Reviewerly/exHarmony huggingface dataset:
An exploratory prioritization of factors affecting current state of information security in Pakistani university libraries

Ever since the increased use of the internet in higher education, libraries have started mass digitisation programs to make their intellectual resources available on the World Wide Web. From the year 2000 onward, Pakistani universities’ libraries have been active in this mass digitisation projects. The purpose of this paper is to examine the data and information security measures taken by university libraries to safeguard the intellectual assets of their respective organisations. Researchers adopted a quantitative approach to conduct this study. Data were collected from 190 professional librarians around the country. The findings of the study revealed that majority of libraries are lagging in standardised measures to secure digital resources. It is hoped that the present study will help in suggesting appropriate standards of data and information security within universities’ libraries. Results can also be replicated in other developing countries to investigate data and information security in libraries.
An efficient hybrid recommendation model based on collaborative filtering recommender systems

In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users’ previous information about items such as books, music, movies, ideas, and so on. Memory-based models are generally referred to as similarity-based CF models, which are one of the most widely agreeable approaches for providing service recommendations. The memory-based approach includes user-based CF (UCF) and item-based CF (ICF) algorithms. The UCF model recommends items by finding similar users, while the ICF model recommends items by finding similar items based on the user-item rating matrix. However, consequent to the ingrained sparsity of the user-item rating matrix, a large number of ratings are missing. This results in the availability of only a few ratings to make predictions for the unknown ratings. The result is the poor prediction quality of the CF model. A model to find the best algorithm is provided here, which gives the most accurate recommendation based on different similarity metrics. Here a hybrid recommendation model, namely ΓUICF, is proposed. The ΓUICF model integrates the UCF and ICF models with the Γ linear regression model to model the sparsity and scalability issue of the user-item rating matrix. Detailed experimentation on two different real-world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.
Rich Embedding Features for One-Shot Semantic Segmentation

One-shot semantic segmentation poses the challenging task of segmenting object regions from unseen categories with only one annotated example as guidance. Thus, how to effectively construct robust feature representations from the guidance image is crucial to the success of one-shot semantic segmentation. To this end, we propose in this article a simple, yet effective approach named rich embedding features (REFs). Given a reference image accompanied with its annotated mask, our REF constructs rich embedding features of the support object from three perspectives: 1) global embedding to capture the general characteristics; 2) peak embedding to capture the most discriminative information; 3) adaptive embedding to capture the internal long-range dependencies. By combining these informative features, we can easily harvest sufficient and rich guidance even from a single reference image. In addition to REF, we further propose a simple depth-priority context module to obtain useful contextual cues from the query image. This successfully raises the performance of one-shot semantic segmentation to a new level. We conduct experiments on pattern analysis, statical modeling and computational learning (Pascal) visual object classes (VOC) 2012 and common object in context (COCO) to demonstrate the effectiveness of our approach.
Operational applications of the diamond norm and related measures in quantifying the non-physicality of quantum maps

Although quantum channels underlie the dynamics of quantum states, maps which are not physical channels -- that is, not completely positive -- can often be encountered in settings such as entanglement detection, non-Markovian quantum dynamics, or error mitigation. We introduce an operational approach to the quantitative study of the non-physicality of linear maps based on different ways to approximate a given linear map with quantum channels. Our first measure directly quantifies the cost of simulating a given map using physically implementable quantum channels, shifting the difficulty in simulating unphysical dynamics onto the task of simulating linear combinations of quantum states. Our second measure benchmarks the quantitative advantages that a non-completely-positive map can provide in discrimination-based quantum games. Notably, we show that for any trace-preserving map, the quantities both reduce to a fundamental distance measure: the diamond norm, thus endowing this norm with new operational meanings in the characterisation of linear maps. We discuss applications of our results to structural physical approximations of positive maps, quantification of non-Markovianity, and bounding the cost of error mitigation.
Cross-Speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis

Cross-speaker style transfer is crucial to the applications of multi-style and expressive speech synthesis at scale. It does not require the target speakers to be experts in expressing all styles and to collect corresponding recordings for model training. However, the performances of existing style transfer methods are still far behind real application needs. The root causes are mainly twofold. Firstly, the style embedding extracted from single reference speech can hardly provide fine-grained and appropriate prosody information for arbitrary text to synthesize. Secondly, in these models the content/text, prosody, and speaker timbre are usually highly entangled, it's therefore not realistic to expect a satisfied result when freely combining these components, such as to transfer speaking style between speakers. In this paper, we propose a cross-speaker style transfer text-to-speech (TTS) model with explicit prosody bottleneck. The prosody bottleneck builds up the kernels accounting for speaking style robustly, and disentangles the prosody from content and speaker timbre, therefore guarantees high quality cross-speaker style transfer. Evaluation result shows the proposed method even achieves on-par performance with source speaker's speaker-dependent (SD) model in objective measurement of prosody, and significantly outperforms the cycle consistency and GMVAE-based baselines in objective and subjective evaluations.
Inconsistency Detection-Based LOD in Smart Homes

