Reviewerly/exHarmony huggingface dataset:
One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization

Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers. However, such model suffers from the limitation that it can only convert the voice to the speakers in the training data, which narrows down the applicable scenario of VC. In this paper, we proposed a novel one-shot VC approach which is able to perform VC by only an example utterance from source and target speaker respectively, and the source and target speaker do not even need to be seen during training. This is achieved by disentangling speaker and content representations with instance normalization (IN). Objective and subjective evaluation shows that our model is able to generate the voice similar to target speaker. In addition to the performance measurement, we also demonstrate that this model is able to learn meaningful speaker representations without any supervision.
Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark.
Analysis of Blockchain System With Token-Based Bookkeeping Method

We propose a token-based blockchain system that streamlines abstractions into a universal token structure. In the proposed system, each token has an identity that enables the implementation of specific rollbacks and governance that make 51% attacks unprofitable. Because the token-based bookkeeping method only verifies and updates the ownership within each transaction, the proposed system supports parallel expansion and cross-chain transactions without limit. The flexible authority management mechanism of the proposed system is regulatory-friendly, as the intensity of supervision and governance can be adapted to accommodate different application scenarios.
Cross-domain network representations

The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning tasks. Recent research has driven significant progress in network representation by employing random walks as the network sampling strategy. Nevertheless, existing approaches rely on domain-specifically rich community structures and fail in the network that lack topological information in its own domain. In this paper, we propose a novel algorithm for cross-domain network representation, named as CDNR. By generating the random walks from a structural rich domain and transferring the knowledge on the random walks across domains, it enables a network representation for the structural scarce domain as well. To be specific, CDNR is realized by a cross-domain two-layer node-scale balance algorithm and a cross-domain two-layer knowledge transfer algorithm in the framework of cross-domain two-layer random walk learning. Experiments on various real-world datasets demonstrate the effectiveness of CDNR for universal networks in an unsupervised way.
Classification Based on Multivariate Contrast Patterns

There is a growing interest in the development of classifiers based on contrast patterns (CPs); partly due to the advantage of them being able to explain classification results in a language that is easy to understand for an expert. CP-based classifiers, when using contrast patterns extracted by miners based on decision trees, attain accuracies comparable with other state-of-the-art classifiers. The existing decision tree-based miners use univariate decision trees (UDTs) to extract CPs. In this paper, we define the concept of multivariate CP. We introduce a multivariate CP miner based on multivariate decision trees (MDTs) as well as a new filtering algorithm for multivariate CPs. From our experimental results, we conclude that our proposed CP miner allows obtaining significantly better classification results than the other state-of-the-art classifiers.
A reversible database watermarking method with low distortion

In this paper, a low distortion reversible database watermarking method based on histogram gap is proposed in view of the large gap in histogram of database integer data. By using the method, the tolerance of the attribute column containing all integer data is firstly calculated and the prediction error is obtained according to the tolerance. Then according to the watermark bits to be embedded, the database tuples will be randomly grouped and the histogram can be constructed by using the prediction error. Finally, the histogram correction rule is used to find the histogram peak bin, the number of consecutive non-zero prediction errors on the left and right sides of the peak is obtained, and the histogram shift is performed on the side with a smaller number of non-zero prediction errors, and then the watermark embedding will be realized. The results of the experiments based on the published dataset of FCTD (Forest Cover Type Dataset) show that compared with the existing GAHSW which also considers distortion, the proposed method significantly reduces the number of histogram column shift while embedding the watermarks, greatly reduces the changes to the carrier data, and effectively reduces the database's data distortion caused by watermark embedding.
Comparing Virtual Reality Telepresence and Traditional Simulation Methods: A Pilot Study

Virtual reality (VR) could enable clinical simulation centers to reach the teaching capacity of traditional hospital practica. This study quantitatively tests VR telepresence against two traditional simulation learning methods using a within-subject design and the Presence Questionnaire. Eight nursing students were randomly assigned and rotated through simulation participation, VR observation, and television observation conditions, completing a questionnaire after each condition. Each condition had a significant effect on presence. Simulation participation yielded the highest perceived presence, followed by VR, and lastly by television observation. This pilot study probed for effect and feedback that will inform a larger experiment.
Lexicase selection of specialists

Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have poor errors on some training cases, if they have great errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting these specialists, which may have poor total error, plays an important role in lexicase selection's observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists much less frequently. We conduct experiments examining this hypothesis, and find that lexicase selection's performance and diversity maintenance degrade when we deprive it of the ability of selecting specialists. These findings help explain the improved performance of lexicase selection compared to tournament selection, and suggest that specialists help drive evolution under lexicase selection toward global solutions.
A Block-Free Distributed Ledger for P2P Energy Trading: Case with IOTA?

Across the world, the organisation and operation of the electricity markets is quickly changing, moving towards decentralised, distributed, renewables-based generation with real-time data exchange-based solutions. In order to support this change, blockchain-based distributed ledgers have been proposed for implementation of peer-to-peer energy trading platform. However, blockchain solutions suffer from scalability problems as well as from delays in transaction confirmation. This paper explores the feasibility of using IOTA’s DAG-based block-free distributed ledger for implementation of energy trading platforms. Our agent-based simulation research demonstrates that an IOTA-like DAG-based solution could overcome the constraints that blockchains face in the energy market. However, to be usable for peer-to-peer energy trading, even DAG-based platforms need to consider specificities of energy trading markets (such as structured trading periods and assured confirmation of transactions for every completed period).
A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack

CAPTCHA authentication has been challenged by recent technology advances in AI. However, many of the AI advances challenging CAPTCHA are either restricted by a limited amount of labeled CAPTCHA data or are constructed in an expensive or complicated way. In contrast, this paper illustrates a low-cost approach that takes advantage of the nature of open source libraries for an AI-based chosen-plaintext attack. The chosen-plaintext attack described here relies on a deep learning model created and trained on a simple personal computer in a low-cost way. It shows an efficient cracking rate over two open-source Python CAPTCHA Libraries, Claptcha and Captcha. This chosen-plaintext attack method has raised a potential security alert in the era of AI, particularly to small-business owners who use the open-source CAPTCHA libraries. The main contributions of this project include: (1) it is the first low-cost method based on chosen-plaintext attack by using the nature of open-source Python CAPTCHA libraries; (2) it is a novel way to combine TensorFlow object detection and our proposed peak segmentation algorithm with convolutional neural network to improve the recognition accuracy.
Dispersive readout of a weakly coupled qubit via the parity-time-symmetric phase transition

For some cavity-quantum-electrodynamics systems, such as a single-electron spin coupled to a passive cavity, it is challenging to reach the strong-coupling regime. In such a weak-coupling regime, the conventional dispersive readout technique cannot be used to resolve the quantum states of the spin. Here we propose an improved dispersive readout method to measure the quantum states of a weakly coupled qubit by harnessing either one or two auxiliary cavities linearly coupled to the passive cavity containing the qubit. With appropriate parameters in both cases, the system excluding the qubit can exhibit a parity-time-symmetric phase transition at the exceptional point (EP). Because the EP can amplify the perturbation induced by the qubit and the parity-time symmetry can narrow the linewidths of the peaks in the transmission spectrum of the passive cavity, we can measure the quantum states of the weakly coupled qubit via this transmission spectrum. Owing to the weak coupling between the qubit and the passive cavity, the backaction due to the measurement of the qubit can also be reduced in comparison with the conventional dispersive readout technique in the strong-coupling regime.
Solving Combinatorial Problems with Machine Learning Methods

