Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Embeddings with Gating Mechanisms for Text-Independent Speaker Verification

In this paper, gating mechanisms are applied in deep neural network (DNN) training for x-vector-based text-independent speaker verification. First, a gated convolution neural network (GCNN) is employed for modeling the frame-level embedding layers. Compared with the time-delay DNN (TDNN), the GCNN can obtain more expressive frame-level representations through carefully designed memory cell and gating mechanisms. Moreover, we propose a novel gated-attention statistics pooling strategy in which the attention scores are shared with the output gate. The gated-attention statistics pooling combines both gating and attention mechanisms into one framework; therefore, we can capture more useful information in the temporal pooling layer. Experiments are carried out using the NIST SRE16 and SRE18 evaluation datasets. The results demonstrate the effectiveness of the GCNN and show that the proposed gated-attention statistics pooling can further improve the performance.
Characterization of the Bitcoin Peer-to-Peer Network (2015-2018)

Based on measurements performed between 2015 and 2018 we provide a characterization of the Bitcoin P2P network. The characterization enables the parametrization and validation of simulation models and the assessment of the reliability of real-world experiments. Furthermore, the network characterization provides insights into the behavior of peers and their operators. For instance, we provide evidence that Sybil events happened in the past in the Bitcoin P2P network. Additionally, our measurements show that the performance and anonymity of transaction and block propagation has been improved by implementation and protocol changes.
An Intrusion Tracking Watermarking Scheme

With the rapid development of edge computing technology, the scale of the network continues to expand.Various types of applications are becoming more widespread.In the edge computing, existing network agents, NAT, IP tunneling technologies, and rapidly evolving anonymous communication systems provide convenience for attackers to hide real IP.In addition, the attacker forms a ''stepping'' chain by breaking through several intermediate systems of the edge computing network.Thereby implementing an invisible intrusion attack across multiple autonomous domains can increase the difficulty of intrusion tracking.Aiming at the problem of insufficient applicability of existing interval centroid based watermarking technique, this paper proposes a histogram specified interval centroid-based watermarking technique.It improves crossdomain collaborative intrusion tracking.This technique improves the resistance of the prior art to multiflow attacks in edge computing.It decreases the time and space overhead of the detector.Compared with other interval centroid based watermarking techniques, this method has stronger concealment.The proposed method can effectively defend against multi-flow attacks of edge computing.The time and space overhead of the detector can be reduced when multiple attack flows are tracked in parallel.Thus, it is suitable for edge computing.The robustness and adaptability are improved by this method.
Capacity-Achieving Private Information Retrieval Codes from MDS-Coded Databases with Minimum Message Size

We consider constructing capacity-achieving linear codes with minimum message size for private information retrieval (PIR) from N non-colluding databases, where each message is coded using maximum distance separable (MDS) codes, such that it can be recovered from reading the contents of any T databases. It is shown that the minimum message size (sometimes also referred to as the sub-packetization level) is significantly, in fact exponentially, lower than previously believed. More precisely, when K > T/ gcd(N, T) where K is the total number of message in the system and gcd(·,·) means the greatest common divisor, we establish, by providing both a novel code construction and a matching converse, the minimum message size as lcm(N -T, T), where lcm(·,·) means the least common multiple. On the other hand, when K is small, we show that it is in fact possible to design codes with a message size even smaller than lcm(N - T, T).
Cryptocurrency Tide and Islamic Finance Development: Any Issue?

