Reviewerly/exHarmony huggingface dataset:
Fast WordPiece Tokenization

Tokenization is a fundamental preprocessing step for almost all NLP tasks.In this paper, we propose efficient algorithms for the Word-Piece tokenization used in BERT, from singleword tokenization to general text (e.g., sentence) tokenization.When tokenizing a single word, WordPiece uses a longest-matchfirst strategy, known as maximum matching.The best known algorithms so far are ( 2 ) (where is the input length) or ( ) (where is the maximum vocabulary token length).We propose a novel algorithm whose tokenization complexity is strictly ( ).Our method is inspired by the Aho-Corasick algorithm.We introduce additional linkages on top of the trie built from the vocabulary, allowing smart transitions when the trie matching cannot continue.For general text, we further propose an algorithm that combines pre-tokenization (splitting the text into words) and our linear-time Word-Piece method into a single pass.Experimental results show that our method is 8.2x faster than HuggingFace Tokenizers and 5.1x faster than TensorFlow Text on average for general text tokenization.
Low-Cost CNN for Automatic Violence Recognition on Embedded System

Due to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models’ validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.
The performance of BERT as data representation of text clustering

Abstract Text clustering is the task of grouping a set of texts so that text in the same group will be more similar than those from a different group. The process of grouping text manually requires a significant amount of time and labor. Therefore, automation utilizing machine learning is necessary. One of the most frequently used method to represent textual data is Term Frequency Inverse Document Frequency (TFIDF). However, TFIDF cannot consider the position and context of a word in a sentence. Bidirectional Encoder Representation from Transformers (BERT) model can produce text representation that incorporates the position and context of a word in a sentence. This research analyzed the performance of the BERT model as data representation for text. Moreover, various feature extraction and normalization methods are also applied for the data representation of the BERT model. To examine the performances of BERT, we use four clustering algorithms, i.e., k-means clustering, eigenspace-based fuzzy c-means, deep embedded clustering, and improved deep embedded clustering. Our simulations show that BERT outperforms TFIDF method in 28 out of 36 metrics. Furthermore, different feature extraction and normalization produced varied performances. The usage of these feature extraction and normalization must be altered depending on the text clustering algorithm used.
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention

Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leveraging the more comprehensive information from both bird's eye view (BEV) and range view (RV). These multi-view methods either refine the proposals predicted from single view via fused features, or fuse the features without considering the global spatial con-text; their performance is limited consequently. In this paper, we propose to adaptively fuse multi-view features in a global spatial context via Dual Cross- VIew SpaTial Attention (VISTA). The proposed VISTA is a novel plug-and-play fusion module, wherein the multi-layer perceptron widely adopted in standard attention modules is replaced with a convolutional one. Thanks to the learned attention mechanism, VISTA can produce fused features of high quality for prediction of proposals. We decouple the classification and regression tasks in VISTA, and an additional constraint of attention variance is applied that enables the attention module to focus on specific targets instead of generic points. We conduct thorough experiments on the benchmarks of nuScenes and Waymo; results confirm the efficacy of our designs. At the time of submission, our method achieves 63.0% in overall mAP and 69.8% in NDS on the nuScenes benchmark, outperforming all published methods by up to 24% in safety-crucial categories such as cyclist.
Applications of deep learning for phishing detection: a systematic literature review

Phishing attacks aim to steal confidential information using sophisticated methods, techniques, and tools such as phishing through content injection, social engineering, online social networks, and mobile applications. To avoid and mitigate the risks of these attacks, several phishing detection approaches were developed, among which deep learning algorithms provided promising results. However, the results and the corresponding lessons learned are fragmented over many different studies and there is a lack of a systematic overview of the use of deep learning algorithms in phishing detection. Hence, we performed a systematic literature review (SLR) to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications. We address nine research questions and provide an overview of how deep learning algorithms have been used for phishing detection from several aspects. In total, 43 journal articles were selected from electronic databases to derive the answers for the defined research questions. Our SLR study shows that except for one study, all the provided models applied supervised deep learning algorithms. The widely used data sources were URL-related data, third party information on the website, website content-related data, and email. The most used deep learning algorithms were deep neural networks (DNN), convolutional neural networks, and recurrent neural networks/long short-term memory networks. DNN and hybrid deep learning algorithms provided the best performance among other deep learning-based algorithms. 72% of the studies did not apply any feature selection algorithm to build the prediction model. PhishTank was the most used dataset among other datasets. While Keras and Tensorflow were the most preferred deep learning frameworks, 46% of the articles did not mention any framework. This study also highlights several challenges for phishing detection to pave the way for further research.
Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions

