Reviewerly/exHarmony huggingface dataset:
Neural generators of sparse local linear models for achieving both accuracy and interpretability

For reliability, it is important for the predictions made by machine learning methods to be interpretable by humans. In general, deep neural networks (DNNs) can provide accurate predictions, although it is difficult to interpret why such predictions are obtained by the DNNs. On the other hand, interpretation of linear models is easy, although their predictive performance is low because real-world data are often intrinsically non-linear. To combine both the benefits of the high predictive performance of DNNs and the high interpretability of linear models into a single model, we propose neural generators of sparse local linear models (NGSLL). Sparse local linear models have high flexibility because they can approximate non-linear functions. NGSLL generates sparse linear weights for each sample using DNNs that take the original representations of each sample (e.g., word sequence) and their simplified representations (e.g., bag-of-words) as input. By extracting features from the original representations, the weights can contain rich information and achieve a high predictive performance. In addition, the prediction is interpretable because it is obtained through the inner product between the simplified representations and the sparse weights, where only a small number of weights are selected by our gate module in NGSLL. In experiments on image, text and tabular datasets, we demonstrate the effectiveness of NGSLL quantitatively and qualitatively by evaluating the prediction performance and visualizing generated weights.
Evaluation of convolutional neural networks for search in 1/f2.8 filtered noise and digital breast tomosynthesis phantoms

With the advent of powerful convolutional neural networks (CNNs), recent studies have extended early applications of neural networks to imaging tasks thus making CNNs a potential new tool for assessing medical image quality. Here, we compare a CNN to model observers in a search task for two possible signals (a simulated mass and a smaller simulated micro-calcification) embedded in filtered noise and single slices of Digital Breast Tomosynthesis (DBT) virtual phantoms. For the case of the filtered noise, we show how a CNN can approximate the ideal observer for a search task, achieving a statistical efficiency of 0.77 for the microcalcification and 0.78 for the mass. For search in single slices of DBT phantoms, we show that a Channelized Hotelling Observer (CHO) performance is affected detrimentally by false positives related to anatomic variations and results in detection accuracy below human observer performance. In contrast, the CNN learns to identify and discount the backgrounds, and achieves performance comparable to that of human observer and superior to model observers (Proportion Correct for the microcalcification: CNN = 0.96; Humans = 0.98; CHO = 0.84; Proportion Correct for the mass: CNN = 0.98; Humans = 0.83; CHO = 0.51). Together, our results provide an important evaluation of CNN methods by benchmarking their performance against human and model observers in complex search tasks.
Incorporating minimal user input into deep learning based image segmentation

Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance. We added a set of sparse surface points as an additional input to supervise the CNNs for more accurate image segmentation. We tested our technique by applying minimal interactions to supervise the networks for segmentation of the prostate on magnetic resonance images. We used U-Net and a new network architecture that was based on U-Net (dual-input path [DIP] U-Net), and showed that our supervising technique could significantly increase the segmentation accuracy of both networks as compared to fully automatic segmentation using U-Net. We also showed DIP U-Net outperformed U-Net for supervised image segmentation. We compared our results to the measured inter-expert observer difference in manual segmentation. This comparison suggests that applying about 15 to 20 selected surface points can achieve a performance comparable to manual segmentation.
Bayesian inference for Gaussian models : Inverse problems and evolution equations

This dissertation studies the Asymptotics of Bayesian nonparametric inference for Gaussian linear models. In Part I, we collect the materials essential for the development of this thesis, including smoothness classes, the Gaussian noise model, and the general approach of Bayesian nonparametric inference to Gaussian linear models. Part II investigates linear inverse problems with Gaussian noise. We examine the inverse problems with continuous observations in smoothness scales in Chapter 6. The inverse problems with discrete observations are studied in Chapter 7 using Gaussian conjugacy and in Chapter 8 by extending the results from Chapter 6. Our results demonstrate two facts. First, the Bayesian contraction rates reach the same order of frequentist minimax optimal rates. Second, there is no deterioration in posterior contraction rates due to the information loss from continuous to discrete observations in inverse problems. Part III covers Bayesian nonparametric inference for stochastic evolution equations. We derive the contraction rates of Gaussian priors for initial conditions and drifts. The results show that the rates are affected by the signals with anisotropic smoothness in higher dimensions.
The Disruptive Effect of Distributed Ledger Technology and Blockchain in the over the Counter Derivatives Market

