Reviewerly/exHarmony huggingface dataset:
Interactive visual data exploration with subjective feedback: an information-theoretic approach

Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she does not know yet. We construct a theoretical model where identified patterns can be input as knowledge to the system. The knowledge syntax here is intuitive, such as "this set of points forms a cluster", and requires no knowledge of maths. This background knowledge is used to find a Maximum Entropy distribution of the data, after which the system provides the user data projections in which the data and the Maximum Entropy distribution differ the most, hence showing the user aspects of the data that are maximally informative given the user's current knowledge. We provide an open source EDA system with tailored interactive visualizations to demonstrate these concepts. We study the performance of the system and present use cases on both synthetic and real data. We find that the model and the prototype system allow the user to learn information efficiently from various data sources and the system works sufficiently fast in practice. We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.
Fast Edge Preserving 2D Smoothing Filter Using Indicator Function

Edge-preserving smoothing filter smoothes the textures while preserving the information of sharp edges. In image processing, this kind of filter is used as a fundamental process of many applications. In this paper, we propose a new approach for edge-preserving smoothing filter. Our method uses 2D local filter to smooth images and we apply indicator function to restrict the range of filtered pixels for edge-preserving. To define the indicator function, we recalculate the distance between each pixel by using edge information. The nearby pixels in the new domain are used for smoothing. Since our method constrains the pixels used for filtering, its running time is quite fast. We demonstrate the usefulness of our new edge-preserving smoothing method for some applications.
Frame Phase-Retrievability and Exact Phase-Retrievable Frames

A phase-retrievable frame $$\{f_{i}\}_{i}^{N}$$ for an n-dimensional Hilbert space is exact if it fails to be phase-retrievable when removing any element from the frame sequence. Unlike exact frames, exact phase-retrievable frames could have different lengths. We shall prove that for the real Hilbert space case, exact phase-retrievable frame of length N exists for every $$2n-1\le N\le n(n+1)/2$$. For arbitrary frames we introduce the concept of redundancy with respect to its phase-retrievability and the concept of frames with exact PR-redundancy. We investigate the phase-retrievability by studying its maximal phase-retrievable subspaces with respect to a given frame which is not necessarily phase-retrievable. These maximal PR-subspaces could have different dimensions. We are able to identify the one with the largest dimension, which can be considered as a generalization of the characterization for phase-retrievable frames. In the basis case, we prove that if M is a k-dimensional PR-subspace, then $$|supp(x)| \ge k$$ for every nonzero vector $$x\in M$$. Moreover, if $$1\le k< [(n+1)/2]$$, then a k-dimensional PR-subspace is maximal if and only if there exists a vector $$x\in M$$ such that $$|supp(x) | = k$$.
eHealth Integrity Model Based on a Permissioned Blockchain

Large eHealth systems should have a mechanism to detect unauthorized changes in patient's medical documentation, access permissions, and logs. This is due to the fact, that the modern eHealth systems interconnect many healthcare providers and many healthcare sites. One of the most promising technologies for implementing integrity protection service is blockchain technology. In this paper, we propose Blockchain based eHealth Integrity Model for ensuring integrity of information in eHealth systems that uses a permissioned blockchain with off-chain storage of information. In contrast to existing solutions, our model allows removing of information, which in many countries' eHealth systems is a legal requirement. Moreover, our proposal can be easily integrated using service oriented architecture, which lowers costs and efforts of systems deployment. The paper also discuss several security problems related to blockchains that must be considered during blockchain development in eHealth systems.
Robust template matching with angle location using dynamic feature pairs updating

This work proposes a novel method for template matching in the wild. Different from the previous methods to search the matching position, our method obtains further ability on the angle location by dynamically updating corresponding feature pairs and rigid transformation parameters, which result in mutual enhancement of both feature extraction and template location. We propose a robust objective function with a valid feature selection for template matching against to noise disturbance, background changing, object deformation and partial occlusion. A hierarchical search strategy is used by adjusting the size of feature patch to improve the matching effectiveness. In addition, we extend the proposed method to match image sequences. It is beneficial to propagate reasonable feature pairs to a sequential object as initialization, recalling a stable tracking result. Extensive experiments are tested on the public database with challenging images and sequences. Experimental results demonstrate the merits of the proposed method compared to some state-of-the-art matching methods.
µL2Q: An Ultra-Low Loss Quantization Method for DNN Compression

