Reviewerly/exHarmony huggingface dataset:
Lift-the-Flap: Context Reasoning Using Object-Centered Graphs.

Children benefit from lift-the-flap books by taking on an active role in guessing what is behind the flap based on the context. In this paper, we introduce lift-the-flap games for computational models. The task is to reason about the scene context and infer what the target behind the flap is in a natural image. Context reasoning is critical in many computer vision applications, such as object recognition and semantic segmentation. To tackle this problem, we propose an object-centered graph representing the scene configuration of the image where each node corresponds to a group of objects belonging to the same category. To infer the target's class label, we introduce an object-centered graph network model consisting of two sub-networks. The classification sub-network takes the complete graph as input and outputs a classification vector assigning the probability for each class. The reinforcement learning sub-network exploits the class label dependencies and learns the joint probability among objects in order to generate multiple reasonable answers for the missing target. To evaluate our model's performance, we carry out human behavioral experiments for lift-the-flap games as a benchmark. Our model makes reasonable inferences compared to humans, and significantly outperforms all the null models. We also demonstrate the usefulness of our object-centered graph network model in context-aware object recognition and target priming in visual search.
Errata to “Guest Editor's Introduction: Special Section on Virtualization and Services for Cloud-Based Application Systems”

Presents corrections to author affiliation information in the paper, “Guest Editor’s Introduction: Special Section on Virtualization and Services for Cloud-Based Application Systems,” (Zhange, Y. et al), IEEE Trans. Serv. Comput., vol. 12, no. 1, pp. 88–90, Jan./Feb. 2019.
Guest Editor's Introduction: Special Section on Services and Software Engineering Towards Internetware

The six papers in this special section focuses on services and software computing. Services computing provides a foundation to build software systems and applications over the Internet as well as emerging hybrid networked platforms motivated by it. Due to the open, dynamic, and evolving nature of the Internet, new features were born with these Internet-scale and service-based software systems. Such systems should be situation- aware, adaptable, and able to evolve to effectively deal with rapid changes of user requirements and runtime contexts. These emerging software systems enable and require novel methods in conducting software requirement, design, deployment, operation, and maintenance beyond existing services computing technologies. New programming and lifecycle paradigms accommodating such Internet- scale and service-based software systems, referred to as Internetware, are inevitable. The goal of this special section is to present the innovative solutions and challenging technical issues, so as to explore various potential pathways towards Internet-scale and service-based software systems.
Trust-based quality awareness using combinatorial auction web service selection in service-based systems

With the immense growth and development of the computing exemplar in service-based systems (SBS), the establishment of the engineering architectures has taken new standards. The computation of trust parameter has become vital and essential to measure the quality of services in such service-based systems. This becomes critical and challenging to the success of multi-tier cloud services. Various approaches have been put forth using the quality aware selection mechanisms, but reliability is a major issue in such systems. In order to establish a more reliable service-based systems, the concept of trust awareness is proposed in the present research work and trust-based quality aware selection using combinatorial auction for service oriented systems is presented. Simulation analysis show that the trust-based quality aware service selection scheme provide better accuracy, high success rate, less cost and reduced overhead than the normal combinatorial auction service selection schemes.
An Efficient Algorithm for Computing Entropic Measures of Feature Subsets

Entropic measures such as conditional entropy or mutual information have been used numerous times in pattern mining, for instance to characterize valuable itemsets or approximate functional dependencies. Strangely enough the fundamental problem of designing efficient algorithms to compute entropy of subsets of features (or mutual information of feature subsets relatively to some target feature) has received little attention compared to the analog problem of computing frequency of itemsets. The present article proposes to fill this gap: it introduces a fast and scalable method that computes entropy and mutual information for a large number of feature subsets by adopting the divide and conquer strategy used by FP-growth – one of the most efficient frequent itemset mining algorithm. In order to illustrate its practical interest, the algorithm is then used to solve the recently introduced problem of mining reliable approximate functional dependencies. It finally provides empirical evidences that in the context of non-redundant pattern extraction, the proposed method outperforms existing algorithms for both speed and scalability. Code related to this chapter is available at: https://github.com/P-Fred/HFP-Growth .
Abstract: HoloLens Streaming of 3D Data from Ultrasound Systems to Augmented Reality Glasses

