Reviewerly/exHarmony huggingface dataset:
Digitalization of the University - What is It?

The structure of a modern university as a social system is considered. The possibility of applying the technologies of the industrial revolution 4.0 for digitalization of the university is evaluated. In particular, the possibilities of cloud services for the digitalization of the educational process are considered. In addition, the application of big data technologies for digitalization and the organizational structure of the university, and project activities is considered. The possibilities of the Internet of things are used to organize “smart laboratories”.
A Confidence-Guided Anomaly Detection Approach Jointly Using Multiple Machine Learning Algorithms

Log data contains very rich and valuable information that records system states and behavior, which can be used to diagnose system failures. Anomaly detection from large-scale log data plays a key role in building secure and trustworthy systems. Anomaly detection model based on machine learning has achieved good results in practical applications. However, logs generated by modern large-scale distributed systems are more complex than ever before in terms of data size and variety. Therefore, the traditional single-machine learning anomaly detection model faces the model aging problem. We design an anomaly detection model that combines multiple machine learning algorithms. By using a conformal prediction, we can calculate the confidence of each algorithm for each log to be detected and use statistical analysis to tag them with a trusted label. The approach was tested on the public HDFS_100k log dataset, and the results show that our model is more accurate.
Multi-Modality Adversarial Auto-Encoder for Zero-Shot Learning

The existing generative Zero-Shot Learning (ZSL) methods only consider the unidirectional alignment from the class semantics to the visual features while ignoring the alignment from the visual features to the class semantics, which fails to construct the visual-semantic interactions well. In this paper, we propose to generate visual features based on an auto-encoder framework paired with multi-modality adversarial networks respectively for visual and semantic modalities to reinforce the visual-semantic interactions with a bidirectional alignment, which ensures the generated visual features to fit the real visual distribution and to be highly related to the semantics. The encoder aims at generating real-like visual features while the decoder forces both the real and the generated visual features to be more related to the class semantics. To further capture the discriminative information of the generated visual features, both the real and generated visual features are forced to be classified into the correct classes via a classification network. Experimental results on four benchmark datasets show that the proposed approach is particularly competitive on both the traditional ZSL and the generalized ZSL tasks.
Deep Extremely Randomized Trees

In this paper, we propose Deep Extremely Randomized Trees (DET), a deep extension of the extremely randomized trees (Extra-Trees) approach. Our approach unifies classification trees with the layered learning method inspired from deep neural networks. The DET is a deep structure where each layer is a set of Extra-Trees. We look at experimental results on machine learning gold standard datasets and find on-par or superior results when compared to state-of-the-art deep models, with much less parameters to tune. It performs faster than deep neural classifiers, and in most cases even faster than gcForest, without losing accuracy.
Detecting Steganography in Inactive Voice-Over-IP Frames Based on Statistic Characteristics of Fundamental Frequency

Steganography in inactive Voice-over-IP frames is a new technique of information hiding, which can achieve large steganographic capacity while maintaining excellent imperceptibility. To prevent the illegitimate use of this technique, the entropy-based and poker test-based steganalysis methods have been presented. However, the detection performance of these two methods is not so good for the cases of having small quantity of inactive frames or low embedding rates. Thus, we present a new steganalysis method based on statistic characteristics of fundamental frequency. Specifically, we employ the statistics for zero-crossing count (ZCC), including the average ZCC of inactive frames, the ratio between the average ZCC of inactive frames and that of all frames, and the difference between the average ZCC of inactive frames and their calibrated versions, to characterize the frame-level dynamic characteristic of speech signals; we utilize the average values of Mel-frequency cepstral coefficients (MFCCs) to represent the invariant characteristic of inactive frames; further, using the feature set consisting of the zero-crossing statistics and average MFCCs, we propose a support-vector-machine based steganalysis for inactive speech frames. The proposed steganalysis method is evaluated with a large number of ITU-T G.723.1 encoded speech samples, and compared with the existing methods. The experimental results demonstrate that the proposed method significantly outperforms the previous ones on detection accuracy, false positive rate and false negative rate for any given embedding rates or using the same number of inactive frames. Particularly, the proposed method can provide accurate detecting results for the existing steganographic methods only using very small quantity of inactive frames, and thereby be employed to detecting potential inactive-frame steganography behaviors in real-time speech streams.
Machine Intelligence and Automation: Deep Learning Concepts Aiding Industrial Applications

