Reviewerly/exHarmony huggingface dataset:
Evaluation of tsunami evacuation planning of the primary school student in Padang

This paper presents an evaluation of the tsunami evacuation of elementary school children in Padang. The objective of the study is to evaluate the evacuation planning using an observation data from an evacuation Experiment initiated by the Padang Regional Disaster Management Agency (BPBD Padang). In this study, the chosen evacuation destination was evaluated based on the available evacuation time, the evacuation methods, walking speed of the students in a group and the provided tools in the classroom for evacuation as in the guidelines of tsunami evacuation for schools in Hawaii.
The Theory of Self-Identification and Categorical Research Method

This article presents a categorical method for analyzing the complex processes of personal identity. Human experiences are a result of conscious generalizations that dominate culture and are fixed in semantic categories. The rapid transformation of society fragments a life into many identifying parameters. Therefore, «a self-concept» and a semantic category of being may not be consistent with each other. The harmonious level of self-organization is manifested in the sensory coherence of people: an intention and an expectation. And fragmentation is a chaos of self-awareness and loss of an emotional stability. In a complicating society, the identity of a person becomes multiple and ambiguous. These studies will determine not only the social level of human self-organization, but begin the search for a method to maintain them. The article attempts to consider a categorical method for analyzing the self-identification properties of a people.
A New Privacy-Enhanced Technology for Fair Matchmaking With Identity Linked Wishes

Matchmaking problems such as coupling for a school dance, voting negotiations in legislative bodies, and recruiting of high level executives involve identity linked wishes (ILW), which are wishes that involve specific identities and are only valid if those particular entities share the common wishes. The challenge of fair and privacy-enhanced matchmaking with ILW, with conflicting goals of anonymity and authentication, was coined the prom problem (TPP). In this paper, we detail phases of the privacy engineering process used for development of a novel privacy-enhanced technology (PET) for TPP. Early stages consisted of problem framing, definition of security and privacy requirements, protocol development, and theoretical security and complexity analyses. A proof-of-concept implementation and feasibility evaluation including performance testing with real-world systems and networks demonstrated the correctness and practicality of the approach, thereby limiting risk prior to incurring the full costs of production system development. We highlight the system architecture, risks, and the regulatory environment. We conclude with lessons learned and recommendations for privacy engineering. In the rapidly evolving landscape of privacy regulations, PETs can afford competitive advantage, while simultaneously limiting compliance risks.
The Effectiveness of Pre-trained Code Embeddings

Few machine learning applications applied to the domain of programming languages make use of transfer learning. It has been shown that in other domains, such as natural language processing, that transfer learning improves performance on various tasks. We investigate the use of transfer learning for programming languages, focusing on two tasks: method name prediction and code retrieval. We find that transfer learning provides improved performance, as it does to natural languages. We also find that these models can be pre-trained on programming languages that are different from the downstream task language and even pre-training models on English language data is sufficient to provide similar performance as pre-training on programming languages.
Modal Logics of Finite Direct Powers of $$\omega $$ Have the Finite Model Property

Let $$(\omega ^n,\preceq )$$ be the n-th direct power of $$(\omega ,\le )$$, natural numbers with the standard ordering, and let $$(\omega ^n,\prec )$$ be the n-th direct power of $$(\omega ,<)$$. We show that for all finite n, the modal algebras of $$(\omega ^n,\preceq )$$ and of $$(\omega ^n,\prec )$$ are locally finite. In particular, it follows that the modal logics of these frames have the finite model property.
Inner Product Optimization for Effective Multiple Kernel Learning

This paper proposes a novel and effective method for multiple kernel learning (MKL) that differs from standard MKL approaches. The new algorithm, named effective multiple kernel learning (EMKL), proposes a learn function space generated by multiple kernels with a group of parameters, as well as constructs a new inner-product with an optimized symmetric positive-definite matrix for the solution space of learning problem. We also describe the proposed EMKL algorithm within a general-purpose image classification framework. The EMKL algorithm has a closed-form solution, thus enabling optimization without the need for the additional iterative operations that are common in standard MKL algorithms. Hence, our proposed EMKL has a lower computational cost than that of conventional MKL techniques. The results of experiments on two benchmark datasets show the effectiveness of the proposed algorithm.
The Logic of Probability and Statistics