Data quantization has been proved to be an effective method to compress deep neural networks (DNNs) by using less bits to represent the parameters and intermediate data. The bit width of the data directly affects the memory footprint, computing capability, and energy consumption during the computation of the DNN models. Although there have been numerous existing studies on data quantization, there is still no quantitative analysis of the existing quantization methods, which results in empirical quantization with unpredictable DNN accuracy loss. To address this problem, we propose an effective method, called ultra-low loss quantization (μL2Q), to provide DNN quantization schemes based on comprehensive quantitative data analysis. μL2Q builds the transformation of the original data to a data space with standard normal distribution, and then find the optimal parameters to minimize the loss of the quantization of a targeted bit width. In addition, we integrate the proposed μL2Q into a popular machine learning framework Caffe for convenient end-to-end DNN design and training. By comparing to the state-of-the-art DNN compression designs, μL2Q shows the greatest ability to maintain DNN accuracy after quantization. In the experiments, our proposed method can deliver 4.42%, 16.70%, 1.95%, 8.26% and 5.63% accuracy improvements on Lenet-5, Cifarnet, VGG7-64 and Resnet-18 (Top1/5), respectively, compared to the state-of-the-art solutions with the same compression ratio.
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network Based on HowNet

Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot effectively identify whether a sentence is semantically rational. Methods based on the language model do not measure the sentence by rationality but by commonness. Methods based on the similarity with human written sentences will fail if human-written references are not available. In this paper, we propose a novel model called Sememe-Word-Matching Neural Network (SWM-NN) to tackle semantic rationality evaluation by taking advantage of the sememe knowledge base HowNet. The advantage is that our model can utilize a proper combination of sememes to represent the fine-grained semantic meanings of a word within specific contexts. We use the fine-grained semantic representation to help the model learn the semantic dependency among words. To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset. Experimental results on this dataset show that the proposed model outperforms the competitive baselines.
A Transfer Learning Based Interpretable User Experience Model on Small Samples

User experience (UX) is a key factor that affects software survival time. A rich line of research has studied the relationships between UX and software factors to modify software and improve user satisfaction. However, the existing machine learning models for predicting UX on small data set is not accurate enough, and research with traditional statistical methods only obtained indistinct relations among UX, user characteristics and software factors. With the goal of improving the accuracy of UX model and obtaining sufficient UX relationships, we propose Transfer in Cart (TrCart) algorithm and Transfer Adaboost in Cart (TrAdaBoostCart) algorithm. To verify this approach, we present the UX study on a desktop game and an android game. According to the experimental results, we find that the TrAdaBoostCart has better accuracy and interpretable results. Hence, the proposed approach provides important guidelines for the design process of mobile applications.
Training a V1 Like Layer Using Gabor Filters in Convolutional Neural Networks

It is suggested from neuroscience that the response of V1 neurons in primate visual streams behaves quite like Gabor filters. As an inspiration from primate neural circuits to artificial neural networks, we propose to replace the first layer as a series of Gabor filters in convolutional neural networks. To enhance the performance of the neural network, we introduce a lateral inhibitory mechanism in Gabor filters, enlightened from the research results of neuroscience. To improve performance, we explore a parameter space and search the best suited parameters using cross validation. Experimental results demonstrate that the accuracy can basically match the results of the original convolutional neural networks. However, the adoption of Gabor filters can greatly reduce the time of training, as well as the memory and storage cost.
Hybrid Fault Tolerance Model for Cloud Dependability

Cloud computing is gaining an increasing popularity and the number of user's applications and interactions with Cloud resources have grown considerably making Cloud services more susceptible to failure. Therefore, fault tolerance is an important property in order to achieve reliability, availability and required quality of service. Several studies were interested in Cloud fault tolerance issues. These studies proposed solutions including checkpoint and replication that are focused on failure of a single Cloud resource, namely virtual machines, and virtualization issues. Such solutions are insufficient given Cloud complexity. Unlike previous work, we propose a solution that takes into consideration not only virtual machines but also physical machines. Our solution is a hybrid fault tolerance strategy that inherits good features and overcome the limitations of traditional fault tolerance strategies. We evaluate the efficiency of our fault tolerance strategy compared to replication and checkpoint strategies by using CloudSim and we prove that our strategy leads to better results.
Semantic Relata for the Evaluation of Distributional Models in Mandarin Chinese

