Reviewerly/exHarmony huggingface dataset:
Embedded Implementation of Early Started Hybrid Denoising Technique for Medical Images with Optimized Loop

This paper represents the architecture of an embedded system for Early Started Hybrid Denoising Technology for Medical Images (ESHDT) using a very popular embedded processor, ATmega processor, which is inexpensive in terms of computation. Embedded implementation of the ESHDT algorithm is chosen because hardware presents a good scope of parallelism and pipelining over software. The Proposed System can work efficiently in a noisy environment. The proposed embedded system architecture has less space complexity because the number of memories is used here. The system starts performing the algorithmic task as soon as two-pixel values are received, and parallelism is achieved by the sixth level of software pipelining and two levels of dedicated hardware pipelining. Due to the design simplicity of our algorithm, it used very few memory resources. Here we used the 8-bit AVR processor which run on low power, and we optimized SD card access so we can say the power consumption by our system is low. Instead of the set of filters, simple predict and update mechanism is introduced. ESHDT provides very good-quality denoised output image with respect to PSNR, UIQI, and MSE.
Measuring UX Capability and Maturity in Organizations

Measuring organizational UX Capability/Maturity (UXCM) has been difficult or inaccurate. Moreover, the lack of empirically developed maturity models, models validated in practice, studies demonstrating their benefit and poor documentation or support for their use, has made this measurement even more problematic. To date, there is no straightforward and efficient method to assess UXCM although such assessment is a prerequisite for the improvement of UX processes. UX artifacts, methods and resources contribute toward the execution of UX processes: the production of UX artifacts demonstrate the execution of a process, whereas the use of UX methods demonstrates the implementation of specific UX processes with specific UX resources. In this paper, we present a measurement structure aiming at assessing organizational capabilities to implement UX processes. This structure consists of a capability scale, a maturity scale, a rating scale and a set of process attributes as measurable characteristics of UX processes. The contribution of this paper is threefold: a description of the measurement structure for the UXCM assessment, a questionnaire for the capability assessment by means of online survey or remote interviews and the documentation of a case study demonstrating the efficiency of the proposed model in an industrial project.
U-Net adaptation for multiple instance learning

Abstract Multiple instance learning (MIL) is a weakly supervised learning method where a single label is assigned to a group of instances. Recent advancement in neural networks makes it possible to achieve great results but the training requires many annotated examples which can be difficult to obtain. In case of medical imaging, such a method can theoretically provide voxel-level annotations basing on the image-level annotations. More precisely, taking a training dataset where each image is given with a label indicating a presence of a region of interest (ROI), the model can be trained to produce voxel labels of the object. We propose a modification of the U-Net architecture for image segmentation that can be trained on weakly labeled datasets and solves the MIL problem. The U-Net architecture is famous for being able to train on small number of samples which can further improve benefits of MIL approach. In this paper we also present results of experiments on synthetic data with investigation of stability to noise and medical images. Experimental results prove that suggested modifications improve the noise stability of the weakly supervised method.
Noise as Dysappearance: Attuning to a Life with Type 1 Diabetes

In this article, we use noise as a metaphor for the overload of information – embodied, technological and online social – that characterizes life with type 1 diabetes. Noise illustrates embodied sensations of fluctuating blood glucose, measurement problems and alarms from digital self-care devices and irrelevant or emotionally disturbing posts on Facebook. Attunement is crucial to the quality of self-care achieved by individuals and comprises: (1) developing skills to receive clear signals from the body, (2) adjusting and individualizing self-care technologies to bodies and daily lives and (3) discerning appropriate distracting and unhelpful self-care information. Ideally, life with type 1 diabetes is harmonious, with clear messages from bodies, technologies and Facebook that enable better self-care.
On Human-Like Performance Artificial Intelligence – A Demonstration Using an Atari Game

Despite the progress made in AI, especially in the successful deployment of deep learning for many useful tasks, the systems involved typically require a huge number of training instances, and hence a long time for training. As a result, these systems are not able to rapidly adapt to changing rules and constraints in the environment. This is unlike humans, who are usually able to learn with only a handful of experiences. This hampers the deployment of, say, an adaptive robot that can learn and act rapidly in the ever-changing environment of a home, office, factory, or disaster area. Thus, it is necessary for an AI or robotic system to achieve human performance not only in terms of the “level” or “score” (e.g., success rate in classification, score in Atari game playing, etc.) but also in terms of the speed with which the level or score can be achieved. In contrast with earlier DeepMind’s effort on Atari games, in which they demonstrated the ability of a deep reinforcement learning system to learn and play the games at human level in terms of score, we describe a system that is able to learn causal rules rapidly in an Atari game environment and achieve human-like performance in terms of both score and time.
Memento: Localized Time‐Warping for Spatio‐Temporal Selection

