Reviewerly/exHarmony huggingface dataset:
Research on College English Teaching under the Background of Artificial Intelligence

Abstract The application of language service products of artificial intelligence has brought great challenges and opportunities to language teaching in colleges and universities: promoting the teaching model innovation; changing the way of students learning; changing the basic functions of teachers. The teaching mode of college English under the background of artificial intelligence should be influenced by several aspects as follows: based on artificial intelligence in college English listening teaching; college English oral teaching based on robot; based on the number of the teaching of artificial intelligence in college English writing; cloud service based artificial intelligence college English in translation teaching.
Characterizing Collision and Second-Preimage Resistance in Linicrypt

Linicrypt (Carmer & Rosulek, Crypto 2016) refers to the class of algorithms that make calls to a random oracle and otherwise manipulate values via fixed linear operations. We give a characterization of collision-resistance and second-preimage resistance for a significant class of Linicrypt programs (specifically, those that achieve domain separation on their random oracle queries via nonces). Our characterization implies that collision-resistance and second-preimage resistance are equivalent, in an asymptotic sense, for this class. Furthermore, there is a polynomial-time procedure for determining whether such a Linicrypt program is collision/second-preimage resistant.
Transfer Learning on Decision Tree with Class Imbalance

Transfer learning has attracted growing attention from the machine learning community. It addresses real-world issues that were not considered in the classical methods, especially with model-based methods when source data is not available. Recent work on transfer learning for Decision Trees has been proposed in the form of two algorithms named SER and STRUT. These methods tackle with success potential changes between source and target data, however several classification tasks may face imbalance data, hence degrading the performance of such transfer methods. In this paper, we study the impact of class imbalance on these algorithms and propose an adaptation for each of them, named SER* and STRUT*. These variants have been tested on real and synthetic data, these latter revealing to be of great use to control at the same time various transformations between source and target along with imbalance conditions. Results on both types of data show the benefits of our approach and suggest several perspectives for further research.
How to Correct Errors in Multi-server PIR

Suppose that there exist a user and $$\ell $$ servers $$S_1,\ldots ,S_{\ell }$$. Each server $$S_j$$ holds a copy of a database $$\mathbf {x}=(x_1, \ldots , x_n) \in \{0,1\}^n$$, and the user holds a secret index $$i_0 \in \{1, \ldots , n\}$$. A b error correcting $$\ell $$ server PIR (Private Information Retrieval) scheme allows a user to retrieve $$x_{i_0}$$ correctly even if and b or less servers return false answers while each server learns no information on $$i_0$$ in the information theoretic sense. Although there exists such a scheme with the total communication cost $$ O(n^{1/(2k-1)} \times k\ell \log {\ell } ) $$ where $$k=\ell -2b$$, the decoding algorithm is very inefficient. In this paper, we show an efficient decoding algorithm for this b error correcting $$\ell $$ server PIR scheme. It runs in time $$O(\ell ^3)$$.
Towards Spoken Medical Prescription Understanding

Prescription Management Systems (PMS) have appeared in health institutions to reduce medication errors which affect several million people worldwide each year. However, practitioners must enter information manually into PMS which decreases the time devoted to care. In this paper, we propose to provide a Natural Language interface to the PMS so that practitioners can record their prescriptions orally through mobile devices at the point of care. We briefly describe the overall approach and focus on the Natural Language Understanding process which was approached through slot-filling. To deal with the paucity of data and the imbalanced class problem, we present a method to artificially generate medical prescriptions. Experiments on the artificial and a realistic dataset with several state-of-the-art NLU systems show that the method makes it possible to learn competitive NLU models and opens the way to experiments on speech corpora.
Machine Learning Based Medicine Distribution System

In this thesis, an intelligent medicine recognition method for medicine vending machines has been proposed. Considering that the name of the medicine is generally the largest character in the medicine box, the medicine name recognition can be converted into the largest character recognition in the picture in this thesis. First, we used Support Vector Machine (SVM) and Connected Component to determine the text region, and found the largest connected region to locate the medicine name. Secondly, we use the method of “fragment link” for text segmentation, which divides the text into two elements: fragment and link, and combines the whole word according to the set rules. Finally, we can directly use Optical Character Recognition (OCR)software for character recognition. Experiments show that this method has a high accuracy of medicine identification.
OPTool—An optimization toolbox for iterative algorithms

