Reviewerly/exHarmony huggingface dataset:
AdvGuard: Fortifying Deep Neural Networks against Optimized Adversarial Example Attack

Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, video recognition, and pattern analysis. However, they are vulnerable to adversarial example attacks. An adversarial example, which is input to which a little bit of noise has been strategically added, appears normal to the human eye but will be misrecognized by the DNN. In this paper, we propose AdvGuard, a method for resisting adversarial example attacks. This defense method prevents the generation of adversarial examples by constructing a robust DNN that provides random confidence values. This method does not require training of adversarial examples, use of other processing modules, or the ability to perform input data filtering. In addition, a DNN constructed using the proposed scheme can defend against adversarial examples while maintaining its accuracy on the original samples. In the experimental evaluation, MNIST and CIFAR10 were used as datasets, and TensorFlow was used as a machine learning library. The results show that a DNN constructed using the proposed method can correctly classify adversarial examples with 100% and 99.5% accuracy on MNIST and CIFAR10, respectively.
Does cryptocurrency pricing response to regulatory intervention depend on underlying blockchain architecture?

Blockchain technology appears to be ready to revolutionise a broad number of industries. However, the blockchain itself contains a number of inefficiencies and areas for improvement, namely: transaction fees and transaction speeds. Directed acyclic graphs (DAGs) address, and improve on these inefficiencies and a number of digital currencies utilising this technology have already begun to appear. This paper provides an explanation of the technology behind DAG-based assets, while identifying and highlighting strategic advantages that DAGs possess over traditional blockchains. We conduct an EGARCH volatility analysis of a range of blockchain-based and DAG-based cryptocurrencies in the aftermath of a range of market shocks, taking the form of regulatory announcements such as bans and broad restrictions for cryptocurrencies. We find that DAG-based assets become increasingly responsive to market shocks as they mature. Such behaviour mirrors that of established cryptocurrencies such as Bitcoin, Ethereum and Litecoin, providing evidence that DAG-based cryptocurrencies now share similar characteristics to traditional blockchain-chain based products.
Preference Cognitive Diagnosis for Student Performance Prediction

Knowledge states modeling is a fundamental issue in online education. One of its tasks is to discover the potential knowledge capacity of students for predicting their performance (i.e., scores on exercises). Current studies either depend on cognitive diagnosis approaches or apply collaborative filtering. However, the prediction accuracy of traditional cognitive diagnosis is insufficient, and collaborative filtering has difficulty ensuring the interpretability of prediction. Actually, students usually read some auxiliary text learning materials that they are interested in, namely, preferred learning material, to consolidate what they have learned. Preference cognitive diagnosis means that the preferred learning materials can reflect the students’ knowledge states (i.e., proficiency for knowledge concepts) to some extent, which is beneficial for predicting students’ performance. Therefore, we propose a preference cognitive diagnosis method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PreferenceCD</i> ) to model students’ knowledge states. Specifically, we first design the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Direct-Indirect</i> method to acquire students’ preferred learning materials. This method mines important information from students’ reading content that can reflect their preference for learning materials to acquire those preferred learning materials directly. Moreover, it discovers preferred learning materials indirectly by analyzing the similarity of students’ learning behaviors during the reading process. Subsequently, we calculate students’ preference degree for knowledge concepts based on the acquired preferred learning materials and diagnose their proficiency for knowledge concepts by applying a cognitive diagnosis model. After that, we combine the above two aspects to model students’ knowledge states and further predict their scores on exercises. The experimental results on a real-world dataset demonstrate the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PreferenceCD</i> with both accuracy and interpretability. The accuracy, root mean square error (RMSE), and mean absolute error (MAE) of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PreferenceCD</i> are 0.7614, 0.4805, and 0.2386, respectively, which outperforms related works by about 2-12% in terms of these evaluation metrics.
Incremental Neural-Network Learning for Big Fraud Data

