Reviewerly/exHarmony huggingface dataset:
ìSPEAK: Using Mobile-Based Online Learning Course to Learn ‘English for the Workplace’

&lt;p class="0abstract"&gt;Due to the rise of the digital era, various industries demand employees with a proficient communicative English skills, but many are still not up to par with the demands. One solution for this is to provide communicative English trainings for employees, especially support staffs, yet face-to-face trainings have many limitations, which can be overcome through mobile learning. Due to the fact that mobile learning can be accessed anytime and anywhere by anyone, the Massive Online Open Courses (MOOC) is one of the platforms that can be used to curb the issue. This paper aimed to identify the perceptions of support staffs towards using ‘ìSPEAK: English for the Workplace’ in MOOC. A total of 30 respondents were selected through purposive sampling and a survey through a 4-point Likert scale questionnaire was given out. The main findings showed that overall, the support staffs have a positive attitude towards using ìSPEAK. Nevertheless, half of them were unsure of the reliability of the sources available in the course. However, the findings of this study implied that ìSPEAK could be one of the training alternatives for support staffs to improve and increase their communicative ability. Future research can look into designing and developing various modules of communicative English in various mobile learning platforms, which could cater to different levels of proficiency of learners.&lt;/p&gt;
DeepSqueeze: Deep Semantic Compression for Tabular Data

With the rapid proliferation of large datasets, efficient data compression has become more important than ever. Columnar compression techniques (e.g., dictionary encoding, run-length encoding, delta encoding) have proved highly effective for tabular data, but they typically compress individual columns without considering potential relationships among columns, such as functional dependencies and correlations. Semantic compression techniques, on the other hand, are designed to leverage such relationships to store only a subset of the columns necessary to infer the others, but existing approaches cannot effectively identify complex relationships across more than a few columns at a time. We propose DeepSqueeze, a novel semantic compression framework that can efficiently capture these complex relationships within tabular data by using autoencoders to map tuples to a lower-dimensional representation. DeepSqueeze also supports guaranteed error bounds for lossy compression of numerical data and works in conjunction with common columnar compression formats. Our experimental evaluation uses real-world datasets to demonstrate that DeepSqueeze can achieve over a 4x size reduction compared to state-of-the-art alternatives.
Integrating automatic question generation with computerised adaptive test

Abstract The present study focuses on the integration of an automatic question generation (AQG) system and a computerised adaptive test (CAT). We conducted two experiments. In the first experiment, we administered sets of questions to English learners to gather their responses. We further used their responses in the second experiment, which is a simulation-based experiment of the AQG and CAT integration. We proposed a method to integrate them with a predetermined item difficulty that enables to integrate AQG and CAT without administering the items in a pretesting. The result showed that all CAT simulations performed better than the baseline, a linear test, in estimating the test taker’s true proficiency.
Image Retargeting Quality Assessment Based on Registration Confidence Measure and Noticeability-Based Pooling

Nowadays, image retargeting approaches have been widely applied to adapt images of various resolutions to heterogenous display devices. To assess the quality of the retargeted images, image retargeting quality assessment (IRQA) has emerged as a critical problem in image quality assessment. In this paper, we address the IRQA problem with a newly proposed framework based on registration confidence measurement (RCM) and noticeability-based pooling (NBP). First, we define the RCM to evaluate the accuracy of image registration, which aligns scenes between the original and retargeted images. We then integrate the proposed RCM with the computed local fidelity of each image block to alleviate the negative influence of inaccurate registration on fidelity measurements. Meanwhile, we present a visual attention fusion (VAF) framework to enhance faces and lines in the saliency map, which are observed to be highly sensitive in the human visual system (HVS). Finally, we propose the NBP strategy, which aggregates the local fidelity of each image block into the overall quality of the retargeted image. Specifically, the NBP strategy sets larger quality ranges for the regions where the visual distortions are more accessible to HVS to reflect the easy noticeability of these regions. Experimental results on the MIT RetargetMe and CUHK datasets demonstrate that the proposed IRQA metric based on RCM and NBP outperforms the state-of-the-art IRQA metrics.
Believe in Literature: Character Education for Indonesia's Youth

Character education has become a significant challenge in Indonesia, as youths have become involved in all sorts of criminal and illicit acts, including casual sex, narcotics, brawls, street racing, and gambling.Even though character education is recognized as necessary for preparing the country's future generations, the current system has been incapable of resolving the issues facing Indonesia's youths or instilling them with desired character traits and values.This article examines literature's potential as an alternative approach and medium for character education.It provides a detailed map of how literary character education has been conducted, as well as how character traits and values have been identified and communicated by students.Data were collected by observing the learning process in a classroom and conducting interviews with teachers and students.Further observation was conducted to understand students' experiences with character education, both within and without the classroom.This study found that through the application of literature as a medium for and approach to character education, students' cognitive, affective, and psychomotor intelligences were improved.Literature contains specific messages and values that help students attain intellectual, emotional, and spiritual maturity.As such, it may be surmised that textual reading and analysis of literature, as a learning medium, can transfer desired values to students and enable them to contextualize said values.To complement the use of literature, this study recommends the development of alternative approaches to character education that make effective evaluation possible.
An Intelligent Recommender System Based on Association Rule Analysis for Requirement Engineering

