Reviewerly/exHarmony huggingface dataset:
CFDIL: a context-aware feature deep interaction learning for app recommendation

The rapid development of mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical usage data to explore users' preferences and then make recommendations. Although traditional methods have achieved certain success, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users' preferences, which is often overlooked by traditional methods. To overcome the aforementioned problems, we proposed a context-aware feature deep interaction learning (CFDIL) method to explore users' preferences and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing novel user and app feature portraits. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which shows that CFDIL outperforms the benchmark methods.
IDC: quantitative evaluation benchmark of interpretation methods for deep text classification models

Abstract Recent advances in deep neural networks have achieved outstanding success in natural language processing tasks. Interpretation methods that provide insight into the decision-making process of these models have received an influx of research attention because of the success and the black-box nature of the deep text classification models. Evaluation of these methods has been based on changes in classification accuracy or prediction confidence when removing important words identified by these methods. There are no measurements of the actual difference between the predicted important words and humans’ interpretation of ground truth because of the lack of interpretation ground truth. A large publicly available interpretation ground truth has the potential to advance the development of interpretation methods. Manual labeling important words for each document to create a large interpretation ground truth is very time-consuming. This paper presents (1) IDC, a new benchmark for quantitative evaluation of interpretation methods for deep text classification models, and (2) evaluation of six interpretation methods using the benchmark. The IDC benchmark consists of: (1) Three methods that generate three pseudo-interpretation ground truth datasets. (2) Three performance metrics: interpretation recall, interpretation precision, and Cohen’s kappa inter-agreement. Findings: IDC-generated interpretation ground truth agrees with human annotators on sampled movie reviews. IDC identifies Layer-wise Relevance Propagation and the gradient-by-input methods as the winning interpretation methods in this study.
PerFlow

Performance analysis is widely used to identify performance issues of parallel applications. However, complex communications and data dependence, as well as the interactions between different kinds of performance issues make high-efficiency performance analysis even harder. Although a large number of performance tools have been designed, accurately pinpointing root causes for such complex performance issues still needs specific in-depth analysis. To implement each such analysis, significant human efforts and domain knowledge are normally required.
Introduction to the Python Hyperspectral Analysis Tool (PyHAT)

Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to support exploratory spectral data analysis, the derivation of mission generated thematic products, and the application of statistical learning methods to spectral data. We present a general overview of the software architecture and identify the classes of users we seek to support. Four case studies demonstrate the use for both orbital and in-situ (landed) hyperspectral data.
Expressive Communication: Evaluating Developments in Generative Models and Steering Interfaces for Music Creation

There is an increasing interest from ML and HCI communities in empowering creators with better generative models and more intuitive interfaces with which to control them. In music, ML researchers have focused on training models capable of generating pieces with increasing long-range structure and musical coherence, while HCI researchers have separately focused on designing steering interfaces that support user control and ownership. In this study, we investigate how developments in both models and user interfaces are important for empowering co-creation where the goal is to create music that communicates particular imagery or ideas (e.g., as is common for other purposeful tasks in music creation like establishing mood or creating accompanying music for another media). Our study is distinguished in that it measures communication through both composer's self-reported experiences, and how listeners evaluate this communication through the music. In an evaluation study with 26 composers creating 100+ pieces of music and listeners providing 1000+ head-to-head comparisons, we find that more expressive models and more steerable interfaces are important and complementary ways to make a difference in composers communicating through music and supporting their creative empowerment.
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model

It is well known that machine learning models are vulnerable to small but cleverly designed adversarial perturbations that can cause misclassification. While there has been major progress in designing attacks and defenses for various adversarial settings, many fundamental and theoretical problems are yet to be resolved. In this paper, we consider classification in the presence of $\ell_0$-bounded adversarial perturbations, also known as sparse attacks. This setting is significantly different from other $\ell_p$-adversarial settings with $p\geq1$, as the $\ell_0$-ball is nonconvex and highly nonsmooth. Under the assumption that data is distributed according to the Gaussian mixture model, our goal is to characterize the optimal robust classifier and the corresponding robust classification error as well as a variety of tradeoffs between robustness, accuracy, and the adversary's budget. To this end, we develop a novel classification algorithm called FilTrun that has two main modules: filtration and truncation. The key idea of our method is to first filter out the nonrobust coordinates of the input and then apply a carefully designed truncated inner product for classification. By analyzing the performance of FilTrun, we derive an upper bound on the optimal robust classification error. We further find a lower bound by designing a specific adversarial strategy that enables us to derive the corresponding robust classifier and its achieved error. For the case that the covariance matrix of the Gaussian mixtures is diagonal, we show that as the input's dimension gets large, the upper and lower bounds converge; i.e., we characterize the asymptotically optimal robust classifier. Throughout, we discuss several examples that illustrate interesting behaviors such as the existence of a phase transition for the adversary's budget determining whether the effect of adversarial perturbation can be fully neutralized or not.
The Construction of Smart Learning Space in Colleges Based on Blended Learning

