Reviewerly/exHarmony huggingface dataset:
Comparative studies on the high-performance compression of SARS-CoV-2 genome collections

Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is fast mutating worldwide. The mutated strains have been timely sequenced by worldwide labs, accumulating a huge amount of viral genome sequences open to public for biomedicine research such as mRNA vaccine design and drug recommendation. It is inefficient to transmit the millions of genome sequences without compression. In this study, we benchmark the performance of reference-free and reference-based compression algorithms on SARS-CoV-2 genome collections extracted from NCBI. Experimental results show that reference-based two-level compression is the most suitable approach to the compression, achieving the best compression ratio 1019.33-fold for compressing 132 372 genomes and 949.73-fold for compressing 416 238 genomes. This enormous file size reduction and efficient decompression have enabled a 5-min download and decompression of $10^5$ SARS-CoV-2 genomes. As compression on datasets containing such big numbers of genomes has been explored seldom before, our comparative analysis of the state-of-the-art compression algorithms provides practical guidance for the selection of compression tools and their parameters such as reference genomes to compress viral genome databases with similar characteristics. We also suggested a genome clustering approach using multiple references for a better compression. It is anticipated that the increased availability of SARS-CoV-2 genome datasets will make biomedicine research more productive.
The Role of Digital Competence in CME Uptake: A Short Communication

The digitisation of society has reached almost every facet of our daily lives. The COVID-19 pandemic has further showcased the role of information and communications technology (ICT) in society and so much so in continuing medical education (CME). This has provided the CME industry with remarkable opportunities to design better educational programmes and reach more audiences. However, for healthcare professionals to take full advantage of these developments, they need to be digitally competent, at least at a basic level. While digital competence influences CME uptake in the internet age, several factors, in turn, can influence digital competence. These factors come from both within and outside the influence of healthcare professionals and educators. In this article, we explore how digital competence influences CME uptake and recommend ways to improve digital competence among healthcare professionals.
A Novel Speech-Driven Lip-Sync Model with CNN and LSTM

Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters. In this paper, we present a combined deep neural network of one-dimensional convolutions and LSTM to generate vertex displacement of a 3D template face model from variable-length speech input. The motion of the lower part of the face, which is represented by the vertex movement of 3D lip shapes, is consistent with the input speech. In order to enhance the robustness of the network to different sound signals, we adapt a trained speech recognition model to extract speech feature, and a velocity loss term is adopted to reduce the jitter of generated facial animation. We recorded a series of videos of a Chinese adult speaking Mandarin and created a new speech-animation dataset to compensate the lack of such public data. Qualitative and quantitative evaluations indicate that our model is able to generate smooth and natural lip movements synchronized with speech.
Interactive Manga Colorization with Fast Flat Coloring

This paper proposes an interactive semi-automatic system for manga colorization. In our system, users can colorize monochrome manga images interactively by scribbling the desired colors. The proposed method creates a high quality colorized image by inputting the original monochrome image after grayscale adjustment and the flat colored image generated from the scribbles to a colorization network. Experiments show that the colorized results yielded by the proposed method are much better than existing methods.
An Approach for Deep Learning in ECG Classification Tasks in the Presence of Noisy Labels

Cardiovascular disease (CVD) is a serial of diseases with global leading causes of death. Electrocardiogram (ECG) is the most commonly used basis for CVD diagnosis due to its low cost and no injury. Due to the great performance shown in classification tasks with large-scale data sets, deep learning has been widely applied in ECG diagnosis. Manual labeling is a time-consuming and labor-intensive job, which makes it error-prone and easy to labeled wrongly. These noisy labels cause deterioration in performance since deep neural network is easy to over-fitting with noisy labels. However, currently, only limited studies have been concerned with this problem. To alleviate the performance degradation caused by noisy labels, we come up with an optimization method combining data clean and anti-noise loss function. Our method filters the noisy data by data-clean method, followed by training the network with boot-hard loss function. The experiment is carried on MIT-BIH arrhythmia database and we take a 1-D CNN model for test. The result indicates that our optimization method can produce an effective improvement for noisy label problems when the proportion of incorrect labels ranging from 10% to 50%.Clinical Relevance— The proposed algorithm can be potentially applied to deal with the noisy label problem in ECG diagnosis task.
A Holistic Model for Individual Noetic-Cognitive Development