Distributional Semantic Models (DSMs) established themselves as a standard for the representation of word and sentence meaning. However, DSMs provide quantitative measurement of how strongly two linguistic expressions are related, without being able to automatically classify different semantic relations. Hence the notion of semantic similarity is underspecified in DSMs. We introduce Evalution-MAN in this paper as an effort to address this underspecification problem. Following the EVALution 1.0 dataset for English, we present a dataset for evaluating DSMs on the task of the identification of semantic relations in Mandarin Chinese. Moreover, we test different types of word vectors on the automatic learning of these semantic relations, and we evaluate them both in a unsupervised and in a supervised setting, finding that distributional models tend, in general, to assign higher similarity scores to synonyms and that deep learning classifiers are the best performing ones in the identification of semantic relations.
Use of Blockchain in Internet of things: A Systematic Literature Review

In recent years, Internet of things have revolutionized the daily life operations by automating routine tasks, changing the way humans interact with each other or with machines. In fact, direct machine to machine interaction is a key enabling feature of IOTs. Their advantages span from small house hold devices to large industrial systems. Although IOTs provide numerous benefits, their implementation possesses different problems mainly because of the critical nature of the tasks they perform and due to their limited computational capabilities. The Blockchain is the future disruptive technology that has gained the interest of large number of industries to address the issues being faced by them. In this paper, we highlighted issues pertaining to the IOT domain and then analyzed the way how these can be solved using Blockchain features. In addition, we also highlighted the problems that arise with the deployment of Blockchain in IOTs.
Information Security: Definitions, Threats and Management in Dubai Hospitals Context

Information technology and high-tech systems used daily in hospitals increase the demand for measuring information security threats in the healthcare industry. The impact of complexity of using new healthcare information and communications technology (ICT) devices places more pressure on the healthcare community to perceive different information systems (IS) security threats as they involve different IS management models. This research study focused on measuring the healthcare community perceptions and on the following issues to include definition of information security; perception of information security threats; perception of information security management; and perception of information security in the context of another healthcare community. To assess to plan, design and implement a national information security strategy for their healthcare sector. A survey was developed and interviewed 60 healthcare employees from three hospitals based in Dubai. The research results assist in creating awareness for easy access to hands-on guidelines for the healthcare community, thereby increasing the awareness levels of information security in the United Arab Emirates (UAE). These results assist the UAE government to further adapt the training and education programs for the healthcare community to increase their effectiveness and efficiency levels of IT and subsequent IS development. A discussion of the current status of Dubai hospitals privacy, confidentiality and security challenge is presented.
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text—without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of ≥10 sentences.
UTILCON 1.0: A Conference Management System trainer in Spanish with strict refereeing control

UTILCON 1.0 is a free web-based conference management system trainer in Spanish, developed and supported at ADVNETLAB in Autonomous University of Mexico City. UTILCON was developed in 2017, version beta was released in June 2018 but version 1.0 was released in November 2018. This Latin American CMS trainer is intended for Latin American students mainly for those undergraduate but mainly for those in social, humanities and sciences not requiring English language. UTILCON is also a trainer for referees and chairs in the mentioned areas in Latin America where not engineering people is not familiar with CMS.
Do Fractional Norms and Quasinorms Help to Overcome the Curse of Dimensionality?

The curse of dimensionality causes well-known and widely discussed problems for machine learning methods. There is a hypothesis that usage of Manhattan distance and even fractional quasinorms lp (for p less than 1) can help to overcome the curse of dimensionality in classification problems. In this study, we systematically test this hypothesis for 37 binary classification problems on 25 databases. We confirm that fractional quasinorms have greater relative contrast or coefficient of variation than Euclidean norm l2, but we demonstrate also that the distance concentration shows qualitatively the same behaviour for all tested norms and quasinorms and the difference between them decays while dimension tends to infinity. Estimation of classification quality for kNN based on different norms and quasinorms shows that the greater relative contrast does not mean the better classifier performance and the worst performance for different databases was shown by the different norms (quasinorms). A systematic comparison shows that the difference in performance of kNN based on lp for p=2, 1, and 0.5 is statistically insignificant.
Efficient Estimation of Node Representations in Large Graphs using Linear Contexts

