Reviewerly/exHarmony huggingface dataset:
Influence of robots service quality on customers' acceptance in restaurants

Purpose This study aims to examine the impact of AI service robots on restaurant customers' engagement and acceptance and the moderating role of robot anthropomorphism on the relationship between AI robot service quality and customer engagement. Design/methodology/approach Using a three-wave time-lagged design, 416 customers of service robots-enabled restaurants participated in the study. Mplus was used to examine the hypotheses. Findings The results confirmed that customers' perception regarding automation, personalization, efficiency and precision of robot service quality determine customer engagement, which further influences customer acceptance of AI service robots. Additionally, robot anthropomorphism moderates the relationships between AI robot service quality in terms of automation, personalization, efficiency and precision and customer engagement. This study confirms that AI service robots-customer engagement contributes to better acceptance of AI service robots. Practical implications The proposed framework can be used as a diagnostic tool to enhance customer acceptance of AI service robots in restaurant settings. This research provides guidelines to restaurant owners to employ AI service robots in front-line services that provide better quality, ultimately enhancing customer engagement and acceptance. Originality/value This study fills the gap in the literature by investigating the influence of AI robot service quality on customer engagement and customer acceptance with the moderating effect of robot anthropomorphism in an emerging market context.
On the implementation of a secured watermarking mechanism based on cryptography and bit pairs matching

Watermarking is one of the most vital digital information hiding technique, which can be used with cryptography mechanism for providing more security to digital data. In image watermarking mechanism mostly LSB substitution is used on the cover image for hiding the secret watermark. In this paper, a novel technique based on the matching of bit pairs and symmetric key cryptography is proposed. Pixel bits of original image and encrypted watermark image are arranged in pairs. The pixel bits are represented in pairs following the proposed algorithm, then the encrypted watermark pixel bit pairs are compared with all bit pairs of original image and accordingly the replacement of bit pairs takes place with the respective matched pair assigned number binary equivalent. If no match is found then go for replacing the 0th pair with watermark bits and replace the two LSB with the value of pair number 0. The proposed mechanism shows good quality of watermarked image along with good PSNR values with a good payload. By comparing the results with some existing algorithms, the proposed scheme shows the valuable results.
Incremental Hash-Bit Learning for Semantic Image Retrieval in Nonstationary Environments

Images are uploaded to the Internet over time which makes concept drifting and distribution change in semantic classes unavoidable. Current hashing methods being trained using a given static database may not be suitable for nonstationary semantic image retrieval problems. Moreover, directly retraining a whole hash table to update knowledge coming from new arriving image data may not be efficient. Therefore, this paper proposes a new incremental hash-bit learning method. At the arrival of new data, hash bits are selected from both existing and newly trained hash bits by an iterative maximization of a 3-component objective function. This objective function is also used to weight selected hash bits to re-rank retrieved images for better semantic image retrieval results. The three components evaluate a hash bit in three different angles: 1) information preservation; 2) partition balancing; and 3) bit angular difference. The proposed method combines knowledge retained from previously trained hash bits and new semantic knowledge learned from the new data by training new hash bits. In comparison to table-based incremental hashing, the proposed method automatically adjusts the number of bits from old data and new data according to the concept drifting in the given data via the maximization of the objective function. Experimental results show that the proposed method outperforms existing stationary hashing methods, table-based incremental hashing, and online hashing methods in 15 different simulated nonstationary data environments.
Meta-mappings for schema mapping reuse

The definition of mappings between heterogeneous schemas is a critical activity of any database application. Existing tools provide high level interfaces for the discovery of correspondences between elements of schemas, but schema mappings need to be manually specified every time from scratch, even if the scenario at hand is similar to one that has already been addressed. The problem is that schema mappings are precisely defined over a pair of schemas and cannot directly be reused on different scenarios. We tackle this challenge by generalizing schema mappings as meta-mappings: formalisms that describe transformations between generic data structures called meta-schemas. We formally characterize schema mapping reuse and explain how meta-mappings are able to: (i) capture enterprise knowledge from previously defined schema mappings and (ii) use this knowledge to suggest new mappings. We develop techniques to infer meta-mappings from existing mappings, to organize them into a searchable repository, and to leverage the repository to propose to users mappings suitable for their needs. We study effectiveness and efficiency in an extensive evaluation over real-world scenarios and show that our system can infer, store, and search millions of meta-mappings in seconds.
Several robust extensions of collaborative representation for image classification

Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in image classification. However, the representation fidelity is usually measured by the ℓ2-norm , which is not robust to outliers. Moreover, CRC and PCRC only consider the global distribution of data and ignore the locality of data. To overcome those problems, in this paper, we propose the residual-based extensions and the weighted version of CRC and PCRC. Specifically, for the residual-based extensions of CRC and PCRC, we measure the representation fidelity by jointing ℓ1-norm and the ℓ2-norm of coding residuals. For the weighted extensions of CRC and PCRC with different coding residuals, we constrained the representation coefficients with locality of data. To verify the effectiveness of the proposed methods, we conduct extensive experiments on six popular face databases and three image databases. Experimental results have demonstrated that the ℓ1-norm of coding residual, jointing both the ℓ1-norm and the ℓ2-norm of coding residuals and the locality constraint of representation coefficients can enhance pattern discrimination effectively.
Self-Tuned Discrimination-Aware Method for Unsupervised Feature Selection

Unsupervised feature selection is fundamentally important for processing unlabeled high-dimensional data, and several methods have been proposed on this topic. Most existing embedded unsupervised methods just emphasize the data structure in the input space, which may contain large noise. Therefore, they are limited to perceive the discriminative information implied within the low-dimensional manifold. In addition, these methods always involve several parameters to be tuned, which is time-consuming. In this paper, we present a self-tuned discrimination-aware (STDA) approach for unsupervised feature selection. The main contributions of this paper are threefold: 1) it adopts the advantage of discriminant analysis technique to select the valuable features; 2) it learns the local data structure adaptively in the discriminative subspace to alleviate the effect of data noise; and 3) it performs feature selection and clustering simultaneously with an efficient optimization strategy, and saves the additional efforts to tune parameters. Experimental results on a toy data set and various real-world benchmarks justify the effectiveness of STDA on both feature selection and data clustering, and demonstrate its promising performance against the state of the arts.
TensorSCONE: A Secure TensorFlow Framework using Intel SGX.

Machine learning has become a critical component of modern data-driven online services. Typically, the training phase of machine learning techniques requires to process large-scale datasets which may contain private and sensitive information of customers. This imposes significant security risks since modern online services rely on cloud computing to store and process the sensitive data. In the untrusted computing infrastructure, security is becoming a paramount concern since the customers need to trust the thirdparty cloud provider. Unfortunately, this trust has been violated multiple times in the past. To overcome the potential security risks in the cloud, we answer the following research question: how to enable secure executions of machine learning computations in the untrusted infrastructure? To achieve this goal, we propose a hardware-assisted approach based on Trusted Execution Environments (TEEs), specifically Intel SGX, to enable secure execution of the machine learning computations over the private and sensitive datasets. More specifically, we propose a generic and secure machine learning framework based on Tensorflow, which enables secure execution of existing applications on the commodity untrusted infrastructure. In particular, we have built our system called TensorSCONE from ground-up by integrating TensorFlow with SCONE, a shielded execution framework based on Intel SGX. The main challenge of this work is to overcome the architectural limitations of Intel SGX in the context of building a secure TensorFlow system. Our evaluation shows that we achieve reasonable performance overheads while providing strong security properties with low TCB.
RAMS: Remote and automatic mammogram screening

About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings. We propose a total screening solution comprised of three main components: a web service for uploading images and reviewing results, a machine learning algorithm for accepting or rejecting images as valid mammograms, and an artificial neural network for locating potential malignancies. Once an image is uploaded to our web service, an image acceptor determines whether or not the image is a mammogram. The image acceptor is primarily a one-class SVM built on features derived with a variational autoencoder. If an image is accepted as a mammogram, the malignancy identifier, a ResNet-101 Faster R-CNN, will locate tumors within the mammogram. On test data, the image acceptor had only 2 misclassifications out of 410 mammograms and 2 misclassifications out of 1,640 non-mammograms while the malignancy identifier achieved 0.951 AUROC when tested on BI-RADS 1, 5, and 6 images from the INbreast dataset.
Sparse Low-Rank Preserving Projection for Dimensionality Reduction