Abstract Interaction techniques for temporal data are often focused on affecting the spatial aspects of the data, for instance through the use of transfer functions, camera navigation or clipping planes. However, the temporal aspect of the data interaction is often neglected. The temporal component is either visualized as individual time steps, an animation or a static summary over the temporal domain. When dealing with streaming data, these techniques are unable to cope with the task of re‐viewing an interesting local spatio‐temporal event, while continuing to observe the rest of the feed. We propose a novel technique that allows users to interactively specify areas of interest in the spatio‐temporal domain. By employing a time‐warp function, we are able to slow down time, freeze time or even travel back in time, around spatio‐temporal events of interest. The combination of such a (pre‐defined) time‐warp function and brushing directly in the data to select regions of interest allows for a detailed review of temporally and spatially localized events, while maintaining an overview of the global spatio‐temporal data. We demonstrate the utility of our technique with several usage scenarios.
Financial Development in Africa

In this chapter we examine the dynamic causal relationship between financial development and economic growth in French- and English-speaking African countries during the period 1990–2014 using a trivariate-panel Granger-causality model. The study uses three proxies of financial development, namely liquid liabilities (FD1), deposit money bank assets (FD2) and bank deposits (FD3), to examine this linkage. Our results show that the causality between financial development and economic growth differs significantly between English-speaking and French-speaking countries. When FD1 and FD3 are used as proxies for financial development, a demand-following response is found to predominate in both French-speaking and English-speaking countries. However, when FD2 is used as a proxy, the study found a unidirectional causal flow from financial development to economic growth to prevail in French-speaking African countries but failed to find any causal relationship between financial development and economic growth in English-speaking countries in either direction.
A Modified Hash Based Least Significant Bits Algorithm for Steganography

New development in computer security has focused on security using only the cryptography and steganography techniques as well, or the combination of both. In steganography, the most widely used technique is the Least Significant Bits method, but this technique is simplistic of all, but it is vulnerable to attacks. However, modifying the LSB method to the point where it would be difficult or impractical to find the hidden message is a domain where extensive work and research is required. Due to the simplistic nature of the LSB method, this technique can be modified easily. This study aims to present a hash based LSB technique by using EXIF data obtained results that are secured utilizing cryptography and steganography using the hash value into the file image. The result shows that the encryption method that further hides the existence of the message into the cover image using (6) six images samples applying the modified hash LSB based method using visual analysis, file size, luminosity histogram, comparison by pixel and the hamming distance-decryption process does not affect the image pixel. There are variations in the size of the file where it shows that the average value of the percentage changes to-0.08 from the decrypted value of -76.36 to the encrypted value of -76.38. The modified hash LSB based method can be used for copyright protection or security of digital image.
Void-and-Cluster Sampling of Large Scattered Data and Trajectories

We propose a data reduction technique for scattered data based on statistical sampling. Our void-and-cluster sampling technique finds a representative subset that is optimally distributed in the spatial domain with respect to the blue noise property. In addition, it can adapt to a given density function, which we use to sample regions of high complexity in the multivariate value domain more densely. Moreover, our sampling technique implicitly defines an ordering on the samples that enables progressive data loading and a continuous level-of-detail representation. We extend our technique to sample time-dependent trajectories, for example pathlines in a time interval, using an efficient and iterative approach. Furthermore, we introduce a local and continuous error measure to quantify how well a set of samples represents the original dataset. We apply this error measure during sampling to guide the number of samples that are taken. Finally, we use this error measure and other quantities to evaluate the quality, performance, and scalability of our algorithm.
Fast top-k similarity search in large dynamic attributed networks

In this paper, we study the problem of retrieving top-k nodes that are similar to a given query node in large dynamic attributed networks. To tackle this problem, we propose a fast Attribute augmented Single-source Path similarity algorithm (ASP). Our ASP constructs an attribute augmented network that integrates both node structure and attribute similarities through similarity scores computed by an efficient single-source path sampling scheme. It also contains simple and effective updating schemes to maintain similarity scores for dynamic edge insertions and deletions. We provide an upper bound of the sampling size of ASP for obtaining an ϵ-approximation estimation of similarity scores with probability at least 1−δ. We theoretically prove that the sampling size and computational complexity of ASP are significantly lower than that of deploying the currently known most effective method Panther. Implementation results from extensive experiments in both synthetic networks and real-world social networks show that our ASP achieves better convergence performance, runs faster than the baselines, and effectively captures both structure and attribute similarities of the nodes. Specifically, we show that our algorithm can return top-k similar nodes for any query node in the real-world network at least 44x faster than the state-of-the-art methods, and update similarity scores in about 1 second for a batch of network modifications in a network of size 1,000,000.
Fractional Krill–Lion Algorithm Based Actor Critic Neural Network for Face Recognition in Real Time Surveillance Videos