The emergence of the internet of things in the smart homes has given rise to many services to meet the user's expectations. It is possible to control the temperature, the brightness, the sound system, and even the security of the house via a smartphone, at the request of the inhabitant or by scheduling it. This growing number of “things” must deal with material constraints such as home network infrastructure, but also applicative due to the number of proposed services. The heterogeneity of users' preferences often creates conflicts between them like turn on and off light or using a heater and an air conditioner in the same time. To manage these conflicts, the authors proposed a solution based on linked open data (LOD). The LOD allows defining the relation between the different services and things in the house and a better exploitation of the attributes of the inhabitant's profile and services. It consists to find inconsistency relation between the equipment using the antonym thesaurus.
Praznik: High performance information-based feature selection

Information filters are an important class of feature selection methods. They bring together strong theoretical background, execution speed and high selection quality. The paper provides a brief review of said methods and introduces praznik, an R package collecting their efficient implementations. Its functionality is discussed with illustrative example analyses on established benchmark datasets, which also demonstrate the package’s competitive accuracy and very high computational efficiency in comparison to other tools. Moreover, praznik exposes its low-level functionality, which aims to support research and development of novel information theory based methods.
Anomaly Detection on Data Streams for Smart Agriculture

Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in the identification of observations that deviate from the norm. This paper proposes an adaptation of an ensemble anomaly detector called enhanced locally selective combination in parallel outlier ensembles (ELSCP). On this basis, we define an unsupervised data-driven methodology for smart-farming temporal data that is applied in two case studies. The first considers harvest data including combine-harvester Global Positioning System (GPS) traces. The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies. Our experiments show that our methodology achieved interesting performance with Area Under the Curve of Precision-Recall (AUCPR) score of 0.972 in the combine-harvester dataset, which is 58.7% better than that of the second-best approach. In the crop dataset, our analysis showed that 30% of the detected anomalies could be directly linked to crop damage. Therefore, anomaly detection could be integrated in the decision process of farm operators to improve harvesting efficiency and crop health.
Improving Face-Based Age Estimation With Attention-Based Dynamic Patch Fusion

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone.However, stateof-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information.In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF).In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet.The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism.The FusionNet uses the discovered patches along with the facial image to predict the age of the subject.Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important).ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered.Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.
Voice Spoofing Countermeasure for Logical Access Attacks Detection

Voice-driven devices (VDDs) like Google Home and Amazon Alexa, which are well-known connected devices in consumer IoT, have applications in various domains i.e., home appliances automation, next-generation vehicles, voice banking, and so on.However, these VDDs that are based on automatic speaker verification systems (ASVs) are vulnerable to voice based logical access (LA) attacks like Text-to-Speech (TTS) synthesis and converted voice signals.Intruders can exploit these attacks to bypass the security of such systems and gain access of victim's bank account or home control.Thus, there exists a need to develop an effective voice spoofing countermeasure that can reliably be used to protect these VDDs against such malicious attacks.This work presents a novel audio features descriptor named as extended local ternary pattern (ELTP) to capture the vocal tract dynamically induced attributes of bonafide speech and algorithmic artifacts in synthetic and converted speeches.We fused our novel ELTP features with the linear frequency cepstral coefficients (LFCC) to further strengthen the capability of our features for capturing the traits of bonafide and spoofed signals.We employ the proposed ELTP-LFCC features to train the deep bidirectional Long Short-Term Memory (DBiLSTM) network for classification of the bonafide and spoof signal (i.e., TTS synthesis, converted speech).Performance of our spoofing countermeasure is measured on the large-scale and diverse ASVspoof 2019 logical access dataset.Experimental results demonstrate that the proposed audio spoofing countermeasure can reliably be used to detect the LA spoofing attacks.
Efficient and Privacy-Preserving Similarity Query With Access Control in eHealthcare

Similarity queries, giving a way to disease diagnosis based on similar patients, have wide applications in eHealthcare and are essentially demanded to be processed under fine-grained access policies due to the high sensitivity of healthcare data. One efficient and flexible way to implement such queries is to outsource healthcare data and the corresponding query services to a powerful cloud. Nevertheless, considering data privacy, healthcare data are usually outsourced in an encrypted form and required to be accessed in a privacy-preserving way. In the past years, many schemes have been proposed for privacy-preserving similarity queries. However, none of them is applicable to achieve data access control and access pattern privacy preservation. Aiming at this challenge, we propose an efficient and access pattern privacy-preserving similarity range query scheme with access control (named EPSim-AC). In our proposed scheme, we first design a novel tree structure, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -d-PB tree, to index healthcare data and introduce an efficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -d-PB tree based similarity query algorithm with access control. Second, to balance the search efficiency and access pattern privacy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -d-PB tree, we also define a weakened access pattern privacy, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -d-PB tree’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -access pattern unlinkability. After that, we preserve the privacy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -d-PB tree based similarity queries with access control through a symmetric homomorphic encryption scheme and present our detailed EPSim-AC scheme. Finally, we analyze the security of our scheme and also conduct extensive experiments to evaluate its performance. The results demonstrate that our scheme can guarantee <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -d-PB tree’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -access pattern unlinkability and has high efficiency.
Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types. In addition, genetic changes in solid tumors primarily act by changing signaling pathways that regulate cell behaviour. In this study, we hypothesized that Deep Learning networks can be trained to directly predict alterations of genes and pathways across a spectrum of solid tumors. We manually outlined tumor tissue in H&E-stained tissue sections from 7,829 patients with 23 different tumor types from The Cancer Genome Atlas. We then trained convolutional neural networks in an end-to-end way to detect alterations in the most clinically relevant pathways or genes, directly from histology images. Using this automatic approach, we found that alterations in 12 out of 14 clinically relevant pathways and numerous single gene alterations appear to be detectable in tissue sections, many of which have not been reported before. Interestingly, we show that the prediction performance for single gene alterations is better than that for pathway alterations. Collectively, these data demonstrate the predictability of genetic alterations directly from routine cancer histology images and show that individual genes leave a stronger morphological signature than genetic pathways.
Word-Object Learning via Visual Exploration in Space (WOLVES): A neural process model of cross-situational word learning.

