Reviewerly/exHarmony huggingface dataset:
Dynamic Learning Convolutional Network with Skip Layers for Image Segmentation

Training deeper networks make an active research topic, but the appropriate is the best for specific tasks. In this paper, we proposed to design a good and stable convolutional neural network to solve many image segmentation tasks. We designed an optional skip connection module, which contains three convolutional holes. We use different sizes of convolution kernels to obtain different size of views. This module connects up sampling and down sampling layers of the same size, and dynamically adjust the parameters of the model through the skip gate unit on each convolution kernel. Our experiments on retinal and lung segmentation tasks to confirm the usability of the module, and the model automatically selects the appropriate convolutional layer and achieves a specific segmentation effect after training. The experiments prove the feasibility of the scheme and provide help for further optimization of the segmentation model with a suitable convolutional layers and parameters.
Impact of Solar Irradiation on the Performance Ratio of the Photovoltaic Module

In this paper, the impact of the solar irradiation on both the average energy production of the PV system installed on the case study house and the performance ratio are studied. Both theoretical and real analysis are performed to show the impact of the solar irradiation on the energy production. The theoretical analysis is carried out by simulating the PV system in SketchUp [3] software. The real analysis is carried out on the measured instantaneous power production from one PV module installed in Kuwait Institute for Scientific Research (KISR). Based on the results the energy production is in direct proportionality to the solar irradiation while the performance ratio is in inveigle proportionality to the solar irradiation. Based on the figures that showed the deployed PV system has a maximum energy production of1150 Kwh and a minimum performance ratio of 16.5% occurs on August 2017 when the solar irradiation is 230 w/m2.
Cybersecurity Awareness Learning System via Thai MOOC

This paper aimed at presenting learning content of cybersecurity awareness, and its evaluation. The learning content was created in Thai MOOC, Thai Massive Learning Online Course. The cybersecurity awareness course consisted of six parts: safe and responsible social networking strategies, safe setting of home network, virus and malware protection, security advice for mobile device users, safe internet use, and helping to stop cyberbullying. All the six parts were provided for home users because they can be forced to acquaint themselves with the risks involved in venturing into cyberspace. The system was also free of charge to whom users are interested in the course of cybersecurity awareness. The evaluation was done within the hybrid course among face-to-face and e-learning for two years.
A Comparison of Stochastic Gradient MCMC using Multi-Core and GPU Architectures

Deep learning models are traditionally used in big data scenarios. When there is not enough training data to fit a large model, transfer learning re-purpose the learned features from an existing model and re-train the lower layers for the new task. Bayesian inference techniques can be used to capture the uncertainty of the new model but it comes with a high computational cost. In this paper, the run time performance of an Stochastic Gradient Markov Chain Monte Carlo method using two different architectures is compared, namely GPU and multi-core CPU. As opposed to the widely usage of GPUs for deep learning, significant advantages from using modern CPU architectures.
Feature reduction using a random-switching genetic algorithm

This paper delves into the use of a specially modified genetic algorithm, termed as Random-Switching GA, for feature reduction in datasets in two different domains – low latency applications and text processing. Comparisons have been made using different fitness functions as well as different prediction functions, while keeping the F1 scores as the metric for evaluation of their viability. The results demonstrate an improvement in the scores with this modified algorithm while keeping the selected feature-set to a minimum.
Singing Cans Prototyping an experimental wind instrument through parametric design integrated with field experiments

We present a study of how parametric design can be linked to field experiments where ready-made plug-ins are not available for performative modelling.The study centres on prototyping `Singing Cans' -an experimental wind instrument made with an assembly of drinking cans that can produce sounds in recognizable pitches by interacting with airflows.We describe how field experiments conducted in a fluid flow lab can generate performative resources linkable to parametric design modelling.In Singing Cans, we focus on how to get airflow through a hole made on drinking can to make sounds.The prototyping process involved a lab-based calibration process to establish the relationship between the air volume of a can, measured by water-filling, and the pitch produced, measured by the Tuner Lite by Piascore.The field experiments resulted in a dataset capturing a can's sound-making behaviour in terms of water volumes and pitches.A parametric model that can take in wind data generated by a CFD package and output a 3D frame for site-specific cans installation is presented.
Compact Code-Based Signature for Reconfigurable Devices With Side Channel Resilience

