Reviewerly/exHarmony huggingface dataset:
Spectroscopy of classical environmental noise with a qubit subjected to projective measurements

We show theoretically how a correlation of multiple measurements on a qubit undergoing pure dephasing can be expressed as environmental noise filtering. The measurement of such correlations can be used for environmental noise spectroscopy, and the family of noise filters achievable in such a setting is broader than the one achievable with a standard approach, in which dynamical decoupling sequences are used. We illustrate the advantages of this approach by considering the case of a noise spectrum with sharp features at very low frequencies. We also show how appropriately chosen correlations of a few measurements can detect the non-Gaussian character of certain environmental noises, particularly the noise affecting the qubit at the so-called optimal working point.
Linguistic Design of In-Vehicle Prompts in Adaptive Dialog Systems

Against the background of current trends towards natural and adaptive in-vehicle Spoken Dialog Systems, this paper aims at evaluating potential factors involved in the perception of naturalness and comprehensibility of system prompts. By conducting an exploratory user study investigating various syntactic paraphrases, we were able to identify several system- and user-sided characteristics which should be considered in the design of system prompts. We conclude from our results that the choice of a syntactic structure for in-vehicle prompts is a relevant question and interestingly depends on several individual user characteristics, such as personality.
Detection of Incumbent Radar in the 3.5 GHz CBRS Band using Support Vector Machines

In the 3.5 GHz Citizens Broadband Radio Service (CBRS), 100 MHz of spectrum will be dynamically shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. This paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of support vector machine (SVM) classifiers for these sensors. We find that a peak analysis classifier and a higher-order statistics classifier perform comparably when the signal is in white Gaussian noise or commercial long term evolution (LTE) emissions, but with out-of-band emissions of adjacent-band systems the peak analysis classifier is far superior. This result also highlights the importance of including adjacent-band emissions in any performance evaluation of 3.5 GHz sensors.
A Machine Learning Framework for Edge Computing to Improve Prediction Accuracy in Mobile Health Monitoring

The great challenges in the aging society and the lack of human resources, especially in health care, remains a formidable task. The cloud centric computing paradigm offers a solution in processing IoT applications in health care. However, due to the large computing and communication overheads, an alternative solution is sought. Here, we consider machine learning in edge computing to detect and improve the predictability accuracy in mobile health monitoring of human activity. With multi-modal sensor data, we conducted pre-processing to sanitize the data and classify the activities in the dataset. We used and compare the processing performance using random forests and SVM machine learning algorithms to identify and classify the activities in the dataset. We achieved approximately 99% accuracy with random forest which was better than SVM, at 98%. We used confusion matrix to identify the majority of mismatched data belonging to initial value of sensors while recording a particular activity, and also used visual representation of the data for better understanding. We extract the activity’s ECG data and classify into four categories to provide more specific information from the person’s activity data. The aforementioned experiments provided promising results and insights on the implementation to improve the prediction accuracy on the health status of people undergoing some activity.
A new batch verification scheme for ECDSA$$^*$$ signatures

In this paper, we propose an efficient batch verification algorithm for ECDSA $$^*$$ (Elliptic Curve Digital Signature Algorithm) $$^*$$ signatures. Our scheme is efficient for both single and multiple signers. ECDSA $$^*$$ signature is a modified version of ECDSA, which accelerates the verification of ECDSA signature by more than 40%. However, the highlighting feature of our proposed scheme is its efficiency for varied batch sizes. The scheme is resistant to forgery attacks by either signer or intruder. The performance of our scheme remains consistent for higher batch sizes too ( $$\ge 8$$ ). Our paper also discusses the possible attacks on ECDSA signatures and also how our scheme is resistant to such attacks.
Feature Extraction of ECG Signal by using Deep Feature

The analysis and classification of Electrocardiogram (ECG) signals have become very important tool to diagnose of heart disorders. Computer-aided techniques are generally used to classify biomedical application areas. In this paper, we aim to feature extraction and classification of ECG signals. Accordingly, an open access ECG database in Physionet was employed in order to separate normal and abnormal of ECG records. Deep feature approach which is based on Convolutional Neural Network (CNN) was applied to taking out important features of heart recordings. Afterward, Extreme Learning Machine (ELM) was applied to the ECG records. The average precision value metric was used to the performance of the classification performed. In this content, it was noticed classification success values were achieved to accuracy % 88.33, sensitivity %89.47 and specificity % 87.80 with ELM.
Trust in Automated Software Repair