Abstract The market for derivatives is substantially different after the 2007/08 financial crisis. Trust fuels business yet the financial crisis undermined this concept and customers lost faith in financial institutions. The then dichotomy, faced by innovators, was to insist with a system based on trust in financial institutions, or explore others where neither trust nor banks, as intermediaries, were indispensable for the successful and safe completion of financial transactions. The aim of this piece of research is to analyse to what extent innovative technology would change the way the ‘Over The Counter’ (OTC) market operates by providing investors with a trustworthy platform for the efficient assessment of the risk behind certain financial instruments. Consequently, the market may not be caught by surprise when another financial crisis strikes.
Superensemble classifier for improving predictions in imbalanced datasets

Learning from an imbalanced data set presents a tricky problem in which traditional learning algorithms perform poorly. Traditional classifiers usually aim to optimize the overall accuracy without considering the relative distribution of each class. To improve predictions in imbalanced classification problems, this article presents a superensemble classifier that maps Hellinger Distance Decision Trees (HDDT) into Radial Basis Function Networks (RBFN). Regularity conditions for universal consistency and the idea of parameter optimization of the proposed model are given in this article. The proposed distribution-free model can be applied for feature selection cum imbalanced classification problems. We have also provided enough experimental evidence using various real-life data sets to assess the performance of the proposed model. Its effectiveness and competitiveness concerning different state-of-the-art models are shown.
Length of the Longest Common Subsequence between Overlapping Words

Given two random finite sequences from $[k]^n$ such that a prefix of the first sequence is a suffix of the second, we examine the length of their longest common subsequence (LCS). If $\ell$ is the length of the overlap, we prove that the expected length of an LCS is approximately $\max(\ell, {E}[L_n])$, where $L_n$ is the length of an LCS between two independent random sequences. We also obtain tail bounds on this quantity.
A Survey on Cloud Federation Architecture and Challenges

Conventional Cloud Computing system possess some limitations. It is difficult for users to switch between various cloud providers due to lack of a standard architecture. There is no standard metering system for cloud computing services. For cloud providers, it is difficult to maintain performance transparency due to changing user requirements, varying loads and limited resources, especially for small and medium providers. Federation of Clouds is the possible solution for these problems. In this paradigm, various cloud providers can federate with each other to expand their business opportunities while achieving optimal resource utilization and cost effectiveness. This collaboration of clouds is fruitful to users in terms of better pricing options, availability of services and overall better Quality of Service. Unlike current Cloud Computing, Federation of Clouds requires a standard architecture to which every participating cloud provider must comply. In nutshell, Federation of Clouds opens a domain of infinite possibilities to reshape the existing world of Cloud Computing and Information Technology, in general. It will provide a level playing field for emerging small and medium level cloud providers to compete among big players. It also has the promise to provide users with better ways to access great computational power and resources.
External Contextual Factors in Information Security Behaviour

Human behaviour is often considered to be irrational, difficult to understand, and challenging to manage.This phenomenon has a direct impact on the way in which humans behave when confronted with information security which, in turn, complicates how security is to be managed.This research attempts to investigate the role that contextual factors play in how humans behave, specifically with regards to information security.Contextual factors are identified that influence human behaviour in general.These factors are conceptualised in relation to existing models of behaviour and subsequently mapped to information security behaviour.A practical research exercise, relating to information security behaviour, is conducted with a university residence as the contextual environment.The specific contextual factors, and how they relate to information security, are discussed.Information security behavioural threshold analysis is employed to evaluate the impact of the identified contextual factors on the residence's security behaviour.The results are reflected upon, based on the results from the threshold analysis.The paper concludes by highlighting the contributions that were made towards understanding contextual factors in information security.
TdBrnn: An Approach to Learning Users' Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM

With the development of Internet technology and the enhancement of people’s concept of the rule of law, online legal consultation has become an important means for the general public to conduct legal consultation. However, different people have different language expressions and legal professional backgrounds. This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation. How to accurately understand the true intentions behind different users’ legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services. Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data, and are not scalable, and often require taxing manual annotation work. This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users’ intention to legal consulting. First, we present the users’ legal consulting statements as a tensor. And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users’ intention of legal consultation, namely the core tensor. The core tensor relies less on the lexical and semantic information of the original users’ legal consulting statements data, it reduces the dimension of the original tensor, and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm. Furthermore, we use a large number of core tensors obtained by the tensor decomposition layer with users’ legal consulting statements tensors as inputs to continuously train Bi-LSTM, and finally derive the users’ legal consultation intention classification model which can comprehensively understand the user’s legal consultation intention. Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks.
Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts

Nowadays, recommender systems (RSes) are becoming increasingly important to individual users and business marketing, especially in the online e-commerce scenarios. However, while the majority of recommendation algorithms proposed in the literature have focused their efforts on improving prediction accuracy, other important aspects of recommendation quality, such as diversity of recommendations, have been more or less overlooked. In the latest decade, recommendation diversity has drawn more research attention, especially in the models based on user-item bipartite networks. In this paper, we introduce a family of approaches to extract fabricated experts from users in RSes, named as the Expert Tracking Approaches (ExTrA for short), and explore the capability of these fabricated experts in improving the recommendation diversity, by highlighting them in a well-known bipartite network-based method, called the Mass Diffusion (MD for short) model. These ExTrA-based models are compared with two state-of-the-art MD-improved models HHP and BHC, with respect to recommendation accuracy and diversity. Comprehensive empirical results on three real-world datasets MovieLens, Netflix and RYM show that, our proposed ExTrA-based models can achieve significant diversity gain while maintain comparable level of recommendation accuracy.
Expanding Learning Environment through Mobile Learning

Technological developments change the point of view in learning. Learning space is not limited to conventional classrooms in schools. With technology, there is an opportunity to expand the learning environment through the development of mo-bile learning. This research design is the basic development of mobile learning in higher education. The purpose is to produce prototypes of online learning materi-als in physical geography courses. The test results are limited to one class show-ing positive responses from prospective users. Flexibility and ease of access make them able to learn according to the time they want. However, the limitations of this research are only limited to certain science courses, and it needs further development for social science courses.
Pedestrian Gender Detection Based on Mask R-CNN

Based on Mask R-CNN, one of the most significant models for instance segmentation, we present our pedestrian gender detection algorithm in this paper. First of all, we verify the effectiveness of Mask R-CNN in pedestrian detection. On this basis, we design our network by merging Mask R-CNN with a gender recognition branch. And due to the lack of current datasets, we not only present a method to train our model, but also build a shopping mall dataset to test our model. The application of our detection model to our small dataset is of great significance to the business planning and the security maintenance of the entertainment center.
“Hello are you available?” Dealing with online frauds and the role of forensic science

On August 6, 2019, the 119 members of the School of criminal justice, forensic science and criminology at the University of Lausanne were the target of an online scammer. His/her modus operandi consisted of email masquerading as the Director of the School in an attempt to induce the victims to buy digital gift cards and to transmit the card usage code to the perpetrator. The first author of this paper is the Director of the School, and the second is an expert in digital forensic science and a professor of the School. They worked together in real time to deal with the fraud. Because the fraud occurred in a School of forensic science and criminology, it raised many questions on a variety of overlapping dimensions. The objective of this study was, therefore, to draw lessons from this case from several perspectives ranging from forensic science to cybersecurity, and from practical to academic. The response to the incident has been treated in four typical distinguishable phases: (1) fraud detection; (2) crisis management; (3) post-incident analysis; and (4) reporting to different communities. We conclude this paper by taking lessons from the case to express the essential role of forensic knowledge and crime analysis in interpreting the information conveyed by digital traces to develop innovative cross-disciplinary models for preventing, detecting, analysing, investigating and responding to online fraud.
Concept Drift Detection with Variable Interaction Networks