Face recognition from low quality videos is one of the major challenges prevailing in video surveillance system. Several works have been contributed towards the face recognition, but have suffered due to the fact that the low quality videos have face part with low resolution. Also, using traditional feature extraction schemes make the recognition process to be tough. This paper introduces a novel feature descriptor and classification scheme for recognizing the face from low quality videos. Here, the video frames are sequentially provided to the Viola–Jones algorithm for detecting the face part, and the quality of the face part is improved by applying bi-cubic interpolation based super resolution scheme. Now, the features of enhanced video frame are extracted using proposed local direction feature descriptor, namely scattering wavelet-based local directional pattern (SW-LDP). Then, the extracted features are fed as the input to the actor critic neural network, where training is done using the newly developed fractional calculus based krill–lion (fractional KL) algorithm. The proposed fractional KL-ACNN algorithm is experimented using the standard FAMED database. From the analysis, it is evident that the proposed classifier achieved low FAR of 3.89%, and low FRR of 4.04%, and high accuracy value of 95%, respectively.
Intuitionistic Fuzzy Dynamic Bayesian Network and its Application to Terminating Situation Assessment

The period of terminating situation in air combat is time-varying and full of information with uncertainty. In order to process the fuzzy information in time series and assess the situation effectively, Intuitionistic Fuzzy Dynamic Bayesian Network (IFDBN) is built. In this model, intuitionistic fuzzy reasoning engine is embedded into dynamic Bayesian Network as a virtual node. Besides, a new method to convert the intuitionistic fuzzy reasoning output into probability which can be input into dynamic Bayesian Network as virtual evidence is proposed. On these bases, the detailed intuitionistic fuzzy reasoning algorithms are formulated and the dynamic Bayesian Network information transferring mode is analyzed. Finally, a case study for terminating situation assessment in air combat is given to prove the feasibility of the proposed model.
Domain Adaptation using a Gradient Reversal Layer with Instance Weighting

We propose a new method for domain adaptation that uses a gradient reversal layer (GRL) with instance weighting. Domain adaptation methods that use GRL have the latent problem of learning data that do not contribute to improving the accuracy with which the target domain is recognized. The proposed method weights each source domain sample. This enables us to control the gradients of training samples that do not contribute to improving accuracy. In an evaluation experiment using computer graphics and real image data, accuracy in recognizing the target domain improved by 5.3% compared with existing domain adaptation methods.
Evolutionary search techniques for the Lyndon factorization of biosequences

A non-empty string x over an ordered alphabet is said to be a Lyndon word if it is alphabetically smaller than all of its cyclic rotations. Any string can be uniquely factored into Lyndon words and efficient algorithms exist to perform the factorization process in linear time and constant space. Lyndon words find wide-ranging applications including string matching and pattern inference in bioinformatics. Here we investigate the impact of permuting the alphabet ordering on the resulting factorization and demonstrate significant variations in the numbers of factors obtained. We also propose an evolutionary algorithm to find optimal orderings of the alphabet to enhance this factorization process and illustrate the impact of different operators. The flexibility of such an approach is illustrated by our use of five fitness functions which produce different factorizations suitable for different downstream tasks.
How XCS can prevent misdistinguishing rule accuracy

On the XCS classifier system, an ideal assumption in the latest XCS learning theory means that it is impossible for XCS to distinguish accurate rules from any other rules with 100% success rate in practical use. This paper presents a preliminary work to remove this assumption. Furthermore, it reveals a dilemma in setting a crucial XCS parameter. That is, to guarantee 100% success rate, the learning rate should be greater than 0.5. However, a rule fitness updated with such a high learning rate would not converge to its true value so rule discovery would not act properly.
Quantifying Differences Between Child and Adult Motion Based on Gait Features

Previous work has shown that motion performed by children is perceivably different from that performed by adults. What exactly is being perceived has not been identified: what are the quantifiable differences between child and adult motion for different actions? In this paper, we used data captured with the Microsoft Kinect from 10 children (ages 5 to 9) and 10 adults performing four dynamic actions (walk in place, walk in place as fast as you can, run in place, run in place as fast as you can). We computed spatial and temporal features of these motions from gait analysis, and found that temporal features such as step time, cycle time, cycle frequency, and cadence are different in the motion of children compared to that of adults. Children moved faster and completed more steps in the same time as adults. We discuss implications of our results for improving whole-body interaction experiences for children.
Hybridizing differential evolution and novelty search for multimodal optimization problems