Cryptocurrencies are hidden monies that are specifically created to be used as digital currencies while assuming the characteristics of real money. Barring the divergent opinions on whether permitted in Islamic law (that is/are halal) or forbidden in Islamic law (that is/are haram), and for which the swing tends to be in favor of its blockchain underlying technology permissibility in Islam, cryptocurrencies are undoubtedly indicating potential for relevance in the global trade, investment, and other contract settlements in some years to come. The potential of the blockchain technology is phenomenal with recent estimates suggesting it will be worth more than $20 trillion in just two years, which is more than the entire American economy. Since fortunes are made by those entrepreneurs and indeed savvy investors who have discerned its future potential earlier on, there exists some great temptation for people to jump on the blockchain bandwagon. Apparently the growing acceptability of digital fiat money as a result of technology development on one hand, and the failure of the paper money to mitigate inflation and other economic disequilibria since the disappearance of the gold standard on the other, various forms of cryptocurrencies including Bitcoins (referred to as the king) appear to roar toward wider recognition. However, an emerging phenomenon associated with cryptocurrency revolution is an observed significant fluctuation (the tide) in its value and thus a subject of discussion within Islamic finance community and beyond. In the midst of this also is the current agitation founded on some of the Islamic law (Sharīʿa) view on the necessity of asset-backed money, to be extended to the current cryptocurrency innovation for its transformation into a Sharīʿa compliant precious metal backed currency. The big question now which this chapter sought to provide the answer is, what are the implications of these developments to a more established and widening global phenomenon of Islamic finance and its development in Muslim world vis-á-vis aspirations for sustained economic development. The work finds that cryptocurrencies would generate three advantages over all forms of money including gold through: establishing a unified financial system through its standard decentralization, being rarer than gold and its significant mitigation of inflation. It is also noted that the prevalent foreign exchange risk resulting from the underlying activities (rather than the currency itself) is free from speculation (Gharar). It is, therefore, recommended that stakeholders in the Islamic Finance world should not be passive but be proactive in commencing processes to develop technical notes, standards, and operational guidelines to partake in the inevitable migration to cryptocurrencies.
Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis

Every day, we deal with a lot of information on the Internet. This information can have origin from many different places such as online review sites and social networks. In the midst of this messy data, arises the opportunity to understand the subjective opinion about a text, in particular, the polarity. Sentiment Analysis and Text Classification helps to extract precious information about data and assigning a text into one or more target categories according to its content. This paper proposes a comparison between four of the most popular Text Classification Algorithms - Naive Bayes, Support Vector Machine, Decision Trees and Random Forest - based on the Amazon Unlocked mobile phone reviews dataset. Moreover, we also study the impact of some attributes (Brand and Price) on the polarity of the review. Our results demonstrate that the Support Vector Machine is the most complete algorithm of this study and achieve the highest values in all the metrics such as accuracy, precision, recall, and F1 score.
Robust Steganography by Modifying Sign of DCT Coefficients

Modern adaptive image steganography with minimizing a distortion function has high performance of undetectability. However, when an image with hidden information is attacked by JPEG compression, its robustness cannot be guaranteed, that remarkably limits its extension from the lab to the real world. In this paper, a novel image steganographic algorithm is proposed that is robust to JPEG compression. First, by using the sign of DCT coefficients, that remains unchangeable before and after JPEG compression, we select the candidate coefficients for resisting JPEG compression. Second, the designed distortion function assigns cost for each candidate DCT coefficient. Finally, relying on both error correction code and Syndrome-Trellis Codes, an encoded message is embedded into the cover image with minimum embedding distortion. Compared with prior arts, extensive experimental results highlight both undetectability and robustness of our proposed algorithm.
Trajectory Optimization and Situational Analysis Framework for Autonomous Overtaking With Visibility Maximization

In this article we present a trajectory generation method for autonomous overtaking of unexpected obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example when overtaking unexpected objects on the vehicle's ego lane on a two-way street. In this case, a human driver would first make sure that the opposite lane is free and that there is enough room to successfully execute the maneuver, and then it would cut into the opposite lane in order to execute the maneuver successfully. We consider the practical problem of autonomous overtaking when the coverage of the perception system is impaired due to occlusion. Safe trajectories are generated by solving, in real-time, a non-linear constrained optimization, formulated as a receding horizon planner that maximizes the ego vehicle's visibility. The planner is complemented by a high-level behavior planner, which takes into account the occupancy of other traffic participants, the information from the vehicle's perception system, and the risk associated with the overtaking maneuver, to determine when the overtake maneuver should happen. The approach is validated in simulation and in experiments in real world traffic.
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends

Early detection of emerging research trends could potentially revolutionise the way research is done. For this reason, trend analysis has become an area of paramount importance in academia and industry. This is due to the significant implications for research funding and public policy. The literature presents several emerging approaches to detecting new research trends. Most of these approaches rely mainly on citation counting. While citations have been widely used as indicators of emerging research topics, they suffer from some limitations. For instance, citations can take months to years to progress and then to reveal trends. Furthermore, they fail to dig into paper content. To overcome this problem, we introduce Leap2Trend, a novel approach to instant detection of research trends. Leap2Trend relies on temporal word embeddings (word2vec) to track the dynamics of similarities between pairs of keywords, their rankings and respective uprankings (ascents) over time. We applied Leap2Trend to two scientific corpora on different research areas, namely computer science and bioinformatics and we evaluated it against two gold standards Google Trends hits and Google Scholar citations. The obtained results reveal the effectiveness of our approach to detect trends with more than 80% accuracy and 90% precision in some cases. Such significant findings evidence the utility of our Leap2Trend approach for tracking and detecting emerging research trends instantly.
Screening PubMed abstracts: is class imbalance always a challenge to machine learning?

The growing number of medical literature and textual data in online repositories led to an exponential increase in the workload of researchers involved in citation screening for systematic reviews. This work aims to combine machine learning techniques and data preprocessing for class imbalance to identify the outperforming strategy to screen articles in PubMed for inclusion in systematic reviews. We trained four binary text classifiers (support vector machines, k-nearest neighbor, random forest, and elastic-net regularized generalized linear models) in combination with four techniques for class imbalance: random undersampling and oversampling with 50:50 and 35:65 positive to negative class ratios and none as a benchmark. We used textual data of 14 systematic reviews as case studies. Difference between cross-validated area under the receiver operating characteristic curve (AUC-ROC) for machine learning techniques with and without preprocessing (delta AUC) was estimated within each systematic review, separately for each classifier. Meta-analytic fixed-effect models were used to pool delta AUCs separately by classifier and strategy. Cross-validated AUC-ROC for machine learning techniques (excluding k-nearest neighbor) without preprocessing was prevalently above 90%. Except for k-nearest neighbor, machine learning techniques achieved the best improvement in conjunction with random oversampling 50:50 and random undersampling 35:65. Resampling techniques slightly improved the performance of the investigated machine learning techniques. From a computational perspective, random undersampling 35:65 may be preferred.
Soft++, a multi-parametric non-saturating non-linearity that improves convergence in deep neural architectures

A key strategy to enable training of deep neural networks is to use non-saturating activation functions to reduce the vanishing gradient problem. Popular choices that saturate only in the negative domain are the rectified linear unit (ReLU), its smooth, non-linear variant, Softplus, and the exponential linear units (ELU and SELU). Other functions are non-saturating across the entire real domain, like the linear parametric ReLU (PReLU). Here we introduce a nonlinear activation function called Soft++ that extends PReLU and Softplus, parametrizing the slope in the negative domain and the exponent. We test identical network architectures with ReLU, PReLU, Softplus, ELU, SELU, and Soft++ on several machine learning problems and find that: i) convergence of networks with any activation function depends critically on the particular dataset and network architecture, emphasizing the need for parametrization, which allows to adapt the activation function to the particular problem; ii) non-linearity around the origin improves learning and generalization; iii) in many cases, non-saturation across the entire real domain further improves performance. On very difficult learning problems with deep fully-connected and convolutional networks, Soft++ outperforms all other activation functions, accelerating learning and improving generalization. Its main advantage lies in its dual parametrization, offering flexible control of the shape and gradient of the function.
Fourier-transform quantum phase estimation with quantum phase noise

For the fundamental task of estimating a phase on an arbitrary quantum process, a variant of Fourier-based quantum phase estimation is devised, which uses a probing signal of multiple entangled qubits. For simple practical implementation, each probing qubit can be applied and measured separately. When the qubits are optimally entangled, the Heisenberg enhanced scaling of the estimation efficiency is obtained. The phase estimation protocol can be applied equally in the presence of quantum phase noise. This enables us to investigate the impact of a generic quantum phase noise on the performance of the Fourier-based phase estimation. Especially it reveals that the strategy found optimal with no noise, gradually loses its optimality as the noise increases. Also, in contrast to the noise-free situation, with noise the presence of entanglement is no longer uniformly beneficial to estimation; there exists an optimal amount of entanglement to maximize the efficiency and above which it becomes detrimental. The results contribute to better knowledge of quantum noise and entanglement for quantum signal and information processing.
A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