In this paper, we present a compact design for the code based signature called LEDAsig with side channel resistance. Existing implementations concentrate on the high-speed feature while few of them have considerations on area or power efficiency which are particularly decisive for low-cost or power constrained IoT applications. We propose an area-efficient FPGA architecture for systematically rotating the QC-LDGM codes amongst the block RAMs with read-first mode. Additionally, the side channel vulnerability of LEDAsig is carefully examined, and protective masking schemes are introduced accordingly to safeguard our design from power analysis attacks. Effectiveness of these schemes is verified on SAKURA-G FPGA board. Up till now, the design presented in this work is the most compact one and also the first side-channel secure one addressing first-order and (univariate) second-order differential power analysis for the code based signature schemes in the open literature. We show for instance that our first-order (second-order) protected implementation can sign a signature in 117 (203) ms on a Xilinx Spartan-6 FPGA, occupying only 622 (1142) slices, and therefore is a prospective candidate for post-quantum signature schemes in low-resource settings.
AR markerless game

The concept of augmented reality markerless tracking is introduced. This concept provides a flexible and practical framework and is especially relevant for video game augmented reality applications. Markerless AR has been comprised of the universal communal. Markerless augmented reality is the encounter proficiency that's now prepared for the primary time. The perception of this report is that the handler's aspect at the live appearance of the camera of their portable phone in three dimensional and the passage that they see that is the certainty is developed by assimilated three- dimensional virtual objects. Augmented reality games have the authority to outspread virtual gaming into real-world circumstances with real people, while improving the senses of the user. This paper designates the AR-zombie game established with the purpose of detected and unindustrialized mobile augmented reality submissions, precisely for gaming, using ground plane detection collaboration techniques. The objective of the AR zombie player is to kill zombies that are identified through the demonstration of the device. Instead of using indicators as a mean of tracking zombies, this game integrates a ground plane detection system, which will augment the user experience by improving the interface of performers with the real world. As the player travels around the environment, the game will show virtual zombies on the display if the detected ground plane is predictable as belonging to the class of the zombies. AR zombie was tested with users to evaluate the interaction with the real world and its types of tracks were evaluated concerning the presentation to confirm a better gaming familiarity.
Repeat-Revolution: Forms of Partaker Agency in the Sound Works of Cage, Holmqvist and Bruys

The works of composer John Cage and sound artist Nathalie Bruys will be coupled to my notion of partaker agency.The participatory aspects will be examined via Susanne Langer and Karen Barad helping to understand that sound has a vital form with its own agency.Supporting Cage's interest in sound as music, I will elaborate on the Buddhist kōan and Kototama chants.The tension between repetition regarded as similitude or difference, will be further examined via the works of Karl Holmqvist and Peter Handke.Arriving at the conclusion, via Jacques Derrida, that repetition might serve as a double mirror, opening up a way out of the realm of logic.
Automatic RDF, Metadata Generation from Legacy Software Models

The resource description framework is a method which help us to interchange and reuse of metadata. Early metadata gives a simple method for performing and reporting Data checks. Checks are indicated in an intuitive way to find at which extend we find the relevant data. Moreover, to create vocabularies to handle metadata of larger data sets and to capture the correct relation between the objects we needed some framework that results us reliable. Resource description framework is XML application which follow organize rules to give clear method of explaining semantics. RDF is a data model. It is simple model which have different statement about resources. Resource can be any object. RDF gave metadata of web data and gave structural information. UML data can also be explained in form of RDF that is quite a fruitful in field of RDF. Transformation from UML to RDF, XML help eminently. Metadata of class diagram acquire through XML then Parsing of XML pledge metadata and retinue the RDF rules to change metadata into RDF Triples. Triple include subject, predicate and object. Which transmits a sense of a sentence. Further we store triples in RDF system. This technique helps to keep data useful for many generations.
Evaluating Distance Approximation for Implicit Curve Fitting

The curve or surface fitting problem to the given data set is treated as the one of comparison of the distance function with its suggested approximation. The comparison is performed in terms of statistical characteristics for the two sets of random variables. The approach is exemplified via the sets generated by single ellipse or a pair of ellipses.
Expanded Game Art and Neurointerfaces as Means of Produsage

