Reviewerly/exHarmony huggingface dataset:
Highly Secured C3I Communication Network Based on Blockchain Technology for Military System

This paper proposes a highly secured and stirring blockchain based system which is decentralized in nature with smart contact and offer new direction for the development of military system. Defense industry deal with the complex procedure of product and supply chain management that is a significant challenge for the industry. Basically today's potential military industry depends on intelligent interface of C3I (command, control, communication and intelligent) systems. However, it is fragile in terms of cyber attacks, secured protocol and data manipulation of hacking where blockchain can be a possible solution. This paper analysis the dissimilar impact of C3I model in the area of artificial intelligence and ensure high security using proper integrity of blockchain platform in military industry. Finally the simulation results prove that proposed model has the potentiality in terms of high accessibility, throughput and security than the typical static system.
Word-Embeddings and Grammar Features to Detect Language Disorders in Alzheimer’s Disease Patients

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects the language production and thinking capabilities of patients. The integrity of the brain is destroyed over time by interruptions in the interactions between neuron cells and associated cells required for normal brain functioning. AD comprises deterioration of the communicative skills, which is reflected in deficient speech that usually contains no coherent information, low density of ideas, and poor grammar. Additionally, patients exhibit difficulties to find appropriate words to structure sentences. Multiple ongoing studies aim to detect the disease considering the deterioration of language production in AD patients. Natural Language Processing techniques are employed to detect patterns that can be used to recognize the language impairments of patients. This paper covers advances in pattern recognition with the use of word-embedding and word-frequency features and a new approach with grammar features. We processed transcripts of 98 AD patients and 98 healthy controls in the Pitt Corpus of the Dementia-Bank database. A total of 1200 word-embedding features, 1408 Term Frequency—Inverse Document Frequency features, and 8 grammar features were extracted from the selected transcripts. Three models are proposed based on the separate extraction of such feature sets, and a fourth model is based on an early fusion strategy of the proposed feature sets. All the models were optimized following a Leave-One-Out cross validation strategy. Accuracies of up to 81.7 % were achieved using the early fusion of the three feature sets. Furthermore, we found that, with a small set of grammar features, accuracy values of up to 72.8 % were obtained. The results show that such features are suitable to effectively classify AD patients and healthy controls.
SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is trained in an end-to-end fashion, which is able to dramatically compress the size of the trained embeddings, thus reduces storage footprint and accelerates retrieval speed. Our evaluation on four real-world networks of diverse characteristics shows that SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, node classification and node recommendation. Moreover, the quantised embedding shows a great advantage in terms of storage and time compared with continuous embeddings as well as hashing methods.
Pedestrian Detection Based on Two-Stream UDN

Pedestrian detection is the core of the driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing down, or emergency stopping to keep pedestrians safe, and improves the security when the vehicle is moving. Suffering from weather, lighting, clothing, large pose variations, and occlusion, the current pedestrian detection still has a certain distance from the practical applications. In recent years, deep networks have shown excellent performance for image detection, recognition, and classification. Some researchers employed deep network for pedestrian detection and achieve great progress, but deep networks need huge computational resources, which make it difficult to put into practical applications. In real scenarios of autonomous vehicles, the computation ability is limited. Thus, the shallow networks such as UDN (Unified Deep Networks) is a better choice, since it performs well while consuming less computation resources. Based on UDN, this paper proposes a new deep network model named two-stream UDN, which augments another branch for solving traditional UDN’s indistinction of the difference between trees/telegraph poles and pedestrians. The new branch accepts the upper third part of the pedestrian image as input, and the partial image has less deformation, stable features, and more distinguished characters from other objects. For the proposed two-stream UDN, multi-input features including the HOG (Histogram of Oriented Gradients) feature, Sobel feature, color feature, and foreground regions extracted by GrabCut segmentation algorithms are fed. Compared with the original input of UDN, the multi-input features are more conducive for pedestrian detection, since the fused HOG features and significant objects are more significant for pedestrian detection. Two-stream UDN is trained through two steps. First, the two sub-networks are trained until converge; then, we fuse results of the two subnets as the final result and feed it back to the two subnets to fine tune network parameters synchronously. To improve the performance, Swish is adopted as the activation function to obtain a faster training speed, and positive samples are mirrored and rotated with small angles to make the positive and negative samples more balanced.
Neural Inheritance Relation Guided One-Shot Layer Assignment Search