Fraud detection systems aim to process a massive amount of data at high speed. To address the issues of data scalability, we introduce a chunk-based incremental classification approach based on a neural network (MLP) and a memory model to tackle the stability-plasticity dilemma. The incremental approach adapts the fraud model sequentially with incoming data chunks and retains past chunks a little more. We employ a large-scale credit-card fraud dataset that we organize into initial and incremental chunks for training and testing. Using data sampling, we solve the data skew problem, a critical issue in fraud detection. After each incremental phase, we evaluate the performance of the adjusted MLP classifier using the testing chunk. The experimental results demonstrate the effectiveness and efficiency of our incremental method and its superiority to the non-incremental MLP.
Comparing World and Screen Coordinate Systems in Optical See-Through Head-Mounted Displays for Text Readability while Walking

Augmented reality (AR) optical-see-through (OST) head-mounted displays (HMD) have developed to a point where browsing information on the go is possible. In this paper, we investigate the readability of text on an AR HMD while the user is walking. There are two common methods of displaying text on a HMD: anchoring the text on the screen coordinate system or the world coordinate system. We report on the results of two laboratory experiments comparing text readability when the text is displayed in these two coordinate systems, and while the participants walked on a treadmill. In the first experiment, the participants read letter strings comprising Sloane letters, whereas the second experiment used English words. In addition to evaluating the text readability and workload experienced by participants, we employed IMU sensors to compare the effects of the text display method on the participants' head movement and gait. In both experiments, the reading speed and head movement were significantly higher and mental workload significantly lower for the world coordinate system than for the screen coordinate system. These results suggest that text readability while walking is better on the world coordinate system, and displaying text with the screen coordinate system results in an unnatural gait owing to the user trying to keep their head still in an effort to stabilize the HMD screen.
Explainable AI in Healthcare

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Constructions of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -uniform and absolutely maximally entangled states beyond maximum distance codes

Pure multipartite quantum states of n parties and local dimension q are called k-uniform if all reductions to k parties are maximally mixed. These states are relevant for our understanding of multipartite entanglement, quantum information protocols, and the construction of quantum error correction codes. To our knowledge, the only known systematic construction of these quantum states is based on classical error correction codes. We present a systematic method to construct other examples of k-uniform states and show that the states derived through our construction are not equivalent to any k-uniform state constructed from the so-called maximum distance separable error correction codes. Furthermore, we use our method to construct several examples of absolutely maximally entangled states whose existence was open so far.
A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data

Abstract Problem solving has been recognized as a central skill that today’s students need to thrive and shape their world. As a result, the measurement of problem-solving competency has received much attention in education in recent years. A popular tool for the measurement of problem solving is simulated interactive tasks, which require students to uncover some of the information needed to solve the problem through interactions with a computer-simulated environment. A computer log file records a student’s problem-solving process in details, including his/her actions and the time stamps of these actions. It thus provides rich information for the measurement of students’ problem-solving competency. On the other hand, extracting useful information from log files is a challenging task, due to its complex data structure. In this paper, we show how log file process data can be viewed as a marked point process, based on which we propose a continuous-time dynamic choice model. The proposed model can serve as a measurement model for scaling students along the latent traits of problem-solving competency and action speed, based on data from one or multiple tasks. A real data example is given based on data from Program for International Student Assessment 2012.
Interpretable Ranking with Generalized Additive Models

Interpretability of ranking models is a crucial yet relatively under-examined research area. Recent progress on this area largely focuses on generating post-hoc explanations for existing black-box ranking models. Though promising, such post-hoc methods cannot provide sufficiently accurate explanations in general, which makes them infeasible in many high-stakes scenarios, especially the ones with legal or policy constraints. Thus, building an intrinsically interpretable ranking model with transparent, self-explainable structure becomes necessary, but this remains less explored in the learning-to-rank setting.
Driver activity recognition using spatial‐temporal graph convolutional LSTM networks with attention mechanism

