Reviewerly/exHarmony huggingface dataset:
Self-Orthogonal Codes Constructed from Posets and Their Applications in Quantum Communication

It is an important issue to search for self-orthogonal codes for construction of quantum codes by CSS construction (Calderbank-Sho-Steane codes); in quantum error correction, CSS codes are a special type of stabilizer codes constructed from classical codes with some special properties, and the CSS construction of quantum codes is a well-known construction. First, we employ hierarchical posets with two levels for construction of binary linear codes. Second, we find some necessary and sufficient conditions for these linear codes constructed using posets to be self-orthogonal, and we use these self-orthogonal codes for obtaining binary quantum codes. Finally, we obtain four infinite families of binary quantum codes for which the minimum distances are three or four by CSS construction, which include binary quantum Hamming codes with length n≥7. We also find some (almost) “optimal” quantum codes according to the current database of Grassl. Furthermore, we explicitly determine the weight distributions of these linear codes constructed using posets, and we present two infinite families of some optimal binary linear codes with respect to the Griesmer bound and a class of binary Hamming codes.
Conveying perspective in multi-user virtual reality collaborations

Effectively conveying the users' visual point of view in a collaborative virtual reality environment is a crucial factor in successful and efficient cooperation. Visual indicators of users' perspectives can reduce the required verbal communication and therefore increase the efficiency of work within remote teams. In this paper we compare three distinct types of visual indicators regarding task completion time, precision and error rate. These perspective conveying methods include a 3D cone indicating the boundaries of a user's field of view, highlighting the object a user is looking at, and displaying a direct video mirror of the user's view port.
Towards an Augmented Reality Cognitive Orthosis to Assist people with Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of neurocognitive disorder in aging. The main impacted activities are instrumental activities of daily living (IADL). Assistance to perform IADL can help people with AD (PwAD) to stay independent longer and live at home. Such assistance can be provided remotely from a caregiver to perform certain tasks using a shared visual representation, where, for instance, audio cues by phone are not sufficient. This paper presents a preliminary design and evaluation of a system to provide a remote assistance from a caregiver to a PwAD who needs assistance, by using a shared visual representation with the caregiver. The PwAD wears an augmented reality head-mounted display and the caregiver can see his environment and add annotations in it through a desktop interface. A preliminary usability and cognitive load study with healthy adults show encouraging results, but several aspects have to be refined.
Data-driven analysis for source apportionment and geochemical backgrounds establishment of toxic elements and REEs in the Tohoku region, Japan

The Tohoku region of Japan is geologically diverse, with a long agriculture and mining history; however, little information about the origins and distribution mechanisms of elements in this region has been reported. This study aims to provide fundamental insights into the effects of geological features and anthropogenic activities on various elements, including toxic elements and rare-earth elements (REEs), in the Tohoku region. A geochemical database (2007, AIST) consisting of data for 53 elements in 485 river sediment samples from the region was used, and a data-driven method combining principal component analysis (PCA) was applied for analysis. GBs for numerous types of areas, including general areas (GGB), natural environments (NGB), high anthropogenic-influenced areas (AGB) and mineralised areas (MGB) were established; especially, MGB was newly proposed in this study to illuminate the role of ore deposits. Both PCA and GBs comparison results show that geological features (especially igneous rock distribution) were the most important factor affecting elemental distribution, rather than anthropogenic activities. In the PCA, the first principal component showed that REE resources were commonly associated with the distribution of granitic rocks and REE-bearing minerals. Anthropogenic contaminations from mining, urban, and anthropogenic areas played important roles as the origin of some toxic elements (e.g. Ni, Pb, Sb). Comparisons between these GBs effectively elucidated the enrichment of certain toxic elements (e.g., Hg, Sb) in ore deposit areas. This data-driven study not only clarified the origins of toxic elements, but also revealed the location of potential REE mineral resources in the Tohoku region.
Implementation of a CNN identifing Modulation Signals on an Embedded SoC

Automatic modulation recognition of signals is one of the primitive tasks in software radio and electronic countermeasures. Nowadays, with the increasingly complex wireless communication channel environment, the accuracy and speed of identification of modulated signals have become increasingly demanding. Thus, this paper proposes a recognition method based on convolutional neural network(CNN) and feature fu-sion to improve the signal recognition accuracy. Using smooth pseudo Wigner-Ville distribution(SPWVD) to convert the one-dimensional signals to images, and the image features extracted by the CNN model are fused with Local binary Patter (LBP) features. CNN running on an embedded platform may not meet the performance requirement, therefore, by optimizing the structure, the network can be embedded on the SOC. The CNN architecture implemented on Xilinx’s ZYNQ platform provides accuracy which is 92.36% and can recognize a image per 0.2344s . Simulation results verify the algorithm’s performance under low signal-to-noise ratio(SNR).
Quantum-Accelerated Global Constraint Filtering