The arrival of the “Internet+” era has made a tremendous impact on the traditional model of talent training in colleges and universities, and the social expectations for college graduates have changed greatly compared with the past. As the cultivation base for talents, colleges and universities must adapt to the trend of the times to reform the mode of talent training. The blended learning mode based on the combination of face-to-face learning and online learning can meet the requirements of the teaching reform in the new era to some extent. This paper discusses how to build the intelligent learning space based on blended learning from the aspects of the technology environment, the teaching process, the learning mode, the smart evaluation and so on and constructs the evaluation index system to be demonstrated, in order to make contributions to the establishment of the new teaching mode, the cultivation of talents, and the establishment of a lifelong learning system.
Unified Question Generation with Continual Lifelong Learning

Question Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG methods mainly focus on building or training models for specific QG datasets. These works are subject to two major limitations: (1) They are dedicated to specific QG formats (e.g., answer-extraction or multi-choice QG), therefore, if we want to address a new format of QG, a re-design of the QG model is required. (2) Optimal performance is only achieved on the dataset they were just trained on. As a result, we have to train and keep various QG models for different QG datasets, which is resource-intensive and ungeneralizable. To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats. Specifically, we first build a format-convert encoding to transform different kinds of QG formats into a unified representation. Then, a method named \emph{STRIDER} (\emph{S}imilari\emph{T}y \emph{R}egular\emph{I}zed \emph{D}ifficult \emph{E}xample \emph{R}eplay) is built to alleviate catastrophic forgetting in continual QG learning. Extensive experiments were conducted on $8$ QG datasets across $4$ QG formats (answer-extraction, answer-abstraction, multi-choice, and boolean QG) to demonstrate the effectiveness of our approach. Experimental results demonstrate that our Unified-QG can effectively and continually adapt to QG tasks when datasets and formats vary. In addition, we verify the ability of a single trained Unified-QG model in improving $8$ Question Answering (QA) systems' performance through generating synthetic QA data.
Deal or no-deal? Using administrative data to explore buyer motivation in online drug purchases and its association with community risk factors in Scotland

The expansion of online drugs markets has widened opportunities to purchase drugs, for both personal use and wider distribution, thus creating new potential public health risks. However, there is little research on the motivation of online drug purchasers or the level of risk that such transactions pose to different communities. Greater insights into the intended use of drug parcels arriving by post, and how this varies across communities could help law enforcement and health services develop more effective policies and better allocate resources to reduce drug-related harms.We use Scottish administrative data about illegal drug parcels seized by the UK Border Force to create a new classification of 'buyer motivation' (based on type of drug, estimated number of doses purchased, and patterns of drug consumption). We identify three potential types of buyer motivation: personal consumption, heavy use or social dealing, and wholesale dealing; and examine the extent to which each type is associated with a range of drug-related community risk factors, thereby identifying potential variation in levels of public health risk.Communities to which drug parcels were destined differed significantly from the Scottish average across a range of factors; however, this varied by buyer motivation. Parcels thought to be purchased for heavy use or social dealing appeared to pose a greater risk within communities characterised by general deprivation, but especially health-related deprivation, with a high youth population but low unemployment rates; whereas those purchased for wholesale dealing appear most likely to pose a risk in communities with higher crime and unemployment rates.Administrative data about intercepted drug parcels could be helpful in classifying the motivation of online drug purchasers and monitoring patterns of variation in potential public health risks at a community level. This could support law enforcement and public health agencies to develop more targeted drug-harm reduction strategies.
The Importance of Human Resources Competencies in Organisational Cyber Risk Management

Although risks are present in any organisation and the importance of their study is obvious, the authors find that risk analysis is an area still in its infancy, as reflected in the small number of existing publications on this topic. Human resources tend to understand risk in an elementary way. The ability of human resources to perceive risk is the ability and competence to identify a potential threat that does not always appear.Aim: The aim of the this chapter was to provide additional knowledge on human resource competencies, in order to avoid the emergence and spread of risks at the organisational and cyber level.Methodology: The authors used the quantitative–comparative analysis, by presenting all the details regarding the competencies of the human resource in order to manage the risks at organisational and cybernetic level.Findings: The findings of this chapter show that the compulsory competencies of the human resource influence both the general competencies and the special competencies: information technology and communications, security ethics and economic ones. These, in turn, can improve or diminish cyber security competencies by almost 50%.Originality of the Study: This study is highlighted by results obtained from the analysis of the capacity of human resources, to integrate theoretical knowledge and practical competencies on the perception of cyber risk. Of particular importance for this research are the analysis of data and the interpretation of results on human resources competencies. In this sense, throughout the chapter are assessed the skills of human resources, necessary for the management of cyber risks at the organisational level. In terms of future research implications, it could be important research to identify a method of assessing the competencies acquired by human resources applied from the perspective of cyber risk.
Identification of markers and artificial intelligence-based classification of radical Twitter data

Purpose Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content. Design/methodology/approach This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated. Findings The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content. Originality/value In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.
CEQE to SQET: A study of contextualized embeddings for query expansion