Layer assignment is seldom picked out as an independent research topic in neural architecture search. In this paper, for the first time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100. Through analyzing this dataset, we discover a neural inheritance relation among the networks with different layer assignments, that is, the optimal layer assignments for deeper networks always inherit from those for shallow networks. Inspired by this neural inheritance relation, we propose an efficient one-shot layer assignment search approach via inherited sampling. Specifically, the optimal layer assignment searched in the shallow network can be provided as a strong sampling priori to train and search the deeper ones in supernet, which extremely reduces the network search space. Comprehensive experiments carried out on CIFAR-100 illustrate the efficiency of our proposed method. Our search results are strongly consistent with the optimal ones directly selected from the architecture dataset. To further confirm the generalization of our proposed method, we also conduct experiments on Tiny-ImageNet and ImageNet. Our searched results are remarkably superior to the handcrafted ones under the unchanged computational budgets. The neural inheritance relation discovered in this paper can provide insights to the universal neural architecture search.
Combined Advertising Sign Classifier

The article describes the problem of classifying photographs of advertising signs of commercial establishments according to the type of services provided. The proposed solution is based on the sharing of textual and visual features. We provide a composite model that includes a text recognition module and an extractor of visual characteristics to improve classification accuracy. We achieve $$F_1$$ of 0.24 exceeding strong baseline quality for 10%.
Learning Multi-Level Dependencies for Robust Word Recognition

Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises. In this paper, we introduce a robust word recognition framework that captures multi-level sequential dependencies in noised sentences. The proposed framework employs a sequence-to-sequence model over characters of each word, whose output is given to a word-level bi-directional recurrent neural network. We conduct extensive experiments to verify the effectiveness of the framework. The results show that the proposed framework outperforms state-of-the-art methods by a large margin and they also suggest that character-level dependencies can play an important role in word recognition. The code of the proposed framework and the major experiments are publicly available1.
AI reflections in 2019

There is no shortage of opinions on the impact of artificial intelligence and deep learning. We invited authors of Comment and Perspective articles that we published in roughly the first half of 2019 to look back at the year and give their thoughts on how the issue they wrote about developed.
Customer identification in currency exchange companies as per FATF recommendations

Purpose The purpose of this paper is to determine if customers due to diligence measures laid down in Financial Action Task Force (FATF) Recommendation no. 10 can be applied to customers of currency exchange companies. Design/methodology/approach Currency exchange financial entities undertake financial transactions with occasional customers, for this reason, this research work is aimed at carrying out a study of the content of FATF Recommendation no. 10 regarding the applicability of due diligence measures to occasional customers. For this purpose, the analytical and interpretative methods have been used. Findings FATF Recommendation No. 10 about customer due diligence measures has been designed primarily for financial entities with regular customers, however, most customers of financial currency exchange companies are occasional customers. For such financial entities, customer identification is mandatory only for transactions above 15,000 USD/EUR, leaving a potential risk of money laundering for financial transactions below that threshold. Furthermore, within currency exchange companies, risk factor analysis and customers’ identity verification are performed only on regular customers. Originality/value Customer due diligence measures in currency exchange financial entities should not be subject to the transaction threshold. Moreover, it is necessary to adopt a centralized control system to avoid currency exchange companies infringement of their control systems.
Encouragement in videogame interactions

This paper explores how participants encourage each other in videogame interactions in private settings. It aims to contribute to a better understanding of encouragement as multimodal practice in videogame interaction. In the paper we focus especially on a comparison of encouragements and instructions and argue that they are used differently: encouragements show another sequence structure than instructions, occur in particular contexts and are characterized by a specific turn design. We consider two types of encouragements. First we analyze encouragements occurring after a display of demotivation and projecting indices of re-motivation of the co-participant. Then we show encouragements produced to incite the encouraged player to continue his ongoing gaming action. This type of encouragements occurs when the instructed action is already going on and it is characterized by lexical repetition and a particular rhythmicity. Our study draws on a corpus of 21h video recorded mostly multiplayer videogame interactions in French.
New discriminant-function-based multidimensional discrimination of mid-ocean ridge and oceanic plateau