With the holistic dynamic approach to the issues of noetic-cognitive development, the conceptual historical cycle initiated by Anaxagoras becomes particularly important. The milestones from his insight are: Nous (Anaxagoras) → Eidos (Plato) → Archetype (C.G. Jung) → Metanoia (J. White). This approach with this concept leads us to a revision of the consciousness ideals as the ultimate holistic and noetic reality. With shifts in peak experiences (based on their ultimate universes of time, space, information and energy) of noetic states of consciousness being measured today. We now can have a better non-reductive look at the transboundary qualities of the holistic realities such as trans-temporality, translocality, transinformativeness, and transergicity. The most authentic levels of holarchy are related to these ultimate universes: emergence with trans-temporality; attractiveness with translocality; fractality with trans-informativeness; and quantumness with transergicity. These levels of holarchy are manifested in the context of a person’s cognitive-noetic development as psychosemantic units of varying complexity such as values, concepts, constructs and symbols. The practical implementation of multilevel holonoetic development is possible in the context of a relevant educational system, which generates new meaning for a person and, as a result, readies them for self-realization. Harmony and synchronicity of holonoetic processes arises and is realized in creative communications, i.e. in transcommunications, which is fundamentally different from mechanical control, which inevitably forces the deprivation of degrees of freedom from the controlled object. Therefore, existential freedom in cognitive-noetic transcommunication is an important condition for creative sense formation. The objective is that the optimal “figure” of a multilevel holarchy of educational environment and a separate module of an individual educational environment will be the shape of a cone or triangle with its apex downward (a metaphor opposite to traditional education). The emergent value-semantic cycle hence would set the largest transdisciplinary scale. Further, the educational cycle would be concretized and deepened through conceptualization, constructiveness and sharp design solutions. The model of psychosemantics of a person’s noetic-cognitive development becomes effective in understanding the mutual conjugation of dynamic and structural psychosemantic modalities. They form an integral structural-dynamic matrix model. Its columns are motivation, sensitive-perception, imagination and emotional vectors. The rows of the matrix are formed by multilevel psychosemantic units: symbolic, constructive, conceptual and valuesemantic. Thus, all psychosemantic modalities form differentiated and structured semantic fields. They can be translated into systems of categories and analytical counting units of content analysis. This allows building analytical and prognostic profiles of both the content spheres of the educational environment and their subjects.
Generation of octapartite entanglement by connecting two symmetric cascaded four-wave mixing processes with one linear beam splitter

We theoretically propose a scheme for generating full octapartite entanglement using two symmetric cascaded four-wave mixing (FWM) processes and one linear beam splitter (BS), in which two independent symmetric cascaded FWM processes are connected by a BS. To present the entanglement properties of the system, we first verify that the generated state is a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi class="MJX-tex-caligraphic" mathvariant="script">H</mml:mi> </mml:mrow> </mml:math> -graph state by exploiting the graphical calculus for Gaussian pure states, and reveal the internal interaction of the generated state by using the derived coupling strength matrix. We proceed with investigating the possibility of this scheme for generating octapartite entanglement by applying the positivity under a partial transposition criterion. The results show that there exists full octapartite entanglement in the whole gain region, and the degree of entanglement increases as the power gain increases. In addition, we investigate the effect of transmissivity of BS on the entanglement properties of the system and the dependence of bipartite entanglement on the power gain and the transmissivity of BS. Our results give a simple way to increase the scale of entanglement by linear optics and cascaded FWM processes.
Motivations to regulate cryptocurrencies: a systematic literature review of stakeholders and drivers

A cryptocurrency is a digital currency that can be used via peer-to-peer networks to execute secure electronic transactions without the need for a central control authority. The goals of this review are to identify how blockchain-based cryptocurrency is affecting the financial system, and to analyse the motivation for establishing cryptocurrency regulation, legislation, law and standards. The research methodology used to conduct this research was the systematic literature review. We conclude that the regulation of cryptocurrency requires the interaction of multiple stakeholders in order to incorporate financial, legal, technological and civil society dimensions. Coordination between several dimensions is necessary since each dimension has specific requirements associated with cryptocurrency usage, and each also has drivers that could involve different stakeholders with potentially competing interests. Most of these drivers are related to the financial, social, legal and technological aspects of regulation, and vice-versa.
ExRec