Motivated by recent advances in quantum algorithms and gate-model quantum computation, we introduce quantum-accelerated filtering algorithms for global constraints in constraint programming. We adapt recent work in quantum algorithms for graph problems and identify quantum subroutines that accelerate the main domain consistency algorithms for the alldifferent constraint and the global cardinality constraint (gcc). The subroutines are based on quantum algorithms for finding maximum matchings and strongly connected components in graphs, and provide speedups over the best classical algorithms. We detail both complete and bounded-probability frameworks for quantum-accelerated global constraint filtering algorithms within backtracking search.
On the synthesis of metadata tags for HTML files

Summary RDFa, JSON‐LD, Microdata, and Microformats allow to endow the data in HTML files with metadata tags that help software agents understand them. Unluckily, there are many HTML files that do not have any metadata tags, which has motivated many authors to work on proposals to synthesize them. But they have some problems: the authors either provide an overall picture of their designs without too many details on the techniques behind the scenes or focus on the techniques but do not describe the design of the software systems that support them; many of them cannot deal with data that are encoded using semistructured formats like forms, listings, or tables; and the few proposals that can work on tables can deal with horizontal listings only. In this article, we describe the design of a system that overcomes the previous limitations using a novel embedding approach that has proven to outperform four state‐of‐the‐art techniques on a repository with randomly selected HTML files from 40 different sites. According to our experimental analysis, our proposal can achieve an F 1 score that outperforms the others by 10.14 % ; this difference was confirmed to be statistically significant at the standard confidence level.
Compression and Rendering of Space Environment Volume Data Based on Improved HVQ Algorithm

Abstract We propose an improved HVQ approach by combining two detail levels to compress space environment volumes, based on the variation characteristics including smooth variation and significantly positive correlation between variations in the same direction at two different scales. First, the space environment data is divided into 4 3 blocks. Then blocks are decomposed into a two-level hierarchical representation and each block is represented by a mean value and a detail vector. Finally, the detail vectors are encoded by a vector quantizer. During the vector quantization process, PCA-split is applied to compute an initial codebook, and then LBG-algorithm is conducted for codebook refinement and quantization. We take advantage of the codebook-retraining method to speed up the quantization of time series with temporal coherence. Furthermore, we employ the progressive rendering based on GPU for realtime interactive visualization. The results of our experiments prove that the algorithm proposed in this paper can significantly improve the compression rate and decoding speed without sacrificing fidelity in space environment domain.
An Inverter-Based True Random Number Generator with 4-bit Von-Neumann Post-Processing Circuit

This paper proposes a small-area low-power inverter-based true random number generator (I-TRNG) which harvests entropy from thermal noise. A single CMOS inverter is used for noise amplification. Clock-feedthrough (CLFT) compensation and body-bias technique provide robustness across a wide range of supply voltage 0.7~1.0 V and temperature -40~100 °C. An on-chip 4-bit Von-Neumann post-processing circuit is implemented for maximum entropy harvesting. I-TRNG is fabricated in 130-nm CMOS technology. It occupies 1495 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.08846 MF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and consumes 0.6585 pJ/bit with a throughput of 0.4456 Mbps (0.1308 Mbits/μW). The random bits generated by I- TRNG pass all FIPS 140-2 and NIST 800-22 tests.
Improving Module Identification and Use in Grammatical Evolution

Exploiting patterns within a solution or reusing certain functionality is often necessary to solve certain problems. This paper proposes a new method for identifying useful modules. Modules are only considered if they are prevalent in the population and they are seen to have a positive effect on an individual's fitness. This is achieved by finding the covariance of an individual's fitness with the presence of a particular subtree in the overall expression.While there are many successful systems that dynamically add modules during Genetic Programming (GP) runs, doing so is not trivial for Grammatical Evolution (GE), due to the fact that it employs a mapping process to produce individuals from binary strings, which makes it difficult to dynamically change the mapping process during a run.We adopt a multi-run approach which only has a single stage of module addition to mitigate the problems associated with continuously adding newly found functionality to a grammar. Based on the well-known Price Equation, our system explores the covariance between traits to identify useful modules, which are added to the grammar, before the system is restarted. Grammar Augmentation through Module Encapsulation (GAME) was tested on seven problems from three different domains and was observed to significantly improve the performance on 3 problems and never showing harmful effects on any problem. GAME found the best individual in 6 of the 7 experiments.
The state, hate speech regulation and sustainable democracy in Africa: a study of Nigeria and Kenya

