Reviewerly/exHarmony huggingface dataset:
Evaluation of forensic soil traces from a crime scene: robbery of a safety deposit box in Brazil

Abstract Soil traces can be used as evidence in criminal investigations due their transferability, great variability and persistence. The soil samples originated from a crime scene that occurred in the Curitiba Metropolitan Region, Brazil. Physical, chemical and mineralogical analyses were carried out on the soil samples. All results were statistically analysed using multivariate analysis (PCA) to verify the relative positioning of soil traces which had been recovered from a stolen safety deposit box (SDB) from a vehicle suspected of being used in the SDB transportation, and from the site allegedly used in the opening of the SDB. The methods employed were effective in discriminating between the sampling sites. The soil from the site used in the opening of the SDB could be excluded as being the location where the soil transferred to the SDB had originated as it was different in many characteristics. Supplementary material: The organic matter content of the samples, and their gibbsite and kaolinite values obtained by thermogravimety analysis (S1), and X-ray diffraction patterns of the silt + clay fractions (S2) are available at https://doi.org/10.6084/m9.figshare.c.4561004
Outlier Detection

This chapter deals with the task of detecting outliers in data from the data mining perspective. It suggests a formal approach for outlier detection highlighting various frequently encountered computational aspects connected with this task. An overview of this book with chapter-wise organization is also furnished here giving an idea of the coverage of the material on this research domain.
Sentiment Analysis

This chapter describes how to perform sentiment analysis using the syuzhet package developed by Jockers. Readers will learn how to extract sentiment values from a text and compare and visualize the emotional arcs of two novels.
Technologies for Digital Inclusion

Digital inclusion proposals are oriented towards specific groups of people. These groups are excluded due to their qualities or lack of them, their attitude, socio-political factors, economic factors, or just by the geographic zone where they live. Since 1999 several strategies have been proposed, either related to educational or technological aspects. The lessons learned are collected with the aim of proposing an adapted model that allows defining a set of good practices. In order to know what technology is the most suited to promote the digital inclusion of each group, multiple variables are analysed. A higher number of variables allow better groups description regarding their context and the relevance of each variable for each group. In this work the authors discuss the dimensions of the group profiles, the factors affecting them, and the infrastructure and software solutions that can change the situation. Finally a simple graphic is proposed to show the dimensions of causes and user groups in order to improve their comparison.
Redactable Blockchain based on Unforgeable Signatures for Supporting Fast Verification

Redactable blockchain is an emerging topic in recent blockchain research. Its purpose is to delete or correct errors and harmful content on the chain without using hard forks. In other words, while preserving the public immutability of the blockchain, to allow some administrators or trusted centers with higher authority to modify the content on the blockchain without violating the integrity of the entire blockchain. In this paper, we proposed a redactable blockchain scheme based on unforgeable signatures. Compared with the existing blockchain editing solutions, this paper's solution can be efficiently and conveniently integrated into the current blockchain technology. This scheme's security is based on the collision-resistant hash function used by the original immutable blockchain and the unforgeability of the signature scheme. Besides, our solution can quickly confirm whether a chain has correctly implemented modifications issued by the central authority through the merge verification of signatures.
Anonymous AE

The customary formulation of authenticated encryption (AE) requires the decrypting party to supply the correct nonce with each ciphertext it decrypts. To enable this, the nonce is often sent in the clear alongside the ciphertext. But doing this can forfeit anonymity and degrade usability. Anonymity can also be lost by transmitting associated data (AD) or a session-ID (used to identify the operative key). To address these issues, we introduce anonymous AE, wherein ciphertexts must conceal their origin even when they are understood to encompass everything needed to decrypt (apart from the receiver's secret state). We formalize a type of anonymous AE we call anAE, anonymous nonce-based AE, which generalizes and strengthens conventional nonce-based AE, nAE. We provide an efficient construction for anAE, NonceWrap, from an nAE scheme and a blockcipher. We prove NonceWrap secure. While anAE does not address privacy loss through traffic-flow analysis, it does ensure that ciphertexts, now more expansively construed, do not by themselves compromise privacy.
Fuzzy Clustering

