Reviewerly/exHarmony huggingface dataset:
The daily swab test collection problem

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science 10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT.
LiDAR-Only Ground Vehicle Navigation System in Park Environment

In this paper, a novel and complete navigation system is proposed for mobile ground vehicles in a park environment. LiDAR map representation and maintenance, dynamic objects detection and removal, hierarchal path planning and model-free local planning are developed in the system. The system is formulated in three layers. In the global layer, given the global point cloud map of the environment, the traverse area is detected and its skeleton graph is extracted to represent the global topology of the environment. Then, in the middle layer, the global map is divided into several submaps and each submap is represented by a modified multi-layer grid map. In the local layer, considering the dynamics of the environment, according to the real-time LiDAR observation, a probabilistic distribution-based representation and its updating mechanism are proposed. Based on the hierarchal environment map representation, the path planning and local planning are performed in a hierarchal way. Considering the complexity of the motion model estimation, a model free local planner is used. Extensive experiments are conducted in the real environment and the source code will be made open for the robotics community.
The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.
Mechanical Property Test of Grass Carp Skin Material Based on the Digital Image Correlation Method

Fish is a common and widely distributed creature. Its skin has a unique physiological structure and plays an important role in many fields. Fish skin also has important potential value for bionics research. This study aims to provide a method and a reliable data for the study of bionics. A method of measuring the mechanical properties of fish skin samples using a binocular stereo digital image correlation (DIC) system combined with a synchronous tensile testing machine was proposed. The mechanical properties (e.g., elastic modulus E and strain) of grass fish skin samples (GFSA) were tested in hydrophilic and dry states. A dual-frequency laser interferometer was used to calibrate the tensile testing machine synchronously, and the feasibility and strain accuracy of DIC in GFSA measurement were verified by finite element method (FEM). The results show differences in the mechanical properties of GFSA between different individuals, different parts, and different states. Under the same stress, the head was easy to deform, and the strain was the largest, and E was the smallest. The tail result was the opposite of the head result.
Learning Effectively from Intervention for Visual-based Autonomous Driving

Imitation learning (IL) approaches like behavioral cloning have been used successfully to learn simple visual navigation policies by learning a large amount of data from expert driving behaviors. However, scaling up to the actual driving scenarios is still challenging for the IL approaches because they rely heavily on expert demonstrations requiring labeling every state the learner visits, which is not practical. Moreover, the expert demonstrations limit the performance upper bound. This work proposes a method to accelerate the learning efficiency inspired by human apprenticeship to promote end-to-end vision-based autonomous urban driving tasks. We employ a hierarchical structure for visual navigation, where the high-level agent is trained with the ground-truth data of the environment, and the trained policy is then executed to train a purely vision-based low-level agent. Moreover, in addition to the labeled demonstrations, the expert intervenes during the training of the low-level agent and brings efficient feedback information, interactively accelerating the training process. Such intervention provides critical knowledge that can be learned effectively for dealing with complex, challenging scenarios. We evaluate the method on the original CARLA benchmark and the more complicated NoCrash benchmark. Compared to the state-of-the-art methods, the proposed method achieves similar good results but requires fewer data and learns faster, effectively improving the sample efficiency.
Towards Engaging Intangible Holographic Public Displays

Public displays are some of the most challenging interfaces to design because of two key characteristics. First, the experience should be engaging, to attract and maintain users' attention. Second, the interaction with the display should be natural, meaning that users should be able to receive the desired output with little or no training. Holographic displays are increasingly popular in public spaces such as museums and concert halls but there is little published research on users' experiences with such displays. Previous research has suggested both tangible and intangible inputs as engaging and natural options for holographic displays, but there is no conclusive evidence on their relative merits. Hence, we run a study to investigate the user experience with a holographic display comparing the level of engagement and feeling of natural experience in the interacting process. We used a mix of surveys, interviews, video recordings, and task-based metrics to measure users' performance on a specific task, the perceived usability, and levels of engagement and satisfaction. Our findings suggest that a tangible input was reported as more natural than the intangible one, however, both tangible and intangible inputs were found to be equally engaging. The latter findings contribute to the efforts of designing intangible public holographic displays and other interactive systems that take into consideration health safety issues, especially during the Covid-19 pandemic era in which contamination can be established with tangible and physical interaction between users and public displays, yet without affecting the level of engagement compared to the tangible experience.
Security Analysis of Archiving System using Notarize File over the Blockchain Protocol (Case Study: Hyperledger Fabric and OpenDocMan)