In the center of the following inquiry stands the structural coupling of play and prosume in very actual forms of Augmented Realities, AR and neurogames. The term prosumer is a portemonteau of the terms production and consumption and finds a correspondence in the technological features of augmentation in a mixed reality, where real world and virtual world elements converge. In Augmented reality games codes call up virtual information, which is usual displayed on everyday mobile technologies as smart phones or tablets. The very latest addition to this already established technosphere of mobile devices and play worlds are biometrical interfaces. Such interfaces, like wristbands, clocks or counters and sensors are embedded or extensions to the mobile gadgetry and ubiquitous computing devices of everyday use. The most spectacular kind of such interfaces are those that claim to measure the states of our very inner selves, brain interfaces. The convergence of play principles and neurointerfaces takes place in a multi layered world of virtual information superimposed to the everyday processes of self-measurement for health improvement and self-optimization. The augmented virtual worlds in health Apps are gamifying life routines, from eating, walking to sleeping. The corresponding interfaces, like wrist bands containing Gyro sensors for movement detection and EEG take our actual personal conditions into account, when productivity and consumer potential is measured. Most directly the required information is gained through the form of consumer neurointerfaces, which are entering the end user market since the last few years, offered by companies with poetically promising names such as Emotiv EPOC and Neurosky. The epistemic promise these interfaces give is to enhance life through better control of the self in playful ways—far beyond gamification play principles are deeply embedded into the use of such tools as social feature. This is crucial to spot here a new form of play produsage: the final product is the healthy and better self. Art games allow a controlled shaping of investigations as artistic research—in sciences.
Efficient Database Search via Tensor Distribution Bucketing

In mass spectrometry-based proteomics, one needs to search billions of mass spectra against the human proteome with billions of amino acids, where many of the amino acids go through post-translational modifications. In order to account for novel modifications, we need to search all the spectra against all the peptides using a joint probabilistic model that can be learned from training data. Assuming M spectra and N possible peptides, currently the state of the art search methods have runtime of O(MN). Here, we propose a novel bucketing method that sends pairs with high likelihood under the joint probabilistic model to the same bucket with higher probability than those pairs with low likelihood. We demonstrate that the runtime of this method grows sub-linearly with the data size, and our results show that our method is orders of magnitude faster than methods from the locality sensitive hashing literature.
Blockchain Cryptography: Part 2

Chapter 6 introduced the concept of symmetric cryptography. As the name suggests, a symmetric encryptionSymmetric encryption scheme uses the same key for both encryption and decryption; the key is known to the sender and recipient of the message but must remain unknown to anyone else. To communicate secretly, a sender uses an invertible cryptographic function to encrypt a plaintextPlaintext m with the key k and then sends the resulting ciphertext c to the recipient.
Joint Multi-source Reduction

The redundant sources problem in multi-source learning always exists in various real-world applications such as multimedia analysis, information retrieval, and medical diagnosis, in which the heterogeneous representations from different sources always have three-way redundancies. More seriously, the redundancies will cost a lot of storage space, cause high computational time, and degrade the performance of learner. This paper is an attempt to jointly reduce redundant sources. Specifically, a novel Heterogeneous Manifold Smoothness Learning (HMSL) model is proposed to linearly map multi-source data to a low-dimensional feature-isomorphic space, in which the information-correlated representations are close along manifold while the semantic-complementary instances are close in Euclidean distance. Furthermore, to eliminate three-way redundancies, we present a new Correlation-based Multi-source Redundancy Reduction (CMRR) method with 2,1-norm equation and generalized elementary transformation constraints to reduce redundant sources in the learned feature-isomorphic space. Comprehensive empirical investigations are presented that confirm the promise of our proposed framework.
An Improved Criminisi’s Image Inpainting Algorithm for Priority and Matching Criteria

There are many applications of image inpainting. Criminisi algorithm is a classical sample-based inpainting method in image inpainting technology. Aiming at the distortion caused by unreasonable priority calculation and similarity matching criteria calculation in the Criminisi algorithm, in this paper, an improved Criminisi algorithm for priority and similarity matching criteria are presented. Experiments show that the improved algorithm has better performance.
A smart system for preventing people with dementia from falling from the chair

This paper presents a smart system that automatically analyzes postures of a person in the chair, estimates the risk of falling and warns both the person and his caregiver when the risk of falling is high. Unlike related designs, the system is inexpensive, robust and operates in real-time.
Design of An Automated Testing Tool for Typesetting of Reflowable Documents

In order to test the typesetting effect of the same reflowable document in a variety of office software, and point out the difference between the typesetting effect, a tool used for automatically testing the typesetting effect of the reflowable document was designed. With the testing tool, a document to be tested is firstly transformed into an image conforming to test requirements. Next, an automated color marking method is designed for the document in the format of OOXML to look up a reverse correlation between layout objects and typesetting elements by means of the editable feature of the reflowable document, while regularization analysis is utilized to analyze documents in other formats based on the image features of typesetting elements. At last, the effect of typesetting elements is analyzed at the pixel level. According to the experimental result, the test contents using the tool designed in this paper can reach 70% of the level-1 common function points. What's more, both the test efficiency and the accuracy of testing results are better than that performed by manual testing.
Nature-Inspired Cryptoanalysis Methods for Breaking Vigenère Cipher

