Reviewerly/exHarmony huggingface dataset:
Will Users Continue Using Banking Chatbots? The Moderating Role of Perceived Risk

AI-powered chatbots have become game-changers for the financial industry. They enable banks to boost customer engagement and improve operational efficiency by lowering the traditional cost of customer support. This study analyses the impact of perceived service quality dimensions on user confirmation, satisfaction and use continuance. The present study also analyses the moderating effect of perceived risk on the relationship between user confirmation, satisfaction and chatbots use continuance. A total of 447 customers, all residing in the Indian city of Bengaluru and having recently used banking chatbot services, were surveyed. Partial least squares structural equation modelling is used to examine the relationships between the variables used in the study. Findings from the study show that all five chatbot service quality dimensions (reliability, interactivity, assurance, responsiveness and understandability) significantly impact the user’s post-use confirmation, influencing their satisfaction and continuance behaviour. Perceived risk negatively moderates the relationship between user confirmation and user satisfaction. Chatbot service developers and e-service providers can leverage these findings to understand user expectations from chatbots. They will also be helpful to other service sectors, such as insurance, travel and tourism, hospitality and healthcare.
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blindspot networks (BSNs). For recent methods targeting real-world denoising, they either suffer from ignoring this spatial correlation, or are limited by the destruction of fine textures for under-considering the correlation. In this paper, we present a novel method called LG-BPN for self-supervised real-world denoising, which takes the spatial correlation statistic into our network design for local detail restoration, and also brings the long-range dependencies modeling ability to previously CNN-based BSN methods. First, based on the correlation statistic, we propose a densely-sampled patch-masked convolution module. By taking more neighbor pixels with low noise correlation into account, we enable a denser local receptive field, preserving more useful information for enhanced fine structure recovery. Second, we propose a dilated Transformer block to allow distant context exploitation in BSN. This global perception addresses the intrinsic deficiency of BSN, whose receptive field is constrained by the blind spot requirement, which can not be fully resolved by the previous CNN-based BSNs. These two designs enable LG-BPN to fully exploit both the detailed structure and the global interaction in a blind manner. Extensive results on real-world datasets demonstrate the superior performance of our method. https://github.com/Wang-XIaoDingdd/LGBPN
New thermometer and oxygen fugacity sensor calibrations for ilmenite- and chromian spinel-bearing peridotitic assemblages

New Fe-Mg exhange thermometers and oxygen sensors have been developed for high pressure peridotitic assemblages that include ilmenite and/or chromian spinel.The calibrations are based on new experimental work to 3.5 GPa designed to investigate f02 and temperature sensitive equilibria in which the effects of Cr203 (eskolaite) substitution in ilmenite and Ti substitution in chromian spinel have been specifically considered.These substitutions are important in mantle-derived ilmenite and chromite solid solutions, particularly those associated with diamond-facies peridotite.The results show that eskolaite substitution in ilmenite has a significant effect on olivine-ilmenite Fe-Mg temperature, in fact, compared to the new calibration, older calibrations yield T differences of up to several hundred °C.These results suggest that Cr-rich ilmenites are likely to be of higher P,T origin than previously assumed.For spinel-bearing assemblages, the effect of Ti substitution in spinel was found to have a relatively small effect on fC>2 and Fe-Mg temperature, however, the new olivine-spinel oxygen sensor and thermometer calibrations have resulted in an improved fit to existing experimental data.The new calibrations offer advances in defining the temperature and redox conditions favourable for diamond stability in the upper mantle and as such may be usefully applied to 'diamond-indicator' mineral and xenolith suites sampled by kimberlite and other rocks of deep-seated origin.Olivine-ilmenite Fe-Mg exchange thermometer
Bank Select Method for Reducing Symbol Search Operations on Stream-Based Lossless Data Compression

Dictionary-based lossless data compression algorithms mainly replace a frequent data pattern in the inputted data to a compressed symbol, and to decompress vice versa. The mechanism potentially has an overhead problem regarding the number of symbol matchings in the table. This work focuses on a technique to reduce the number of searches in the dictionary using a bank separation technique. This poster presentation shows design and implementation of the technique applied to LCT-DLT.
An Approach to Cross-Lingual Voice Conversion