Infants, children, and adults have been shown to track co-occurrence across ambiguous naming situations to infer the referents of new words. The extensive literature on this cross-situational word learning (CSWL) ability has produced support for two theoretical accounts-associative learning (AL) and hypothesis testing (HT)-but no comprehensive model of the behavior. We propose Word-Object Learning via Visual Exploration in Space (WOLVES), an implementation-level account of CSWL grounded in real-time psychological processes of memory and attention that explicitly models the dynamics of looking at a moment-to-moment scale and learning across trials. We use WOLVES to capture data from 12 studies of CSWL with adults and children, thereby providing a comprehensive account of data purported to support both AL and HT accounts. Direct model comparison shows that WOLVES performs well relative to two competitor models. In particular, WOLVES captures more data than the competitor models (132 vs. 69 data values) and fits the data better than the competitor models (e.g., lower percent error scores for 12 of 17 conditions). Moreover, WOLVES generalizes more accurately to three "held-out" experiments, although a model by Kachergis et al. (2012) fares better on another metric of generalization (Akaike Information Criterion [AIC]/Bayesian Information Criterion [BIC]). Critically, we offer the first developmental account of CSWL, providing insights into how memory processes change from infancy through adulthood. WOLVES shows that visual exploration and selective attention in CSWL are both dependent on and indicative of learning within a task-specific context. Furthermore, learning is driven by real-time synchrony of words and gaze and constrained by memory processes over multiple timescales. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
An intelligent recognition framework of access control system with anti-spoofing function

&lt;abstract&gt; &lt;p&gt;Under the background that Covid-19 is spreading across the world, the lifestyle of people has to confront a series of changes and challenges. This also presents new problems and requirements to automation facilities. For example, nowadays masks have almost become necessities for people in public places. However, most access control systems (ACS) cannot recognize people wearing masks and authenticate their identities to deal with increasingly serious epidemic pressure. Consequently, many public entries have turned to an attendant mode that brings low efficiency, infection potential, and high possibility of negligence. In this paper, a new security classification framework based on face recognition is proposed. This framework uses mask detection algorithm and face authentication algorithm with anti-spoofing function. In order to evaluate the performance of the framework, this paper employs the Chinese Academy of Science Institute of Automation-Face Anti-spoofing Datasets (CASIA-FASD) and Reply-Attack datasets as benchmarks. Performance evaluation indicates that the Half Total Error Rate (HTER) is 9.7%, the Equal Error Rate (EER) is 5.5%. The average process time of a single frame is 0.12 seconds. The results demonstrate that this framework has a high anti-spoofing capability and can be employed on the embedded system to complete the mask detection and face authentication task in real-time.&lt;/p&gt; &lt;/abstract&gt;
Adversarial Multiview Clustering Networks With Adaptive Fusion

The existing deep multiview clustering (MVC) methods are mainly based on autoencoder networks, which seek common latent variables to reconstruct the original input of each view individually. However, due to the view-specific reconstruction loss, it is challenging to extract consistent latent representations over multiple views for clustering. To address this challenge, we propose adversarial MVC (AMvC) networks in this article. The proposed AMvC generates each view’s samples conditioning on the fused latent representations among different views to encourage a more consistent clustering structure. Specifically, multiview encoders are used to extract latent descriptions from all the views, and the corresponding generators are used to generate the reconstructed samples. The discriminative networks and the mean squared loss are jointly utilized for training the multiview encoders and generators to balance the distinctness and consistency of each view’s latent representation. Moreover, an adaptive fusion layer is developed to obtain a shared latent representation, on which a clustering loss and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\ell _ {1,2}}$ </tex-math></inline-formula> -norm constraint are further imposed to improve clustering performance and distinguish the latent space. Experimental results on video, image, and text datasets demonstrate that the effectiveness of our AMvC is over several state-of-the-art deep MVC methods.