Requirement gathering is a vital step in software engineering. Even though many recent researches concentrated on the improvement of the requirement gathering process, many of their works lack completeness especially when the number of users is large. Data Mining techniques have been recently employed in various domains with promising results. In this work, we propose an intelligent recommender system for requirement engineering based on association rule analysis, which is a main category in Data Mining. Such recommender would contribute in enhancing the accuracy of the gathered requirements and provide more comprehensive results. Conducted experiments in this work prove that FP Growth outperformed Apriori in terms of execution and space consumption, while both methods were efficient in term of accuracy.
An Adaptive Approach on Credit Card Fraud Detection Using Transaction Aggregation and Word Embeddings

Due to the surge of interest in online retailing, the use of credit cards has been rapidly expanded in recent years. Stealing the card details to perform online transactions, which is called fraud, has also seen more frequently. Preventive solutions and instant fraud detection methods are widely studied due to critical financial losses in many industries. In this work, a Gradient Boosting Tree (GBT) model for the real-time detection of credit card frauds on the streaming Card-Not-Present (CNP) transactions is investigated with the use of different attributes of card transactions. Numerical, hand-crafted numerical, categorical and textual attributes are combined to form a feature vector to be used as a training instance. One of the contributions of this work is to employ transaction aggregation for the categorical values and inclusion of vectors from a character level word embedding model which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feature engineering strategy and the automated training set generation methodology are evaluated on the real credit card transactions.
SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications

The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the "on" and "off" state of global positioning system sensor in smartphones. To address this problem, this paper proposes "SenseCrypt", a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.
Power entity recognition based on bidirectional long short-term memory and conditional random fields

With the application of artificial intelligence technology in the power industry, the knowledge graph is expected to play a key role in power grid dispatch processes, intelligent maintenance, and customer service response provision. Knowledge graphs are usually constructed based on entity recognition. Specifically, based on the mining of entity attributes and relationships, domain knowledge graphs can be constructed through knowledge fusion. In this work, the entities and characteristics of power entity recognition are analyzed, the mechanism of entity recognition is clarified, and entity recognition techniques are analyzed in the context of the power domain. Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated, and the two methods are comparatively analyzed. The results indicated that the CRF model, with an accuracy of 83%, can better identify the power entities compared to the BLSTM. The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.
Implementation of Decentralized Blockchain E-voting

E-voting reduced the cost of election and provided convenience to some extent as compared to the traditional approach of pen and paper but it was considered to be unreliable as anyone having access to the machine physically can obstruct the machine and alter the votes. Also in order to control the e
Generating universal adversarial perturbation with ResNet

Adversarial machine learning, as a research area, has received a great deal of attention in recent years. Much of this attention has been devoted to a phenomenon called adversarial perturbation, which is human-imperceptible and can be used to craft adversarial examples. The deep neural networks are vulnerable to adversarial examples, which raises security concerns on learning algorithms due to the potentially severe consequences. It was shown there exist universal perturbations that are image-agnostic can fool the network when added to the majority of images. Since different attack strategies proposed for generating universal perturbation are still suffering from attack success rate, attack efficiency, and transferability. In this paper, we design an attack framework that uses a residual network (ResNet) to create universal perturbation. We introduce a trainable residual network generator that converts random noise into universal adversarial perturbation, which can be used to efficiently generate perturbations for any instance after being trained. Unlike traditional methods, moreover, we use a loss network to guarantee the similarity of images in content. The new generator structure and objective function make our method achieve better attack results than the existing methods. A variety of experiments conducted on the CIFAR-10 dataset reveal that our proposed attack framework constitutes an advance in the creation of universal adversarial perturbation, as it can achieve a success rate of 89%, which outperforms the similar methods, along with low perturbation norms.
Deep Ensemble Feature Network for Gastric Section Classification

In this paper, we propose a novel deep ensemble feature (DEF) network to classify gastric sections from endoscopic images. Different from recent deep ensemble learning methods, which need to train deep features and classifiers individually to obtain fused classification results, the proposed method can simultaneously learn the deep ensemble feature from arbitrary number of convolutional neural networks (CNNs) and the decision classifier in an end-to-end trainable manner. It comprises two sub networks, the ensemble feature network and the decision network. The former sub network learns the deep ensemble feature from multiple CNNs to represent endoscopic images. The latter sub network learns to obtain the classification labels by using the deep ensemble feature. Both sub networks are optimized based on the proposed ensemble feature loss and the decision loss which guide the learning of deep features and decisions. As shown in the experimental results, the proposed method outperforms the state-of-the-art deep learning, ensemble learning, and deep ensemble learning methods.
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning

A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://github.com/DevinJake/MARL.
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering

We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue.First, three language modeling tasks are used to pre-train the transformers, token-and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts.Then, multitask learning between the utterance prediction and the token span prediction is applied to finetune for span-based question answering (QA).Our approach is evaluated on the FRIENDSQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.
A Two-Step Rule-Extraction Technique for a CNN

The explanation of the decisions provided by a model are crucial in a domain such as medical diagnosis. With the advent of deep learning, it is very important to explain why a classification is reached by a model. This work tackles the transparency problem of convolutional neural networks(CNNs). We propose to generate propositional rules from CNNs, because they are intuitive to the way humans reason. Our method considers that a CNN is the union of two subnetworks: a multi-layer erceptron (MLP) in the fully connected layers; and a subnetwork including several 2D convolutional layers and max-pooling layers. Rule extraction exhibits two main steps, with each step generating rules from each subnetwork of the CNN. In practice, we approximate the two subnetworks by two particular MLP models that makes it possible to generate propositional rules. We performed the experiments with two datasets involving images: MNISTdigit recognition; and skin-cancer diagnosis. With high fidelity, the extracted rules designated the location of discriminant pixels, as well as the conditions that had to be met to achieve the classification. We illustrated several examples of rules by their centroids and their discriminant pixels.
Multi-Channel Graph Neural Networks

The classification of graph-structured data has be-come increasingly crucial in many disciplines. It has been observed that the implicit or explicit hierarchical community structures preserved in real-world graphs could be useful for downstream classification applications. A straightforward way to leverage the hierarchical structure is to make use the pooling algorithms to cluster nodes into fixed groups, and shrink the input graph layer by layer to learn the pooled graphs.However, the pool shrinking discards the graph details to make it hard to distinguish two non-isomorphic graphs, and the fixed clustering ignores the inherent multiple characteristics of nodes. To compensate the shrinking loss and learn the various nodes’ characteristics, we propose the multi-channel graph neural networks (MuchGNN). Motivated by the underlying mechanisms developed in convolutional neural networks, we define the tailored graph convolutions to learn a series of graph channels at each layer, and shrink the graphs hierarchically to en-code the pooled structures. Experimental results on real-world datasets demonstrate the superiority of MuchGNN over the state-of-the-art methods.
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation

Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue. Existing methods largely rely on either external knowledge or statistical bias information to alleviate this problem. In this paper, we tackle this issue from another two aspects: (1) scene-object interaction aiming at learning specific knowledge from a scene via an additive attention mechanism; and (2) long-tail knowledge transfer which tries to transfer the rich knowledge learned from the head into the tail. Extensive experiments on the benchmark dataset Visual Genome on three tasks demonstrate that our method outperforms current state-of-the-art competitors. Our source code is available at https://github.com/htlsn/issg.
Leveraging Document-Level Label Consistency for Named Entity Recognition

Document-level label consistency is an effective indicator that different occurrences of a particular token sequence are very likely to have the same entity types. Previous work focused on better context representations and used the CRF for label decoding. However, CRF-based methods are inadequate for modeling document-level label consistency. This work introduces a novel two-stage label refinement approach to handle document-level label consistency, where a key-value memory network is first used to record draft labels predicted by the base model, and then a multi-channel Transformer makes refinements on these draft predictions based on the explicit co-occurrence relationship derived from the memory network. In addition, in order to mitigate the side effects of incorrect draft labels, Bayesian neural networks are used to indicate the labels with a high probability of being wrong, which can greatly assist in preventing the incorrect refinement of correct draft labels. The experimental results on three named entity recognition benchmarks demonstrated that the proposed method significantly outperformed the state-of-the-art methods.
‘AI-Generated Inventions’: Time to Get the Record Straight?

Abstract This article attempts to clarify the notion of an ‘AI-generated’ invention, an issue which has triggered an intense debate on the future of patent law and policy. While there is a general consensus that such inventions are incompatible with the concept of human inventorship, it remains largely unclear to what extent concerns regarding ‘non-human’ ingenuity can be justified. Most uncertain is how AI ‘autonomously generates’ inventions, and in what way 'AI-generated' inventions differ from inventions developed with the aid of AI. Drawing on the extensive literature review, this article depicts AI techniques as methods of computational problem solving. It emphasises that such methods should not be equated with a computer’s ‘cognitive autonomy’. Further, it clarifies that the types of AI that have been most debated in the patent law literature ‒ artificial neural networks and evolutionary algorithms ‒ essentially require detailed instructions that determine how the relation between inputs and outputs is derived through computation. Accordingly, it is argued that, as long as computers rely on instructions defined by a human as to how solve a problem, the separation between human and non-human (algorithmic) ingenuity is, in itself, artificial. Ultimately, the article calls for a broader technical inquiry that would elucidate the relevance of the currently debated normative concerns over ‘non-human inventorship’ against the background of the technological state of the art.