The most prevalent multilingual Text-to-Speech (TTS) synthesis systems encounter an unnatural speaker shift at the language boundaries. This is observed when they are employed for code-mixed TTS synthesis. For the very fact that the collection of polyglot speech is non-trivial, many alternative approaches have been in focus. Cross-Lingual Voice Conversion (CLVC) has been one of those to generate speech with desired speaker and language identities. Our aim in this paper is to design a light-weighted CLVC framework between a pair of Mandarin-English speakers. CLVC is challenging when compared to traditional Voice Conversion (VC) because of its nature of accommodating unaligned corpus from the source and target speakers. We thus focus on generating a parallel corpus for CLVC and bridging the gap between speakers and languages. We perform a text-independent voice conversion with a three-layered conventional Neural Network (NN) for this purpose. The main contributions include i) Source similarity in both training and conversion stages of CLVC, ii) generation of a parallel corpus and iii) text independent and transcription free CLVC. We exploit two variants of a Neural Network in the proposed framework, i) an autoencoder to enable the source similarity and generation of parallel corpus, ii) a traditional DNN for feature mapping between the source and target. The subjective and objective evaluations show that the proposed method is indeed capable of performing a CLVC with an auto-encoded speech.
28th Annual Computational Neuroscience Meeting: CNS*2019

F1 The geometry of abstraction in hippocampus and pre-frontal cortexSilvia Bernardi1, Marcus K. Benna2, Mattia Rigotti3, Jérôme Munuera4, Stefano Fusi1, C. Daniel Salzman1 1Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States of America; 2Columbia University, Center for Theoretical Neuroscience, Zuckerman Mind Brain Behavior Institute, New York, NY, United States of America; 3IBM Research AI, Yorktown Heights, United States of America, 4Columbia University, Centre National de la Recherche Scientifique (CNRS), École Normale Supérieure, Paris, FranceCorrespondence: Marcus K. Benna (mkb2162@columbia.edu)BMC Neuroscience 2019, 20(Suppl 1):F1Abstraction can be defined as a cognitive process that finds a common feature—an abstract variable, or concept—shared by a number of examples. Knowledge of an abstract variable enables generalization to new examples based upon old ones. Neuronal ensembles could represent abstract variables by discarding all information about specific examples, but this allows for representation of only one variable. Here we show how to construct neural representations that encode multiple abstract variables simultaneously, and we characterize their geometry. Representations conforming to this geometry were observed in dorsolateral pre-frontal cortex, anterior cingulate cortex, and the hippocampus in monkeys performing a serial reversal-learning task. These neural representations allow for generalization, a signature of abstraction, and similar representations are observed in a simulated multi-layer neural network trained with back-propagation. These findings provide a novel framework for characterizing how different brain areas represent abstract variables, which is critical for flexible conceptual generalization and deductive reasoning.
A Case Representation and Similarity Measurement Model with Experience-Grounded Semantics

Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential of experienced-grounded semantics in case representation and semantic similarity measurement. The main contents in this study are as follows: (i) a case representation model based on experience-grounded semantic is proposed, (ii) a novel semantic similarity measurement method with multi-strategy reasoning is introduced, and (iii) a case-based reasoning software for urban firefighting field based on the proposed model is designed and implemented. Theoretically, compared with traditional structured case representation methods, the proposed model not only represents case in a fully formalized way, but also provides a novel metric for computing the strength of the semantic relationship between cases. The proposed model has been applied in an intelligent decision-support software for urban firefighting.
Orchini Similarity User Authentication Based Streebog Hash Function for Secured Data Storage in Cloud

Cloud provides a flexible on-demand data outsourcing services for the individuals and organizations to store their data in the server. Security of cloud storage is guaranteed through providing data confidentiality. The cloud computing provides several benefits but the major risk to provide security to the user’s data since the cloud infrastructures vulnerable to various threats. In order to improve the secure cloud storage, an Orchini Similarity Authentication based Streebog Hashing Secured Data Storage (OSA-SHSDS) mechanismis introduced. The main aim of OSA-SHSDS mechanism is to store the cloud user data with higher confidentiality rate and lesser space complexity. In order to store the data in the cloud server, the cloud user personal details are to be registered. After registering, the cloud server generates the ID and password for every registered cloud user. Wheneverthe userwants to store the data, the user has to login with ID and password to the cloud server for authentication. The cloud server verifies whether the user has authenticatedthe user or not through orchini similarity measure. After authentication, the cloud user is allowed to store the data in the cloud server.. This helps to increase the data confidentiality rate and to minimize the space complexity. The experimental evaluation of OSA-SHSDS mechanism and existing methods are performed using Amazon dataset with the metrics such as authentication accuracy, computation time, data confidentiality rate and space complexity. The observed results reveal that the proposed OSA-SHSDS mechanism attains higher authentication accuracy and data confidentiality with minimum computation time as well as space complexity than the other state -the- art- methods.
Efficient Pattern Matching Algorithms for DNA Sequences