Deduction is certainty via the Principle of the Excluded Middle, where every premise must be true or false, not both true and false, and not neither true nor false. Not every scientific claim is always true or always false. In response, the partial syllogism was created where “some” was substituted for “all” and “all X’s are Y’s” becomes “some X’s are Y’s”. For discovery, the partial syllogism had greater utility. The utility was extended by substituting a probability, P(X), for “some.” Thus “some X’s are Y’s” becomes “there is a probability that X’s are Y’s”. Probabilities often are enumerated, 100 X’s are encountered, 80 were Y’s for a probability of 0.8 (80%) that an X is a Y. On another enumeration, the probability may be different, leading to the need for probabilities of probabilities, hence statistics. Reproducibility and productive irreproducibility depend on the logical certainty of the probability of probabilities.
Fast document area detection for scanned images

Digitization of many documents in public institutions is performed with the use of optical scanner with fixed scanned area size. Therefore, scanned document does not always fill all the image area and may contain additional marginal noise. A major difficulty is the appearance of borders area, which can have varying pixel intensity with additional distortions. In this paper, a novel algorithm for fast detection of the document area in scanned images is proposed. It consists of three main stages: preprocessing, edge density projections and black run analysis. The experimental results on several scanned images with different size of document have demonstrate the 86.1% accuracy document content with fast processing speed.
Research and application of a novel randomly encoded hybrid grating interferometric wavefront sensor

In this paper, a novel randomly encoded hybrid grating (REHG) interferometric wavefront sensor with the features of high-precision, high-resolution, high-dynamic-range and anti-vibration is proposed. The REHG consists of a randomly encoded binary amplitude grating and a phase chessboard. The far filed Fraunhofer diffractions only contain &plusmn;1 orders in two orthogonal directions. Different from the cross grating lateral shearing interferometer (CGLSI), there is no need of order selection mask for quadriwave lateral shearing interference. Without the influence of periodical Talbot effect, a continuously variable shear ratio can be obtained with the REHG, which makes it possible to control the dynamic range and measurement sensitivity of the wavefront sensor. A high-precision calibration method for shear ratio based on the shearing wavefront feature extraction and the generalized wavefront retrieval algorithm are employed to ensure the accuracy of the wavefront measurement results. The REHG wavefront sensor can work in collimated beam and convergent beam modes. Due to self-referenced and common-path characteristics, the REHG wavefront sensor can applied to different application fields <i>in situ</i>. Compared to the ZYGO interferometer, the results of the optical aberration and spherical surface measured by the REHG are highly precise and also show good repeatability. By applying two REHG wavefront sensors with different shear ratio, a wideband sensitivity-enhanced interferometric microscopy with real-time visualization can retrofit existing bright-field microscopes into quantitative phase microscopes.
Sasthyakathan - A Health Based Social Robot

Bangladesh has the highest rate of malnutrition around the world. More than 9.5 million children suffer from malnutrition and 50 to 60 percent of women in Bangladesh have BMI<18.5 kg/m2 [1]. Malnutrition in children, adolescents and women is a major concern. Furthermore, health expert human shows disinterest to provide healthcare solution in a remote location or in rural areas. For the purpose of solving this problem, we build an autonomous robot that interacts and communicates with humans which can questions and answers regarding healthcare queries intelligently, can recognize food and shows essential food nutrients separately and provide nutrition Plan, health report individually.
Composing Ecosystemically in Responsive Environments with Gestural Media, Objects and Textures

In this workshop, participants will try their hand at a variety of tangible, embodied, and embedded sensing and feedback technologies including vibrotactile instruments, expressive mechatronics, gesturally modulated fields of light, sound, mist; realtime steerable immersive atmospheres. Working through hands-on experience by theme, participants will be introduced to compositional and experimental methodologies. In the second half of the workshop, participants will compose together some simple "ecosystems" using the Synthesis Center's hardware-software media choreography architecture (sc), in the iStage experimental theater-scale blackbox space.
Entanglement, CP-Maps and Quantum Communications

In this chapter we review the employment of quantum entanglement as a resource for information processing and transmission. In particular we introduce and discuss the notion of completely positive maps operating on observable algebras of physical systems in order to have a model to construct communication channels based on quantum processes. Then we discuss advantages and limitations of entanglement-assisted quantum communication schemes like quantum teleportation and dense coding.
Adherent Raindrop Detection Based on Morphological Operations

The presence of raindrops in an image can highly impact the performance of applications used in Advanced Driver Assistance Systems (ADAS). Therefore, the detection of raindrops remains a necessity, especially in rainy conditions to improve the ADAS performance. In this paper, we present a new approach to detect adherent raindrops in a single image based on the raindrops characteristics and using a combination of image processing techniques.
Accurate imagery recovery using a multi-observation patch model