With the development of machine learning in various fields, it can also be applied to combinatorial optimization problems, automatically discovering generic and fast heuristic algorithms based on training data, and requires fewer theoretical and empirical knowledge. Pointer network improves the attention mechanism, instead of allocating different attention to hidden states of encoder to generate context vectors, using attention as a pointer to select an element of the input sequence at every step of decoding, which solves the problem of variable dictionary size of the output sequence. Pointer net (Ptr-Net) applied to three combinatorial optimization problems, convex hull, Delaunay triangulation, and traveling salesman problem (TSP), obtains good approximate solutions. Point matching is also a special kind of combinatorial optimization problems that is to obtain the optimal corresponding references, which can be modeled by Ptr-Net. However, Ptr-Net can't be used to solve point matching problem because it doesn't take full advantage of the correspondences between the two point sets. We propose multi-pointer network, which draws the idea from multi-label classification, to address this limitation by pointing out a set of input elements. These applications are all based on supervised learning to approximate expected known solutions. However, high-quality labeled data is often expensive, unreliable, or simply unavailable and may be infeasible for new problem statements, making supervised learning being unpractical. Reinforcement learning, as another research hotspot in the field of machine learning, does not require labeled sample data. It interacts with the environment through trial-and-error mechanism and focuses more on learning problem-solving strategies. We introduce a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning, focusing on the traveling salesman problem. We also introduce a framework, a unique combination of reinforcement learning and graph embedding network, to solve graph optimization problems, focusing on maximum cut (MAXCUT) and minimum vertex cover (MVC) problems.
Cloud storage security assessment through equilibrium analysis

With ever greater amounts of data stored in cloud servers, data security and privacy issues have become increasingly important. Public cloud storage providers are semi-trustworthy because they may not have adequate security mechanisms to protect user data from being stolen or misused. Therefore, it is crucial for cloud users to evaluate the security of cloud storage providers. However, existing security assessment methods mainly focus on external security risks without considering the trustworthiness of cloud providers. In addition, the widely used third-party mediators are assumed to be trusted and we are not aware of any work that considers the security of these mediators. This study fills these gaps by assessing the security of public cloud storage providers and third-party mediators through equilibrium analysis. More specifically, we conduct evaluations on a series of game models between public cloud storage providers and users to thoroughly analyze the security of different service scenarios. Using our proposed security assessment, users can determine the risk of whether their privacy data is likely to be hacked by the cloud service providers; the cloud service providers can also decide on strategies to make their services more trustworthy. An experimental study of 32 users verified our method and indicated its potential for real service improvement.
FDREnet: Face Detection and Recognition Pipeline

Face detection and recognition are being studied extensively for their vast applications in security, biometrics, healthcare, and marketing. As a step towards presenting an almost accurate solution to the problem in hand, this paper proposes a face detection and face recognition pipeline - face detection and recognition embedNet (FDREnet). The proposed FDREnet involves face detection through histogram of oriented gradients and uses Siamese technique and contrastive loss to train a deep learning architecture (EmbedNet). The approach allows the EmbedNet to learn how to distinguish facial features apart from recognizing them. This flexibility in learning due to contrastive loss accounts for better accuracy than using traditional deep learning losses. The dataset’s embeddings produced from the trained FDREnet result accuracy of 98.03%, 99.57% and 99.39% for face94, face95, and face96 datasets respectively through SVM clustering. Accuracy of 97.83%, 99.57%, and 99.39% was observed for face94, face95, and face96 datasets respectively through KNN clustering.
Attention-Based Personalized Encoder-Decoder Model for Local Citation Recommendation

With a tremendous growth in the number of scientific papers, researchers have to spend too much time and struggle to find the appropriate papers they are looking for. Local citation recommendation that provides a list of references based on a text segment could alleviate the problem. Most existing local citation recommendation approaches concentrate on how to narrow the semantic difference between the scientific papers' and citation context's text content, completely neglecting other information. Inspired by the successful use of the encoder-decoder framework in machine translation, we develop an attention-based encoder-decoder (AED) model for local citation recommendation. The proposed AED model integrates venue information and author information in attention mechanism and learns relations between variable-length texts of the two text objects, i.e., citation contexts and scientific papers. Specifically, we first construct an encoder to represent a citation context as a vector in a low-dimensional space; after that, we construct an attention mechanism integrating venue information and author information and use RNN to construct a decoder, then we map the decoder's output into a softmax layer, and score the scientific papers. Finally, we select papers which have high scores and generate a recommended reference paper list. We conduct experiments on the DBLP and ACL Anthology Network (AAN) datasets, and the results illustrate that the performance of the proposed approach is better than the other three state-of-the-art approaches.
On a stochastic neuronal model integrating correlated inputs