The considerable growth of biological data motivates the researchers to solve the different problems in computational bioinformatics. Pattern matching is applied as an important operation in different phases of the computational pipelines. For instance, by pattern matching, the users are enabled to find the locations of particular DNA subsequences in a DNA sequence or biological database. In addition, some patterns of these expanding biological databases are updated over time. Quick pattern matching algorithms are required to perform faster searches. To speed up searches on large DNA sequences, this paper proposes two pattern matching algorithms. The proposed algorithms raise performance by finding the windows of the sequence via the first and last characters of the pattern and also by employing word-based processing instead of the characterbased processing presented in previous works. The experimental results reveal the superiority of the presented algorithms over the other simulated algorithms in terms of time cost.
TEACHING MATHEMATICS THROUGH ONLINE COLLABORATIVE ENVIRONMENT IN THE HIGHER EDUCATION CONTEXT

The higher education sector is continuously working towards the improvement of support mechanism by means of strengthening of education technology and various other strategies.This requirement differs from the nature of the subject.For technical subjects, it is much easier to take the assistance of technology, such as online tools, videos, animation, interactive applications and much more.Whereas, subjects like Mathematics and Language learning, has a totally different scenario.However, the higher educational institutions are adopting a various innovative approach to enhance the student learning experience.This research paper focuses on some of those methods, which are adopted to improve Mathematics education at the foundation level in one of the higher educational institution in the Sultanate of Oman.The foundation level education was introduced to bridge a gap of basic requirements, which some of the students lack while completing their higher school and takes admission in the diploma or bachelor studies.Foundation Mathematics is one of the subjects which is being taught at this level.Although, there is a provision that student can opt for clearing a challenge exam and get admitted to the first semester, still many of the students opt for studying foundation due to various factors.In their school education, they are not allowed to use smartphones or devices during their classes.Whereas, in college, they can use their devices for educational purposes.The module leader prepares a session plan according to the learning outcomes and maps various activities with the support of various tools.Some of these tools are online, which are accessible on mobile phones.One such useful tool is Kaizala, which allows students to collaborate with each other to participate in various activities being conducted by the faculty members.It supports almost all the multimedia content such as audio, video, animation, interactive quizzes, polling, sharing of images, etc.In this research paper, the authors have described the implementation of such collaborative tools in various ways, to enhance student understanding.Finally, the method was found to be very useful and it has improved the results.Part-time students also found it quite useful, as they were able to participate in the class activities even if they are unable to make-up to some of their classes due to some official reasons.There were students who were repeating the subject for more than one time and who have also found such methods as quite useful ones.Overall, the experience of introducing online collaborative tools to improve the learning mechanism of Mathematics related subject was found to be very good.
Experience of Using Project-Based Learning in the URFU Hypermethod e-Learning System

ECEL is now a well-established event on the academic research calendar and now in its 18 th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them.The scope of papers will ensure an interesting two days.The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research.The opening keynote presentation is given by Anthony "Skip" Baisel, from the Queen Mary University of London on the topic of Higher Education Pedagogy using Game Design.The second day of the conference will open with interactive collaborative keynote by Mie Buhl, Bente Meyer, Rikke Ørngreen, on the topic of Does IT work?Investigating factors at play in e-learning research.
Using model-based neuroimaging to adjudicate structured and continuous representational accounts in same-different categorization and beyond

The capacity to categorize using the concepts same and different plays a central role in cognition. However, in any given circumstance, it can be difficult to tell whether a person or animal is performing same and different categorization using structured relational rules from propositional logic or perceptual change detection processes. Discrete, logical behavior can often be produced from continuous perceptual spaces, and continuous behavior can arise from systems relying on structured logical rules. Model-based neuroimaging, when used in conjunction with advances in task development, can aid in understanding how people accomplish same and different categorization. We review recent publications employing the model-based neuroimaging paradigm to isolate latent brain processes associated with use of structured, logical rules and continuous perceptual processes.
The Application of Artificial Intelligence Technology in Sports Competition

Abstract In order to respond to the call of the country to build a strong sports country, the workers in the sports industry should speed up the pace of sports development. Artificial intelligence is a high-end industry, but also a key technology to guide the construction of sports power. Artificial intelligence technology can not only help social development, but also help human autonomy and self-reliance development. The combination of artificial intelligence technology and sports has been common, the future application scope will be more and more extensive, the integration will be deeper and deeper, this paper takes the sports competition as an example, This paper briefly analyzes the application advantage and application status of artificial intelligence technology in sports competition.
A Secure IoT Framework Based on Blockchain and Machine Learning

