models. The first studies devoted to the application of artificial intelligence in the tasks of recognizing images on drawings began to appear in the early 90s of the 20th century. The analysis of approaches to the recognition of drawings allows us to consider the potential of using different methods of artificial intelligence in the task of recognizing drawings and transforming two-dimensional models into three-dimensional models. To analyze the potential of improving the work of CNN, as well as its architecture, without resorting to extensive expansion of the convolutional neural network (CNN) architecture, as well as taking into account the need to solve the task related to the logical vectorization of primitives and/or conditional graphics recognized by means of a convolutional neural network markings on drawings to perform 2D to 3D transformation. In the future, this stimulates researchers to look for alternative methods and models for image recognition systems on drawings.
Conceptualizing a Teacher Training for Identifying STEAM-Lab Spaces to Address Diversity Gaps

Nowadays, the diversity gap in STEM, with a special focus on the gender gap, is a worldwide challenge. A sustainable society cannot be achieved until society is represented in the STEM sectors that have such an impact on its development. The CreaSTEAM project is focused on mapping best practices in STEAM and creating STEAM-Labs in secondary schools in order to reduce diversity gaps, for example, gender, social or cultural background. With the aim to create adequate spaces according to the characteristics and possibilities of schools, this chapter addresses how to design and implement a training process for teachers and institutions that allows the design and implementation of STEAM-Labs in educational centers.
ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing

This paper presents a deep learning-based video compression framework (ViSTRA3), which has been employed to generate compression results for the ISCAS 2022 Grand Challenge on Neural Network-based Video Coding. The proposed framework intelligently adapts video format parameters of the input video before encoding, subsequently employing a CNN at the decoder to restore their original format and enhance reconstruction quality. ViSTRA3 has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under the Joint Video Exploration Team Common Test Conditions. Bjønegaard Delta (BD) measurement results show that the proposed framework consistently outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7% based on the assessment of PSNR and VMAF.
An Improved Dark Channel Prior for Fast Dehazing of Outdoor Images

Object detection has a vital role in many of the real time out door computer vision-based applications. For these applications haze removal become a pre-processing technique, that can recover clear images from foggy ones, which is necessary for object detection. An efficient fast haze removal method based on the atmosphere scattering model and the dark channel prior method is proposed. Instead of using single global atmospheric light to restore foggy image, a local atmospheric light estimation method is applied in the proposed design to achieve optimal results. The existing DCP based methods are time consuming as the transmission map estimation can be executed only after the atmospheric light estimation. The proposed methods exploit the depth map of a image for the transmission map estimation, which results in much more faster dehazing algorithm. The validity of the proposed algorithm is verified using the databases with (O-HAZY) and without ground truth information. The performance of the algorithm is analyzed on a pair of ground truth and hazy images whose PSNR, RMSE have been evaluated and analogized with the existing algorithms. The performance metrics of the proposed algorithm are found to be improved than the other exsisting algorithms.
Imposing Connectome-Derived Topology on an Echo State Network

Can connectome-derived constraints inform computation? In this paper we investigate the contribution of a fruit fly connectome's topology on the performance of an Echo State Network (ESN) - a subset of Reservoir Computing which is competitive in chaotic time series prediction. Specifically, we replace the reservoir layer of a classical ESN - normally a fixed, random graph represented as a 2-d matrix - with a particular (female) fruit fly connectome-derived connectivity matrix. We refer to this experimental class of models (with connectome-derived reservoirs) as “Fruit Fly ESNs” (FFESNs). We train and validate the FFESN on a chaotic time series prediction task; here we consider four sets of trials with different training input sizes (small, large) and train-validate splits (two variants). We compare the validation performance (Mean-Squared Error) of all of the best FFESN models to a class of control model ESNs (simply referred to as “ESNs”). Overall, for all four sets of trials we find that the FFESN either significantly outperforms (and has lower variance than) the ESN; or simply has lower variance than the ESN.
Virtual Reality Exposure Therapy for Claustrophobia : Evaluating usability and usefulness by clinicians

One of the most common anxiety disorders affecting the general population is claustrophobia. This article presents a Virtual Reality (VR) application that simulates an elevator that can manage the patients' fear in a controlled way and help them handle their claustrophobia. Seven clinical professionals evaluated the application through eight sessions. Data were collected via the Systematic Usability Scale (SUS), interviews, and observations by professionals expecting to use it. The results show the usefulness of the application and its ability to manage anxiety attacks. In addition, it shows the importance of considering separate evaluations from professionals already in the design of applications. They are enabling the use of this technology in practice, and their view illustrates the importance of obtaining meaningful feedback when developing practical therapeutic technologies.
A Matching Logic Foundation for Alk

Alk is an educational platform designed to help in learning algorithms and acquiring algorithmic thinking. This paper describes how the semantics of Alk and the algorithm’s properties can be formally described using matching logic such that the reasoning about algorithms, described as Alk programs, can be done in a uniform way. Challenges are coming from the specificity of the Alk Language: abstract descriptions for the values, no declarations for variable or for parameter types, and various algorithmic paradigms (e.g., non-deterministic algorithms). The main consequence of this approach is that we may use in a sound way the symbolic execution for proving algorithms’ properties.
Augmented Reality tool for the facilitation of the UAV flight inspection process during aircraft de-icing as a maintenance procedure