Although fully online learning is now the ‘new normal’ in many parts of the world, its implementation is often beset by challenges such as the lack of student self-regulation, and the sense of isolation. In this paper, we explored the use of chatbots to support student goal setting (Study 1) and social presence (Study 2) in online activities. In Study 1, participants in a fully online course were invited to complete a goal setting activity prior to attending class via a goal-setting chatbot. The chatbot engaged participants with five questions developed based on the SMART (specific, measurable, achievable, realistic, and timely) goal setting framework. In Study 2, English-as-Foreign-Language participants in a fully online course were tasked to complete listening practices. The learning buddy chatbot was designed based on the social presence framework (interpersonal communication, open communication, cohesive communication) to guide students through listening exercises. In both Study 1 and 2, we evaluated participants’ behavioral engagement by measuring their conversation records with the chatbots, as well as participants’ perceived usefulness and ease of use of the chatbots. We also gathered in-depth interview data concerning the participants’ perceptions of interacting with the chatbots. Overall, our findings found positive learner experiences with both chatbots with regard to the chatbots’ perceived usefulness and perceived ease of use. We also provided suggestions for instructors to apply chatbots in teaching and learning.
Empowering learners for the age of artificial intelligence

Although studied for decades by the research community, artificial intelligence (AI) in education has recently sparked much public debate with the wide-spread popularity of systems such as ChatGPT and DALL-E. Existing literature offers a wealth of research on design, deploying and evaluating AI-driven systems in education. However, the challenges related to the growing influence of AI in the society, calls for revisiting research foundations of AI in education in order to inform decision-making in policy and guide future research. This special issue of Computers & Education: Artificial Intelligence brings 11 papers that explore the theme of empowering learners for the age of AI. The contributions of these papers can be broadly grouped into seven main themes: intersection between AI and humans that looks at the space of coordination; assessment that explores challenges and opportunities afforded by the use of AI in educational assessment; explainability in AI as a critical need for humans in education to understand and trust AI; design for learning that offers principles for designing AI-driven systems and educational opportunities; conceptual AI and learning exploring the need for the development of new theories of learning and their connections with existing theoretical foundations in education; accurate predictions and their role in future education; and applications of AI in classrooms and educational systems. The findings of these studies highlight pressing research and policies challenges and opportunities that arise with the broad penetration of AI in education. They also emphasize the need for future research that addresses issues of ethics, bias and farness in the use of AI in education; challenges associated with data sources and ownership as the key fuel and enabler of present-day AI generation; AI literacies and competencies of stakeholders who use and are impacted by AI in education; identification of effective learning and teaching practices with the use of AI; and policy development to increase responsiveness of education systems to rapid changes driven by AI.
Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field.
Integrity verification for digital Holy Quran verses using cryptographic hash function and compression

Data Integrity is one of the primary concepts in information security. The main task of the cryptographic hash function is to assure the integrity of the transmitted data. In this work, integrity verification methods for digital verses of the Holy Quran are proposed. The first method uses cryptographic hash functions and generate the hash table of the Holy Quran. SHA256 and RIPEMD160 hash functions are chosen in this work. The second method is a single compression technique which manipulates data during the run time. The compression method makes use of the two bytes in Unicode UTF-8 for the Arabic characters set. The results show that for a digital copy of the Holy Quran encoded in Unicode UTF-8, the sizes of the hash tables generated by SHA256 and RIPEMD160 are 84.73% and 90.46% (6.55-fold and 10.48-fold) smaller respectively. The compressed version of the Holy Quran has a size of 47.24% (1.9-fold) smaller than the original copy. In addition, a second preimage attack on a hash table for the digital Holy Quran produced by using CRC32 hash function is conducted to study the impact of such attacks on the two hash tables proposed in this work.
Unified Fine-Grained Access Control for Personal Health Records in Cloud Computing

Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for fine-grained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access sub-policy, which is based on different patients' access policies. Then, the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, we prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.
Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes

We propose a deep convolutional neural network (CNN) method for natural image matting. Our method takes multiple initial alpha mattes of the previous methods and normalized RGB color images as inputs, and directly learns an end-to-end mapping between the inputs and reconstructed alpha mattes. Among the various existing methods, we focus on using two simple methods as initial alpha mattes: the closed-form matting and KNN matting. They are complementary to each other in terms of local and nonlocal principles. A major benefit of our method is that it can "recognize" different local image structures and then combine the results of local (closed-form matting) and nonlocal (KNN matting) mattings effectively to achieve higher quality alpha mattes than both of the inputs. Furthermore, we verify extendability of the proposed network to different combinations of initial alpha mattes from more advanced techniques such as KL divergence matting and information-flow matting. On the top of deep CNN matting, we build an RGB guided JPEG artifacts removal network to handle JPEG block artifacts in alpha matting. Extensive experiments demonstrate that our proposed deep CNN matting produces visually and quantitatively high-quality alpha mattes. We perform deeper experiments including studies to evaluate the importance of balancing training data and to measure the effects of initial alpha mattes and also consider results from variant versions of the proposed network to analyze our proposed DCNN matting. In addition, our method achieved high ranking in the public alpha matting evaluation dataset in terms of the sum of absolute differences, mean squared errors, and gradient errors. Also, our RGB guided JPEG artifacts removal network restores the damaged alpha mattes from compressed images in JPEG format.
Multi-Matching Network for Multiple Choice Reading Comprehension