The protection of sensitive data against unauthorized access remains a primary concern of modern life. Over time, many different approaches have been introduced to tackle this problem, from substitution ciphers in classic cryptography to post-quantum cryptography as a representative of modern cryptography. In this paper, we focus on a polyalphabetic substitution cipher, precisely the Vigenere cipher. For a cryptoanalysis of the latter, we utilized five nature-inspired algorithms, i.e., Differential Evolution, Firefly Algorithm, Particle Swarm Optimization, Artificial Bee Colony Algorithm, and Cuckoo Search, were utilized. Furthermore, different key lengths were analysed to investigate the search behaviour of the selected algorithms. The results of the experiment show that the applicability of the nature- inspired algorithms for cryptoanalysis is very promising. Out of the tested algorithms, the Differential Evolution outperformed other algorithms.
Fast and Parallelizable Ranking with Outliers from Pairwise Comparisons

In this paper, we initiate the study of the problem of ordering objects from their pairwise comparison results when allowed to discard up to a certain number of objects as outliers. More specifically, we seek to find an ordering under the popular Kendall tau distance measure, i.e., minimizing the number of pairwise comparison results that are inconsistent with the ordering, with some outliers removed. The presence of outliers challenges the assumption that a global consistent ordering exists and obscures the measure. This problem does not admit a polynomial time algorithm unless NP $$ \subseteq $$ BPP, and therefore, we develop approximation algorithms with provable guarantees for all inputs. Our algorithms have running time and memory usage that are almost linear in the input size. Further, they are readily adaptable to run on massively parallel platforms such as MapReduce or Spark.
YouTube-Based Keystream Generator — Secure and Lightweight Secret Key Generation for Near Field Communication Devices

The technology of today is completely going behind data transfer. Voluminous data has been accessed to bring about innovation and invention. In this notion we need to focus on different modes of data transfer that will not only contribute for effective data transfer but also aid in the transmission of data in a secured manner. This paper focusses on the Near Field Communication (NFC) mode of data transfer and the lightweight encryption security strategies that helps to ensure data integrity and consistency. Basically, NFC is a communication protocol that is operating onto the network layer. The key features of NFC include wireless technology, short range (about 4-6 cm), high frequency and low speed connection. Unlike Bluetooth, NFC connections are established at once in a time duration of about one tenth of a second. Owing to the short range of NFC the security is more intense compared to the Bluetooth technology. Still, NFC has its own cons in terms of security issues like packet sniffing, Man In The Middle (MITM) attacks etc. To tackle these issues we have paved attention to the lightweight encryption algorithms which have been intricately designed in a way to suit specialized environments such as Radio Frequency Identification (RFID) tags, contactless sensors etc.We therefore propose a lightweight solution to the issue of secret key generation in the context of NFC devices which uses the natural entropy of images for producing randomness.
Partial Occlusion Handling using Quadrant-wise Sparsity for Face Image Recognition

In the past few years, Sparse representation technique for classification of face images has provided very good classification accuracy. The main problems associated with unconstrained face recognition are partial occlusion and expression changes. In case of partial face occlusion, a large portion of discriminative information gets lost, which deteriorates the classification accuracy. In this paper a novel quadrant-wise sparsity-based classifier is proposed which is based on implementing sparse representation in two different quadrants simultaneously. Proposed classifier utilizes the facial information available in upper and lower quadrants independently to perform classification of test image. Thus, it provides correct classification even if one quadrant is completely occluded. The efficiency of proposed method is showcased using detailed experiments carried out on standard AR database. The experimental results of different experiments carried out with different number of training images and training sets are compared with simple sparse representation method in terms of mean classification accuracy. The performance of proposed method is observed with a maximum improvement of 18.82 % in terms of classification accuracy.
Adaptive Partial Image Secret Sharing

In contrast to encrypting the full secret image in classic image secret sharing (ISS), partial image secret sharing (PISS) only encrypts part of the secret image due to the situation that, in general, only part of the secret image is sensitive or secretive. However, the target part needs to be selected manually in traditional PISS, which is human-exhausted and not suitable for batch processing. In this paper, we introduce an adaptive PISS (APISS) scheme based on salience detection, linear congruence, and image inpainting. First, the salient part is automatically and adaptively detected as the secret target part. Then, the target part is encrypted into n meaningful shares by using linear congruence in the processing of inpainting the target part. The target part is decrypted progressively by only addition operation when more shares are collected. It is losslessly decrypted when all the n shares are collected. Experiments are performed to verify the efficiency of the introduced scheme.