Everything begins from a single origin with a unique point in space and time. This is the spark of innovation that fuels the most amazing breakthroughs. It is the passion for discovery that unveils the genesis of all that exists in the universe. Today the power of AI helps computer achieve superhuman capabilities in image recognition and lets scientists save our precious resources by analyzing in 1 month what used to take 10 years. Everyday devices translate even the most complex languages from voice into text and images into words helping the visually impaired recognize an old friend or letting a visually impaired woman read to her child for the first time. Autonomous vehicles give us the freedom to reimagine our city streets where there are no streets, to help the lost to find their way home. We see robots teach themselves to perform simple tasks. We even watch them all as they take their first step. And today a 2500-year-old game meets its match as a computer competes with one of the greatest human champions of all time and wins. This is a collective imagination fuelled by forward-looking technologies and the beginning of the most amazing discoveries yet to come. There are many applications of deep learning. Machine learning provides us an incredible set of tools. If there is a difficult problem in hand, we need not find an algorithm for it, it finds out by itself what is important about the problem and tries to solve it on its own. In many problem domains they perform better than human experts. One of its applications is toxicity detection for different chemical structures by means of deep learning. It is so efficient that it could find the toxic property of the chemicals. Next one is mitosis detection from large images. Mitosis means that certain nuclei are undergoing different transformations that are quite harmful and quite difficult to detect. The technique called convolution neural networks outperforms professional radiologists at their own task. Learning algorithm can read a website and find out whether fake content is discussed there as real content or not. Because of the imperfection of the 3D scanning procedures, 3D scanned furniture is too noisy to use as they are. However, there are techniques to look at these really noisy models and try to figure out how they should look by learning the symmetries and other properties of real furniture. This algorithm can also do an excellent job at predicting how different fluids behave in time and the attire for expected to be super useful in physical simulation in the following years. Another application where a computer algorithm is called deep Q learning plays pong against itself and eventually achieves expertise.
Smart Shopping Cart Learning Agents Development

The paper describes the design, implementation and user evaluation of utility and goal-based intelligent learning agents for smart shopping cart. In keeping user’s shopping habits rules and shopping list, they guide visitors through the shops and the goods in the shopping center or according to new promotions in the shops, respectively. It is envisaged that concrete implementation of the shopping agents will be running on each shopping cart in the shopping centers. The k-d decision tree, the smallest identification tree and reinforcement-learning algorithm are used for agents learning. Some initial user opinions of the shopping cart agents are presented.
PADE: Supporting Collaborative Visual Analysis of Patient Administrative Systems Data with a Large Touch Display System

We present PADE, a visual analytics tool for collaboratively exploring data from patient administrative systems on large touch displays in meeting contexts. Large touch displays are becoming commercially available, but we have limited knowledge about how they might be used in such a context. We designed PADE based on inquiries with healthcare data analysts tasked with understanding expenses in a healthcare system that serve about six million residents. Our goals in designing the system were to enable the analysts to collaboratively construct hypotheses, quickly generate and execute strategies, and support ad hoc discussions and Q&A sessions during meetings. We created a set of interaction techniques that let users create new visualizations and combine parts of existing ones. We illustrate these possibilities through a collaborative analysis scenario. Finally, we discuss the possibilities and limitations of PADE, its interaction techniques, and future work in this direction.
Multi-Representation Multi-Heuristic A* Motion Planning for a Dual-Arm Underwater Vehicle Manipulation System

Recently, Intervention-Autonomous Underwater Vehicles (I-AUVs), started to grab researchers attention. While prior systems rely on variations of the task-priority redundancy control framework, our recent research demonstrated experimental results using search-based motion planning for a single arm floating-based intervention in known and unknown environments. In this work, we present a search-based motion planning algorithm applied to the coordinated manipulation problem of a dual-arm Intervention AUV, in an object transportation mission (e.g. pipe transportation). Two Challenges are considered: the high-dimensionality of the system, and the motion coordination between the mobile base and both working arms. The latter challenge is a major one if accurate execution is required, especially considering the floating nature of the AUV. Our approach relies on exploiting the loose coupling between the different system components. We show for the first time the use of a search-based planner on a dual-arm underwater manipulator. In addition, we support our claims with a simulated demonstration, successfully performing a coordinated dual-arm underwater intervention.
Radar Emitter Identification under Transfer Learning and Online Learning