The current state-of-the-art in iterative optimization algorithms for differentiable cost functions is scattered throughout the literature, which hinders their comparison for the specific program at hand. Depending on the research area, theoretical optimal parameters and convergence rates are available in different formulations. Consequently, this toolbox aims at providing a benchmarking software for the various gradient-descent-based algorithms and implements functions to return the optimal parameters whenever possible. Researchers can focus on the development of new algorithms and test them against the ones present in the literature and made them available under a common framework. The structure of the software is tailored such that novel contributions can be easily added through the design of a function implementing a single step of the algorithm. The ease of usage is illustrated by examples in the literature.
Regressive Whale Optimization for Workflow Scheduling in Cloud Computing

Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three major constraints, such as resource utilization, energy, and the Quality of Service (QoS). Therefore, the proposed task scheduling requires minimum time and cost for executing the task in the virtual machines. The performance of the proposed method is analyzed using the four experimental setups, and the results of the analysis prove that the proposed multi-objective task scheduling method performs well than the existing methods. The evaluation metrics considered for analyzing the performance of the proposed workflow scheduling method are resource utilization, energy, cost, and time. Resource utilization is the process of making the most of the resources available for performing tasks. Energy is the quantitative property of the resource to perform tasks. The proposed method attains the maximum resource utilization at a rate of 0.0334, minimal rate of energy, scheduling cost, and time as 0.2291, 0.0181, and 0.0007, respectively.
Integrating Digital Forensics and Digital Discovery to Improve E-mail Communication Analysis in Organisations

In Digital Forensics andSathiyanarayanan, Mithileysh Digital Discovery, e-mail communication analysisFadahunsi, Odunayo has become an important part of the litigation process. Integrating these two can improve e-mail communication analysis in organisations and help both legal and technical professionals achieve goals of conducting analysis in a manner that is legally defensible and forensically sound. In this forensic discovery process, digital evidence plays an increasingly vital role in the court to prove or disprove an individual or a group of individual’s actions in order to secure a conviction. However, e-mail investigations are becoming increasingly complex and time consuming due to the multifaceted large data involved, and investigators find themselves unable to explore and conduct analysis in an appropriately efficient and effective manner. This situation has prompted the need for improved e-mail communication analysis that can be capable of handling large and complex investigations to detect suspicious activities. So, our interactive visualisations aims to improve digital forensics discovery ability to search and analyse a vast amount of e-mail information quickly and efficiently.
Tuning of Control Parameters of Grey Wolf Optimizer using Fuzzy Inference

The Grey Wolf Optimizer (GWO) is a recent metaheuristic that can be explored in many applications. This paper proposes a mechanism to tune the control parameters that influence the hunting process in the GWO in order to improve its efficiency. This adjustment is made by a fuzzy inference system that uses the normalized fitness value of each wolf and the hunting mechanism control parameters of the GWO. The proposed fuzzy mechanism is tested and compared with the conventional GWO and another version that uses a fuzzy system as input information the ratio of the current iteration number and the maximum number of iterations. For performance analysis of the proposed fuzzy mechanism, all tested optimizers in ten benchmark optimization functions ran 1000 times. Simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional GWO and it is competitive in relation to another fuzzy version adopted in the GWO design.
AdaBits: Neural Network Quantization with Adaptive Bit-Widths

Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource budgets. In this paper, we investigate a novel option to achieve this goal by enabling adaptive bit-widths of weights and activations in the model. We first examine the benefits and challenges of training quantized model with adaptive bit-widths, and then experiment with several approaches including direct adaptation, progressive training and joint training. We discover that joint training is able to produce comparable performance on the adaptive model as individual models. We further propose a new technique named Switchable Clipping Level (S-CL) to further improve quantized models at the lowest bit-width. With our proposed techniques applied on a bunch of models including MobileNet-V1/V2 and ResNet-50, we demonstrate that bit-width of weights and activations is a new option for adaptively executable deep neural networks, offering a distinct opportunity for improved accuracy-efficiency trade-off as well as instant adaptation according to the platform constraints in real-world applications.
NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension

Real-world question answering systems often retrieve potentially relevant documents to a given question through a keyword search, followed by a machine reading comprehension (MRC) step to find the exact answer from them. In this process, it is essential to properly determine whether an answer to the question exists in a given document. This task often becomes complicated when the question involves multiple different conditions or requirements which are to be met in the answer. For example, in a question What was the projection of sea level in the assessment report?, the answer should properly satisfy several conditions, such as increases (but not decreases) and fourth (but not third). To address this, we propose a neural question requirement inspection model called NeurQuRI that extracts a list of conditions from the question, each of which should be satisfied by the candidate answer generated by an MRC model. To check whether each condition is met, we propose a novel, attention-based loss function. We evaluate our approach on SQuAD 2.0 dataset by integrating the proposed module with various MRC models, demonstrating the consistent performance improvements across a wide range of state-of-the-art methods.
MMLUP: Multi-source &amp; Multi-task Learning for User Profiles in Social Network

With the rapid development of the mobile Internet, users generate massive data in different forms in social network every day, and different characteristics of users are reflected by these social media data. How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services, marketing, and recommendation systems. In this paper, we propose Multi-source & Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user. Firstly, we design their own feature extraction models for multiple heterogeneous data sources. Secondly, we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning. Thirdly, we design each task’s own unique presentation layer for discriminant output of specific-task. Finally, we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task. Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender, age and region of social media users.
Comparing Artificial Intelligence Platforms for Histopathologic Cancer Diagnosis.

Two machine learning platforms were successfully used to provide diagnostic guidance in the differentiation between common cancer conditions in veteran populations.
2019 CAD Contest: Logic Regression on High Dimensional Boolean Space

Using sampling patterns is always a powerful method to save efforts for the problems with large input space since it can quickly help identify cases' properties. The meaning behind these sampling results can be informative and useful, but these results may be unreadable to humans. Therefore, in 2019 CAD Contest [1], we formulate a problem of “logic regression on high dimensional Boolean space”. Given a blackboxed input-output relation generator, contestants are required to find a minimal Boolean logic circuit which matches the input-output relations of the given generator. In this contest, we provide benchmarks that address industrial applications of logic regression with several scenarios and different scales of input space to evaluate contestants' algorithms. We expect that the contest results can help industrial application and attract interesting academic research.
Realistic Sonar Image Simulation Using Generative Adversarial Network

Sonar sensors are widely utilized underwater because they can observe long-ranged objects and are tolerant to measurement conditions, such as turbidity and light conditions. However, sonar images have low quality and hard to collect, so development and application of sonar-based algorithms are difficult. This paper proposes a method to generate realistic sonar images or to segment real sonar image, to better utilize the sonar sensors. A simple sonar image simulator was implemented using a ray-tracing method. The simulator could calculate semantic information of real sonar images including properties of highlight, background, and shadow regions. Then, a generative adversarial network translated the simulated images into more realistic images or real sonar images into simulated-like images. The proposed method can be used to augment or pre-process sonar images.
SGAP-Net: Semantic-Guided Attentive Prototypes Network for Few-Shot Human-Object Interaction Recognition

Extreme instance imbalance among categories and combinatorial explosion make the recognition of Human-Object Interaction (HOI) a challenging task. Few studies have addressed both challenges directly. Motivated by the success of few-shot learning that learns a robust model from a few instances, we formulate HOI as a few-shot task in a meta-learning framework to alleviate the above challenges. Due to the fact that the intrinsic characteristic of HOI is diverse and interactive, we propose a Semantic-Guided Attentive Prototypes Network (SGAP-Net) to learn a semantic-guided metric space where HOI recognition can be performed by computing distances to attentive prototypes of each class. Specifically, the model generates attentive prototypes guided by the category names of actions and objects, which highlight the commonalities of images from the same class in HOI. In addition, we design a novel decision method to alleviate the biases produced by different patterns of the same action in HOI. Finally, in order to realize the task of few-shot HOI, we reorganize two HOI benchmark datasets, i.e., HICO-FS and TUHOI-FS, to realize the task of few-shot HOI. Extensive experimental results on both datasets have demonstrated the effectiveness of our proposed SGAP-Net approach.
A Density-based Clustering Algorithm Using Adaptive Parameter K-Reverse Nearest Neighbor

Density-based clustering method DBSCAN is one of the most widely used clustering methods, this algorithm does not need to input the number of clustering, and can find clusters of any shape. However, DBSCAN algorithm requires two parameters, with different parameters, the clustering results are quite different. This paper proposes a density-based clustering algorithm using adaptive parameter k-reverse nearest neighbor: ARKNN-DBSCAN, which can effectively identify and separate clusters with different densities by analyzing the correlation through the reverse k-nearest neighbor of the observation in the dataset without input parameters. Our results show that this algorithm is more accurate than other density-based clustering algorithms using adaptive parameter.
Integrating AR and VR for Mobile Remote Collaboration