Multimodal optimization has shown to be a complex paradigm underneath real-world problems arising in many practical applications, with particular prevalence in physics-related domains. Among them, a plethora of cases within the computational design of aerospace structures can be modeled as a multimodal optimization problem, such as aerodynamic optimization or airfoils and wings. This work aims at presenting a new research direction towards efficiently tackling this kind of optimization problems, which pursues the discovery of the multiple (at least locally optimal) solutions of a given optimization problem. Specifically, we propose to exploit the concept behind the so-called Novelty Search mechanism and embed it into the self-adaptive Differential Evolution algorithm so as to gain an increased level of controlled diversity during the search process. We assess the performance of the proposed solver over the well-known CEC'2013 suite of multimodal test functions. The obtained outcomes of the designed experimentation supports our claim that Novelty Search is a promising approach for heuristically addressed multimodal problems.
Usage Comparison between English-Language and French-Language e-Reference in a Bilingual Canadian University

This article reports the results of a quantitative and systematic analysis comparing usage rates between English-language and French-language e-reference titles at Laurentian University, Canada. With the library undergoing major cuts to its collection budget, it became imperative to determine if French-language e-resource usage justified their expenditures. Searches and full-text viewings or downloads were collected as usage metrics. Both English- and French-language e-reference collections exhibited very weak linear relationships between collection size and usage. The French-language e-reference collection experienced a higher level of full-text use, relative to size. The cost-per-viewing for the English-language e-reference collection was three times lower than that of the French-language collection. The difference is even greater for the cost-per-search, with the English-language e-reference collection being 12 times lower than that calculated for the French-language e-reference collection.
Cryptanalysis of an NTRU-Based Proxy Encryption Scheme from ASIACCS’15

In ASIACCS 2015, Nu $$\tilde{\mathrm{n}}$$ ez, Agudo, and Lopez proposed a proxy re-encryption scheme, NTRUReEncrypt, based on NTRU, which allows a proxy to translate ciphertext under the delegator’s public key into a re-encrypted ciphertext that can be decrypted correctly by delegatee’s private key. In addition to its potential resistance to quantum algorithm, the scheme was also considered to be efficient. However, in this paper we point out that the re-encryption process will increase the decryption error, and the increased decryption error will lead to a reaction attack that enables the proxy to recover the private key of the delegator and the delegatee. Moreover, we also propose a second attack which enables the delegatee to recover the private key of the delegator when he collects enough re-encrypted ciphertexts from a same message. We reevaluate the security of NTRUReEncrypt, and also give suggestions and discussions on potential mitigation methods.
Study on the Test Data Fault Mining Technology Based on Decision Tree

Aiming at the shortage of greatness of test data and the traditional decision-making arithmetic of decision tree, the paper put forward a way to diagnose faults based on vehicle test data mining. Attribute list was built by the way of training data set in tests, fault decision tree fabricated based on better-deciphered arithmetic –C4.5. To overcome the time-consuming shortage of C4.5 in the discretization process of continuous valves, the paper studied Fayyad theorem, prospered improved arithmetic by calculating via dividing line, experiments proved the fault detection and diagnose strategy effectively and exactitude
Comparative Study on Two Pretreatment Processes for Chemical Phase Analysis of Gold in Geological Samples by Atomic Absorption Spectrometry

Sample pretreatment is important for chemical phase analysis of elements. In this study, the geological samples of the Laozuoshan gold mine are chosen to pretreat by ultrasonic centrifugation and cyclotron oscillation, and the content of gold in eight chemical phases (water-soluble, ion exchange and clay adsorption, organic matter bound, iron-manganese oxide bound, naked or seminaked, carbonate bound, sulfide bound, and insoluble silicate states) is determined by atomic absorption spectrometry. The results show that the gold content of water-soluble, ion exchange and clay adsorption, iron-manganese oxide, and naked or seminaked states in the rock and ore samples is low, and some samples have high gold content of insoluble silicate states in the two methods. However, the gold content of organic matter bound, carbonate bound, and sulfide bound states obtained by ultrasonic centrifugation and cyclotron oscillation methods is significantly different. According to the X-ray fluorescence spectrometry data and the actual geological condition, the result given by the cyclotron oscillation method is more reasonable. The gold content of sulfide bound state in sediment samples is the highest and consistent with the mineral information, which could be applied to preliminarily predict the rock and ore conditions in the corresponding mining areas. In contrast with ultrasonic centrifugation, the cyclotron oscillation method has the advantages of simplicity, high efficiency, practicality, and environmental protection, and it can be better used for the determination of gold chemical phase state in geological samples by atomic absorption spectrometry.