Automation and autonomous systems are becoming increasingly pervasive in society, as are the software systems that control them. There is a need for safe and secure software systems. Automated code repair provides a promising solution. The present research investigates programmers’ perceptions of trustworthiness and trust in automated code repair, how those perceptions and intentions differed from code ostensibly repaired by a human, and the effects of repair transparency. The present research comprises two studies, each with a unique sample. The first sample included inexperienced developers (N = 24), and the second sample included experienced developers (N = 24). Participants were presented with five different pieces of code before and after being repaired by an automated code repair program, and were asked to rate the trustworthiness of the repairs and whether they would endorse using the code. Each study was a 2 × 2 between-subjects design with repeated measures. The first factor manipulated the purported source of the repairs (human vs automated code repair program). The second factor manipulated the transparency of the repairs (deleted vs commented out). Results suggest that inexperienced developers find automated code repair more trustworthy than repairs made by a human. Both experienced and inexperienced developers trusted the human repairer less after reviewing the repairs, but did not significantly differ in their intentions to trust the automated code repair program after reviewing the repairs.
A Method of Detecting Storage Based Network Steganography Using Machine Learning

Today, the techniques of network steganography are widely applied. In addition to the outstanding advantages about the ability to hide and transmit secret information, it has a huge disadvantage that is being exploited by hackers to transmit information or communicate with the control host. Network steganography storage method is one of the network steganography techniques that is being much applied. Due to the characteristics of the storage based network steganography are different from other network steganography techniques, the detection of this techniques is difficult. The traditional tools and methods used to detect steganography are difficult to detect the signs of steganographic packets that use this technique. Therefore, in this paper, the authors propose using machine learning to detect abnormal behavior of steganographic packets.
Network Embedding Using Semi-Supervised Kernel Nonnegative Matrix Factorization

Network embedding, aiming to learn low-dimensional representations of nodes in networks, is very useful for many vector-based machine learning algorithms and has become a hot research topic in network analysis.Although many methods for network embedding have been proposed before, most of them are unsupervised, which ignores the role of prior information available in the network.In this paper, we propose a novel method for network embedding using semi-supervised kernel nonnegative matrix factorization (SSKNMF), which can incorporate prior information and thus to learn more useful features from the network through introducing kernel methodology.Besides, it can improve robustness against noises by using the objective function based on L 2,1 norm.Efficient iterative update rules are derived to resolve the network embedding model using the SSKNMF, and the convergence of these rules are strictly proved from the perspective of mathematics.The results from extensive experiments on several real-world networks show that our proposed algorithm is effective and has better performance than the existing representative methods.
An efficient concentrative photovoltaic solar system with Bayesian selection of optimal solar tracking algorithms

As the importance of sustainable energy has been rapidly growing, a concentrative photovoltaic (CPV) solar system is drawing much attention. In order for a system to operate efficiently, a deliberate solar tracking system must be equipped because an optimal tilt of solar panel is changed as the Sun orbits its trajectory. However, many existing tracking methods did not clearly consider the effect of various weather conditions, even though the performance of tracking method is subject to them. In this paper, we propose a CPV solar system that chooses the most proper solar tracking method among the group of heterogeneous tracking algorithms, based on an inference on the current weather conditions with Bayesian network (BN). We use 13 features derived from image processing and implement four tracking algorithms which have relative performance depending on nine different weather conditions. We constructed the working CPV system and collected the 1630 image data every three minutes for five hours over a period of 16 days. The proposed BN shows 93.9% accuracy for inferencing weather conditions, and the proposed system shows 16.58% higher power productivity, compared to a pinhole system and other existing methods.
Deep spatial attention hashing network for image retrieval

Hashing is one of the most popular image retrieval technique since its fast-computational speed and low storage cost. Recently, deep hashing methods have greatly improved the image retrieval performance in contrast to traditional hashing method. However, the binary hashing representation is only generated from the global image region, which may result in sub-optimal hashing code. Inspired by the latest advance in spatial attention mechanism, we propose an novel end-to-end deep hashing framework which composes of two sub-networks. One sub-network uses spatial attention model to determine the local features from more specific region of interest, another sub-network extracts the global features from original image. By combining the local and global features with learnable hash functions, the proposed deep hashing framework can optimize the deep hash function and high-quality binary code jointly. Numerous experiments on two large scale image benchmarks datasets have shown that the proposed method is superior to other existing methods for image retrieval.
Evolution prediction of unconventional emergencies via neural network: An empirical study of megacities