Clustering and fuzzy clustering can be analyzed, developed and used by having two perspectives in mind. On the one hand, these techniques create one of the generic tools of data analytics aimed at revealing a structure in data and are regarded as a essential prerequisite for a slew of detailed algorithms (Anderberg in Cluster analysis for applications. Academic Press, New York, 1973 []). There are numerous applications of clustering (Bezerra et al. in Inf Sci 518:13–28, 2020 []; D’Urso et al. in Spat Stat 30:71–102, 2019 []; Tao et al. in Neurocomputing, 2019 []) including Big Data (Ianni et al. in Future Gener Comput Syst 102:84–94, 2020 [; Shukla and Muhuri in Eng Appl Artif Intell 77:268–282, 2019 []). On the other hand, in virtue of the underlying principle of clustering (group similar objects together), clusters can be sought as some information granules that offer an abstract, condensed view at the data. From this perspective, clustering can be sought as a conceptually sound and algorithmically appealing vehicle to construct fuzzy sets, viz. determine their membership grades (functions). Because the clustering process revolves about available data, we refer to this method of building fuzzy sets as a data-driven approach.
Negotiation Based Combinatorial Double Auction Mechanism in Cloud Computing

Cloud computing is a demanding business platform for services related to the field of IT.The goal of cloud customers is to access resources at a sustainable price, while the goal of cloud suppliers is to maximize their services utilization.Previously, the customers would bid for every single resource type, which was a limitation of cloud resources allocation.To solve these issues, researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle.Still, in this allocation mechanism, some drawbacks need to be tackled, such as due to the lower average bid price the users are dropped from the auction process.To solve this problem, we proposed a "Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation (N-CDARA) in cloud computing".The proposed method negotiates with dropped users.Lower average bid price users are asked by our proposed mechanism to increase their bids, as by the quoted bids they will be dropped by the auctioneer.Most of the users that are close to winning accept the proposal and increase their bid prices.The proposed mechanism is implemented in a CloudSim simulation toolkit.Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4% than the existing schemes.
Methodology

Abstract Naturally, human do the search for truth in various ways. All of that becomes methodological, but not a methodology. The right way and produce truth by involving the principle of believing the truth, getting a way, and having a purpose. So, its not justification. Three basic principles of methodology are not enough. However, the methodology becomes the method science, it needs to be systematic that flows the truth logically, and needs a structure to form the truth logically. Thus, abstraction is helped by rules or formulations that describe the structure, at least from the point formation, as definitions, lemmas, propositions, theorems, or corollaries.
Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 80% of collected speech data is used for training the acoustic models and 20% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, the proposed features shows an improvement in Word Recognition Accuracy (WRA) of 1.79% over baseline features.
Group Belief

While logical formalizations of group notions of knowledge such as common and distributed knowledge have received considerable attention in the literature, most approaches being based on modal logic, group notions of belief have received much less attention. In this paper we systematically study standard notions of group knowledge and belief under different assumptions about which properties knowledge and belief have. In particular, we map out (lack of) preservation of knowledge/belief properties against different standard definitions of group knowledge/belief. It turns out that what is called group belief most often is not actually belief, i.e., does not have the properties of belief. In fact, even what is called group knowledge is sometimes not actually knowledge either. For example, under the common assumption that belief has the KD45 properties, distributed belief is not actually belief (it does not satisfy the D axiom). In the literature there is no detailed completeness proof for axiomatizations of KD45 with distributed belief that we are aware of, and there has been some confusion regarding soundness of such axiomatizations related to the mentioned lack of preservation. In this paper we also present a detailed completeness proof for a sound axiomatization of KD45 with distributed belief.
Unsupervised Deep Learning Architectures

The cascade of multiple layers of a deep learning architecture can be learnt in an unsupervised manner for the tasks like pattern analysis. A deep learning architecture can be trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine. Unsupervised deep learning algorithms are important because unlabeled data is more abundant than the labeled data. For applications with large volumes of unlabeled data, a two-step procedure is used: in the first step, a deep neural network is pretrained in an unsupervised manner; in the second step, a small portion of the unlabeled data is manually labeled, and then used for supervised fine-tuning of the deep neural network.
Rural versus urban fiction in contemporary Chinese literature - Quantitative approach case study

Abstract The study examined contemporary Chinese novels that depicted the social changes in rural and urban areas. We analyzed the texts using several quantitative methods of stylometry (lexical richness, activity, descriptivity, nominality, cluster analysis of the most frequent words) and corpus linguistics (keywords). We focused on the differences (both in style and in content) between contemporary fiction depicting life in Chinese rural and urban areas. The results revealed that rural stories are more dynamic (active), focusing more on the plot with a simpler description and a smaller vocabulary.
Hybrid Embroidery: Exploring Interactive Fabrication in Handcrafts