The tourism industry has been promoting its products and services based on the reviews that people often write on travel websites like TripAdvisor.com, Booking.com and other platforms like these. These reviews have a profound effect on the decision making process when evaluating which places to visit, such as which restaurants to book, etc. In this contribution is presented a cloud based software tool for the massive analysis of this social media data (TripAd-visor.com). The main characteristics of the tool developed are: i) the ability to aggregate data obtained from social media; ii) the possibility of carrying out combined analyses of both people and comments; iii) the ability to detect the sense (positive, negative or neutral) in which the comments rotate, quantifying the degree to which they are positive or negative, as well as predicting behaviour patterns from this information; and iv) the ease of doing everything in the same application (data downloading, pre-processing, analysis and visualisation). As a test and validation case, more than 33.500 revisions written in English on restaurants in the Province of Granada (Spain) were analysed.
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design

The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each parameter in the search space, manually tuning of DNN hyperparameters is impractical. Automatic joint DNN and hardware hyperparameter optimization is indispensable for such problems. Bayesian optimization-based approaches have shown promising results for hyperparameter optimization of DNNs. However, most of these techniques have been developed without considering the underlying hardware, thereby leading to inefficient designs. Further, the few works that perform joint optimization are not generalizable and mainly focus on CMOS-based architectures. In this work, we present a novel pseudo agent-based multiobjective hyperparameter optimization (PABO) for maximizing the DNN performance while obtaining low hardware cost. Compared to the existing methods, our work poses a theoretically different approach for joint optimization of accuracy and hardware cost and focuses on memristive crossbar based accelerators. PABO uses a supervisor agent to establish connections between the posterior Gaussian distribution models of network accuracy and hardware cost requirements. The agent reduces the mathematical complexity of the co-optimization problem by removing unnecessary computations and updates of acquisition functions, thereby achieving significant speed-ups for the optimization procedure. PABO outputs a Pareto frontier that underscores the trade-offs between designing high-accuracy and hardware efficiency. Our results demonstrate a superior performance compared to the state-of-the-art methods both in terms of accuracy and computational speed (~100x speed up).
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency

Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (i.e., identifying the boundaries of the terms and definitions). The previous works for DE have only focused on one of the two approaches, failing to model the inter-dependencies between the two tasks. In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies. Our model features deep learning architectures to exploit the global structures of the input sentences as well as the semantic consistencies between the terms and the definitions, thereby improving the quality of the representation vectors for DE. Besides the joint inference between sentence classification and sequential labeling, the proposed model is fundamentally different from the prior work for DE in that the prior work has only employed the local structures of the input sentences (i.e., word-to-word relations), and not yet considered the semantic consistencies between terms and definitions. In order to implement these novel ideas, our model presents a multi-task learning framework that employs graph convolutional neural networks and predicts the dependency paths between the terms and the definitions. We also seek to enforce the consistency between the representations of the terms and definitions both globally (i.e., increasing semantic consistency between the representations of the entire sentences and the terms/definitions) and locally (i.e., promoting the similarity between the representations of the terms and the definitions). The extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.1
Working Together with Conversational Agents: The Relationship of Perceived Cooperation with Service Performance Evaluations

Conversational agents are gradually being deployed by organizations in service settings to communicate with and solve problems together with consumers. The current study investigates how consumers' perceptions of cooperation with conversational agents in a service context are associated with their perceptions about agents' anthropomorphism, social presence, the quality of the information provided by an agent, and the agent service performance. An online experiment was conducted in which participants performed a service-oriented task with the assistance of conversational agents developed specifically for the study and evaluated the performance and attributes of the agents. The results suggest a direct positive link between perceiving a conversational agent as cooperative and perceiving it to be more anthropomorphic, with higher levels of social presence and providing better information quality. Moreover, the results also show that the link between perceiving an agent as cooperative and the agent's service performance is mediated by perceptions of the agent's anthropomorphic cues and the quality of the information provided by the agent.