Reviewerly/exHarmony huggingface dataset:
Training Provably Robust Models by Polyhedral Envelope Regularization

Training certifiable neural networks enables us to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to obtain a provable adversarial-free region in the neighborhood of the input data by a polyhedral envelope, which yields more fine-grained certified robustness than existing methods. We further introduce polyhedral envelope regularization (PER) to encourage larger adversarial-free regions and thus improve the provable robustness of the models. We demonstrate the flexibility and effectiveness of our framework on standard benchmarks; it applies to networks of different architectures and with general activation functions. Compared with state of the art, PER has negligible computational overhead; it achieves better robustness guarantees and accuracy on the clean data in various settings.
An Improved Deep Model for Knowledge Tracing and Question-Difficulty Discovery

Knowledge Tracing (KT) aims to analyze a student’s acquisition of skills over time by examining the student’s performance on questions of those skills. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, DKT and its variants often lead to oscillation results on a skill’s state may due to it ignoring the skill’s difficulty or the question’s difficulty. As a result, even when a student performs well on a skill, the prediction of that skill’s mastery level decreases instead, and vice versa. This is undesirable and unreasonable because student’s performance is expected to transit gradually over time. In this paper, we propose to learn the knowledge tracing model in a “simple-to-difficult” process, leading to a method of Self-paced Deep Knowledge Tracing (SPDKT). SPDKT learns the difficulty of per question from the student’s responses to optimize the question’s order and smooth the learning process. With mitigating the cause of oscillations, SPDKT has the capability of robustness to the puzzling questions. The experiments on real-world datasets show SPDKT achieves state-of-the-art performance on question response prediction and reaches interesting interpretations in education.
Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation

Moving objects have special importance for Autonomous Driving tasks. Detecting moving objects can be posed as Moving Object Segmentation, by segmenting the object pixels, or Moving Object Detection, by generating a bounding box for the moving targets. In this paper, we present a Multi-Task Learning architecture, based on Transformers, to jointly perform both tasks through one network. Due to the importance of the motion features to the task, the whole setup is based on a Spatio-Temporal aggregation. We evaluate the performance of the individual tasks architecture versus the MTL setup, both with early shared encoders, and late shared encoder-decoder transformers. For the latter, we present a novel joint tasks query decoder transformer, that enables us to have tasks dedicated heads out of the shared model. To evaluate our approach, we use the KITTI MOD [23] data set. Results show 1.5% mAP improvement for Moving Object Detection, and 2% IoU improvement for Moving Object Segmentation, over the individual tasks networks.
Multilingual-GAN: A Multilingual GAN-based Approach for Handwritten Generation

Handwritten Text Recognition (HTR) is a difficult problem because of the diversity of calligraphic styles. To enhance the accuracy of HTR systems, a large amount of training data is required. The previous methods aim at generating handwritten images from input strings via RNN models such as LSTM or GRU. However, these methods require a predefined alphabet corresponding to a given language. Thus, they can not well adapt to a new languages. To address this problem, we propose an Image2Image-based method named Multilingual-GAN, which translates a printed text image into a handwritten style one. The main advantage of this approach is that the model does not depend on any language alphabets. Therefore, our model can be used on a new language without re-training on a new dataset. The quantitative results demonstrate that our proposed method outperforms other state-of-the-art models. Code is available at https://github.com/HoSyTuyen/MultilingualGAN
Human and Scene Motion Deblurring Using Pseudo-Blur Synthesizer

Present-day deep learning-based motion deblurring methods utilize the pair of synthetic blur and sharp data to regress any particular framework. This task is designed for directly translating a blurry image input into its restored version as output. The aforementioned approach relies heavily on the quality of the synthetic blurry data, which are only available before the training stage. Handling this issue by providing a large amount of data is expensive for common usage. We answer this challenge by providing an on-the-fly blurry data augmenter that can be run during training and test stages. To fully utilize it, we incorporate an unorthodox scheme of deblurring framework that employs the sequence of blur-deblur-reblur-deblur steps. The reblur step is assisted by a reblurring module (synthesizer) that provides the reblurred version (pseudo-blur) of its sharp or deblurred counterpart. The proposed module is also equipped with hand-crafted prior extracted using the state-of-the-art human body statistical model. This prior is employed to map human and non-human regions during adversarial learning to fully perceive the characteristics of human-articulated and scene motion blurs. By engaging this approach, our deblurring module becomes adaptive and achieves superior outcomes compared to recent state-of-the-art deblurring algorithms.
Safety-Aware Metric for People Detection

