Reviewerly/exHarmony huggingface dataset:
An Adaptive Binarization Method for Cost-efficient Document Image System in Wavelet Domain

In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu&#x2019;s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.
Towards an Approach for Validating the Internet-of-Transactional-Things

This paper examines the impact of transactional properties, known as pivot, retriable, and compensatable, on Internet-of-Things (IoT). Despite the ever-growing number of things in today’s cyber-physical world, a limited number of studies examine this impact while considering things’ particularities in terms of reduced size, restricted connectivity, continuous mobility, limited energy, and constrained storage. To address this gap, this paper proceeds first, with exposing things’ duties, namely sensing, actuating, and communicating. Then, it examines the appropriateness of each transactional property for each duty. During the performance of transactional things, (semi)-atomicity criterion is adopted allowing to approve when these things’ duties could be either canceled or compensated. A system that runs a set of what-if experiments is presented in the paper allowing to demonstrate the technical doability of transactional things.
Audience Meter: a Use Case of Deploying Machine Learning Algorithms over 5G Networks with MEC

Audience Meter is an application that estimates the number of attendees in an event throughout the time by applying a state-of-the-art face detection algorithm. In order to detect faces at large distances, high resolution images are required. This makes unfeasible the computation of the algorithm on the device and requires the use of MEC modules from the emerging 5G networks to boost performance. An average boost factor of 10 × is achieved by using MEC in comparison with CPU devices for high resolution images.
An Improved Attention-Based Spatiotemporal-Stream Model for Action Recognition in Videos

Action recognition is an important yet challenging task in computer vision.Attention mechanism not only tells where to focus but when to focus.It plays a key role in extracting discriminative spatial and temporal features for solving the task.In this paper, we propose an improved spatiotemporal attention model based on the two-stream structure to recognize the different actions in videos.Specifically, we first extract the intra-frame spatial features and inter-frame optical flow features for each video data.Then we implement an effective attention module, which sequentially infers attention maps along three separate dimensions: channel, spatial and temporal.After adaptive feature refinement based on the attention maps, we perform a temporal pooling process to squeeze the temporal dimension.Then, these achieved spatial and temporal features are fed into the spatial LSTM and temporal LSTM, respectively.Finally, we fuse the spatial feature, temporal feature and two-stream fusion feature to classify the actions in videos.Additionally, we also collect and construct a new Ping-Pong action dataset for subsequent human-robot interaction task from YouTube.It contains 2400 labeled videos for 4 categories.We compare with other action recognition algorithms and validate the feasibility and effectiveness of the proposed method on Ping-Pong action dataset and HMDB51 dataset.
Combiners for AEAD

The Authenticated Encryption with Associated Data (AEAD) primitive, which integrates confidentiality and integrity services under a single roof, found wide-spread adoption in industry and became indispensable in practical protocol design. Recognizing this, academic research put forward a large number of candidate constructions, many of which come with provable security guarantees. Nevertheless, the recent past has shaken up with the discovery of vulnerabilities, some of them fatal, in well-regarded schemes, stemming from weak underlying primitives, flawed security arguments, implementation-level vulnerabilities, and so on. Simply reacting to such findings by replacing broken candidates by better(?) ones is in many cases unduly, costly, and sometimes just impossible. On the other hand, as attack techniques and opportunities change over time, it seems venturous to propose any specific scheme if the intended lifetime of its application is, say, twenty years.In this work we study a workable approach towards increasing the resilience against unforeseen breaks of AEAD primitives. Precisely, we consider the ability to combine two AEAD schemes into one such that the resulting AEAD scheme is secure as long as at least one of its components is (or: as long as at most one component is broken). We propose a series of such combiners, some of which work with fully generic AEAD components while others assume specific internal structures of the latter (like an encrypt-then-MAC design). We complement our results by proving the optimality of our constructions by showing the impossibility of combiners that get along with less invocations of the component algorithms.
Mutually Orthogonal Softmax Axes for Cross-Domain Retrieval