Internet of Things (IoT) network consists of many devices that communicate together and exchange data.IoT network has many applications especially in smart city and smart campus.IoT devices usually produce a huge amount of data that are stored in the cloud to be analyzed later.Data in general and IoT devices data in particular suffer from major security issues such as the availability and the integrity of data.Blockchain is a new technology that offers an interesting solution for the security of sensitive IoT data by protecting data against malicious tampering.However, the data stored in the blockchain cannot be altered, therefore, it should be validated before being stored in the blockchain especially that IoT devices are vulnerable to attacks.Machine learning algorithms are very useful to detect compromised IoT devices to ensure that only reliable data are stored in the blockchain.The main aim of this paper is to propose a framework for storing reliable IoT data in a secure way that preserves the integrity and availability properties.First, to detect compromised IoT devices, several machine learning algorithms are evaluated.The IoTID20 dataset for anomalous activity detection in IoT network is used to build our machine learning model.IoTID20 dataset contains 80 network features and 625783 records.For our experiment, 4000 records are selected randomly from the dataset.Two algorithms are used to select features namely Logistic Regression and Pearson's correlation.A total of 15 features are selected in order to classify packets as (Normal, Anomaly).Several machine learning algorithms are trained: Logistic Regression, Random Forest, Decision Tree, K Nearest Neighbors, AdaBoost, and Naïve Bayes.These algorithms are compared based on the following measurements: accuracy, precision, recall, F1 score, and the time for classification.The results reveal that Random Forest and AdaBoost classifiers give very close results and are considered the best classifiers based on all performance metrics used in the paper except the time.Regarding the time required to detect malicious records, Decision Tree and Naïve Bayes are the best as the time required to predict 4000 records is 0.015 seconds.A private blockchain is built to store normal data received from IoT devices after being filtered.The data is signed and then distributed to all nodes in the network for verification.To provide scalability, the consensus algorithm of the blockchain is based on the proof of authority algorithm.As a proof of concepts, the blockchain is developed using Python programming language and the time required to verify the signatures is computed.
Techniques to enhance a QUBO solver for permutation-based combinatorial optimization

Many combinatorial optimization problems can be formulated as a problem to determine the order of sequence or to find a corresponding mapping of the objects. We call such problems permutation-based optimization problems. Many such problems can be formulated as a quadratic unconstrained binary optimization (QUBO) or Ising model by introducing a penalty coefficient to the permutation constraint terms. While classical and quantum annealing approaches have been proposed to solve QUBOs to date, they face issues with optimality and feasibility. Here we treat a given QUBO solver as a black box and propose techniques to enhance its performance. First, to ensure an effective search for good quality solutions, a smooth energy landscape is needed; we propose a data scaling approach that reduces the amplitudes of the input without compromising optimality. Second, we need to tune the penalty coefficient. In this paper, we illustrate that for certain problems, it suffices to tune the parameter by performing random sampling on the penalty coefficients to achieve good performance. Finally, to handle possible infeasibility of the solution, we introduce a polynomial-time projection algorithm. We apply these techniques along with a divide-and-conquer strategy to solve some large-scale permutation-based problems and present results for TSP and QAP.
An Efficient Method for Breast Mass Classification Using Pre-Trained Deep Convolutional Networks

Masses are the early indicators of breast cancer, and distinguishing between benign and malignant masses is a challenging problem. Many machine learning- and deep learning-based methods have been proposed to distinguish benign masses from malignant ones on mammograms. However, their performance is not satisfactory. Though deep learning has been shown to be effective in a variety of applications, it is challenging to apply it for mass classification since it requires a large dataset for training and the number of available annotated mammograms is limited. A common approach to overcome this issue is to employ a pre-trained model and fine-tune it on mammograms. Though this works well, it still involves fine-tuning a huge number of learnable parameters with a small number of annotated mammograms. To tackle the small set problem in the training or fine-tuning of CNN models, we introduce a new method, which uses a pre-trained CNN without any modifications as an end-to-end model for mass classification, without fine-tuning the learnable parameters. The training phase only identifies the neurons in the classification layer, which yield higher activation for each class, and later on uses the activation of these neurons to classify an unknown mass ROI. We evaluated the proposed approach using different CNN models on the public domain benchmark datasets, such as DDSM and INbreast. The results show that it outperforms the state-of-the-art deep learning-based methods.