A modified LIF-type stochastic model is considered with a non-delta correlated stochastic process in place of the traditional white noise. Two different mechanisms of reset are specified with the aim to model endogenous and exogenous correlated input stimuli. Ornstein-Uhlenbeck processes are used to model the two different cases. An equivalence between different ways to include currents in the model is also shown. The theory of integrated stochastic processes is evoked and the main features of involved processes are obtained, such as mean and covariance functions. Finally, a simulation algorithm of the proposed model is described; simulations are performed to provide estimations of firing densities and related comparisons are given.
Could Blockchain Technology Empower Patients, Improve Education, and Boost Research in Radiology Departments? An Open Question for Future Applications

Blockchain can be considered as a digital database of cryptographically validated transactions stored as blocks of data. Copies of the database are distributed on a peer-to-peer network adhering to a consensus protocol for authentication of new blocks into the chain. While confined to financial applications in the past, this technology is quickly becoming a hot topic in healthcare and scientific research. Potential applications in radiology range from upgraded monitoring of training milestones achievement for residents to improved control of clinical imaging data and easier creation of secure shared databases.
Brain dynamics and temporal trajectories during task and naturalistic processing

Human functional Magnetic Resonance Imaging (fMRI) data are acquired while participants engage in diverse perceptual, motor, cognitive, and emotional tasks. Although data are acquired temporally, they are most often treated in a quasi-static manner. Yet, a fuller understanding of the mechanisms that support mental functions necessitates the characterization of dynamic properties. Here, we describe an approach employing a class of recurrent neural networks called reservoir computing, and show the feasibility and potential of using it for the analysis of temporal properties of brain data. We show that reservoirs can be used effectively both for condition classification and for characterizing lower-dimensional "trajectories" of temporal data. Classification accuracy was approximately 90% for short clips of "social interactions" and around 70% for clips extracted from movie segments. Data representations with 12 or fewer dimensions (from an original space with over 300) attained classification accuracy within 5% of the full data. We hypothesize that such low-dimensional trajectories may provide "signatures" that can be associated with tasks and/or mental states. The approach was applied across participants (that is, training in one set of participants, and testing in a separate group), showing that representations generalized well to unseen participants. Taken together, we believe the present approach provides a promising framework to characterize dynamic fMRI information during both tasks and naturalistic conditions.
A framework for effective threat hunting

In today's dynamic cyber security environment, with its rapidly changing threat landscape, companies are becoming increasingly aware of the necessity of getting ahead of new cyber attack trends. It is for this reason that threat hunting has become so popular. A new wave of attacks has become exceptionally proficient in successful, undetected intrusions, breaching network defences, exploiting system vulnerabilities and obtaining access to organisations’ systems and data.
Secure Hash Function Constructing for Future Communication Systems and Networks

The application of Web technologies and forms of electronic document circulation in the process of information exchange between users though simplifies this process. However, generates a number of new threats for the confidentiality, integrity, and availability of information and the appearance of previously unknown vulnerabilities. One of the most common methods of protection is using the digital certificates that ensure the confidential exchange of data between a client and a server by encrypting and authenticating a digital certificate. A digital certificate is a public key, certified by the EDS of the certification center. Nevertheless, a digital certificate is not just a public key with information, but a so-called signature of a server or Web resource that is implemented using the hash functions. Information technologies development and the emergence of new attack types lead to increasing the amount of disadvantages of existing hash functions. Thus, in the paper a new hashing function was proposed, which was based on the SHA-2 hash function. Improvements involved a number of changes: increased the size of words and an increase in the message digest; at the preprocessing stage, the incoming message is supplemented by a pseudorandom sequence; the numbers of nonlinear functions are increased. The proposed changes allow to reduce the number of rounds in the compression function, which will guarantee at least similar stability indicators with simultaneous increase in data processing speed.