In this paper, we introduce and address a crucial but less accentuated problem in cross-domain retrieval task. We first highlight the challenge caused by diversity of inter-class similarities across different domains. For example, bear and teddy bear classes have almost the same instances in a sketch domain whereas they are greatly different in photos. In other words, two classes can be mixed together or even far away in the feature space depending on the modality. A key component of the problem, mapping feature representations of different domains to a common space while preserving semantic similarities, is a tough task due to the domain gap. In order to address such diversity, we introduce a novel feature learning approach by forcing the decision axes of a softmax-based classifier to be mutually orthogonal. Since the feature spaces of different domains share a regularized structure thanks to the orthogonal decision axes, we formulate the mapping to a common space as a simple linear transformation. Our proposed method is extensively evaluated on four different cross-domain retrieval benchmarks consisting of various data modalities, and we found that our method significantly outperforms the state-of-the-art techniques.
Author Correction: Multi-layer adaptation of group coordination in musical ensembles

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Featured Surface Matching Method for Liver Image Registration

This research devises a method to match marker-less surfaces for liver image registration.Surgeons usually glean preoperative liver information, such as anatomy and the locations of liver tumors or large intrahepatic vessels, from preoperative liver images that are obtained using Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) or ultrasound.During minimally invasive surgery, surgeons use a laparoscope to obtain information about the intraoperative liver surface and identify an intraoperative liver tumor or the locations of vessels using the preoperative information.However, the liver can be lifted, shifted, flipped, squeezed or turned over during surgery.These manual operations can lead to severe deformation, so it is difficult to identify the location of intraoperative liver tumors or vessels.It is also difficult to accurately remove a liver tumor while avoiding injury to large intrahepatic vessels.This research proposes a method to determine the location of intraoperative vessels or tumors.The proposed method uses CT scans to construct a preoperative biomechanical volume model and uses a novel surface matching method to determine the relationship between the preoperative and intraoperative surfaces.The preoperative volume model is deformed by the finite element model in terms of the relationship that is defined by the proposed surface matching method, so that it aligns with the intraoperative surface model and shows the location of intraoperative vessels and tumors.The method of target registration error is measured for an ex vivo porcine liver to validate the proposed method.The results show that the error in the internal marker (which represents the location of the tumor and the vessel) is 4.54 ± 3.55 mm and the error in the surface marker is 2.98 ± 1.09 mm, which demonstrates the feasibility and high degree of accuracy of the proposed method.
Generalization of Pauli channels through mutually unbiased measurements

We introduce a new generalization of the Pauli channels using the mutually unbiased measurement operators. The resulting channels are bistochastic but their eigenvectors are not unitary. We analyze the channel properties, such as complete positivity, entanglement breaking, and multiplicativity of maximal output purity. We illustrate our results with the maps constructed from the Gell-Mann matrices and the Heisenberg-Weyl observables.
Extracting Actionable Knowledge From Social Networks Using Structural Features

In conventional data mining methods, the output is either a description of input data or a prediction of unseen data. But the real-world problems usually require interventions in order to alter the current data specifications towards a desirable goal. Actionable knowledge discovery is a field of study specifically developed for this matter. Existing methods rarely tackled the problem of extracting actionable knowledge from social networks. Moreover, due to the dependencies among the underlying network data, extracted actions should be evaluated since the changes suggested by the actions may not be described by the model constructed so far. This enforces the refinement of the model to preserve the quality of extracted actions. In this paper we propose a new method for action mining which incorporates an action evaluation process overcoming the mentioned problem while focusing specifically on social network data. Such data contains valuable information based on the links inside the network where a change in some feature values may result in a chain of changes in others due to the dependencies conveyed by the links in the network. We use a state-of-the-art structural feature extraction method to capture the information of the dependencies inside the network. Our proposed method iteratively updates structural features which are incorporated in the action extraction process. In this process, we thoroughly examine the effects of the application of actions by discovering the impact of possible changes in the network. We call this phenomenon “change propagation”. According to our experimentations, our method outperforms the state-of-the-art methods in terms of action effectiveness and reliability with comparable efficiency.
An Improved Ensemble Machine Learning Algorithm for Wearable Sensor Data Based Human Activity Recognition

Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this chapter, we propose an improved machine learning method based on the ensemble algorithm to boost the performance of these machine learning methods for HAR.
Side-Channel Countermeasures’ Dissection and the Limits of Closed Source Security Evaluations