The debates on hate speech regulation have divided scholars and practitioners. While the liberals largely argue that hate speech ban is anti-democratic and counter-productive for preservation of fundamental human rights, the humanists posit that hate speech is harmful and its regulation is relevant in order to avert the collateral damage/harm it inflicts on human dignity and equality of human beings. Despite the significant efforts in Africa to regulate hate speech, its debates focus on United States and Western Europe. Thus, the account of hate speech regulation in African context is largely understudied. This study therefore examines the impact state regulation of hate speech on sustainable democracy in Africa. Using the qualitative dominant mixed methods approach and data generated from Nigeria and Kenya, the paper argues that state regulation of hate speech presents an opportunity for fostering order, advancing national cohesion, reducing hate speech and promoting inclusive governance for all, irrespective of age, sex, disability, race, ethnicity, origin, religion and economic status. The paper concludes that implementing hate speech legislation alongside other non-legal, dialogue-based, egalitarian, voluntary approaches are relevant for diminishing hate speech and the harms it engender as well as promoting sustainable democracy in Africa and beyond.
Blockchain-Based Product Traceability Beyond Proof of Existence

Contemporary consumers are demanding greater visibility in products they consume, and practices used to produce them. Blockchain-based product traceability solutions attempt to integrate the data silos of suppliers into an immutable and distributed ledger to provide consumers with a transparent and trustworthy system to trace back a product at hand. A common limitation of these solutions is the use of blockchain to provide only the proof of existence of data. We propose a solution where data related to enforcement of product-quality specifications and production limits, merging and splitting products as batches, and mixing of ingredients are handled within the block-chain itself to provide greater consistency, transparency, and trust. Our solution is based on a graph data structure built into the blockchain nodes, smart contracts, and facilitation of different roles in a supply chain. We demonstrate the utility of the proposed solution by implementing it on the Hyperledger Fabric platform. Performance results confirmed that both the graph data structure and blockchain has acceptable throughput and latency characteristics.
Real-Time Outlier Detection in Time Series Data of Water Sensors

Dutch water authorities are responsible for, among others, the management of water levels in waterways. To perform their task properly, it is important that data is of high quality. We compare several univariate and multivariate methods for real time outlier detection in time series data of water sensors from Dutch water authority "Aa en Maas". Their performance is assessed by measuring how well they detect simulated spike, jump and drift outliers. This approach allowed us to uncover the outlier parameter values (i.e. drift or jump magnitude) at which certain detection thresholds are reached. The experiments show that the outliers are best detected by multivariate (as opposed to univariate) models, and that a multi-layer perceptron quantile regression (QR-MLP) model is best able to capture these multivariate relations. In addition to simulated outliers, the QR-MLP model is able to detect real outliers as well. Moreover, specific rules for each outlier category are not needed. In sum, QR-MLP models are well-suited to detect outliers without supervision.
On the Parameterization of Cartesian Genetic Programming

In this work, we present a detailed analysis of Cartesian Genetic Programming (CGP) parametrization of the selection scheme ($\mu+\lambda$), and the levels back parameter l. We also investigate CGP's mutation operator by decomposing it into a self-recombination, node function mutation, and inactive gene randomization operators. We perform experiments in the Boolean and symbolic regression domains with which we contribute to the knowledge about efficient parametrization of two essential parameters of CGP and the mutation operator.
Dynamic Control Method for Tenants’ Sensitive Information Flow Based on Virtual Boundary Recognition

In the cloud environment, owing to the large-scale sharing of the upper application instance and the underlying virtual machine resources, the tenants' information flow boundary in the shared virtual machine is fuzzy and difficult to identify. In addition, protection of tenant information flow between processes is inadequate, resulting in the leakage of sensitive information of tenants. Therefore, a dynamic control method for tenants' sensitive information flow based on virtual boundary recognition is proposed. By analyzing the behavior and operation log of tenants, the behavior feature vectors of tenants are constructed, and an automatic recognition algorithm of tenant virtual boundary based on the dynamic spiking neural network is designed. This algorithm can realize dynamic identification of the tenant virtual security boundary when the application service demand changes dynamically. Further, combined with the concept of centralized and decentralized information flow control, a dynamic control method of sensitive information flow is established. The security label is formally defined by using the lattice structure, and the control rules of tenants' information flow and the rules of tenant label encryption-declassification are designed. Thus, the independent, dynamic and secure control of tenants' information flow inside and outside the tenant virtual boundary. Finally, the detailed design of a dynamic security control application system for cloud tenants' sensitive information flow is provided. Experiments confirm that the proposed algorithm can identify the security boundary of tenants more accurately and efficiently than the traditional spiking neural network classification methods. Further, the security and effectiveness of the method is verified by the intransitive noninterference theory and the experiment of information flow control.
A blind quantum audio watermarking based on quantum discrete cosine transform