The representation-based learning methods, such as sparse representation-based classification and low-rank representation, show effective and robust for image clustering and classification. However, these methods essentially belong to the transductive methods and they cannot deal with the new samples. Meanwhile, the original high-dimensional data contains a large amount of redundant information. If the original data are directly performed, it will not only degrade the performance of the algorithm but also lead to a sharp increase in the amount of computation. Therefore, a novel robust sparse low-rank preserving projection (SLRPP) is presented for dimensionality reduction, in which both the essential similarity structure of the observed data and the optimal feature representation are simultaneously obtained. By alternatively iterating the augmented Lagrangian multiplier method and the eigendecomposition, the framework of the SLRPP can be solved. The experimental results on six image databases proved that our SLRPP algorithm can achieve a competitive performance compared with the state-of-the-art subspace learning methods.
An integrated haptic-enabled virtual reality system for orthognathic surgery planning

Conventional Orthognathic surgery (OGS) planning involves cephalometric analyses and dental casts to be mounted on an articulator. Dental segments are subsequently identified, cut and repositioned to allow the fabrication of intraoral wafers that guide the positioning of the osteotomy bone segments. This conventional planning introduces many inaccuracies that affect the post-surgery outcomes. Although computer technologies have advanced computational tools for OGS planning, they have failed in providing a practical solution. Many focuses only on some specific stages of the planning process, and their ability to transfer preoperative planning data to the operating room is limited. This paper proposes a new integrated haptic-enabled virtual reality (VR) system for OGS planning. The system incorporates CAD tools and haptics to facilitate a complete planning process and is able to automatically generate preoperative plans. A clinical pre-diagnosis is also provided automatically by the system based on the patient’s digital data. A functional evaluation based on a real patient case study demonstrates that the proposed virtual OGS planning method is feasible and more effective than the traditional approach at increasing the intuitiveness and reducing errors and planning times.
Controllable two-qubit swapping gate using superconducting circuits

In this paper, we investigate a linear chain of qubits and determine that it can be configured into a conditional two-qubit swapping gate, where the first and last qubits of the chain are the swapped qubits, and the remaining middle ancilla qubits are controlling the state of the gate. The swapping gate introduces different phases on the final states depending on the initial states. In particular, we focus on a chain of four qubits and show the swapping gate it implements. We simulate the chain with realistic parameters, and decoherence noise and leakage to higher excited states, and find an average fidelity of around 0.99. We propose a superconducting circuit which implements this chain of qubits and present a circuit design of the circuit. We also discuss how to operate the superconducting circuit such that the state of the gate can be controlled. Lastly, we discuss how the circuit can be straightforwardly altered and may be used to simulate Hamiltonians with nontrivial topological properties.
Reservoir computing based on transverse modes in a single optical waveguide

In this work, a passive reservoir computing scheme is presented, which relies on the excitation and guiding of transverse modes in a large cross-section photonic waveguide. The optical modes act similar to conventional waveguide-based reservoir nodes, whereas random defects act as the operational equivalent of internode connections. Therefore, the proposed scheme’s number of nodes and connections can scale with the waveguide’s volume, contrary to planar implementations. The principle of operation, alongside the role of mode defects to performance, is validated through a numerical model, whereas the classification of time-dependent, 2D analog/digital image streams is used as an application-relevant benchmark test.
On Quantum Advantage in Information Theoretic Single-Server PIR

In (single-server) Private Information Retrieval (PIR), a server holds a large database $${\mathtt {DB}}$$ of size n, and a client holds an index $$i \in [n]$$ and wishes to retrieve $${\mathtt {DB}}[i]$$ without revealing i to the server. It is well known that information theoretic privacy even against an “honest but curious” server requires $$\varOmega (n)$$ communication complexity. This is true even if quantum communication is allowed and is due to the ability of such an adversarial server to execute the protocol on a superposition of databases instead of on a specific database (“input purification attack”). Nevertheless, there have been some proposals of protocols that achieve sub-linear communication and appear to provide some notion of privacy. Most notably, a protocol due to Le Gall (ToC 2012) with communication complexity $$O(\sqrt{n})$$ , and a protocol by Kerenidis et al. (QIC 2016) with communication complexity $$O(\log (n))$$ , and O(n) shared entanglement. We show that, in a sense, input purification is the only potent adversarial strategy, and protocols such as the two protocols above are secure in a restricted variant of the quantum honest but curious (a.k.a specious) model. More explicitly, we propose a restricted privacy notion called anchored privacy, where the adversary is forced to execute on a classical database (i.e. the execution is anchored to a classical database). We show that for measurement-free protocols, anchored security against honest adversarial servers implies anchored privacy even against specious adversaries. Finally, we prove that even with (unlimited) pre-shared entanglement it is impossible to achieve security in the standard specious model with sub-linear communication, thus further substantiating the necessity of our relaxation. This lower bound may be of independent interest (in particular recalling that PIR is a special case of Fully Homomorphic Encryption).
Machine Learning for Phishing Detection and Mitigation