At construction sites and off-road scenarios people are often in danger, because drivers of heavy machinery have to focus on their tasks and might recognize dangerous situations too late. They, therefore, heavily rely on driver assistance systems. For people detection in such environments not all inference results (true positives, false positives or false negatives) are equally relevant. In this paper we propose a novel metric for people detection, providing an increased emphasis on human hazard prevention. Most works blindly allocate efforts for improving detection performance using situation-agnostic metrics like mean average precision or F1-score. The objective of this work is to determine solid pragmatic variables that directly relate to a person's safety and judiciously combine them, coming up with a safety-aware metric for people detection. To set up a system focusing on safety and reliability, the results of a neural network have to be subsequently verified. We therefore propose a new detection plausibility check based on an extension of the stixel algorithm and a multi-object tracker. Our results are evaluated on a new self-recorded off-road dataset for people detection and compared with standard evaluation metrics. Furthermore, results obtained from training are compared against the proposed plausibility algorithm.
Design and Implementation of Knowledge Graph Platform of Power Marketing

Knowledge graph technology has developed rapidly in recent years, and it is widely used in various scenarios, such as intelligent semantic search, intelligent in-depth question answering and mobile personal assistants. However, power marketing services face many problems such as low service response efficiency, poor customer experience and a lack of real-time online services. Thus, it is necessary to design a power marketing knowledge graph platform that can integrate scattered knowledge points in the power marketing field, promote knowledge utilization, improve internal and external service, and strengthen active perception and service functions/capabilities. Based on the establishment of the Neo4j power marketing graph database, this paper further combined rules, dictionaries and models to extract knowledge, and builds an application architecture of knowledge graph platform with knowledge management applications and extraction service functions. It expected to accurately identify the subject of the inquiry from the diversified questions expressed by users, and the answer can be found from the power marketing knowledge graph.
Time-Aware Recommender System via Continuous-Time Modeling

The overload of information on the Internet becomes ubiquitous nowadays, which makes the role of recommender systems more important. In recommender systems, the interest of users and popularity of items are not static, but can change drastically. Thus modeling the temporal dynamic of user-item interactions is crucial in recommender systems. The newly proposed Neural Ordinary Differential Equation (NODE) method is able to modeling the temporal mechanism of a system with neural networks. By using the ODE-LSTM method, which unites the ability of NODE to handle continuous time and that of LSTM to address sequential data, in this paper we achieve significant improvements for the recommendation task on several real-world datasets with the time irregularity. To handle sessions with different timestamps in ODE-LSTM, we propose a collective timeline technique that contributes a lot to the performance improvement. Moreover, we find that reducing the scale of time intervals in sessions significantly improves the recommendation performance.
3D reconstruction of insects: an improved multifocus stacking and an evaluation of learning-based MVS approaches

3D reconstruction of insects from photographs is a challenging task as it requires to tackle several problems such as strong out-of-focus areas in macro-photography, thin structures (insect legs and hairs), flat-colored surfaces (insects shells), non-Lambertian (shells specularities) and even translucent surfaces (wings). In this work, we first present a new lens-based image registration technique for accurate multi-focus stacking suitable for 3D reconstruction purposes while other methods create in-focus images for viewing purpose only. We then evaluate and compare the classical Multi-View-Stereo (MVS) reconstruction pipeline for small and complex objects with recent deep learning-based reconstruction methods such as the Neural Radiance Fields (NeRF) and the Neural Sparse Voxel Fields (NSVF). We present an assessment of different sources of errors for the considered methods. The results are compared both quantitatively and qualitatively across the different methods. From this analysis we present a series of practical guidelines for addressing the common issues of the reconstruction of small objects under challenging conditions.
A Dendritic Neuron Model for Disease Prediction

Disease prediction is one of the common applications in machine learning. On account of the powerful ability of data learning and exploration, machine learning has significant advantages in disease prediction and assisting clinical decision-making, which can effectively promote the development of medical work. The structure of traditional neural network is complex and requires a large number of neurons. In this paper, the dendritic neuron model is introduced to predict the disease. The experiment proves that the model has the advantages of simple structure, high prediction accuracy and fast convergence, which provides a feasible idea for the application of machine learning in disease prediction.
Study of Various Techniques for the Classification of Hateful Memes