At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identification methods usually include an offline training stage and online identifying stage, which cannot achieve the real-time identification of the radar emitter. Aimed at the above problems, this paper proposes a radar emitter identification method based on transfer learning and online learning. First, for the case where the target domain contains only a small number of labeled samples, the TrAdaBoost method is used as the basic learning framework to train a support vector machine, which can obtain useful knowledge from the source domain to aid in the identification of the target domain. Then, for the case where the target domain does not contain labeled samples, the Expectation-Maximization algorithm is used to filter the unlabeled samples in the target domain to generate the available training data. Finally, to make the identification quickly and accurately, we propose a radar emitter identification method, based on online learning to ensure real-time updating of the model. Simulation experiments show that the proposed method, based on transfer learning and online learning, has higher identification accuracy and good timeliness.
Adaptive Regularized Semi-Supervised Clustering Ensemble

Although semi-supervised clustering ensemble methods have achieved satisfactory performance, they fail to effectively utilize the constrained knowledge such as cannot-link and must-link when generating diverse ensemble members. In addition, they ignore negative effects brought about by redundancies and noisy data. To address the above shortcomings, in this paper we propose an approach to combine multiple semi-supervised clustering solutions via adaptively regularizing the weights of clustering ensemble members, which is referred to as ARSCE. First, we generate a series of feature subspaces by randomly selecting feature without replacement to avoid the scenario where there are two identical feature subspaces. Second, we conduct feature transformation on the above obtained feature subspaces while considering the pairwise constraints to find new clustering-friendly spaces, where clustering methods are exploited to generate various clustering solutions. Finally, we design a novel fusion strategy to integrate multiple clustering solutions into a unified clustering partition, where weights are designated for each clustering ensemble member. Extensive experiments are conducted on multiple real-world benchmarks, and experimental results demonstrate the effectiveness and superiority of our proposed method ARSCE over other counterparts.
Breast cancer diagnosis based on a new improved Elman neural network optimized by meta‐heuristics

Abstract In this article, a new optimized method for diagnosing and analyzing breast cancer from the mammography images is presented. In this regard, preprocessing is used to remove the Gaussian noises that are used to happen in the mammography images and also to remove the additional areas. Then, image segmentation is performed on the images to determine the areas where the contrast material is perceptible. Afterward, combined feature extraction based on a discrete wavelet transform and gray‐level co‐occurrence matrix is proposed for extracting the important information fromthe images. Finally, a new classification model based on an improved Elman neural network (ENN) has been proposed. The ENN is optimized by an improvedversion of the collective animal behavior algorithm. Simulation results areimplemented to the Mammographic Image Analysis Society database and the resultsare compared with three different methods.
Data Collection and End-to-End Learning for Conversational AI.

A fundamental long-term goal of conversational AI is to merge two main dialogue system paradigms into a standalone multi-purpose system. Such a system should be capable of conversing about arbitrary topics (Paradigm 1: open-domain dialogue systems), and simultaneously assist humans with completing a wide range of tasks with well-defined semantics such as restaurant search and booking, customer service applications, or ticket bookings (Paradigm 2: task-based dialogue systems).The recent developmental leaps in conversational AI technology are undoubtedly linked to more and more sophisticated deep learning algorithms that capture patterns in increasing amounts of data generated by various data collection mechanisms. The goal of this tutorial is therefore twofold. First, it aims at familiarising the research community with the recent advances in algorithmic design of statistical dialogue systems for both open-domain and task-based dialogue paradigms. The focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. In theory, learning end-to-end from data offers seamless and unprecedented portability of dialogue systems to a wide spectrum of tasks and languages. From a practical point of view, there are still plenty of research challenges and opportunities remaining: in this tutorial we analyse this gap between theory and practice, and introduce the research community with the main advantages as well as with key practical limitations of current end-to-end dialogue learning.The critical requirement of each statistical dialogue system is the data at hand. The system cannot provide assistance for the task without having appropriate task-related data to learn from. Therefore, the second major goal of this tutorial is to provide a comprehensive overview of the current approaches to data collection for dialogue, and analyse the current gaps and challenges with diverse data collection protocols, as well as their relation to and current limitations of data-driven end-to-end dialogue modeling. We will again analyse this relation and limitations both from research and industry perspective, and provide key insights on the application of state-of-the-art methodology into industry-scale conversational AI systems.
Personal Identity on Blockchain