In order to meet the increasingly stringent throughput and latency requirements in datacenter networks, several innovative network architectures based on reconfigurable optical topologies have been proposed. Examples include demand-oblivious reconfigurable topologies such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020), and Sirius (SIGCOMM 2021), as well as demand-aware topologies such as ProjecToR (SIGCOMM 2016). All these architectures feature attractive performance properties using specific prototypes. However, reproducing these experiments is often difficult due to missing hardware and publicly available software. This paper presents a flexible framework for reconfigurable networks based on off-the-shelf hardware, which supports experimentation and reproducibility at a small scale. We describe how our framework, ExReC, can be instantiated with different configurations, allowing us to emulate existing architectures and to study their trade-offs. Finally, we demonstrate the application of our approach to different use cases and workloads, including distributed machine learning training.
Design of Safety and Integrated Disaster Prevention System Based on Big Data Technology

Abstract With the development of Internet technology, with the continuous increase of data volume, it has become more and more difficult to maintain the traditional centralized data storage method. Data is easy to copy, difficult to share, high storage costs, and low data usage efficiency. Further trigger the demand for more efficient data storage technology. This article aims to study the application of blockchain technology in the data security storage and sharing system. On the basis of analyzing the problems of data sharing and cryptography, the functional modules of the data security storage and sharing system are designed. Encryption uses public key encryption algorithm to ensure encryption performance. The simulation experiment results show that the system is effective for file sharing, and the average generation time of the algorithm in this paper is within the controllable range.
SISO Decoding of ℤ<sub>4</sub> Linear Kerdock and Preparata Codes

Some nonlinear codes, such as Kerdock and Preparata codes, can be represented as binary images under the Gray map of linear codes over rings. This paper introduces MAP decoding of Kerdock and Preparata codes by working with their quaternary representation (linear codes over Z4 ) with the complexity of O(N2log2N), where N is the code length in Z4. A sub-optimal bitwise APP decoder with good error-correcting performance and complexity of O(Nlog2N) that is constructed using the decoder lifting technique is also introduced. This APP decoder extends upon the original lifting decoder by working with likelihoods instead of hard decisions and is not limited to Kerdock and Preparata code families. Simulations show that our novel decoders significantly outperform several popular decoders in terms of error rate.
Platform responsibility for online harms: towards a duty of care for online hazards

Outpaced by the development of the internet, current regulatory approaches do not protect users from online harms transmitted over online platforms like Facebook, Twitter and Reddit. The Draft Online Safety Bill attempts to improve safety online by developing online analogues for responsibility practices, but is limited by a focus on platforms as service providers hosting content. Online safety requires more than safe services, because platforms are more than content hosts. Platforms, in the social interactions they facilitate, establish online spaces that are governed by platform architecture and algorithms. These spaces present online hazards in the ways they curate content and interactions online, establishing environments that enable online harms. Such consequences are a product of the space that enabled them: of unmanaged online hazards. Incorporating platform responsibility for online spaces and their hazards into the Online Safety Bill’s risk assessment duties enables protection from the full extent of online harms.
Dimensionality Reduction for Categorical Data

Categorical attributes are those that can take a discrete set of values, e.g., colours. This work is about compressing vectors over categorical attributes to low-dimension discrete vectors. The current hash-based methods compressing vectors over categorical attributes to low-dimension discrete vectors do not provide any guarantee on the Hamming distances between the compressed representations. Here we present <monospace>FSketch</monospace> to create sketches for a sparse categorical data and an estimator to estimate the pairwise Hamming distances among the uncompressed data only from their sketches. We claim that these sketches can be used in the usual data mining tasks in place of the original data without compromising the quality of the task. For that we ensure that the sketches also are categorical, sparse, and the Hamming distance estimates are reasonably precise. Both the sketch construction and the Hamming distance estimation algorithms require just a single-pass; furthermore, changes to a data point can be incorporated into its sketch in an efficient manner. The compressibility depends upon how sparse the data is and is independent of the original dimension – making our algorithm attractive for many real-life scenarios. Our claims are backed by rigorous theoretical analysis of the properties of <monospace>FSketch</monospace> and supplemented by extensive comparative evaluations with related algorithms on some real-world datasets. We show that <monospace>FSketch</monospace> is significantly faster, and the accuracy obtained by using its sketches are among the top for the standard unsupervised tasks of <inline-formula><tex-math notation="LaTeX">$\mathrm{RMSE}$</tex-math></inline-formula> , clustering and similarity search.
A Rate Control Algorithm for Video-based Point Cloud Compression

