Reviewerly/exHarmony huggingface dataset:
Generative Aging of Brain MR-Images and Prediction of Alzheimer Progression

Predicting the age progression of individual brain images from longitudinal data has been a challenging problem, while its solution is considered key to improve dementia prognosis. Often, approaches are limited to group-level predictions, lack the ability to extrapolate, can not scale to many samples, or do not operate directly on image inputs. We address these issues with the first approach to artificial aging of brain images based on Wasserstein Generative Adversarial Networks. We develop a novel recursive generator model for brain image time series, and train it on large-scale longitudinal data sets (ADNI/AIBL). In addition to thorough analysis of results on healthy and demented subjects, we demonstrate the predictive value of our brain aging model in the context of conversion prognosis from mild cognitive impairment to Alzheimer’s disease. Conversion prognosis for a baseline image is achieved in two steps. First, we estimate the future brain image with the Generative Adversarial Network. This follow-up image is passed to a CNN classifier, pre-trained to discriminate between mild cognitive impairment and Alzheimer’s disease. It estimates the Alzheimer probability for the follow-up image, which represents an effective measure for future disease risk.
On the Applicability of Secret Share Algorithms for Saving Data on IoT, Edge and Cloud Devices

A common practice to store data is to use remote Cloud-based storage systems. However, storing files in remote services can arise privacy and security issues, for example, they can be attacked or even discontinued. A possible solution to solve this problem is to split files into chunks and add redundancy by means of Secret Share techniques. When it comes to Internet of Things (IoT), Edge and Cloud environments, these techniques have not been evaluated for the purpose of storing files. This work aims to address this issue by evaluating two of the most common Secret Share algorithms in order to identify their suitability for different environments, while considering the size of the file and the availability of resources. In particular, we analysed Shamir's Secret Share schema and the Redundant Residue Number System (RRNS) to gauge their efficiency regarding storage requirement and execution time. We made our experiments for different file sizes (from 1kB up to 500MB), number of parallel threads (1 to 4) and data redundancy (0 to 7) in all aforementioned environments. Results were promising and showed that, for example, to have seven degrees of redundancy, Shamir uses eight times more storage than RRNS; or, Shamir is faster than RRNS for small files (up to 20 kB). We also discovered that the environment on which the computation should be performed depends on both file size and algorithm. For instance, when employing RRNS, files up to 500kB can be processed on the IoT, up to 50MB on the Edge, and beyond that on the Cloud; whereas, in Shamir's schema, the threshold to move the computation from the IoT to the Edge is about 50kB, and from the Edge to the Cloud is about 500kB.
Dynamic Interface and Access Model by Dead Token for IoT Systems

Communication between users and intelligent devices is normally done through a graphical user interface. In addition, devices that communicate using Bluetooth are also implementing a control interface. Thus, most of the devices in an enclosure such as home or work can be remotely controlled. This implies that each device can have an interface and an IP assignment for its own control. In this way, users must learn and manage several communication interfaces. In this paper, we present a model of a general graphical user interface to control different smart devices that can consume HTTP requests or that are controlled by Bluetooth. In addition, we present an authentication approach for the Internet of Things that uses the proposed model.
Soil organic carbon predictions in Subarctic Greenland by visible–near infrared spectroscopy

Release of carbon from high-latitude soils to the atmosphere may have significant effects on Earth’s climate. In this contribution, we evaluate visible–near-infrared spectroscopy (vis-NIRS) as a time- and cost-efficient tool for assessing soil organic carbon (SOC) concentrations in South Greenland. Soil samples were collected at two sites and analyzed with vis-NIRS. We used partial least square regression (PLS-R) modeling to predict SOC from vis-NIRS spectra referenced against in situ dry combustion measurements. The ability of our approach was validated in three setups: (1) calibration and validation data sets from the same location, (2) calibration and validation data sets from different locations, and (3) the same setup as in (2) with the calibration model enlarged with few samples from the opposite target area. Vis-NIRS predictions were successful in setup 1 (R2 = 0.95, root mean square error of prediction [RMSEP] = 1.80 percent and R2 = 0.82, RMSEP = 0.64 percent). Predictions in setup 2 had higher errors (R2 = 0.90, RMSEP = 7.13 percent and R2 = 0.78, RMSEP = 2.82 percent). In setup 3, the results were again improved (R2 = 0.95, RMSEP = 2.03 percent and R2 = 0.77, RMSEP = 2.14 percent). We conclude that vis-NIRS can obtain good results predicting SOC concentrations across two subarctic ecosystems, when the calibration models are augmented with few samples from the target site. Future efforts should be made toward determination of SOC stocks to constrain soil–atmosphere carbon exchange.
Negated LAMA: Birds cannot fly