Archives are important documents for an organization. Manual archive management has many drawbacks. However, digitizing archives can also cause new problems, namely information security issues (one of which is data manipulation). This can be overcome by implementing blockchain which ensures the security of information on the system. Blockchain requires additional protocols to access and store documents. One of the protocols that can be used is Notarize File over the Blockchain (NFB). NFB allows users to archive, retrieve, and verify the existence of an archive in the system. The NFB protocol can be said to be unique because on the one hand it combines a distributed system (blockchain: Hyperledger Fabric) and centralized (DMS: OpenDocMan). But on the other hand, this contradicts the initial concept of the formation of blockchain which is a distributed system with the aim of increasing the security of information on the system. Therefore, a security analysis was carried out on three aspects (confidentiality, integrity, availability) on the system as a whole. There are 2 indicators on the confidentiality aspect, 4 indicators on the integrity, and 1 indicator on the availability aspect. The results of the security analysis show that the system has met all indicators in all three aspects.
VR Training for Laboratory Environments

VR apps in the categories of gaming, leisure, social, fitness, work and education are becoming increasingly popular. The aim of this paper is to determine the potential for increasing efficiency and conserving resources in laboratory processes. For this purpose, literature research was conducted on the one hand, and on the other hand, a virtual simulation environment in the field of cell culture was created within a Laminar Flow Hood (Flow) using the Unity development environment and the Oculus Quest 2 VR goggles. Outstanding features of the app are that, in addition to the possibility of being used via cable using a PC (PCVR), it can also be operated wireless as an Android standalone package, and that it can be used with controllers as well as with hand tracking, i.e., freehand. The Laminar Flow application was extensively tested on a group of 10 employees and students of the Department of Biomedical Engineering. The data obtained from the app, as well as questionnaires completed by the test persons, show that a usability-friendly application can be used to train workflows efficiently and, in a resource saving manner. Furthermore, the more often the workflow has been worked through by a person, a greater improvement in the workflow was achieved. In addition, a strong acceptance and high commitment of the users in the technical environment can be seen. Regarding comparable studies and although the test group is subject to some limitations the enormous potential that IVR offers to support location-based training sessions in companies and especially in laboratories and university facilities can be seen. In view of the decreasing prices, the technical advancement of VR devices and the paradigm shift of work and education towards increased home office driven by COVID-19, intensified future research in the field of IVR can be justified in combination with these positive results.
Bridging the Gap Between Image Coding for Machines and Humans

Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In many use cases, such as surveillance, it is also important that the visual quality is not drastically deteriorated by the compression process. Recent works on using neural network (NN) based ICM codecs have shown significant coding gains against traditional methods; however, the decompressed images, especially at low bitrates, often contain checkerboard artifacts. We propose an effective decoder finetuning scheme based on adversarial training to significantly enhance the visual quality of ICM codecs, while preserving the machine analysis accuracy, without adding extra bitcost or parameters at the inference phase. The results show complete removal of the checkerboard artifacts at the negligible cost of -1.6% relative change in task performance score. In the cases where some amount of artifacts is tolerable, such as when machine consumption is the primary target, this technique can enhance both pixel-fidelity and feature-fidelity scores without losing task performance.
Extracting Effective Subnetworks with Gumbel-Softmax

Large and performant neural networks are often overparameterized and can be drastically reduced in size and complexity thanks to pruning. Pruning is a group of methods, which seeks to remove redundant or unnecessary weights or groups of weights in a network. These techniques allow the creation of lightweight networks, which are particularly critical in embedded or mobile applications.In this paper, we devise an alternative pruning method that allows extracting effective subnetworks from larger untrained ones. Our method is stochastic and extracts subnetworks by exploring different topologies which are sampled using Gumbel Softmax. The latter is also used to train probability distributions which measure the relevance of weights in the sampled topologies. The resulting subnetworks are further enhanced using a highly efficient rescaling mechanism that reduces training time and improves performance. Extensive experiments conducted on CIFAR show the outperformance of our subnetwork extraction method against the related work.
A Cube Attack on a Reduced-Round Sycon

The cube attack was proposed at the 2009 Eurocrypt. The attack derives linear polynomials for specific output bits of a BlackBox cipher. Cube attacks target recovery keys or secret states. In this paper, we present a cube attack on a 5-round Sycon permutation and a 6-round Sycon permutation with a 320-bit state, whose rate occupies 96 bits, and whose capacity is 224 bits. We found cube variables related to a superpoly with a secret state. Within the cube variables, we recovered 32 bits of the secret state. The target algorithm was Sycon with 5-round and 6-round versions of permutation. For the 5-round Sycon, we found a cube variable and recovered a state with a total of 2192 Sycon computations and 237 bits of memory. For the 6-round Sycon, we found cube variables and recovered a state with a total of 2192 Sycon computations and 270 bits of memory. When using brute force in a 5-round attack, 2224 operations were required, but the cube attack proposed in this paper had 248 offline operations, and 232 operations were required. When using brute force in a 6-round attack, 2224 operations were required, but the cube attack proposed in this paper required 295 offline operations, and 263 operations were required. For both attacks, offline could be used continuously after performing only once. To the best of our knowledge, this is the first cube attack on Sycon.
“It’s cleaner, definitely”: Collaborative Process in Audio Production