Megacities occupy an important position in the country they locate and even the whole world. Therefore it is of great significance to investigate the evolution mechanism of unconventional emergencies and to explore the probability of predicting the secondary disasters in megacities. In this paper we study the evolution prediction problem of unconventional emergencies and formulate it as multi-label classification. A novel multi-label learning vector quantization (LVQ) neural network is proposed to construct the prediction model able to forecast the type of sub-events. A real data set of 85 megacities all over the world is collected and used as a case study in the experiments. The prediction performance is measured by the match between the real label set and retrieved label set. The empirical results demonstrate the effectiveness of using LVQ in a multi-label scenario for predicting the type of secondary disasters. The model is beneficial for emergency managers to predict the potential secondary disasters and thus make informed decisions for disaster prevention and mitigation.
LiSSA: Localized Stochastic Sensitive Autoencoders

The training of autoencoder (AE) focuses on the selection of connection weights via a minimization of both the training error and a regularized term. However, the ultimate goal of AE training is to autoencode future unseen samples correctly (i.e., good generalization). Minimizing the training error with different regularized terms only indirectly minimizes the generalization error. Moreover, the trained model may not be robust to small perturbations of inputs which may lead to a poor generalization capability. In this paper, we propose a localized stochastic sensitive AE (LiSSA) to enhance the robustness of AE with respect to input perturbations. With the local stochastic sensitivity regularization, LiSSA reduces sensitivity to unseen samples with small differences (perturbations) from training samples. Meanwhile, LiSSA preserves the local connectivity from the original input space to the representation space that learns a more robustness features (intermediate representation) for unseen samples. The classifier using these learned features yields a better generalization capability. Extensive experimental results on 36 benchmarking datasets indicate that LiSSA outperforms several classical and recent AE training methods significantly on classification tasks.
Efficient Debanding Filtering for Inverse Tone Mapped High Dynamic Range Videos

When a low or standard dynamic range video is inverse tone mapped to high dynamic range (HDR), there can be banding artifacts in the output HDR video. We design a highly integrated architecture that can detect and alleviate banding artifacts while preserving true edges and details in an extremely efficient way. This is achieved by a 7-tap edge-aware selective sparse filter. Coding artifacts, such as blocky artifacts, can also be reduced by this filter. The filter includes some parameters that depend on the strengths of the banding artifacts. A parameter selection mechanism is presented which considers smoothness of the banding regions and fidelity of the filtering output. The filter yields significant PSNR gain at the regions of artifacts. Subjective tests demonstrate the great quality improvement achieved by the proposed filter, compared to the quality before filtering. The visual quality provided by the filter is better than or similar to that of algorithms which are far more complex.
Automatic Diacritics Restoration for Tunisian Dialect

Modern Standard Arabic, as well as Arabic dialect languages, are usually written without diacritics. The absence of these marks constitute a real problem in the automatic processing of these data by NLP tools. Indeed, writing Arabic without diacritics introduces several types of ambiguity. First, a word without diacratics could have many possible meanings depending on their diacritization. Second, undiacritized surface forms of an Arabic word might have as many as 200 readings depending on the complexity of its morphology [12]. In fact, the agglutination property of Arabic might produce a problem that can only be resolved using diacritics. Third, without diacritics a word could have many possible parts of speech (POS) instead of one. This is the case with the words that have the same spelling and POS tag but a different lexical sense, or words that have the same spelling but different POS tags and lexical senses [8]. Finally, there is ambiguity at the grammatical level (syntactic ambiguity). In this article, we propose the first work that investigates the automatic diacritization of Tunisian Dialect texts. We first describe our annotation guidelines and procedure. Then, we propose two major models, namely a statistical machine translation (SMT) and a discriminative model as a sequence classification task based on Conditional Random Fields (CRF). In the second approach, we integrate POS features to influence the generation of diacritics. Diacritics restoration was performed at both the word and the character levels. The results showed high scores of automatic diacritization based on the CRF system (Word Error Rate (WER) 21.44% for CRF and WER 34.6% for SMT).
Multi-Modal Face Authentication using Deep Visual and Acoustic Features

User authentication on smartphones is the key to many applications, which must satisfy both security and convenience. We propose a multi-modal face authentication system, which pushes the limit of state-of-the-art image based face recognition solutions by incorporating a new dimension of sensing modality - acoustics. It actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and extracts features from the echoes using a customized convolutional neural network, which are fused with sophisticated visual features extracted from state-of-the-art face recognition models, for secure face authentication. Because the echo features depend on 3D facial geometries and material, our multi-modal design is not easily spoofed by images or videos like image based face recognition systems. It does not require any special sensors thus eliminating the extra costs in solutions like FaceID. Experiments show that our design achieves comparable face recognition performance to the state-of-the-art image based face authentication, while able to block image/video spoofing.
Quantum hacking of free-space continuous-variable quantum key distribution by using a machine-learning technique