Multiple-choice machine reading comprehension is an important and challenging task where the machine is required to select the correct answer from a set of candidate answers given passage and question. Existing approaches either match extracted evidence with candidate answers shallowly or model passage, question and candidate answers with a single paradigm of matching. In this paper, we propose Multi-Matching Network (MMN) which models the semantic relationship among passage, question and candidate answers from multiple different paradigms of matching. In our MMN model, each paradigm is inspired by how human think and designed under a unified compose-match framework. To demonstrate the effectiveness of our model, we evaluate MMN on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Empirical results show that our proposed model achieves a significant improvement compared to strong baselines and obtains state-of-the-art results.
<i>Doccurate</i>: A Curation-Based Approach for Clinical Text Visualization

Before seeing a patient, physicians seek to obtain an overview of the patient's medical history. Text plays a major role in this activity since it represents the bulk of the clinical documentation, but reviewing it quickly becomes onerous when patient charts grow too large. Text visualization methods have been widely explored to manage this large scale through visual summaries that rely on information retrieval algorithms to structure text and make it amenable to visualization. However, the integration with such automated approaches comes with a number of limitations, including significant error rates and the need for healthcare providers to fine-tune algorithms without expert knowledge of their inner mechanics. In addition, several of these approaches obscure or substitute the original clinical text and therefore fail to leverage qualitative and rhetorical flavours of the clinical notes. These drawbacks have limited the adoption of text visualization and other summarization technologies in clinical practice. In this work we present Doccurate, a novel system embodying a curation-based approach for the visualization of large clinical text datasets. Our approach offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text. We discuss findings of a formal qualitative evaluation conducted with 6 domain experts, shedding light onto physicians' information needs, perceived strengths and limitations of automated tools, and the importance of customization while balancing efficiency. We also present use case scenarios to showcase Doccurate's envisioned usage in practice.
Keyed Hash Function Using Hyper Chaotic System With Time-Varying Parameters Perturbation

Secure hash function plays an important role in cryptography. This paper constructs a hash algorithm using the hyperchaotic Lorenz system, which serves as a sponge function to absorb input message via multiple parameters time-varying perturbation. First, the input message is divided into four 1D arrays, to generate four perturbation sequences via parameter updating rule, the perturbed parameters are still within their significant intervals, to make the system remain a hyperchaotic state. Each iteration of the discrete hyperchaotic system uses updated parameters, and the last state variable values are extracted to generate a len-bit hash value by transformation algorithm. The algorithm is so flexible that it can generate 256, 512, 1024 or longer hash value through parameter switcher. The experimental evaluation and comparison demonstrated the hash function's resistance to differential attack and second pre-image attack. The proposed hash function can be applied in the identification, information integrity, and figure signature.
Cooperative Learning of Disjoint Syntax and Semantics

Serhii Havrylov, Germán Kruszewski, Armand Joulin. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art

Pedestrian detection is one of the important computer vision problems in automotive safety and driver assistance domain. It is a major component of the advanced driver assistance system (ADAS) which help the driver to drive safely. Recent literature shows a number of research activities addressing object detection/tracking in general and pedestrian detection in particular. The solutions proposed by different researchers vary in detection methods, detection scenario, feature descriptors, classification schemes, detection performance, as well as computational complexity. However, the average detection accuracy is not much promising even after many years of research. The fail-safe and real-time human detection from real life road scenes, even in standard resolution, is far from reality. Safety critical systems in the automotive industry have to follow well established stringent safety standards like ISO26262. Since the pedestrian detection system deals with human safety, it also has to follow these standards before integrating to the vehicle electronics. This paper is a study of different techniques used in pedestrian detection specific to the automotive application, along with a description of generic pedestrian detection solution architecture.
An efficient human action recognition framework with pose-based spatiotemporal features

In the past two decades, human action recognition has been among the most challenging tasks in the field of computer vision. Recently, extracting accurate and cost-efficient skeleton information became available thanks to the cutting edge deep learning algorithms and low-cost depth sensors. In this paper, we propose a novel framework to recognize human actions using 3D skeleton information. The main components of the framework are pose representation and encoding. Assuming that human actions can be represented by spatiotemporal poses, we define a pose descriptor consisting of three elements. The first element contains the normalized coordinates of the raw skeleton joints information. The second element contains the temporal displacement information relative to a predefined temporal offset and the third element keeps the displacement information pertinent to the previous timestamp in the temporal resolution. The final descriptor of the whole sequence is the concatenation of frame-wise descriptors. To avoid the problems regarding high dimensionality, Principal Component Analysis (PCA) is applied on the descriptors. The resulted descriptors are encoded with Fisher Vector (FV) representation before they get trained with an Extreme Learning Machine (ELM). The performance of the proposed framework is evaluated by three public benchmark datasets. The proposed method achieved competitive results compared to the other methods in the literature.