Abstract Working from vague client instructions, how do audio producers collaborate to diagnose what specifically is wrong with a piece of music, where the problem is and what to do about it? This paper presents a design ethnography that uncovers some of the ways in which two music producers co-ordinate their understanding of complex representations of pieces of music while working together in a studio. Our analysis shows that audio producers constantly make judgements based on audio and visual evidence while working with complex digital tools, which can lead to ambiguity in assessments of issues. We show how multimodal conduct guides the process of work and that complex media objects are integrated as elements of interaction by the music producers. The findings provide an understanding how people currently collaborate when producing audio, to support the design of better tools and systems for collaborative audio production in the future.
Adaptation Consistency of Distributed Role-Oriented Applications Based on the Actor Model of Computation

Complex software is expected to adapt to changing circumstances autonomously. In software engineering, structural adaptations of objects can be modeled using contexts and roles. Existing role concepts support autonomous adaptation but provide only limited support for distributed setups. This paper addresses two major issues of distributed adaptive systems. Firstly, the mapping of roles to the actor model, which facilitates the concurrent execution of independent objects and their communication via message passing. Secondly, a concept for coordinating adaptations across multiple distributed nodes, relying on a strong or eventual consistent adaptation of distributed role-based applications. The former approach always ensures a globally consistent state of the system. The latter increases the system’s availability in the case of network congestions or failures of devices during the adaptation process by superseding a rollback of all intermediate changes. We discuss the cases for applying different consistency criteria and show the feasibility of our approach by presenting an empirical case study of a file server application in a prototypical programming language.
Invariance measures for neural networks

Invariances in neural networks are useful and necessary for many tasks. However, the representation of the invariance of most neural network models has not been characterized. We propose measures to quantify the invariance of neural networks in terms of their internal representation. The measures are efficient and interpretable, and can be applied to any neural network model. They are also more sensitive to invariance than previously defined measures. We validate the measures and their properties in the domain of affine transformations and the CIFAR10 and MNIST datasets, including their stability and interpretability. Using the measures, we perform a first analysis of CNN models and show that their internal invariance is remarkably stable to random weight initializations, but not to changes in dataset or transformation. We believe the measures will enable new avenues of research in invariance representation.
Containerization in cloud computing for OS-level virtualization

Cloud computing uses virtualization techniques to supply many sorts of computing resources through the internet. It enables users to access data from a data center or a centralized server on demand. Virtualization plays an essential role in delivering different computing services in a cloud computing environment. The process of generating a virtual representation of something is known as virtualization, such as virtual application, server, storage, and network. Virtualization allows concurrent execution of multiple applications on the same physical machine. Different virtualization features are efficient utilization of resources, faster provisioning of applications and resources, faster workload deployment, simplified data center management, reduced capital and operating costs, increased IT productivity, and many more. In this chapter, we will present the different types of virtualization techniques available in cloud computing, the limitation of system-level virtualization and how it is overcome in OS-level virtualization, different approach to OS-level virtualization, how to implement OS-level virtualization using containers, container architecture and their management, what are the software resources available for OS-level virtualization, and finally we will conclude the chapter with few research challenges and future directions.
A New Intraoral Six-Degrees-of-Freedom Jaw Movement Tracking Method Using Magnetic Fingerprints

We proposed a novel jaw movement tracking method that can measure in six degrees of freedom. The magnetic field generated by a permanent magnet paired with a small, low-power-consumption Hall effect magnetic sensor is used to estimate the relative distance between two objects—in this instance, the lower and upper jaws. By installing a microelectromechanical system (MEMS) orientation sensor in the device, we developed a mouthpiece-type sensing device that can measure voluntary mandibular movements in three-dimensional orientation and position. An evaluation of individuals wearing this device demonstrated its ability to measure mandibular movement with an accuracy of approximately 3 mm. Using the movement recording feature with six degrees of freedom also enabled the evaluation of an individual’s jaw movements over time in three dimensions. In this method, all sensors are built onto the mouthpiece and the sensing is completed in the oral cavity. It does not require the fixation of a large-scale device to the head or of a jig to the teeth, unlike existing mandibular movement tracking devices. These novel features are expected to increase the accessibility of routine measurements of natural jaw movement, unrestricted by an individual’s physiological movement and posture.
Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes

We consider the problem of modeling gestational diabetes in a clinical study and develop a domain expert-guided probabilistic model that is both interpretable and explainable. Specifically, we construct a probabilistic model based on causal independence (Noisy-Or) from a carefully chosen set of features. We validate the efficacy of the model on the clinical study and demonstrate the importance of the features and the causal independence model.