As an important security technology, recently quantum watermarking attracted wide research attention. This study presents a blind quantum audio watermarking scheme based on quantum discrete cosine transform (qDCT). In the proposed scheme, the quantum audio signal in quantum representation of discrete signal (QRDS) is transformed into frequency domain using qDCT. The medium frequency components are selected as the target of watermarking. The qubits of previously scrambled watermark image are embedded in three qubits of each frequency component, and inverse qDCT is performed. The reverse process carried out to extract watermark qubits, in which majority voting policy employed to select correct watermark qubit. For every procedure of the proposed scheme, the quantum circuit and complexity analysis is presented. The novelty of the study is to embed watermark data in the medium frequency band of audio signal which is an optimal target for carrying hidden data. Furthermore, the proposed scheme employs parallel processing, the intrinsic powerful property of quantum computing, to perform embedding process efficiently and reduce quantum gates in presented quantum circuits. Based on transparency and robustness evaluation results, the proposed method demonstrated a better robustness than previous works, while keeping the same capacity, without significant change in transparency.
Boundary Constraint-Handling Methods in Differential Evolution for Mechanical Design Optimization

This paper presents an experimental comparison of nine boundary constraint-handling methods found in the specialized literature added to differential evolution when solving four mechanical design optimization problems. The experimental part considers a comparison of final results besides performance measures used in evolutionary constrained optimization as well as the number of vectors and variables repaired. The Kruskal-Wallis non-parametric test and the Bonferroni post-hoc test are computed to validate the findings. The final results suggest that the Projection method provides a better overall performance when compared with other approaches. However, Centroid 1+1 was the method which promotes less repairs.
An Intelligent Dashboard for Assisted Tweet Composition in the Cultural Heritage Area (Work-in-progress)

Cultural Heritage institutions are nowadays using social media to communicate with citizens and tourists. However, providing actual effective communication is not an easy task, as every day millions of messages are posted through social media. Thus, getting visibility is not trivial. In this paper we present the architecture of a dashboard, accessible by mobile Android devices, to support museum social media managers in composing effective tweets by providing suggestions to improve message drafts. At this aim, the application exploits machine learning techniques over data related to tweets posted by museums in the past.
Bayesian indirect inference for models with intractable normalizing functions

Inference for doubly intractable distributions is challenging because the intractable normalizing functions of these models include parameters of interest. Previous auxiliary variable MCMC algorithms are infeasible for multi-dimensional models with large data sets because they depend on expensive auxiliary variable simulation. We develop a fast Bayesian indirect algorithm by replacing an expensive auxiliary variable simulation from a probability model with a computationally cheap simulation from a surrogate model. We learn the relationship between the surrogate model parameters and the probability model parameters using Gaussian process approximations. We apply our methods to challenging examples, and illustrate that the algorithm addresses both computational and inferential challenges for doubly intractable distributions. Especially for a large social network model with 10 parameters, we show that our method can reduce computing time from about 2 weeks to 5 hours, compared to the previous method.
A Novel Attention-based Aggregation Function to Combine Vision and Language

The joint understanding of vision and language has been recently gaining a lot of attention in both the Computer Vision and Natural Language Processing communities, with the emergence of tasks such as image captioning, image-text matching, and visual question answering. As both images and text can be encoded as sets or sequences of elements - like regions and words - proper reduction functions are needed to transform a set of encoded elements into a single response, like a classification or similarity score. In this paper, we propose a novel fully-attentive reduction method for vision and language. Specifically, our approach computes a set of scores for each element of each modality employing a novel variant of cross-attention, and performs a learnable and cross-modal reduction, which can be used for both classification and ranking. We test our approach on image-text matching and visual question answering, building fair comparisons with other reduction choices, on both COCO and VQA 2.0 datasets. Experimentally, we demonstrate that our approach leads to a performance increase on both tasks. Further, we conduct ablation studies to validate the role of each component of the approach.