We take advantage of a recently published open source implementation of the AES protected with a mix of countermeasures against side-channel attacks to discuss both the challenges in protecting COTS devices against such attacks and the limitations of closed source security evaluations. The target implementation has been proposed by the French ANSSI (Agence Nationale de la Sécurité des Systèmes d’Information) to stimulate research on the design and evaluation of side-channel secure implementations. It combines additive and multiplicative secret sharings into an affine masking scheme that is additionally mixed with a shuffled execution. Its preliminary leakage assessment did not detect data dependencies with up to 100,000 measurements. We first exhibit the gap between such a preliminary leakage assessment and advanced attacks by demonstrating how a countermeasures’ dissection exploiting a mix of dimensionality reduction, multivariate information extraction and key enumeration can recover the full key with less than 2,000 measurements. We then discuss the relevance of open source evaluations to analyze such implementations efficiently, by pointing out that certain steps of the attack are hard to automate without implementation knowledge (even with machine learning tools), while performing them manually is straightforward. Our findings are not due to design flaws but from the general difficulty to prevent side-channel attacks in COTS devices with limited noise. We anticipate that high security on such devices requires significantly more shares.
Analysis of Suitable Natural Feature Computer Vision Algorithms for Augmented Reality Services

The first step in working with object augmentation in an augmented reality system is to identify the target object, so its pose in respect to the camera can be determined for precise and accurate augmented content generation over the target object.In modern augmented reality systems natural feature detection algorithms are widely used for detecting, identifying and tracking planar textured objects.All-natural feature algorithms detect interest points or keypoints (detector) in an image (scene) and/or calculate descriptors for keypoints (extractor).Algorithms can include both parts, detection and extraction, and can have just one of them realized.There is a variety of algorithms available nowadays for developers to use.Starting from floating point descriptor-based ones as SIFT and SURF and a row of binary descriptor-based algorithms such as BRIEF, ORB, BRISK, FREAK, KAZE, A-KAZE, LATCH.In addition, there are algorithms which only detect interest points, as FAST or A-GAST.Furthermore, it is possible to use one algorithm for keypoint detection and afterwards use another for descriptor extraction.Given such a variety of available algorithms, it is necessary to analyse them by understanding their working principles, so they can be classified by their strengths and weaknesses and in what situations the use of one or another algorithm is more appropriate.Since it is possible to use combinations of algorithms, a table of possible cases is provided.For clarity we must mention that various algorithms, which are not mentioned here, are available but we take an overview of the above listed as all of them are included in the OpenCV library and are widely used in the industry.
Design and implementation of intelligent accounting data analysis platform based on industrial cloud computing

Abstract In order to improve the ability of accounting data analysis and statistics, a method of constructing modern accounting data analysis platform based on industrial cloud computing is proposed. The time series model of modern accounting data analysis is constructed by using block bit sequence analysis method, and the association rule characteristic quantity of accounting data is extracted. Combined with cloud computing technology, the modern accounting data analysis platform is constructed, and the fuzzy C-means clustering algorithm is used to realize the clustering of modern accounting data, which can improve the ability of parallel computing and statistical analysis of accounting data. The simulation results show that the intelligent data analysis platform designed makes the statistical analysis ability of accounting data better and the parallel computing efficiency higher.
USGS Cascades Volcano Observatory news media management guide — General protocols and templates

First posted March 25, 2020 For additional information, contact: Volcano Science CenterCascades Volcano ObservatoryU.S. Geological Survey1300 SE Cardinal CourtVancouver, WA, 98683 This guide describes general protocols and provides templates for news media management at the U.S. Geological Survey (USGS) Cascades Volcano Observatory (CVO) and is intended for use by the CVO scientist-in-charge, communications staff, scientists, and guest communications colleagues. This public version, with CVO names and contact information removed, may be useful to other agencies developing their own protocols and templates. This guide evolved from a smaller document hastily assembled out of necessity during the complex and overwhelming news media interest in the 2004–2008 Mount St. Helens eruption. News media interest exceeded the need for life-saving crisis communication and foretold of the need for future multi-faceted and well-coordinated news media and social media responses during future volcanic events.This guide accompanies the USGS Volcano Science Center’s (VSC’s) general guidelines and protocols for how communications staff at all VSC observatories will work together to respond to news media requests. The protocols and templates are applicable to (1) normal conditions when CVO has an opportunity to be proactive with its messages and to raise general awareness, (2) general responses to news media and TV documentary inquiries, (3) intense news media interest where the responsibility to communicate information and hazards rests primarily with staff at CVO, and (4) intense and overwhelming news media interest that requires a multiagency response. This guide reflects general protocols in effect at the time of publication. The information will be modified as conditions change. Although “news media” generally refers to traditional outlets such as TV, radio, and newspapers, the protocols used to engage these traditional outlets apply also when responding to bloggers, online news services, and social media comments.
Quantum Teleportation via Qutrit Entangled State