In many complex tasks, a remote expert may need to assist a local user or to guide his or her actions in the local user's environment. Existing solutions also allow multiple users to collaborate remotely using high-end Augmented Reality (AR) and Virtual Reality (VR) head-mounted displays (HMD). In this paper, we propose a portable remote collaboration approach, with the integration of AR and VR devices, both running on mobile platforms, to tackle the challenges of existing approaches. The AR mobile platform processes the live video and measures the 3D geometry of the local environment of a local user. The 3D scene is then transited and rendered in the remote side on a mobile VR device, along with a simple and effective user interface, which allows a remote expert to easily manipulate the 3D scene on the VR platform and to guide the local user to complete tasks in the local environment.
Emerging Trends in Usable Security and Privacy

Abstract New technologies are constantly becoming part of our everyday life. At the same time, designers and developers still often do not consider the implications of their design choices on security and privacy. For example, new technologies generate sensitive data, enable access to sensitive data, or can be used in malicious ways. This creates a need to fundamentally rethink the way in which we design new technologies. While some of the related opportunities and challenges have been recognized and are being addressed by the community, there is still a need for a more holistic understanding. In this editorial, we will address this by (1) providing a brief historical overview on the research field of ‘Usable Security and Privacy’; (2) deriving a number of current and future trends; and (3) briefly introducing the articles that are part of this special issue and describing how they relate to the current trends and what researchers and practitioners can learn from them.
Performance Assessment of Multiple Machine Learning Classifiers for Detecting the Phishing URLs

In the field of information security, phishing URLs detection and prevention has recently become egregious. For detecting, phishing attacks several anti-phishing systems have already been proposed by researchers. The performance of those systems can be affected due to the lack of proper selection of machine learning classifiers along with the types of feature sets. A details investigation on machine learning classifiers (KNN, DT, SVM, RF, ERT and GBT) along with three publicly available datasets with multidimensional feature sets have been presented on this paper. The performance of the classifiers has been evaluated by confusion matrix, precision, recall, F1-score, accuracy and misclassification rate. The best output obtained from Random Forest and Extremely Randomized Tree with dataset one and three (binary class feature set) of 97% and 98% accuracy accordingly. In multiclass feature set (dataset two), Gradient Boosting Tree provides highest performance with 92% accuracy.
A Social Robot-Based Platform for Prevention of Childhood Obesity

Childhood obesity is a major public health concern since it is associated with obesity during adulthood and the occurrence of diseases such as diabetes and cancer. In this paper we present a platform for childhood obesity prevention, which is based on interactive games played between a social robot and the child. The interactions between the robot and the child occur within four distinct stages: a) Familiarization with the robot, b) education and knowledge assessment (e.g., for a balanced diet), c) behavioural data collection & empowerment, and d) personalized goal-setting. The platform was evaluated in terms of usability with 10 participants achieving a System Usability Score (SUS) of 93.8. Overall, our results show the feasibility of the adopted approach and the potential of social robots in playing a major role for promoting healthy behaviours and preventing childhood obesity.
Individual differences in the desirable difficulty effect during lexical acquisition.

Reading in difficult or novel fonts results in slower and less efficient reading (Slattery & Rayner, 2010); however, these fonts may also lead to better learning and memory (Diemand-Yauman, Oppenheimer, & Vaughan, 2011). This effect is consistent with a desirable difficulty effect such that more effort during encoding results in better long term retention (Bjork, 1994). The effect is robust and found in many contexts; however, it has not yet been applied to the process of lexical acquisition. The purpose of the current study was to investigate whether readers would learn words better when presented in Sans Forgetica, a font designed to induce desirable difficulty. One hundred sixty participants were randomly assigned to one font type and read sentences with 15 very low-frequency English words presented in two different informative contexts while their eye movements were monitored. They completed a spelling skill assessment and tests of their orthographic and semantic acquisition of the words. Linear mixed effects analyses were conducted and indicate that high-skill spellers learned the spelling and meaning of words better in the desirable difficulty font, but low-skill spellers did not show the same benefit. This pattern highlights the importance of individual differences in learning such that a desirable difficulty for one reader may be an undesirable difficulty for another. (PsycINFO Database Record (c) 2020 APA, all rights reserved).