Driver activity engagement while driving plays a vital role that leads to negative outcomes of driving safety. To reduce traffic accidents and ensure driving safety, real-time driver activity recognition architecture is proposed in this study. Specifically, a total of eight kinds of common driving-related activities are identified, which include the normal driving, left or right checking, texting, answering the phone, using media, drinking, and picking up objects. Raw experiment videos are collected via onboard monocular cameras, which are used for the upper body skeleton information extraction of the driver. Then, the graph convolutional networks (GCN) are constructed for spatial structure feature reasoning in a single frame, which is consecutively followed by long short-term memory (LSTM) networks for temporal motion feature learning within the sequence. Moreover, the attention mechanism is further utilised to emphasise the keyframes to select discriminative sequential information. Finally, a large-scale driver activity dataset, consisting of both naturalistic driving data and simulative driving data, is collected for model training and evaluations. Experimental results show that the general recall ratio of those eight driving-related activities reaches up to 88.8% and the real-time recognition efficiency can reach up to 24 fps, which would satisfy the real-time requirements of engineering applications.
Cryptographic Fault Diagnosis using VerFI

Historically, fault diagnosis for integrated circuits has singularly dealt with reliability concerns. In contrast, a cryptographic circuit needs to be primarily evaluated concerning information leakage in the presence of maliciously crafted faults. While Differential Fault Attacks (DFAs) on symmetric ciphers have been known for over 20 years, recent developments have tried to structurally classify the attackers' capabilities as well as the properties of countermeasures. Correct realization of countermeasures should still be manually verified, which is error-prone and infeasible for even moderate-size real-world designs. Here, we introduce the concept of Cryptographic Fault Diagnosis, which revises and shapes the notions of fault diagnosis in reliability testing to the needs of evaluating cryptographic implementations. Additionally, we present VerFI, which materializes the idea of Cryptographic Fault Diagnosis. It is a fully automated, open-source fault detection tool processing the gate-level representation of arbitrary cryptographic implementations. By adjusting the bounds of the underlying adversary model, VerFI allows us to rapidly examine the desired fault detection/correction capabilities of the given implementation. Among several case studies, we demonstrate its application on an implementation of LED cipher with combined countermeasures against side-channel analysis and fault-injection attacks (published at CRYPTO 2016). This experiment revealed general implementation flaws and undetectable faults leading to successful DFA on the protected design with full-key recovery.
Shape autotuning activation function

The choice of activation function is essential for building state-of-the-art neural networks. At present, the most widely-used activation function with effectiveness is ReLU. However, ReLU suffers from the weakness including non-zero mean, negative missing, and unbounded output, thus it has potential disadvantages in the optimization process. In this paper, we propose a novel activation function, namely "Shape Autotuning Activation Function" (SAAF), to overcome these three challenges simultaneously. The SAAF inherits merits of smooth activation functions (such as Sigmoid and Tanh) and piecewise activation functions (such as ReLU and its variants), and avoids their deficiencies. Specifically, the SAAF adaptively adjusts a pair of independent trainable parameters to capture negative information and provide a near-zero mean output, resulting in better generalization performance and faster learning speed. At the same time, it provides bounded outputs to ensure a more stable distribution of output during network training. We evaluated SAAF on deep networks applied to a variety of tasks, including image classification, machine translation, and generative modeling. Comprehensive comparison study shows that the proposed SAAF is superior to state-of-the-art activation functions.
Application of particle swarm optimization in optimal placement of tsunami sensors

Rapid detection and early warning systems demonstrate crucial significance in tsunami risk reduction measures. So far, several tsunami observation networks have been deployed in tsunamigenic regions to issue effective local response. However, guidance on where to station these sensors are limited. In this article, we address the problem of determining the placement of tsunami sensors with the least possible tsunami detection time. We use the solutions of the 2D nonlinear shallow water equations to compute the wave travel time. The optimization problem is solved by implementing the particle swarm optimization algorithm. We apply our model to a simple test problem with varying depths. We also use our proposed method to determine the placement of sensors for early tsunami detection in Cotabato Trench, Philippines.
Physical reviews of solar radiation models for estimating global solar radiation in Indonesia