Two-dimensional ultrasound (US) imaging is one of the most common tools for diagnostic procedures. However, this imaging modality requires highly experienced and skilled operators to mentally reconstruct three-dimensional (3D) anatomy from these images. Additionally, the physician’s gaze is focused on the screen of the US system instead of the probe and patient. In order to overcome these problems, we propose real-time 3D US in combination with augmented reality (AR) glasses (specifically Microsoft HoloLens) to render the volume relative to the US probe [1].
Natural Language Processing Using PyTorch

Natural language processing is an important branch of computer science. It is the study of human language by computers performing various tasks. Natural language study is also known as computational linguistics. There are two different components of natural language processing: natural language understanding and natural language generation. Natural language understanding involves analysis and knowledge of the input language and responding to it. Natural language generation is the process of creating language from input text. Language can be used in various ways. One word may have different meanings, so removing ambiguity is an important part of natural language understanding.
Time Will Tell

This chapter provides an overview of the book. It addresses major themes and terms such as synchrony, entrainment, and driving and driven rhythms, all of which are likely to be novel to the intended reading audience. The basic ideas that underlie the dynamic attending involving modes of synchrony, manifest in entrainment, are sketched. For each chapter, certain relevant constructs are previewed and briefly described. This chapter also outlines the rationale for developing a dynamic approach to attending. This involves providing contrasting view of attention that are currently prominent in psychology. Finally, this chapter provides a brief outline of each chapter in the book and discusses the rationale for its organization.
What Are Data Breaches?

We live in a technical society that is both exciting and terrifying at the same time. Never before have we had instant access to information and services at the touch of a button—from the devices we carry in our pockets, the computers on our desks, or the TVs in our houses. While this unprecedented level of access to online services is exciting, it also brings a lot of personal risks. To access these services, we have to give up our information to companies that we must entrust with it. Unfortunately, although we trust these companies with our data, we are continually hearing in the news about data accounts being stolen. In this chapter we will explore what data breaches are, their impacts, and then set the expectation for the rest of the book.
Authorization for secured cloud storage through SHA-256

Cloud computing has become one of the most inﬂuential paradigms in the IT industry in recent years. This emerging computing technology demands the user to believe their data is secure to cloud providers, there have been increasing security and privacy concerns on outsourced data. Many Schemes emphasis on Attribute-Based Encryption (ABE) have been made for access control of user’s data in cloud computing; however, they lack from some major security issues. In order to realize flexible, reliable, and ﬁne grained access control of available data in cloud computing, A Secured Hashing Algorithm is proposed which is a one-way cryptography technique with no decryption. This online storage mechanism is an integrated cloud backup suite that gives end-to-end visibility and full control of your data storage resources at corresponding rates. Cloud offers high scalability for all your storage needs your scalable data demands met from highly scalable archival to storage, equip your enterprises with the comprehensive storage suites needs. Cloud storage allows you to store unlimited data in an always available, highly durable and fully secure environment with Enterprises-Grade System Management for your commercial and budgetary needs. The key benefits are scalability, durability, support and predictable pricing. The proposed scheme not only achieves security due to its Secured Hashing Algorithm (SHA), but also inherits scalability and efficient access control in supporting compound attributes of ASBE (Attribute Set Based Encryption).
The International Conference on Intelligent Biology and Medicine 2018: Medical Informatics Thematic Track (MedicalInfo2018)

In this editorial, we first summarize the 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) that was held on June 10–12, 2018 in Los Angeles, California, USA, and then briefly introduce the six research articles included in this supplement issue. At ICIBM 2018, a special theme of Medical Informatics was dedicated to recent advances of data science in the medical domain. After peer review, six articles were selected in this thematic issue, covering topics such as clinical predictive modeling, clinical natural language processing (NLP), electroencephalogram (EEG) network analysis, and text mining in biomedical literature.
Body Gesture Modeling for Psychology Analysis in Job Interview Based on Deep Spatio-Temporal Approach

Social psychologists have long studied job interviews with the aim of knowing the relationships between behaviors, interview outcomes, and job performance. Several companies give great importance to psycho-test based on observation of the candidate is behavior more than the answers they even especially in sensitive positions like trade, marketing, investigation, etc. Our work will be a combination between two interesting topics of research in the last decades which are social psychology and affective computing. Some techniques were proposed until today to analyze automatically the candidate is non verbal behavior. This paper concentrates in body gestures which is an important non-verbal expression channel during affective communication that is not very studied in comparison to facial expressions. We proposed in this work a deep Spatio-temporal approach, it merges the temporal normalization method which is the energy binary motion information (EBMI) with deep learning based on stacked auto-encoder (SAE) for emotional body gesture recognition in job interview and the results prove the efficiency of our proposed approach.
Can gaze behaviour predict stress response and coping during acute psychosocial stress? A virtual-reality-based eyetracking study