This paper presents Hybrid Embroidery, a framework for interactive fabrication that leverages computational methods to broaden the possibilities of the craft of embroidery. Combining embroidery techniques, generative design methods, computer vision and a computerized embroidery machine, we show how this framework elicits a variety of innovative fabrication experiences that emphasize open-ended exploration, improvisation and play. The paper documents this framework, a series of sample results, challenges and next steps. It further outlines some of its implications for supporting creative exploration through real-time and direct manipulation of materials and close human-machine interaction.
Person Re-ID through unsupervised hypergraph rank selection and fusion

Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals across multiple cameras that have no overlapping views. Most of the approaches require labeled data, which is not always available, given the huge amount of demanded data and the difficulty of manually assigning a class for each individual. Recently, studies have shown that re-ranking methods are capable of achieving significant gains, especially in the absence of labeled data. Besides that, the fusion of feature extractors and multiple-source training is another promising research direction not extensively exploited. We aim to fill this gap through a manifold rank aggregation approach capable of exploiting the complementarity of different person Re-ID rankers. In this work, we perform a completely unsupervised selection and fusion of diverse ranked lists obtained from multiple and diverse feature extractors. Among the contributions, this work proposes a query performance prediction measure that models the relationship among images considering a hypergraph structure and does not require the use of any labeled data. Expressive gains were obtained in four datasets commonly used for person Re-ID. We achieved results competitive to the state-of-the-art in most of the scenarios.
Using CNN for solving two-player zero-sum games

We study a two-player zero-sum game (matrix game for short) with the objective of finding the saddle point and its value. We develop a novel convolutional neural network (CNN for short) approach to achieve the goal. We propose a complete training pipeline, including a specific CNN model structure to handle varying game sizes, generating training datasets, and model fitting. The experiment results show that our proposed method outperforms the traditional linear programming (LP for short) method and two regret minimization learning algorithms in terms of computational efforts. • We use a novel CNN method to solve two-player zero-sum games. • Concrete training algorithms are proposed to train the CNN model for the games. • Our CNN model can handle different game sizes and untrained generation distributions. • Our CNN approach shows great potential in terms of computational efficiency.
Accurate and Efficient Quantum Computations of Molecular Properties Using Daubechies Wavelet Molecular Orbitals: A Benchmark Study against Experimental Data

The molecular orbitals for H_2, LiH, and water are calculated using a quantum algorithm based on the Daubechies wavelet theory: Accurate results are achieved, while keeping the number of qubits minimal.
Parameterized bipartite entanglement measure

Abstract We propose a novel parameterized entanglement measure α -concurrence for bipartite systems. By employing positive partial transposition and realignment criteria, we derive analytical lower bounds for the α -concurrence. Moreover, we calculate explicitly the analytic expressions of the α -concurrence for isotropic states and Werner states.
A Multi-Dimensional and Multi-Factor Trust Computation Framework for Cloud Services

In this paper, we propose a novel trust computation framework (TCF) for cloud services. Trust is computed by taking into consideration multi-dimensional quality of service (QoS) evidence and user feedback. Feedback provides ample evidence regarding the quality of experience (QoE) of cloud service users. However, in some cases, users may behave maliciously and report false feedback. Users can carry out collusion and Sybil attacks to slander/self-promote cloud services. Trust computed in such cases could be misleading and inaccurate. Evaluating the credibility of user feedback can help in not only preventing the collusion and Sybil attacks but also remunerating the affected cloud services. Despite the advantages of credibility evaluation, very few studies take into consideration feedback credibility and multi-dimensional evaluation criteria. Considering the limitations of existing studies, we propose a new TCF in which trust is computed by aggregating multi-dimensional evidence from QoS and QoE. We have used multi-dimensional QoS attributes to compute the objective trust of cloud services. The QoS attributes are divided into three dimensions, i.e., node profile, average resource consumption, and performance. The node profile of a cloud service is attributed to CPU frequency, memory size, and hard disk capacity. The average resource consumption is quantified based on the current CPU utilisation rate, current memory utilisation rate, current hard disk utilisation rate, and energy consumption. Moreover, the performance of a cloud service is measured by the average response time and task success ratio. Besides that, the credibility of feedback is evaluated to prevent the malicious behaviour of cloud users. Our results demonstrate the effectiveness of our proposed TCF in computing accurate trust in cloud services.
What to expect when you are expecting earthquake early warning