A patch-based non-local self-similarity prior underpins many of the current state-of-the-art results in image-recovery problems. The associated methods tend to exploit such priors either heuristically in terms of the correlations between similar patches, or implicitly using hand-crafted models. Both approaches have a limited ability to represent image-specific self-similarity statistics, which limits the accuracy of the results. To address this problem, we propose a novel multi-observation patch model (MOPM) for image recovery. The MOPM enables the recovery of a clean patch from multiple noisy observations by using a linear filtering operation on a specific manifold. More importantly, it can be adaptively learned from the intermediate recovered image with a latent variable-based Bayesian learning approach. Thus, the MOPM obtains better representation of the image-specific internal statistics. In addition, the MOPM is naturally integrated into a half-quadratic splitting framework, in which the MOPM can be constantly refined through iterations and ultimately produce promising results. The experimental results on denoising and compressive sensing demonstrate the effectiveness of the MOPM for image recovery.
Synonym-antonym and reinforce-void patterns

In this chapter, we introduce two families of analytic patterns. The first is synonym-antonym and is shown to be an analytic analogue to the Hegelian dialectic. Its broad applicability to any analytic discrimination problem is highlighted. The second pattern family is that of reinforce-void, which is shown to provide an interesting balance between consensus-driven approaches and rule-based approaches. Methods in which to drive understanding from using these two patterns simultaneously are provided. The applicability of the patterns to other multiphase counterbalanced approaches is given.
A Lightweight Ciphertext-Policy Attribute-Based Encryption for Fine-Grained Access Control

With the expansion of data, companies start to look for new efficient and cheap alternatives to store, share and manage their data. In this regard, cloud storage services appeared to fill this gap by providing a huge amount of computing resources ensuring data availability and efficient data management with low cost due to the pay-as-you-go payment-model adopted by cloud providers. However, by outsourcing their sensitive data (financial data, health records …), companies will no longer be in control of them, so this arises big challenges related to the confidentiality and data security against attackers (in case of compromised cloud servers) and even against curious cloud providers. Therefore, encrypting data before outsourcing them is the only way that gives to the data owners the control over their data. However, using traditional public key cryptography in a data-sharing context produces an unnecessary communication and computation overhead since for each targeted user, the data owner needs to encrypt a copy of data with the corresponding user’s public key. To fix this problem, many attribute-based encryption (ABE) schemes were proposed. In the ABE model, the encryption process is done based on the attributes instead of a unique public key, and users with matching attributes can decrypt the ciphertext, so data owners in this model don’t need to generate many copies of the same data as in the traditional cryptosystems. However, these schemes still require a huge computational power and communication cost since they are based on expensive bilinear pairing and modular exponentiation operations. We propose a lightweight version of the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) using elliptic curve cryptography and scalar point multiplications, instead of bilinear pairings and modular exponentiations, to ensure a fine-grained access control with less computation cost and shorter keys and ciphertexts. The results show that our scheme improves the execution efficiency and requires a low-cost communication and storage.
Person reidentification on video surveillance data

Person reidentification is a very challenging problem nowadays because of a big amount of video surveillance systems used. The data from such systems is processed to analyze events or emergency situations, find specific people, etc. One of the ways of solving the problem of an area security is the development of person reidentification algorithms. In this paper we propose an algorithm for person reidentification based on RGB histogram features calculation. On the first stage HOG descriptor is selected to detect a person on an image. Then we used k-means++ clustering algorithm to remove background on a person image. Finally, Bayes and SVM classification methods were used for person reidentification. Experimental results showed that the proposed solution can be used for person reidentification with high precision (not less than 82%). To carry out research 3D People Surveillance Dataset was used.
S‐boxes representation and efficiency of algebraic attack

Algebraic analysis of block ciphers aims at finding the secret key by solving a collection of polynomial equations that describe the internal structure of a cipher for chosen observations of plaintext/ciphertext pairs. Although algebraic attacks are addressed for cryptanalysis of block and stream ciphers, there is a lack of understanding of the impact of algebraic representation of the cipher on efficiency of solving the resulting collection of equations. The study investigates some different S-box representations and their effect on complexity of algebraic attacks. In particular, the authors observe that a S-box representation defined in the work as forward–backward (FWBW) leads to a collection of equations that can be solved efficiently. They show that the SR (10,2,1,4) cipher can be broken with algebraic cryptanalysis using standard algebra software Singular and FGb. This is the best result achieved so far. The effect of description of S-boxes for some light-weight block ciphers is investigated. A by-product of this result is that some improvements have been achieved on the algebraic cryptanalysis of LBlock, PRESENT and MIBS light-weight block ciphers. The authors’ study and experiments confirm a counter-intuitive conclusion that algebraic attacks work best for the FWBW S-box representation. This contradicts a common belief that algebraic attacks are more efficient with quadratic S-box representation.
Electric Load Forecasting Based on Sparse Representation Model