Building on Petroni et al. (2019), we propose two new probing tasks analyzing factual knowledge stored in Pretrained Language Models (PLMs). (1) Negation. We find that PLMs do not distinguish between negated (Birds cannot [MASK]) and non-negated (Birds can [MASK]) cloze questions. (2) Mispriming. Inspired by priming methods in human psychology, we add misprimes to cloze questions (Talk? Birds can [MASK]). We find that PLMs are easily distracted by misprimes. These results suggest that PLMs still have a long way to go to adequately learn human-like factual knowledge.
PoTN

As one of the fundamental characteristics of distributed IoT networks, blockchain has attracted tremendous interests from both the academics and industries. Recently, the key to support distributed IoT networks through blockchain is the consensus protocol with multi-miners selection. However, most of them are fixed-miners based, and thus offering opportunities for attackers to launch DoS attack against the fixed miners or manipulate the creation of blocks by bribing the fixed miners. In this paper, we propose a novel consensus protocol called proof-of-trust negotiation (PoTN). With negotiation rules, trust management is introduced to evaluate the trustworthiness of miners. In this basis, the random-honest miners selection can be achieved by the design of trusted random selection algorithm on the miner team. Simulation results show that PoTN is more effective than traditional consensus protocols in block creation.
OctShuffleMLT: A Compact Octave Based Neural Network for End-to-End Multilingual Text Detection and Recognition

In recent years, scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exist many challenges in applying very deep networks to many real-world applications, that have hardware limitations, such as robots, and smartphones. To address these challenges, in this paper, we propose the OctShuffleMLT, an effective fully convolutional neural network, with fewer layers and parameters, which can precisely detect multilingual scene text. Our proposed model is based on the Octave Convolutions that use compact blocks, which reduces memory inference by 13.16%, FLOPS by 71.86%, and the number of parameters by 34.04% when compared to the baseline system. Extensive experiments were conducted on ICDAR 2015 and ICDAR 2017 datasets. Experimental results show that our model can produce accurate detection recognition results on both datasets. The code for the paper is made available on the GitHub repository https://github.com/victoic/OctShuffle-MLT.
How to generate cryptographically strong sequences of pseudo random bits

research-article Share on How to generate cryptographically strong sequences of pseudo random bits Authors: Manuel Blum University of California University of CaliforniaView Profile , Silvio Micali University of California University of CaliforniaView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 227–240https://doi.org/10.1145/3335741.3335751Published:04 October 2019Publication History 3citation85DownloadsMetricsTotal Citations3Total Downloads85Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Randomness Analysis for the Generalized Self-Shrinking Sequences

In cryptography, the property of randomness in pseudo-random generators is very important to avoid any pattern in output sequences, to provide security against attacks, privacy and anonymity. In this article, the randomness of the family of sequences obtained from the generalized self-shrinking generator is analyzed. Moreover, the characteristics, generalities and relationship between the t-modified self-shrinking generator and the generalized self-shrinking generator are presented. We find that the t-modified self-shrunken sequences can be generated from a generalized self-shrinking generator. Then, an in-depth analysis of randomness focused on the generalized sequences by means of complete and powerful batteries of statistical tests and graphical tools is done, providing a useful vision of the behaviour of these sequences and proving that they are suitable to be used in cryptography.
The Impact of Virtual Human Voice on Learner Trust

The current study investigates if a virtual human’s voice can impact the user’s trust in interacting with the virtual human in a learning setting. It was hypothesized that trust is a malleable factor impacted by the quality of the virtual human’s voice. A randomized alternative treatments design with a pretest placed participants in either a low-quality Text-to-Speech (TTS) engine female voice (Microsoft speech engine), a high-quality TTS engine female voice (Neospeech voice engine), or a human voice (native female English speaker) condition. All three treatments were paired with the same female virtual human. Assessments for the study included a self-report pretest on knowledge of meteorology, which occurred before viewing the instructional video, and a measure of system trust. The current study found that voice type impacts a user’s trust ratings, with the human voice resulting in higher ratings compared to the two synthetic voices.
Issues using logistic regression with class imbalance, with a case study from credit risk modelling