Video-based point cloud compression (V-PCC) has been an emerging compression technology that projects the 3D point cloud into a 2D plane and uses high efficiency video coding (HEVC) to encode the projected 2D videos (geometry video and color video). In this work, we propose a rate control algorithm for the all-intra (AI) configuration of V-PCC. Specifically, based on the quality-dependency existing in the projected videos, we develop an optimization formulation to allocate target bits between the geometry video and the color video. Furthermore, we design a two-pass method for HEVC to adapt to the new characteristics of projected videos, which significantly improves the accuracy of rate control. Experimental results demonstrate that our algorithm outperforms V-PCC without rate control in R-D performance with just 0.43% bitrate error.
An Improved Medical Image Watermarking Technique Based on Weber’s Law Descriptors

In distributed m-health communication, it is a major challenge to develop an efficient blind watermarking method to protect the confidential medical data of patients. This paper proposes an efficient blind watermarking for medical images, which boasts a very high embedding capacity, a good robustness, and a strong imperceptibility. Three techniques, namely, discrete cosine transform (DCT), Weber’s descriptors (WDs), and Arnold chaotic map, were integrated to our method. Specifically, the Arnold chaotic map was used to scramble the watermark image. Then, the medical image was partitioned into non-over lapping blocks, and each block was subjected to DCT. After that, the scrambled watermark image data were embedded in the middle-band DCT coefficients of each block, such that two bits were embedded in each block. Simulation results show that the proposed watermarking method provides better imperceptibility, robustness, and computational complexity results with higher embedding capacity than the contrastive method.
The many faces of learning-guided cognitive control.

The guiding question of this special issue is how people learn to adapt control in a context-sensitive manner ("control learning"). Broadly speaking, the hypothesis probed by the articles herein is that this occurs via learning about regularities in the (task) environment, which in turn guides the engagement of control. This can take place in the form of incremental learning of the contextual likelihood of control demands (e.g., the accumulating realization that the current block of trials seems to be of high difficulty), and/or by associating specific stimuli or "events" with spe- cific control demands, which can subsequently be retrieved in response to those stimuli/events. However, depending on context, adaptive control and the processes of learning and memory can also be at odds with one another. The studies in this special issue tackle three key themes surrounding learning-control interactions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
deepstruct – linking deep learning and graph theory

deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models. For this, deepstruct provides deep neural network models with different restrictions which can be created based on an initial graph. Further, tools to extract graph structures from trained models are available. This step of extracting graphs can be computationally expensive even for models of just a few dozen thousand parameters and poses a challenging problem.deepstruct supports research in pruning, neural architecture search, automated network design and structure analysis of neural networks.
Person Localization Model Based on a Fusion of Acoustic and Visual Inputs

PLEA is an interactive, biomimetic robotic head with non-verbal communication capabilities. PLEA reasoning is based on a multimodal approach combining video and audio inputs to determine the current emotional state of a person. PLEA expresses emotions using facial expressions generated in real-time, which are projected onto a 3D face surface. In this paper, a more sophisticated computation mechanism is developed and evaluated. The model for audio-visual person separation can locate a talking person in a crowded place by combining input from the ResNet network with input from a hand-crafted algorithm. The first input is used to find human faces in the room, and the second input is used to determine the direction of the sound and to focus attention on a single person. After an information fusion procedure is performed, the face of the person speaking is matched with the corresponding sound direction. As a result of this procedure, the robot could start an interaction with the person based on non-verbal signals. The model was tested and evaluated under laboratory conditions by interaction with users. The results suggest that the methodology can be used efficiently to focus a robot’s attention on a localized person.
Area Coverage With Multiple Capacity-Constrained Robots

The area coverage problem is the task of efficiently servicing a given two-dimensional surface using sensors mounted on robots such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). We present a novel formulation for generating coverage routes for multiple capacity-constrained robots, where capacity can be specified in terms of battery life or flight time. Traversing the environment incurs demands on the robot resources, which have capacity limits. The central aspect of our approach is transforming the area coverage problem into a line coverage problem (i.e., coverage of linear features), and then generating routes that minimize the total cost of travel while respecting the capacity constraints. We define two modes of travel: (1) servicing and (2) deadheading, which correspond to whether a robot is performing task-specific actions or not. Our formulation allows separate and asymmetric travel costs and demands for the two modes. Furthermore, the cells computed from cell decomposition, aimed at minimizing the number of turns, are not required to be monotone polygons. We develop new procedures for cell decomposition and generation of service tracks that can handle non-monotone polygons with or without holes. We establish the efficacy of our algorithm on a ground robot dataset with 25 indoor environments and an aerial robot dataset with 300 outdoor environments. The algorithm generates solutions whose costs are 10% lower on average than state-of-the-art methods. We additionally demonstrate our algorithm in experiments with UAVs.