An Internet meme is a catchphrase, concept, or piece of media that is spread by means of the Internet, regularly through online media platforms and particularly for comical purposes. Detection of Online Hateful speech in the form of memes presents as a major challenge due to its multimodal nature. This study explores various methods for the classification of hateful memes on multimodal data using several pre-trained models like VGG19 and Xception in combination with machine learning models like Support Vector Machine and Naïve Bayes. We have experimented several techniques where one of them being integrated stacked model technique for the image modality. In our multimodal experiment approach, we have used early fusion technique and GloVe Embedding for which our model obtained 0.584 as the highest f1-score in a multimodal experiment consisting of an LSTM and VGG16 model.
The impact of the software architecture on the developer productivity

Abstract Distinct technological trends seriously influence the modern software architectures. In this paper, four different software architectures and framework combinations were generally compared. The basis for the analysis is the developer's productivity. In a 3 year-long research and development project, a real-world telemedicine application was efficiently implemented four times with various software architectures and architectural patterns. More than 5,000 person-hours were spent on carrying out them. At present, a unique dataset is available, which provides the opportunity to compare the cost of spent person-hours in different approaches. The goal of this research is to describe the measurement approach, the dataset and the applied architectures considering the software developer's productivity.
The Application of Cloud Computing Intelligent Optimization Algorithm in the Investigation of College Students’ English Autonomous Learning under the Multimedia Teaching Mode

Autonomous learning is what college students need to learn. For some students who have difficulties in self-study, this paper develops a cloud computing intelligent optimization algorithm in the multimedia teaching mode of college students’ English autonomous learning system. This platform can help self-learners learn translation, listening, speaking, and other skills. At the same time, the platform also includes a self-assessment system including diagnostic evaluation, formative evaluation, and summative evaluation. The final test shows that the recognition rate of the system can reach more than 95%, which can meet the needs of use.
Jura

The initial public offering (IPO) market in Hong Kong is consistently one of the largest in the world. As part of its regulatory responsibilities, Hong Kong Exchanges and Clearing Limited (HKEX) reviews annual reports published by listed companies (issuers). The number of issuers has grown at a fast pace, reaching 2,538 as the end of 2020. This poses a challenge for manually reviewing these annual reports against the many diverse regulatory obligations (listing rules). We propose a system named Jura to improve the efficiency of annual report reviewing with the help of machine learning methods. This system checks the compliance of an issuer's published information against listing rules in four steps: panoptic document recognition, relevant passage location, fine-grained information extraction, and compliance assessment. This paper introduces in detail the passage location step, how it is critical for speeding up compliance assessment, and the various challenges faced. We argue that although a passage is a relatively independent unit, it needs to be combined with document structure and contextual information to accurately locate the relevant passages. With the help of Jura, HKEX reports saving 80% of the time on reviewing issuers' annual reports.
Smart Multimedia Compressor—Intelligent Algorithms for Text and Image Compression

Abstract A number of text compression algorithms have been proposed in the past decades, which have been very effective and usually operate on conventional character/word-based approaches. A novel data compression perspective of data mining is explored in this research and the paper focuses on novel frequent sequence/pattern mining approach to text compression. This work attempts to make use of longer-range correlations between words in languages for achieving better text compression. We propose a novel and efficient method by making the compression of any word-level text in a universal manner for corpora across domains referred as Universal Huffman Tree-based encoding. The major contribution of this work is in terms of avoidance of code table communication to the decoder. Simulation results over benchmark datasets indicate that Universal Huffman encoding employing frequent sequence mining (achieves [20%, 89%] improvement in compression in reduced time. The paper also contributes a usable interface for Data compression that employs the proposed frequent sequent mining-based data compression algorithm. The interface supports features such as feedback, consistency, usability, navigation, visual appeal, performance and accessibility on par with existing compression softwares. The work results in an intelligent data compression software employing knowledge engineering perspective.
The shape of view