The class imbalance problem arises in two-class classification problems, when the less frequent (minority) class is observed much less than the majority class. This characteristic is endemic in many problems such as modeling default or fraud detection. Recent work by Owen [19] has shown that, in a theoretical context related to infinite imbalance, logistic regression behaves in such a way that all data in the rare class can be replaced by their mean vector to achieve the same coefficient estimates. We build on Owen's results to show the phenomenon remains true for both weighted and penalized likelihood methods. Such results suggest that problems may occur if there is structure within the rare class that is not captured by the mean vector. We demonstrate this problem and suggest a relabelling solution based on clustering the minority class. In a simulation and a real mortgage dataset, we show that logistic regression is not able to provide the best out-of-sample predictive performance and that an approach that is able to model underlying structure in the minority class is often superior.
An Ontology-based Approach for User Modelling and Personalization in E-Learning Systems

Intelligent tutoring systems are becoming an important mean of education delivery and have changed the ways of teaching and learning. One of their most important aspects concerns the personalization and the adaptation of the learning procedures to learners' characteristics. This is mainly achieved with the use of student modelling which is employed to record user context and to enhance the learning experience of the students. In this work, we present a generic ontology that incorporates concepts and properties used to model user profiles in order to formulate concrete, complete and extensible user modelling in e-learning systems. The ontology consists of learners' personal, cognitive and social information as well as information about their performance and skills. In order to keep the student model updated, semantic rules are utilized to analyse student learning and to extract knowledge regarding their performance. The ontological approach is domain independent and can be utilized as a reference model for learner representation in intelligent tutoring systems in higher and distance education.
Accuracy Measurement on Indonesian Non-formal Affixed Word Stemming With Levenhstein

Algorithm stemming of Nazief and Andriani has been developed in terms of speed and accuracy. One of the developments is Non-Formal Affix Algorithm. This algorithm increases the accuracy of stemming in non-formal affixed words. In its development, Indonesian language is used in two forms: standard and non-standard. Non-formal language is commonly used in non-formal situation such as in daily conversation, post, or comment on social media (Facebook, Twitter, Instagram, etc.). Non-formal Affix Stemming Algorithm is used to get the basic word of a non-formal word. However, this algorithm has limitation in non-formal affixed word stemming. Thus, this research aims to focus on the modification of non-formal affix algorithm to increase accuracy in non-formal affixed words stemming. The result of this research shows that modification of the combination of Levenshtein Distance and frequently used words dictionary has accuracy level of 88.33% with precision of 1 and error rate of 0.2. Meanwhile, Non-formal Affix algorithm has accuracy level of 73.3% at stemming 60 non-formal affixed words with precision of 1 and error rate of 0.26. Therefore, it can be concluded that Lenshtein Distance approach can increase the accuracy of Non-formal Affix algorithm in non-formal affixed words stemming.
Remarks for Scaling Up a General Gaussian Process to Model Large Dataset with Sub-models

Gaussian process models (GPs) have proven to be effective to approximate expensive responses (e.g. high-fidelity simulations and experiments) and widely used for industrial designs. With rapid development of computational resources and a large amount of inter-connected data, it’s becoming necessary to process large dataset efficiently for industrial designs. However, a major challenge for the standard GP is the prohibitively expensive training time with complexity O(n^3) leaving modeling data of size n=〖10〗^4 impractical. One strategy for handling large dataset is sub-modeling in which multiple smaller models are developed from sub-dataset and combined for prediction. The sub-modeling speeds up various numerical processes with GP training including Cholesky decomposition, matrix multiplications. This paper proposes intelligent sub-modeling for efficient sub-sampling and modeling from the dataset. Intelligence comes from two aspects: (1) the sub-dataset is adaptively selected to maximize the prediction accuracy of a sub-model, (2) the number of sub-models is determined based on identified stopping criterion, therefore, no need to model all the dataset. A series of techniques are integrated to enhance the intelligent sub-modeling: (1) the likelihood acquisition function for error reduction, (2) combined prediction to improve exploration of sub-sampling, (3) parameter sharing of the initial sub-model for faster training, (4) batch sampling to improve training speed and exploration of the dataset. The effect of each technique has been quantified with a test function. Comparison between a standard GP and the proposed intelligent sub-modeling is provided regarding prediction mean, uncertainty, global sensitivity and sample size. The effect of multi-objective modeling is also demonstrated with a Cantilevered Beam function.
A Depthwise Separable Convolution Neural Network for Small-footprint Keyword Spotting Using Approximate MAC Unit and Streaming Convolution Reuse