Learning distributed representations in graphs has a rising interest in the neural network community. Recent works have proposed new methods for learning low dimensional embeddings of nodes and edges in graphs and networks. Several of these methods rely on the SkipGram algorithm to learn distributed representations, and they usually process a large number of multi-hop neighbors in order to produce the context from which node representations are learned. This is a limiting factor for these methods as graphs and networks keep growing in size. In this paper, we propose a simple alternate method which is as effective as previous methods, but being much faster at learning node representations. Our proposed method employs a restricted number of permutations over the immediate neighborhood of a node as context to generate its representation, thus avoiding long walks and large contexts while learning the representations. We present a thorough evaluation showing that our method outperforms state-of-the-art methods in six different datasets related to the problems of link prediction and node classification, being one to three orders of magnitude faster than baselines when generating node embeddings for very large graphs.
A novel model to simulate cloud dynamics with cellular automaton

In real system simulations, the application of cellular automata has been shown as an interesting option, because it can represent an emergent behavior and its implementation is simple. This paper presents a method for simulating thermodynamic systems, such as cloud dynamics, with cellular automata. In accordance with thermodynamic principles, this paper presents an isolated system model that describes temperature dynamics. The model uses the Von Neumann neighborhood of five cells, each with two possible states: the presence or absence of a cloud or a part of it. Our model uses three weather properties, as follows, condensed cloud water particles, temperature and outer winds. Four types of experiments were performed to validate the model proposed: (i) a warm body in the environment, (ii) a cloud dynamic simulation, (iii) a stability analysis, and (iv) a comparison between the dynamic of the proposed model and satellite images of clouds.
Speech Recognition Engine using ConvNet for the development of a Voice Command Controller for Fixed Wing Unmanned Aerial Vehicle (UAV)

Current interfaces between humans and UAVs do little to recognize the human affinity for verbal communication or the accepted (and empirically effective) practice of guiding aircraft through verbal commands. Incorporating speech recognition into a technology solution is one way to attempt to determine what, exactly, was said by a human. Speech Recognition is an end to end process of mapping audio speech data to textual sentences or key phrases. The paper presents the implementation of the speech recognition engine for a voice command controller for fixed-wing Unmanned Aerial Vehicle (UAV) using a deep Convolutional Neural Network that process and classifies voice samples. The architecture was an adaptation of an image processing CNN, programmed in Python using Keras model-level library and Tensor Flow back-end. The original contribution of the paper is: the training of the CNN with a set of recordings in the English Language for basic Fixed-Wing UAV Maneuver commands like backward, down, follow, forward, go, left, off, on, right, stop, up, and addition of some commands for future purposes such as tree and visual. The proposed speech recognition engine is intended for the development of a voice command controller for Fixed Wing UAV.
DEVELOPMENT OF THE CONTENT OF PROFESSIONAL COMPETENCIES OF THE EDUCATIONAL STANDARD OF AN IT SPECIALIST BASED ON THE REQUIREMENTS OF OCCUPATIONAL STANDARDS AND EMPLOYERS' NEEDS

The article deals with the problems of developing Basic Professional Educational Programs of Higher Education on Information Systems and Technologies in accordance with the Federal State Higher Education Standard (3++) and the requirements of occupational standards and employers’ needs. The article presents the stages of developing Basic Professional Educational Programs taking into account these requirements. The federal regulatory acts governing the application of occupational standards in the development of Basic Professional Educational Programs, as well as official data on the required number of IT specialists in the context of digitalization of the country’s economy, are analyzed. On the basis of the correspondence between the hierarchical levels of the requirements of the educational standard and the occupational standard in web technologies, the article demonstrates an approach to determining the list and designing the content of professional competencies, based on the formation of competency passports and ensuring the integrity of the main professional educational program of university. The article studies one of the professional competencies of the Information Systems and Technologies educational program to show its structure and content development and suggests possible wording of competency indicators and descriptors used to assess the competency formed.