Accurate electric load forecasting can prevent the waste of power resources and plays a crucial role in smart grid. The time series of electric load collected by smart meters are non-linear and non-stationary, which poses a great challenge to the traditional forecasting methods. In this paper, sparse representation model (SRM) is proposed as a novel approach to tackle this challenge. The main idea of SRM is to obtain sparse representation coefficients by the training set and the part of over-complete dictionary, and the rest part of over-complete dictionary multiplied with sparse representation coefficients can be used to predict the future load value. Experimental results demonstrate that SRM is capable of forecasting the complex electric load time series effectively. It outperforms some popular machine learning methods such as Neural Network, SVM, and Random Forest.
Hybrid Ways to Disseminate Interaction Design Research

This studio will constructively explore hybrid publication formats for the dissemination of interaction design research in an academic context. We start from the premise that there is a gap between the richness of the artefacts created in interaction design research and the formal publication formats that exist today. Interactive artefacts and the experiences they elicit in use, possess a richness that is difficult to communicate in text and images alone. Through exploratory prototyping, we will explore this gap: what are key elements of artefact based research, that might benefit from alternative media, and how might we design such media to become useful supplements to existing dissemination formats? By bringing together a diverse group of participants from the TEI community to explore this topic, we aim at bringing forth a discussion on how we might stretch the boundaries of academic publication of interaction design research.
Objective analysis and bit rate analysis of HEVC compressed 4D‐fMRI images

Abstract Medical imaging and clinical diagnostics are complementary to one another since their analysis is typical and contains critical information. The growing volume of data has become one of the biggest challenges, as the acquisition of medical modalities is currently having high resolution from the improved and efficient machines (3 to 7 T or more). Moreover, image and video compression is a need with the consideration that there should not be any gap for losing the important information. Less bitrate requirement with high compression ratio without sacrificing important detail is a challenge these days. The current study, is dealing with the compression of 4D‐functional medical resonance images (fMRI) with a codec, that is, high‐efficient video coding (HEVC/H.265) and its objective analysis along with its predecessor that is advanced video coding (AVC/H.264) and with VP8 (WebM Project of Google) reported here. Further, the bit rate analysis that has been conducted, also accounts in conjunction with the bitrate investigation, which is an imperative perspective vital for the telemedicine field. The simulation results reported here represents the compression ratio (CR = 118.23:1) with HEVC/H.265 codec over the compression ratio (CR = 20.52:1) provided by AVC/H.264 and VP8 (CR = 78.29:1). There has been significant improvement observed in alignment of the peak signal‐to‐noise ratio (APSNR), structural similarity (SSIM), and mean squared error (MSE) metrics. Overall, the performance of the anticipated technique is satisfactory for the forthcoming telemedicine or clinical use.
T-IBE-T

T-IBE-T applies identity-based encryption (IBE) to inter-tile communication in tiled multi-processor system-on-chip (MPSoC) hardware architectures. There, a network-on-chip (NoC) enables communication among topologically disconnected parts of an application. When such an MPSoC is shared while tiles are exclusively allocated, with malicious applications present, the NoC becomes a weak point. Yet minimal memory footprint, as well as scalability, are necessary preconditions on any security mechanism in this scenario. We show that T-IBE-T provides each component such as the tiles' OS instances, applications and tile application instances with a secure key exchange while ensuring asynchronicity, minimal latency and providing no-cost key distribution.
Image Retrieval Using Inception Structure with Hash Layer for Intelligent Monitoring Platform

In view of the problem of low efficiency and accuracy in traditional image retrievals, a method using inception structure with hash layers of image retrieval is presented for intelligent monitoring platform. The main idea of our work is to add hash layers into the inception structure of deep neural network, which can be used to transform the global average pooling features into low dimensional binary hash codes. Our method is utilized to not only ensure the sparseness of the neural network, but also avoid the overfitting phenomenon. Experimental results via the MNIST and CIFAR-10 datasets show that the retrieval efficiency and accuracy can be higher using our methods than before.
Multiple S-Box Correlation Energy Analysis Model Based on Particle Swarm Optimization

Aiming at the problem that the amount of calculation of correlation energy analysis is too large in the process of attacking multiple S-box corresponding keys, this paper proposes a multiple S-box correlation energy analysis model based on particle swarm optimization. The particle swarm optimization algorithm has the characteristics of simple structure, fast search speed and memory, therefore, our model simultaneously attacks multiple S-boxes, which can reduce the amount of calculation, thereby achieving the goal of recovering the key efficiently and correctly. Finally, experimental analysis and verification results of the DES algorithm indicate that our new energy analysis model has about 55% improvement in efficiency and 30% improvement in accuracy over traditional energy analysis models.