In recent years, many applications of voice wake-up technology have entered people's lives and the key technology is Keyword Spotting (KWS). The keyword spotting system needs to detect the ambient voice and wait for a wake-up at any time, which requires low power consumption and high recognition accuracy. We mainly aim at reducing the power consumption of real-time keyword spotting systems in this paper. Based on Google's speech commands dataset (GSCD), a deep neural network model with Depthwise Separable Convolution (DS-Conv) is constructed and trained. We propose a kind of Approximate Multiply and Accumulate Unit (AP-MAC) and a data reuse method called Streaming Convolution Reuse (SCR) and prove that the neural network with AP-MACs saves 37.7% ~ 42.6% of computing power and achieves similar Word Error Rate (WER) compared to the same model using traditional MAC units in KWS task. Also, SCR allows the model to reuse convolution results for multiple audio frames and saves 94% of activations storage. By combining these two methods, the computing power and memory storage per audio frame of the baseline model are reduced by 98.5% ~ 98.7% and 94% respectively.
A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming

Gene Expression Programming(GEP), a variant of Genetic Programming(GP), is a well established technique for automatic generation of computer programs. Due to the flexible representation, GEP has long been concerned as a classification algorithm for various applications. Whereas, GEP cannot be extended to multi-classification directly, and thus is only capable of treating an $M$-classification task as $M$ separate binary classifications without considering the inter-relationship among classes. Consequently, GEP-based multi-classifier may suffer from output conflict of various class labels, and the underlying conflict can probably lead to the degraded performance in multi-classification. This paper employs evolutionary multitasking optimization paradigm in an existing GEP-based multi-classification framework, so as to alleviate the output conflict of each separate binary GEP classifier. Therefore, several knowledge transfer strategies are implemented to enable the interation among the population of each separate binary task. Experimental results on 10 high-dimensional datasets indicate that knowledge transfer among separate binary classifiers can enhance multi-classification performance within the same computational budget.
Missing value recovery for encoder signals using improved low-rank approximation

Rotary encoders have been increasingly equipped in high precision machinery, and their data missing may pose great challenges for both numeric control and health monitoring. In view of this limitation, a new methodology, termed improved low-rank approximation (ILRA), is proposed for encoder signal recovery. In this approach, phase space representation (PSR) is first introduced to transform the one-dimensional encoder signal into a matrix for low-rank exploration. Bidirectional centralization with cumulative energy ratio is then proposed to identify and extract the principal components embedded in phase space. Finally, an improved low-rank approximation scheme is employed to recover the missing values in an iterative manner. The performance of the proposed ILRA is evaluated by both simulation and experimental analysis. The results show that the proposed method can recover the missing values efficiently. Furthermore, the method yields satisfactory results for subsequent instantaneous angular speed estimation. Therefore, it may offer a promising tool for numerical data recovery in industrial applications.
A Cross-Institutional Study of eBook Demand-Driven Acquisition (DDA) Use and Efficacy of Eight Large Academic Libraries

This study is the first of its kind to analyze and compare demand-driven acquisition (DDA) ebook programs on a large scale by using eight academic libraries. The purpose is to understand which factors contribute to successful collection management practices and sustainability. Study findings also offer insight into weeding practices and suggest that ebooks removed from the discovery pool too soon may impact service to library users. Furthermore, findings based on formula analysis show that return on investment (ROI) for serviceable content is better achieved through a sustained straight DDA model without short-term loans.
Mitigate the Reverberant Effects on Speaker Recognition via Multi-training

Speaker recognition techniques have been developed into a relatively mature status over the past few decades through continuous research and development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security forensics robustness and reliability of the system is crucial. The reverberation condition can be represented by two main parameters namely Reverberation Time (RT) and Direct to Reverberation Ratio (DRR) (which represent the distance of the microphone to the source). This paper presents an efficient method to mitigating or at least alleviates the impacts of reverberation upon speaker verification. Multi-condition training approaches are investigated to alleviate such detrimental effects. Three multi-condition training methods are then investigated to mitigate such detrimental effects. The first uses matched train/test speaker models based on estimated reverberation time (RT) values. The second utilizes two-condition training where clean and reverberant models are used. Lastly, a four-condition training setup is proposed and conducted to improve the system performance. The utilized data set building, for SV experiments, training, and speech test material are obtained from the University of Salford Anechoic chamber database (SALU-AC). Experimental results show the first and the last types of multi-condition training providing significant gains in performance relative to the baseline.