Qutrit is the natural extension of qubit in quantum information processing and has quite a few advantages that outperform qubit. In this paper, we investigate the feasibility of teleportation of an unknown qubit state, as well as an unknown qutrit state using a two-qutrit entangled pair. We show that by carefully constructing the measurement bases, both the qubit and the qutrit can be faithfully teleported from Alice to Bob with a two-qutrit maximally entangled state.
Analysis Security of SIA Based DSS05 on COBIT 5 Using Capability Maturity Model Integration (CMMI)

A secure academic information system is part of the college. The security of academic information systems is very important to maintain information optimally and safely. Along with the development of technology, academic information systems are often misused by some irresponsible parties that can cause threats. To prevent these things from happening, it is necessary to know the extent to which the security of the academic information system of universities is conducted by evaluating. So the research was conducted to determine the Maturity Level on the governance of the security of University Ahmad Dahlan academic information system by using the COBIT 5 framework on the DSS05 domain. The DSS05 domain on COBIT 5 is a good framework to be used in implementing and evaluating related to the security of academic information systems. Whereas to find out the achievement of evaluation of academic information system security level, CMMI method is needed. The combination of the COBIT 5 framework on the DSS05 domain using the CMMI method in academic information system security is able to provide a level of achievement in the form of a Maturity Level value. The results of the COBIT 5 framework analysis of the DSS05 domain use the CMMI method to get a Maturity level of 4,458 so that it determines the achievement of the evaluation of academic information systems at the tertiary level is Managed and Measurable. This level, universities are increasingly open to technological developments. Universities have applied the quantification concept in each process, and are always monitored and controlled for performance in the security of academic information systems.
Progressive Data Hiding in Integer Wavelet Transform of Electrocardiogram by Using Simple Decision Rule and Coefficient Calibration

Based on one dimensional (1D) integer wavelet transform (IWT) domain, patient's data can be effectively embedded in electrocardiogram (ECG) via the proposed criterion and coefficient alignment.Multiple data bits can be sequentially hidden in host bundles (with different lengths) of the low and high subbands of IWT coefficients.Simulations indicated that the average signal-to-noise ratio (SNR) and the payload of the proposed method are superior to those of existing techniques.Additionally, the proposed method exhibited a robustness that has rarely been observed in conventional ECG steganography.Our method is capable of resisting attacks such as cropping, inversion, scaling, translation, truncation, and Gaussian noise addition.Because bit embedding and extraction procedures are quite simple, our method can be applied in portable biometric devices.
Real-Time Human Fall Detection and Emotion Recognition using Embedded Device and Deep Learning

The fall detection is one of the most discussed topic among the researchers because of its usage in home care and medical places for the patients.Because death due to falls is considered as most dangerous and injuries sustained from these incidents are pretty fatal and serious.Sometimes if the young age person falls he might suffer from minor injuries but we cannot say the same to the older aged person.Sometimes even though the person is suffered from the fall we can save the person by providing immediate care.Because there are some cases in which the death occurred by not responding quickly.Raspberry pi is used as a embedded device and Mog2 algorithm is used for video analysis.After detecting the fall the people are notified using internet based REST APIs like TWILIO.And for emotion recognition it is mostly used to detect the emotional state of the person.This system is mostly used in health care and marketing sectors to provide better results for the people.For emotion recognition system we use CNN (Convolutional Neural Network) since it considered as the best classifier for the video based recognition system.A data set with various emotions is trained and introduced to the system.For both these systems by using deep learning algorithm with embedded device and OpenCV we can provide better results and the performance of the systems are improved.