We introduce a novel method to solve the joint problem of correspondence and triangulation of points from multiple calibrated perspective views and show its application to counting the number of leaves present on plants photographed from multiple angles in phenotyping facilities. Assuming there is a set of n calibrated views of an object, the input to our algorithm is a set of 2D points (e.g., leaf tips, fruits, flowers) detected in each of the n views, and the output is a set of 3D points corresponding to the 2D points when re-projected on views. Our approach is robust to noise and occlusion. In particular, it is not required for all points to be visible from one of the views, as our algorithm can infer that a point is occluded by reasoning on the 3D geometry of the scene. Our algorithm is suitable for many points (approx. thousands) reconstructed from a reduced set of views (up to 15). For example, our implementation finds the correspondence of 20 points captured by six cameras in about one second on consumer hardware. We evaluate the performance on synthetic data as well as real examples. We show that the accuracy of leaf counting from multi-view images is drastically improved by our algorithm.
Social Engineering: Hacking into Humans

In this world of digitalization, the need for data privacy and data security is quite important. The IT companies today prefer their data over everything. Not only for companies, the data privacy important for any individual. But no matter how secure is the company, how advanced is the technology used or how much up to date their software is, there's still a vulnerability in every sector known as ‘Human'. The art of gathering sensitive information from a human being is known as Social Engineering. Technology has increased drastically in the past few years but the threat of Social engineering is still a problem. Social engineering attacks are ince=reasing day by day due to lack of awareness and knowledge. Social engineering is a really common practice to gather information and sensitive data through the use of mobile numbers, emails, SMS or direct approach. Social engineering can be really useful for the attacker if done in a proper manner.'Kevin Mitnik' is the most renowned social engineers of all time. In this paper, we are going to discuss Social Engineering, its types, how it affects us and how to prevent these attacks. Also, many proofs of Concepts are also presented in this paper.
A New Entropy for Hypergraphs

This paper introduces a new definition of entropy for hypergraphs. It takes into account the fine structure of a hypergraph by considering its partial hypergraphs, leading to an entropy vector. This allows for more precision in the description of the underlying complexity of the hypergraph. Properties of the proposed definitions are analyzed.
High-efficient Black-box Calibration of Large-scale Silicon Photonics Switches by Bacterial Foraging Algorithm

Black-box calibration algorithm is highly required for various large-scale silicon photonics devices. We demonstrated bacterial-foraging algorithm to calibrate random phase errors in 32×32 matrix switch by simulating switch as black-box, which promised ≤2min calibration time.
Firefly Algorithm for Swarm Intelligence Clustering Method

Swarm Intelligence (SI) is a kind of artificial intelligence (AI) discipline that is popular in these days. Firefly algorithm (FA) is recently developed for new SI. The objective of this paper is to propose the FA to find the optimal data clustering solution with number of clusters. Our proposed FA is suitable for highly nonlinear and multimodal optimization because FA can consider the attractiveness and distance between the fireflies (solutions) simultaneously. We use the silhouette as valid index to evaluate the solution with number of clusters considering the intra-cluster and inter-cluster distance at the same time. We also use the relative distance rate (VSUBj/SUB) of each data j which is used to generate the good initial solutions to save the much computation time for silhouette evaluations. The performance of our proposed FA using silhouette with relative distance rate is validated using several real data sets by experiment and analysis.
CARE

Travelling is not always fun for wheelchair users in the built environment (both indoor and outdoor) in presence of various unknown barriers, such as, uneven sidewalks, curb heights, stairs, ramps, cobbled streets, etc. Also, elderly individuals are more likely to require assistance while using self-propelled and non-motorized wheelchairs. However, along with barriers there are also various wheelchair-friendly facilities, such as, supervised crosswalks and elevators, which improve accessibility of environments and facilities. In this project, we plan to develop a Campus-wide Accessible Route Estimation (CARE) System which works through a large-scale vibration data analysis (created by wheelchair movement) from different built or natural surfaces and aims to classify the surfaces into accessible and inaccessible. The goal is to find accessible routes for wheelchair users.
Automatic analysis of old documents: taking advantage of an incomplete, heterogeneous and noisy corpus