SUMMARY We present a strategy for earthquake early warning (EEW) alerting that focuses on providing users with a target level of performance for their shaking level of interest (for example, ensuring that users receive warnings for at least 95 per cent of the occurrences of that shaking level). We explore the factors that can affect the accuracy of EEW shaking forecasts including site conditions (which can make shaking more or less varied), the share of earthquake hazard that comes from outlier events, and even the metric of shaking being forecast (with modified Mercalli intensity being the most uncertain quantity to forecast). To provide different locations with the same level of EEW performance, the alert issuance criteria will need to change based on these factors. Finally, we present a hierarchy of information approach with which to communicate EEW system design and performance to the public and other stakeholders, including how frequently alerts will be issued and how accurate those alerts will be.
Context-Aware Transfer Attacks for Object Detection

Blackbox transfer attacks for image classifiers have been extensively studied in recent years. In contrast, little progress has been made on transfer attacks for object detectors. Object detectors take a holistic view of the image and the detection of one object (or lack thereof) often depends on other objects in the scene. This makes such detectors inherently context-aware and adversarial attacks in this space are more challenging than those targeting image classifiers. In this paper, we present a new approach to generate context-aware attacks for object detectors. We show that by using co-occurrence of objects and their relative locations and sizes as context information, we can successfully generate targeted mis-categorization attacks that achieve higher transfer success rates on blackbox object detectors than the state-of-the-art. We test our approach on a variety of object detectors with images from PASCAL VOC and MS COCO datasets and demonstrate up to 20 percentage points improvement in performance compared to the other state-of-the-art methods.
The Prospects of Monte Carlo Antibody Loop Modelling on a Fault-Tolerant Quantum Computer

Quantum computing for the biological sciences is an area of rapidly growing interest, but specific industrial applications remain elusive. Quantum Markov chain Monte Carlo has been proposed as a method for accelerating a broad class of computational problems, including problems of pharmaceutical interest. Here we investigate the prospects of quantum advantage via this approach, by applying it to the problem of modelling antibody structure, a crucial task in drug development. To minimize the resources required while maintaining pharmaceutical-level accuracy, we propose a specific encoding of molecular dihedral angles into registers of qubits and a method for implementing, in quantum superposition, a Markov chain Monte Carlo update step based on a classical all-atom force field. We give the first detailed analysis of the resources required to solve a problem of industrial size and relevance and find that, though the time and space requirements of using a quantum computer in this way are considerable, continued technological improvements could bring the required resources within reach in the future.
Engineering entanglement between resonators by hot environment

Abstract Autonomous quantum thermal machines do not require an external coherent drive or work input to perform the desired tasks, making them a promising candidate for thermal management in quantum systems. Here, we propose an autonomous quantum thermal machine in which two uncoupled macroscopic mechanical resonators or microwave resonators achieve considerable entanglement via a hot thermal bath. This becomes possible by coupling the resonators to a common two-level system or third harmonic oscillator and driving it by the hot incoherent thermal bath. The critical step to make the entanglement involves suitable engineering of the hot bath, realized by bath spectrum filtering. Our results suggest that the bath spectrum filtering can be an alternative to typical non-autonomous reservoir engineering schemes to create exotic quantum states.
A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering

Due to the rapid increase in data volumes, clustering algorithms are now finding applications in a variety of fields. However, existing clustering techniques have been deemed unsuccessful in managing large data volumes due to the issues of accuracy and high computational cost. As a result, this work offers a parallel clustering technique based on a combination of the K-means and Multi-Agent System algorithms (MAS). The proposed technique is known as Multi-K-means (MK-means). The main goal is to keep the dataset intact while boosting the accuracy of the clustering procedure. The cluster centers of each partition are calculated, combined, and then clustered. The performance of the suggested method's statistical significance was confirmed using the five datasets that served as testing and assessment methods for the proposed algorithm's efficacy. In terms of performance, the proposed MK-means algorithm is compared to the Clustering-based Genetic Algorithm (CGA), the Adaptive Biogeography Clustering-based Genetic Algorithm (ABCGA), and standard K-means algorithms. The results show that the MK-means algorithm outperforms other algorithms because it works by activating agents separately for clustering processes while each agent considers a separate group of features.