In this work, we study recent advances in context-sensitive language models for the task of query expansion. We study the behavior of existing and new approaches for lexical word-based expansion in both unsupervised and supervised contexts. For unsupervised models, we study the behavior of the Contextualized Embeddings for Query Expansion (CEQE) model. We introduce a new model, Supervised Contextualized Query Expansion with Transformers (SQET) that performs expansion as a supervised classification task and leverages context in pseudo-relevant results. We study the behavior of these expansion approaches for the tasks of ad-hoc document and passage retrieval. We conduct experiments combining expansion with probabilistic retrieval models as well as neural document ranking models. We evaluate expansion effectiveness on three standard TREC collections: Robust, Complex Answer Retrieval, and Deep Learning. We analyze the results of extrinsic retrieval effectiveness, intrinsic ability to rank expansion terms, and perform a qualitative analysis of the differences between the methods. We find out CEQE statically significantly outperforms static embeddings across all three datasets for Recall@1000. Moreover, CEQE outperforms static embedding-based expansion methods on multiple collections (by up to 18% on Robust and 31% on Deep Learning on average precision) and also improves over proven probabilistic pseudo-relevance feedback (PRF) models. SQET outperforms CEQE by 6% in P@20 on the intrinsic term ranking evaluation and is approximately as effective in retrieval performance. Models incorporating neural and CEQE-based expansion score achieves gains of up to 5% in P@20 and 2% in AP on Robust over the state-of-the-art transformer-based re-ranking model, Birch.
Multistability Analysis and MultiSim Simulation of A 12-Term Double-Scroll Hyperchaos System with Three Nonlinear Terms, Bursting Oscillations and Its Cryptographic Applications

Using three nonlinear terms (two quadratics and one cubic), a new 12-term hyperchaos system with a double-scroll attractor is proposed in this chapter. Using bifurcation analysis, dynamical properties are exemplified and multistability property with coexisting hyperchaos attractors is established. MultiSim circuit simulation of the new hyperchaos system aids invalidation and practical application. Secure communication devices make use of dynamic systems having hyperchaos properties. Due to the good nonlinear properties of the presented hyperchaos system, we introduce its applications in designing secure substitution boxes (S-boxes) and generating pseudo-random number generators (PRNGs). Analyses of results prove the effectiveness of the presented S-box and PRNG mechanisms.
On the Testability of Artificial Intelligence and Machine Learning Systems

This article examines current testing techniques for the quality assurance of artificial intelligence and machine learning systems. It organizes them based on the granularity of testing level and explores design tactics using these techniques.
CondNAS: Neural Architecture Search for Conditional CNNs

As deep learning has become prevalent and adopted in various application domains, the need for efficient convolution neural network (CNN) inference on diverse target platforms has increased. To address the need, a neural architecture search (NAS) technique called once-for-all, or OFA, which aims to efficiently find the optimal CNN architecture for the given target platform using genetic algorithm (GA), has recently been proposed. Meanwhile, a conditional CNN architecture, which allows early exits with auxiliary classifiers in the middle of a network to achieve efficient inference without accuracy loss or with negligible loss, has been proposed. In this paper, we propose a NAS technique for the conditional CNN architecture, CondNAS, which efficiently finds a near-optimal conditional CNN architecture for the target platform using GA. By attaching auxiliary classifiers through adaptive pooling, OFA’s SuperNet is successfully extended, such that it incorporates the various conditional CNN sub-networks. In addition, we devise machine learning-based prediction models for the accuracy and latency of an arbitrary conditional CNN, which are used in the GA of CondNAS to efficiently explore the large search space. The experimental results show that the conditional CNNs from CondNAS is 2.52× and 1.75× faster than the CNNs from OFA for Galaxy Note10+ GPU and CPU, respectively.
Automatic identification of semi-tracks on apatite and mica using a deep learning method

Fission track dating is a widely used thermochronological approach to constrain the thermal history of rocks. Conventionally this approach requires manual identification of fission tracks under the microscope which can be time-consuming and labor-intensive. In this study, we proceed with the newly developed approach to identify fission tracks on transmitted light images based on a deep learning method. In this new approach, we use a convolution neural network (CNN) to extract semi-tracks through image semantic segmentation. Considering the boundary ambiguity inherent in the CNN, we also extract the multi-scale boundary of the images in order to refine the semantic segmentation. We then calculate an area threshold of semi-tracks to determine whether semi-tracks are overlapping or not. These non-overlapping tracks are counted directly from the refined semantic segmentation images. For these overlapping tracks, we develop a boundary-superimposed method by using the refined semantic segmentation and the multi-scale boundary images with the help of the reflected-light images to split them before counting. We used 101 images of spontaneous fission tracks and 7 images of induced fission tracks for training with this new approach and tested the resulting convolutional neural networks on 114 spontaneous fission track images and 60 induced fission track images. Most of the test samples show high precision, recall, F1-score, and overall accuracy, highlighting the potential usage of this approach to identify fission tracks automatically.