Internet video providers rely on alerting workflows to identify and remedy incidents that can impact users (e.g., outages or buggy players). There is growing evidence for the need for viewership-based analytics---detecting and diagnosing incidents that manifest through changes in viewership patterns but not in other (e.g., QoE) metrics. However, both detection and diagnosis of viewership anomalies is challenging due to the contextual nature of anomalies, non-stationarity of viewership, and complex dependencies between the structure of events and how they impact different subpopulations of viewers. We present Proteas, an alerting framework for video viewership anomalies that tackles these challenges. Proteas builds on key spatiotemporal structural insights. First, across different sub-populations of viewers and days of the week, we find that the shape of the viewership curve remains invariant over multiple weeks, thus enabling anomaly detection. Second, we use the hierarchy of viewership groups to produce compact alerts. Finally, we find that common anomalies manifest with spatiotemporal signatures, which enables us to classify anomalies to produce actionable alerts. We evaluate Proteas using 3 months of real viewership data (including the onset of the COVID-19 pandemic) and show that Proteas is accurate with over 80% True Positive Rate, average precision of over 86% (i.e., few false positives) and doesn't miss any major events. In addition, we find that approximately half of Proteas's alerts refer to events not caught by other alerting workflows, thus adding value to operators' existing toolkit.
Solar Power Forecasts

Power generation from solar and wind energy systems is highly variable due to its dependence on meteorological conditions. An efficient use of these fluctuating energy sources requires reliable forecast information for management and operation strategies. We give an overview of different applications and state-of-the-art models for solar photovoltaic power forecasting. These include physical, regressive, machine learning and time series models and generate either point or probabilistic forecsts. Additionally, techniques of collecting information on solar irradiance, cloud movement, and weather predictions are discussed, including on-site measurements by sensor networks, all-sky imaging, satellite imaging and numerical weather prediction-based models. Finally, we present an overview of error metrics that are commonly used in solar power forecasting to evaluate the performance of the forecast models.
Optical method for identification and quantification of full-field stress distributions and evolution in assembled lining structures based on additively printed models and phase-shifting methods

The accurate knowledge and characterization of the full-field stress distributions and evolution in assembled lining structures underlie the design, construction, and maintenance of tunnel linings. However, it is challenging for traditional field monitoring and laboratory experiments to identify directly and quantify accurately such distributions and evolution. Numerical simulations, as alternative solutions, are limited, as their results are significantly dependent on the accuracy of mechanical parameters, constitutive laws, failure criteria, mesh optimization, and boundary conditions, which are difficult to determine experimentally. In this study, a plane-stress-model-based photoelastic testing method was developed for the direct observation and quantification of the full-field stress distribution and evolution in an assembled lining structure based on additive manufacturing or three-dimensional printing technology and phase shifting methods. Three-dimensional printing techniques and stress- sensitive photopolymers were employed to fabricate transparent assembled models based on the information of real lining structures. The model split-merge technique, phase-shifting method, and unwrapping algorithm were combined to determine the full-field distribution of the principal stress difference and shear stress within the assembled segments. The proposed method was verified based on a comparison of the calculated and measured fringe orders at different loading stages. The experimental results indicated that the largest stress concentration was around the joint that connected the adjacent Segment L1 and basic Segment B1, which formed the potential critical failure zone of the assembled structure. An approximate X-shaped shear band was also identified. Comparisons of the results verified that the boundary and contact conditions, which are difficult to simulate, can lead to significantly different simulation results with respect to the distribution and evolution of the principal stress difference and shear stress. This study shows that the proposed method can effectively and quantitatively characterize the full-field stress distributions and evolution within complex assembled lining structures.
Cache-based characterization: A low-infrastructure, distributed alternative to network-based traffic and application characterization

It is important for network operators to carry out traffic and application characterization to gain insights into the activity of their networks. Several studies proposed methods that extract features from network traffic to characterize it, or to classify the application that produced it, based on a “man in the middle” network interception point that can analyze the entire network traffic of an organization. This network topology, however, is increasingly becoming irrelevant, due to mobile and remote traffic joining the corporate network by passing through VPN channels or relay networks. In this work we propose an edge-oriented lightweight traffic characterization method, based on measuring contention on the last-level CPU cache. In contrast to previous traffic characterization methods, which track network traffic from a central location, our method performs measurements directly on user machines, using an unprivileged JavaScript-based webpage. Our evaluation shows that the accuracy of our cache-based method is equivalent to that of network-based methods, both over VPN and over non-VPN networks.