During the past few years, industry has begun to recognize the operational and economic value of Unmanned Aerial Vehicles (UAVs) operations. Internet of Things (IoT), Augmented Reality (AR), UAVs etc. are being continuously applied within Industry 4.0 applications due to their major capabilities on the optimization of modern manufacturing concepts. The scope of this paper is the development of an AR application to support the inspection process performed by UAVs during the de-icing procedure on aircrafts before take-off. A mixed reality interface is being designed for providing virtual feedback to the operator regarding the position and extension of ice on the aircraft. The main objective is to significantly reduce the inspection time and increase the accuracy of detecting ice through an immersive environment, exceeding the standard time consuming pre-flight methods relying on visual inspection. To broaden applicability, two different development strategies are being implemented: for Android devices and for Microsoft HoloLens Head-Mounted Display. Both scenarios have been tested in a mixed reality to evaluate the performance of the Augmented Reality application.
Learning Generalisable Representations for Offline Signature Verification

Current offline signature verification methods based on deep learning have achieved promising results, but these methods degrade greatly in cross-domain settings. An efficient offline signature verification model with both high performance and for deployment cross-domain without any adaptation. In this paper, we propose a novel approach to learning generalisable representations for offline signature verification. Firstly, we use the Siamese network combined with Triplet loss and Cross Entropy (CE) loss to learn discriminative features. Secondly, we introduce Instance Normalization (IN) into the network to cope with cross-domain discrepancies and propose an Inference Layer Normalization Neck (ILNNeck) module to further improve model generalization. We evalute the method on our self-collected Multilingual Signature dataset (MLSig) and three public datasets: BHSig-H, BHSig-B, and CEDAR. Results show that while our method achieves comparable results in single-domain setting, it is obviously superior to state-of-the-art methods in cross-domain setting.
A Key Recovery Protocol for Multiparty Threshold ECDSA Schemes

Recently, threshold ECDSA schemes have received much attention from the security community, due to the need of efficient key management in the blockchain system. For the practical use of threshold cryptosystem, a key recovery protocol is essential for users who lost their own secret shares to recover them. It was studied for a long time in the proactive secret sharing area, but the main aim of recent studies in that area is to achieve stronger security and so they are immoderate for the currently existing threshold ECDSA schemes. In this paper, we provide a new key recovery protocol for threshold ECDSA schemes that is secure against static corruptions by malicious adversaries, as in the common adversary model of the state-of-the-art threshold ECDSA schemes. Our proposed protocol reduces both the computational and communication costs to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> is the threshold of the schemes, that is, the minimum number of users required for generating a valid signature. According to our experimental results, when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> = 2 with 128-bit security, while the previous result takes 10.46 ms in total for all computations (excluding the transmission time on the network), our protocol takes 4.21 ms, which improves by a factor of about 2.48 times. The advantage of our protocol over the previous result is bigger when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> is larger. For example, when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> = 9 with 128-bit security, while the previous result requires 333.42 ms in total for all computations, our protocol requires 56.61 ms, which outperforms the previous result by a factor of about 5.89 times.
Need Only One More Point (NOOMP): Perspective Adaptation Crowd Counting in Complex Scenes

Recently, solving the crowd counting problem under occlusion and complex perspective is a hot but difficult topic. Existing methods mainly constructed counters in parallel perspective, but when facing complex perspective, such as the influences of height difference and heavy occlusions, they fail to get good accuracy. To alleviate these problems, this work proposes a novel and interesting framework NOOMP (Need Only One More Point) for perspective adaptation crowd counting task in complex nature scenes. Firstly, this work considers that the common scenes in our daily life usually have the height difference, which brings complex perspective to crowd counting. So, a new labeled method, Absolute-geometry Gaussian Generation is proposed, which only needs one more point for each person in image and gets better accuracy. Secondly, the NOOMP framework consists of meta-learning structure and uses the few-shot way to train the counting model, which can implement the perspective adaptation effective and solve the problem of high label cost. Thirdly, for fitting the characteristic of few-shot learning, this work proposes a new Multi-head Parallel Network (MPNet) for NOOMP. The feature of crowd is extracted by MPNet, which is a hybrid structure composed of shallow network and deep network. This network can save the features of shallow network and the deeper network effectively, which makes MPNet performs well in NOOMP. In addition, this work collects a new dataset, named Multiple Height Differences in Mall (MHDM) for NOOMP, which contains images of different views and height differences from shopping malls and supermarkets. Experiments based on MHDM and other benchmarks show that the NOOMP has good performances in model accuracy and works well for solving perspective change problem.
Use of blockchain in health sensor networks to secure information integrity and accountability

Accountability and integrity are likely to play a key role in emerging health sensor network environments. Since health sensor devices are low capability devices, they are particularly susceptible to modern cyber threats. Blockchain technology is an emerging security technology that was explored by researchers in the past few years. Blockchain technology has been designed to protect information integrity and accountability. In a distributed manner making forgery and destruction of data can be very difficult. In this survey paper, we are specifically focused on how blockchain platforms adopt to the healthcare industry and achieve data integrity and accountability. Also, we will discuss the benefits and challenges of blockchain technology, and future blockchain research directions. Blockchain technology can be used to prevent unauthorized information access and enhance the security of sensor data. The use of blockchain technology in health sensor networks may address many of the integrity and accountability concerns of health sensor networks.