The problem of fraud identity documents is a fatal problem. This severity is due to the method currently being employed to create and authenticate legitimate documents. Creation of identity document requires an individual to convince appropriate government authority by producing all supporting proofs of identity declaration. This process is vulnerable as it can result in the accumulation of power to the individual issuing the identity document. Also, the authentication process involves sharing the copy of already issued identity proof. Many a time, identity proofs are shared with individuals who cannot be trusted for their integrity. This can result in misuse of the identity documents for socially evil activities. Blockchain provides a decentralized trust-less network which nobody owns, but everyone can use. The proposed system provides solution to the identity management problem by putting personal identifiers on blockchain. Using blockchain technology, a system has been proposed to create, manage and confirm an individual’s identity and credentials in a much secure way. Sharing of relevant information would be possible only with the identity owner’s will. Hence, creation of false identity documents will be prevented.
Binary Matrix Factorization applied to Netflix dataset analysis

In this paper we aim at assessing the potential of Binary Matrix Factorization (BMF) in the implementation of recommendation systems, by analyzing a Netflix dataset. In particular, we study the explanatory power and the prediction capability of a particular BMF algorithm based on a post non-linear mixture model, namely the Post NonLinear Penalty Function (PNL-PF) algorithm. Unlike the majority of BMF methods, PNL-PF is capable of efficiently handling the difficult case of correlated rank-1 binary terms. We show that BMF represents an interesting alternative to classical matrix factorization methods in terms of explanatory power and prediction capability.
Micro-Accreditation for Matching Employer E-Hire Needs

This paper presents a novel way to help match employers' knowledge requirements with students' knowledge earned using blockchain's smart contracts to assure credentials and track student records. This decentralized approach proposes using the micro-accreditation of topics from the CAE framework to courses and associated tasks, while introducing a revolutionary idea of a blockchain-based peer-reviewed rigor score assignment. Our work and result metrics were completed in Ethereum and connected test networks. We concluded this new approach is mostly efficient and scalable depending on the network load, with faster transaction times when the miners are properly incentivized. Future work will include further fine-tuning of the transaction algorithms to improve time, as well as an investigation into a better consensus model for peer review and rigor determination.
Protecting Medical Research Data Using Next Gen Steganography Approach

In this paper our main aim is to protect medical research information, when data either images or information shared via internet or stored on hard drive 3rd person can't access without authentication. As needs be, there has been an expanded enthusiasm for ongoing years to upgrade the secrecy of patients' data. For this we combined different techniques to provide more security. Our approach is a combination of cryptography, Steganography & digital watermarking we named this technique as Next Gen. We used cryptography for encrypting the patient's information even if they find image is stegonized and digital watermarking for authenticity and for Steganography we used most popular least significant bit algorithm (LSB). The experimental outcomes with various inputs show that the proposed technique gives a decent tradeoff between security, implanting limit and visual nature of the stego pictures.
Aspect-based approach to measure performance of financial services using voice of customer

Banking institution is a critical part of any country’s economy, which provides a variety of services for an individual or a business entity. Due to vast availability of banks and their services, it is a cumbersome task for a person to choose a bank for his/her specific need. In this regard, we introduced a computational framework for ranking a set of banking institutions based on people’s opinion. The introduced framework is based on aspect-based sentiment analysis and multi-criteria decision making (MCDM) approaches. To evaluate our methodology, we developed a sentiment dataset comprising reviews on four Indian nationalized banks, whose quality is evaluated using 9 aspects/attributes. In order to come up with holistic ranking, we employed simple majority voting on ranking obtained using three multi-criteria decision making methods viz. analytic hierarchy process, VIKOR, and fuzzy multi-attribute decision making. We observed that final ranking obtained using our method has a strong resemblance with real-life outreach of selected four Indian banks.
The Good, the Bad, and the Rational: Aspects of Character in Logical Agents

Common wisdom holds that each person has a character which is a rather stable, but revisable, system of conditional beliefs and preferences that causally directs the person’s behavior. Can the same be said of an artificial agent? In particular, does it make sense to talk of (the typically cold) logical agents, studied in logic-based artificial intelligence, as having characters? I argue that the answer is certainly “yes”, at least in the trivial sense that behaviors of such agents are directed by stable, yet revisable, systems of beliefs and preferences. What is interesting is investigating whether and how nontrivial variations in character, as exhibited by humans, can be witnessed in artificial logical agents. In this paper, I present an algebraic logical language which is expressive enough to facilitate the identification of a number of dimensions along which characters of logical agents may vary in interesting ways. Chief among these dimensions are high-level properties of belief and intention, and general constraints on the logic of good and bad.