Phishing e-mails refer to fraudulent e-mails sent with the aim of deceiving people and tricking them into revealing confidential information, such as passwords, usernames and credit card information. Such information may be related to the bank account. This is done to make an illegal financial gain. Due to such e-mails, the present study aims to identify and discuss machine-learning approaches for fighting against and detecting phishing e-mails. The present chapter aims to identify and discuss machine-learning approaches for fighting against and detecting phishing e-mails. These approaches are used for filtering the phishing attacks. Researchers also carried out an assessment for several phishing e-mails-filtering methods; they also conducted a comparison between them. Such a comparison was conducted in terms of advantages and limitations. Thus, the present study aims to deepen one’s understanding of matters related to phishing attacks. It also aims to promote awareness about the methods used for fighting against and filtering phishing e-mails.
A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

At present, the coverless information hiding has been developed. However, due to the limited mapping relationship between secret information and feature selection, it is challenging to further enhance the hiding capacity of coverless information hiding. At the same time, the steganography algorithm based on object detection only hides secret information in foreground objects, which contribute to the steganography capacity is reduced. Since object recognition contains multiple objects and location, secret information can be mapped to object categories, the relationship of location and so on. Therefore, this paper proposes a new steganography algorithm based on object detection and relationship mapping, which integrates coverless information hiding and steganography. In this method, the coverless information hiding is realized by mapping the object type, color and secret information in object detection method. At the same time, the object detection method is used to find the safe area to hide secret messages. The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. In this work, we empirically investigate the representational power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results quantify how well various approaches can represent the requisite value functions, and help us identify issues that can impede good performance.
Blockchain and Healthcare

Distributed Ledger Technologies are promised to be genuinely disruptive in a variety of industries and sectors, from the food supply, pharmaceutical, real estate, financial services, academia and healthcare. Blockchain technologies vastly improve the flow of data within and among companies and people and transform the way that the Internet is used to exchange value. The use of distributed applications can revolutionise and simplify complex system tasks and create new substructures in a fully developing distributed economy as part of Industry 4.0. Blockchain technologies have the enormous potential to transform healthcare and existing security, privacy and interoperability issues allowing more flexibility and freedom to patients and their data. New economies of scale are emerging for health information exchange that makes the management of electronic records easier while eliminating fictions and costs associated with current intermediaries. This chapter seeks to explore the idiosyncrasies of Blockchain technology and its adaptation in healthcare with an emphasis to its core features and applications. We expand to the key risks and opportunities of Blockchain and briefly discuss this pilot studies in healthcare.
VR For Your Ears: Dynamic 3D audio is key to the immersive experience by mathias johansson · illustration by eddie guy

Put on your virtual-reality headset and be transported to a distant planet, ducking the crossfire in a battle between alien species. Laser rifle shots whiz by your head; military shuttles hover before you; the frantic calls of comrades hail from all directions. ¶ Change the channel. ¶ Now you are courtside at a basketball game. You hear players trash- talking on the court in front of you and coaches yelling from the bench to your left. Turn your head and the sound spins with you; the announcers in the broadcast booth are in front of you, the court sounds behind. ¶ Change the channel. ¶ Now you're at the Gothenburg Concert Hall, whose acoustics are ranked among the best in the world. From your front-row seat, the 109-person orchestra before you simmers quietly at first, then roars to life, its sound enveloping you. Turn your head to the left to hear the violins more strongly; turn it to the right and hear the cellos and brass section a little above the rest.