The transmission coefficient of the free-space channel is fluctuating due to the occurrence of random fluctuations of the refractive index and noise. The Gaussian-modulated quantum states of light may degrade into a non-Gaussian mixture at a certain probability after being transmitted through the free-space channel. In such a situation, Eve can apply an entanglement-distillation attack to steal secret information without it being revealed if Alice and Bob regard the practical fluctuation losses as constant. However, the entanglement-distillation attack is available only when the transmitting states are a non-Gaussian mixture; otherwise, Eve will expose herself. Exploiting this loophole and considering the limitations of the attack, we enhance Eve's eavesdropping ability by integrating a machine-learning technique with the entanglement-distillation attack. With the help of convolutional neural network (CNN, one of the most popular machine leaning tools), Eve can choose the best opportunity to launch the entanglement-distillation attack. Therefore, Eve has a chance to bias the estimation of the parameters by improving the entanglement resource of the mixed states, which allows Eve to render the final keys shared between the legitimate parties insecure. Our proposed CNN-based entanglement-distillation attack does not require additional resources and can be implemented using simple means based on the present technologies.
Efficient and Privacy-Preserving Outsourced SVM Classification in Public Cloud

Data classification has become an important and prevailing technique for big data analytics. Typically, a data classifier is designed and outsourced to a public cloud. A service provider then can easily provide various services and handle frequent and massive classification requests from users. With privacy concerns as well as Intellectual Property(IP) protection issues, the valuable classifier and the sensitive user data cannot be directly exposed to the public cloud. In this paper, we focus on the Support Vector Machine (SVM), one of the most popular classifiers, and propose an efficient and privacy-preserving outsourcing scheme for SVM classification in public clouds. Specifically, the service provider is allowed to transform the traditional SVM classifier to fixed hyper-rectangles and the order-preserving encryption is utilized to encrypt these hyper-rectangles as the encrypted classifier. Afterwards, the encrypted classifier is outsourced to the public cloud, and a user can submit an encrypted range query to the cloud and obtain the classification results back. Security analysis and extensive experimental evaluation demonstrate that our scheme can protect the confidentiality of classifier and users' data and achieves efficient SVM classification in terms of computational cost.
Monitoring-Aware Optimal Deployment for Applications Based on Microservices

Modern cloud applications are required to be distributed, scalable, and reliable. The microservice architectural style enables developers to achieve this goal with reduced effort. Nonetheless, microservices deployment is not trivial due to the heterogeneity of the microservices in terms of both functional and non-functional requirements. This is also true when considering the monitoring requirements that are specific to each microservice and must be satisfied in order to enable the verification of the application objectives satisfaction. However, not all providers offer the same set of metrics with the same quality. The goal of this paper is to provide an approach for supporting the deployment of microservices in multi-cloud environments focusing on the Quality of Monitoring. Adopting a multi-objective mixed integer linear optimisation problem, our approach supports the application owner in finding the optimal deployment for satisfying all the constraints and maximising the quality of monitored data, while minimising the costs. To this end, a knowledge base is introduced to mediate between the perspectives of the cloud provider and the application owner, while a Bayesian Network is adopted to enhance the provider’s monitoring capabilities by estimating metrics requested by the application owners that the cloud provider is not able to monitor.
Serverless architecture for workflow scheduling with unconstrained execution environment

Cloud computing, together with all its boons, has taken the world of computing by a storm, reaping benefits across various domains. Introduction of serverless computing, and more precisely Function-as-a-Service (FaaS), removed many orchestration and maintenance issues that system designers were facing. This inspired an emerging field of research on utilization and optimization of serverless computing. Existing body of work on this topic is, to the best of authors knowledge, focused on using serverless functions (e.g. AWS Lambda, Google Cloud Functions) exclusively. Such functions suffer constraints in the context of their execution environment, time or available space. In this paper we present a more general approach that improves upon existing architectures that revolve around cloud functions. By leveraging AWS Fargate technology, we propose a fully serverless and infinitely scalable architecture that is based on producer-consumer pattern and can be shaped to satisfy wide range of requirements. As worker nodes, Docker containers are used, which helps avoid aforementioned constrains. This concept is put to use in a system for acquisition of high frequency data.