From a compilation of geochemical data for the discrimination of the tectonic settings of mid-ocean ridge (MOR; 3730 samples) and oceanic plateau (OP; 3656 samples), we present two new discriminant functions and diagrams obtained from censored multivariate discordant outlier-free datasets. Ten different sets of data (original concentrations as well as isometric log-ratio transformed (ilr) variables; all 10 major (M) elements as well as all 10 major and 6 trace elements MT) were used to evaluate the quality of discrimination from linear discriminant analysis (LDA) and canonical analysis. Two selected multidimensional models ilrM (9 ilr transformed variables of multi-normally distributed 10 major elements) and ilrMT (15 ilr transformed variables of multi-normally distributed combined 10 major and 6 trace elements), considered as the best or most representative (total of 5650 samples for ilrM and 2858 for ilrMT), provided percent success values, respectively, of 80.9% for the MOR and 81.1% for the OP (ilrM) and 88.5% for the MOR and 90.1% for the OP (ilrMT). Both processes (log-ratio transformation and multi-normality) rendered the percent success values similar for both groups (MOR and OP). The respective discriminant functions were successfully used for four tests from known tectonic settings and four application cases (two for ophiolites and two for Precambrian rocks), documenting thus the utility of the new discrimination procedure for the MOR and OP tectonic settings. Furthermore, we showed that our multidimensional procedure is robust against analytical errors or uncertainties, as well as post-emplacement compositional changes caused by element mobility from both low or high temperature alteration. The robustness against the gain or loss of a single element at a time was also documented, from which the ilrMT model was evaluated as more robust than the ilrM model. A new online computer program MOROPdisc was written in Java Framework ZK, which is freely available for use at our web portal http://tlaloc.ier.unam.mx.
An Improved Dynamic Window Approach Integrated Global Path Planning

Aiming at the shortcomings of dynamic windowing algorithm (DWA) into local optimal solution. This work studys a modified DWA algorithm integrated global path planning. By introducing the result of global path planning as a reference trajectory, a novel evaluation function is designed which makes the optimal trajectory be guaranteed. In addition, the work also proposes the evaluation sub-function of direction, the evaluation sub-function of smoothing speed and the evaluation sub-function of acceleration, separately, ensures the direction, smoothness and speed of movement. The algorithm is verified in mobile robot, experimental results show that the algorithm optimizes the path of mobile robot, simultaneously, the advantages of the traditional DWA, such as the practicability and continuity of motion are retained.
Design of Smart Devices for Older People: A User Centered Approach for the Collection of Users’ Needs

In this paper, it will be related a part of a design doctoral research that concerns the choice of the typology of users to analyzed to design smart devices for elderly people with physical symptoms related to gait and movement. The author will also explore which are the most advantageous ways to extrapolate from users’ contributions their needs, that in User Centered Design are considered essential to elaborate the project requirements.
Leo Breiman's “Two Cultures”

Abstract Simon Raper continues his exploration of key moments in the history of statistics with a close look at Leo Breiman's 2001 critique of the data modelling and algorithmic modelling cultures. Almost 20 years on, Breiman's words are perhaps more relevant than ever
Recognizing Variables from Their Data via Deep Embeddings of Distributions

A key obstacle in automated analytics and meta-learning is failing to recognize when different datasets contain measurements of the same variable. Because provided attribute labels are often uninformative in practice, this task may be more robustly addressed by leveraging the data values themselves, rather than relying on their arbitrarily selected variable names. Here, we present a computationally efficient method to identify high-confidence variable matches between a given set of data values and a large repository of previously encountered datasets. Our approach enjoys numerous advantages over distributional similarity based techniques because we leverage learned vector embeddings of datasets which adaptively account for natural forms of data variation encountered in practice. Based on the neural architecture of deep sets, our embeddings can be computed for both numeric and string data, and empirically outperform standard statistical techniques for dataset search.
Effect of class imbalance in heterogeneous network embedding: An empirical study