In this article we try to tackle some problems arising with noisy and heterogeneous data in the domain of digital humanities. We investigate a corpus known as the mazarinades corpus which gathers around 5,500 documents in French from the 17th century. First of all, we show that this set of documents is not strictly speaking a corpus since its coverage has not been thoroughly defined. Then, we advocate that it is possible to get interesting results even in the case of such an incomplete, heterogeneous and noisy dataset by strictly limiting the amount of pre-treatments necessary fro processing texts. Finally, we present some results on a case study on document dating where we aim to complete missing metadata in the mazarinades corpus. We exploit a method based on character strings analysis which is robust to noisy data and can even take advantage of this noise for improving the quality of the results.
Corrigendum to: “FreeSurfer subcortical normative data” [Data in Brief 9 (2016) 732–736]

[This corrects the article DOI: 10.1016/j.dib.2016.10.001.].
Decoy state quantum-key-distribution by using odd coherent states without monitoring signal disturbance

Recently, a novel quantum-key-distribution (QKD) protocol, called Round-robin-differential-phase-shift (RRDPS) QKD, has been proposed to share a secure key without monitoring the signal disturbance. In this paper, we propose a decoy state RRDPS-QKD protocol with odd coherent states (OCS). We implement a one-intensity decoy state method into the RRDPS-QKD with OCS to estimate the key rate. The results show that both the maximum transmission distance and the key rate of our protocol are significantly improved. Moreover, only one-intensity decoy state is sufficient for the protocol to approach the asymptotic limit with infinite decoy states.
Medical image super-resolution using phase stretch anchored regression (Conference Presentation)

Medical imaging is fundamentally challenging due to absorption and scattering in tissues and by the need to minimize illumination of the patient with harmful radiation. Imaging modalities also suffer from low spatial resolution, limited dynamic range and low contrast. These predicaments have fueled interest in enhancing medical images using digital post processing. Recent progress in image super resolution using machine learning and in particular convolutional neural networks (CNNs) may offer new possibilities for improving the quality of medical images. However, the tendency of CNNs to hallucinate image details is detrimental for medical images as it may lead to false diagnostics. Also, these techniques require prohibitively large computational resource, a problem that is exacerbated by the large size of medical images. Rapid and Accurate Image Super Resolution (RAISR) method provides a computationally efficient solution for image upscaling. In this paper, we propose ARAISR, an improved variant of RAISR, which inherits the local features and regression model of RAISR but instead of utilizing cluster anchored points to represent image feature space. This algorithm combines the low computing complexity of RAISR with the feature enhancement advantage of phase stretch transform (PST), a new computational approach that is inspired by the physics of photonic time stretch technique. We obtain improved quality (i.e. maximum 1dB PSNR better than RAISR) and hallucination-free performance for medical images super resolution.
Teleporting digital images

During the last 30 years the scientific community has coexisted with the most fascinating protocol due to Quantum Physics: quantum teleportation, which would have been impossible if quantum entanglement, so questioned by Einstein, did not exist. In this work, a complete architecture for the teleportation of Computational Basis States (CBS) is presented. Such CBS will represent each of the possible 24 classical bits commonly used to encode every pixel of a 3-color-channel image (red–green–blue, or cyan–yellow–magenta). For this purpose, a couple of interfaces: classical-to-quantum and quantum-to-classical are presented with two versions of the teleportation protocol: standard and simplified.
Desarrollos en elaboración de documentación técnica: lenguajes de marcado ligero

Lightweight markup languages have been consolidated as the lingua franca for the preparation of technical documents. Their foundations and history are revised, as well as the main languages and documentation generators.
Editorial Introducing the IEEE Transactions on Biometrics, Behavior, and Identity Science

It gives us immense pleasure to introduce IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM). Biometrics and Identity Science is a core opportunity area with a large social impact that is becoming a major component in several large-scale identity management projects. For example, Office of Biometric Identity Management (OBIM), India's Unique Identification Authority of India (UIDAI - Aadhaar) project, the FBI's Next Generation Identification (NGI) system, Canada Border Services, and other nation-wide identity management programs have shown the usefulness of biometrics for identity management.
Rounding Modulation for Transparent Data-Hiding Scheme in High-Quality Audio File

In this paper, a novel digital data-hiding technique for the high-quality audio file is introduced. The data is embedded into the frequency domain of the host audio signal utilizing the Fast Fourier Transform (FFT). The data is embedded into the phase element of the selected samples using the rounding concept of the traditional Phase Shift Keying (PSK) technique. The perceptual quality of the embedded audio file is changed as minimum as possible which means that there will be no noticeable difference between the original and the embedded audio file by the human ears.