The current development of today’s production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the condition of plants and products in future production lines will be continuously analyzed with the objective to predict any kind of breakdown and trigger preventing actions proactively. Such ambitious predictions are commonly performed with support of machine learning algorithms. In this work, we utilize these algorithms to model complex systems, such as production plants, by focussing on their variable interactions. The core of this contribution is a sliding window based algorithm, designed to detect changes of the identified interactions, which might indicate beginning malfunctions in the context of a monitored production plant. Besides a detailed description of the algorithm, we present results from experiments with a synthetic dynamical system, simulating stable and drifting system behavior.
Spatial-Temporal Aware Long-Term Object Tracking

Most existing trackers perform well in the occurrence of short-term occlusions and appearance and illumination variations, but struggle with the challenges of long-term tracking which include heavy or long-term occlusions, and out-of-view objects. We propose a spatial-temporal aware long-term object tracking method in this paper, to manage the challenges of long term tracking. Firstly, we present a spatial-temporal aware correlation filter, which jointly models the spatial and temporal information of the target within the correlation filter framework to locate a visible target from frame to frame. Then, we design a tracking uncertainty detection mechanism to activate the re-detector in case of track failure. The mechanism relies on the variations of correlation response measured spatially, and the appearance similarity estimated temporally. Finally, we propose a spatial-temporal aware long-term re-detector to recover the target when it becomes visible. In the re-detection process, a large number of candidates are sampled, evaluated, and refined spatially to obtain accurate locations. Additionally, reliable memory retained through conservative template updating enables the recovery of the target by similarity matching. The spatial-temporal information is explicitly encoded in each component, which operates collaboratively to boost the overall performance. Extensive experimental results conducted on publicly available benchmark datasets demonstrate that the proposed method performs favorably when compared to other state-of-the-art trackers.
Level differentiation technology in vocational education

Differentiated learning technology has been used for a long time. However, with the introduction of innovative educational technologies, it acquires new features and therefore requires additional research. Higher schools are in the process of searching for elements based on a personality-focused approach and introduce the most effective technologies in this process. The purpose of the article is to discusses the implementation of technologies that provide a single differentiation in the process of preparing students. The characteristic of students' work in differentiated educational conditions is presented. Teaching and training students is arranged in groups. As a result, there is a way to prepare competent students, making them highly qualified professionals. The study shows that there are great opportunities in teaching students of a higher educational institution. The formation of competencies with the help of differentiated learning technology becomes more effective, since individual characteristics of each student are taken into account and their interest and motivation to study a particular course increases. Differentiated tasks allow you to monitor dynamics of the development of students' competence, the systematic implementation of a certain type of tasks that are suitable for certain groups of students.The article presents a study of the level of training of students of the University of the 3rd year of study. The study was conducted in 2018 throughout the school year. Our results are based on data obtained from two groups of students with the same number of subjects. The introduction of differentiated learning technology can improve the quality of training of higher education institutions students. With the help of these technologies, each student, building training in accordance with their personal characteristics and professional needs, achieves success in mastering the material, quickly forms professional competence. The results of the study focused on the quality of training showed that differentiated technologies improve students’ performance.
A study on the development of artificial intelligence(AI) chatbot by pre-service student teachers

This study has evaluated 9 AI chatbots developed by a class of 19 pre-service teacher students in order to find out whether those chatbots work properly and pre-service student teachers’ attitudes to developing chatbot experiences. The summary of the study is as follows. First, four types of evaluation are used: 1) asking questions specific to the entity, 2) evaluating whether prompts work properly, 3) evaluating whether or not the partial utterances were accepted depending on the work of prompts and finally, 4) performing the actual learning task. According to the analysis, 1) the chatbot had a 57.4% success rate in setting up the entity. 2) Prompt operation in relation to the entity showed a success rate of 50%. 3) Initiating prompts by part of speech was 57.4% successful. 4) Finally, six chatbots have succeeded in performing learning task. Second, 9 out of 10 items in the active and collaborative learning domain show more than 3.0 and positive recognition. Six out of seven items in the learning strategy category have scored at least 3.5, indicating that the collaborative task had students actively communicate with peers and contribute to effectively completing the task. In addition, the average time spent for finishing the task was 34 hours or more, which is a surprising result considering that the total semester hours for the course work are 30 hours per semester. The findings of this study suggest that it’s the only beginning of a collective effort to implement AI in education. We are supposed to face the music and give it a try.