Network science has been extensively explored in solving various bibliometrics tasks such as Co-authorship prediction, Author classification, Author clustering, Author ranking, Paper ranking, etc. While majority of the past studies exploit homogeneous bibliographic network (consists of singular type of nodes and edges), in recent past there is a surge in using heterogeneous bibliographic entities and their inter-dependencies using heterogeneous information networks (HIN). Unlike homogeneous bibliographic networks, a bibliographic HIN consists of multi-typed nodes such as Author, Paper, Venue, etc. and corresponding relations. Thus bibliographic HIN is more complex and captures rich semantics of underlying bibliographic data as well as poses more challenges. Since a real-world HIN may have different number of instances for different node types, class imbalance is ubiquitous. Recent studies discuss class imbalance in brief and exploit meta-path-based strategies to address the issue. However, there is no work which quantitatively study the effect of class imbalance in regards to solving real-world bibliometrics tasks. Therefore, this paper first proposes a metric to estimate class imbalance in HIN and study the effects of class imbalance over two bibliometrics tasks, namely (i) Co-authorship prediction and (ii) Author's research area classification, using node features generated by network embedding-based frameworks for DBLP dataset. From various experimental analysis, it is evident that class imbalance in bibliographic HIN is an inherent characteristic and for better performance of the above-mentioned bibliometrics tasks, the bibliographic HINs must consider Author, Paper, and Venue as node types.
Identity Verification in Virtual Education Using Biometric Analysis Based on Keystroke Dynamics

Virtual education has become one of the tools most widely used by students at all educational levels, not just because of its convenience and flexibility, but also because it can expand educational coverage. All these benefits also bring along multiple issues in terms of security and reliability in the evaluation the of student’s knowledge because traditional identity verification strategies, such as the combination of username and password, do not guarantee that the student enrolled in the course really takes the exam. Therefore, a system with a different type of verification strategy should be designed to differentiate valid users from impostors. This study proposes a new verification system based on distances computed among Gaussian Mixture Models created with different writing task. The proposed approach is evaluated in two different modalities namely intrusive verification and non-intrusive verification. The intrusive mode provides a false positive rate of around 16 %, while the non-intrusive mode provides a false positive rate of 12 % In addition, the proposed strategy for non-intrusive verification is compared to a work previously reported in the literature and the results show that our approach reduces the equal error rate in about 24.3 %. The implemented strategy does not need additional hardware; only the computer keyboard is required to complete the user verification, which makes the system attractive, flexible, and practical for virtual education platforms.
Damerau Levenshtein Distance for Indonesian Spelling Correction

Word correction used to find an incorrect word in writing. Levenshtein distance is one of algorithm to correcting typing error. It is an algorithm that calculates a difference between two strings. The operations that used to the calculation are insert, delete, and substitution. However, this algorithm has a disadvantage that it cannot overcome two switched letters in the same word. The algorithm that can solve those issues is a damerau Levenshtein. This research aims to analyse a damerau Levenshtein algorithm that used to correcting Indonesian spelling. The dataset in this research consists of two fairy tale stories with a total of 1266 words and 100 typing errors. From these two algorithms, the accuracy is up to 73% on Levenshtein distance and 75% on damerau Levenshtein.
Towards cognitive artificial intelligence device: an intelligent processor based on human thinking emulation

The intervention of computer technology began the era of a more intelligent and independent instrumentation system based on intelligent methods such as artificial neural networks, fuzzy logic, and genetic algorithm. On the other hand, processor with artificial cognitive ability has also been discovered in 2016. The architecture of the processor was designed based on knowledge growing system (KGS) algorithm, a new concept in artificial intelligence (AI) which is focused on the emulation of the process of the growing of knowledge in human brain after getting new information from human sensory organs. KGS is considered as the main method of a new perspective in AI called as cognitive artificial intelligence (CAI). The design is to obtain the architecture of the data path of the processor. We found that the complexity of the processor circuit is determined by the number of combinations of sensors and hypotheses as the main inputs to the processor. This paper addresses the development of an intelligence processor based on cognitive AI in order to realize an Intelligence Instrumentation System. The processor is implemented in field programmable gate array (FPGA) and able to perform human thinking emulation by using KGS algorithm.