Indonesia is a tropical country that has an equator that has the intensity of the sun throughout the year. Mapping of the potential solar intensity has not been done much, even the measurement data is still very limited. The purpose of this paper is to review 10 solar intensity calculation models that are frequently used. Of the 10 models that provide different physical characteristics, wherefrom the physical characteristics it concludes a suitable model for the Indonesian region. From the statistical evaluation, the solar radiation model proposed by Liu and Jordan shows in a good agreement with the measurement result. The mean bias error for this model is 8.3 W/m2 and root mean square error is 100.9 W/m2, this model shows the lowest error than other models. Additionally, based on the physical factor analysis of environmental conditions in each model, Liu and Jordan’s model is suitable for estimating global solar radiation on the defined location in Indonesia.
Adaptive Entropy Index Histogram Equalization for Poor Contrast Images

Hidden details and lack of image contrast can be attributed to limited user experience, poor device quality, environment settings during image acquisition, and illumination.To address these problems, techniques based on histogram equalization (HE) have been frequently used to reduce these problems and to improve image contrast.However, the resultant images obtained by techniques often appear unnatural possibly due to washed-out effects and unwanted artifacts.This study proposes a new technique called adaptive entropy index histogram equalization (AEIHE) that belongs to the local sub-class of HE-based contrast enhancement techniques.AEIHE initially divides the image into three sub-images to enhance and highlight its local details.Each of these sub-images uses a different contextual region and clip limit based on the richness of their information and their structure, both of which are adaptively determined by AEIHE.A new parameter called Entropy-Index is then used to ensure the high information richness of the resultant sub-image while preserving its structure.AEIHE guarantees the production of an excellent resultant image by combining enhanced sub-images.Quantitative evaluations of 819 images show that AEIHE has successfully produced excellent resultant images with improved contrast, highlighted local details, and minimized effects of artifacts and unwanted noise.Therefore, AEIHE has a high application potential in the medical imaging, machine vision, and industrial domains.
How Does Library Migration Impact Software Quality and Comprehension? An Empirical Study

The process of migration between different third-party software libraries, while being an typical library reuse practice, is complex, time consuming and error-prone. Typically, during a library migration process, developers opt to replace methods from a retired library with other methods from a new library without altering the software behavior. However, the extent to which the process of migrating to new libraries will be rewarded with improved software quality is still unknown. In this paper, our goal is to study the impact of library API migration on software quality. We conducted a large-scale empirical study on 9 popular API migrations, collected from a corpus of 57,447 open-source Java projects. We computed the values of commonly-used software quality metrics before and after a migration occurs. The statistical analysis of the obtained results provides evidence that library migrations are likely to improve different software quality attributes including significantly reduced coupling, increased cohesion, and improved code readability. Furthermore, we released an online portal that helps software developers to understand the impact of a library migration on software quality and recommend migration examples that adopt best design and implementation practices to improve software quality. Finally, we provide the software engineering community with a large scale dataset to foster research in software library migration.
Evaluating the Performance of Apple’s Low-Latency HLS

In its annual developers conference in June 2019, Apple has announced a backwards-compatible extension to its popular HTTP Live Streaming (HLS) protocol to enable low-latency live streaming. This extension offers new features such as the ability to generate partial segments, use playlist delta updates, block playlist reload and provide rendition reports. Compared to the traditional HLS, these features require new capabilities on the origin servers and the caches inside a content delivery network. While HLS has been known to perform great at scale, its low-latency extension is likely to consume considerable server and network resources, and this may raise concerns about its scalability. In this paper, we make the first attempt to understand how this new extension works and performs. We also provide a 1:1 comparison against the low-latency DASH approach, which is the competing low-latency solution developed as an open standard.
Learn to Floorplan through Acquisition of Effective Local Search Heuristics

Automatic heuristic design through reinforcement learning opens a promising direction for solving computationally difficult problems. Unlike most previous works that aimed at solution construction, we explore the possibility of acquiring local search heuristics through massive search experiments. To illustrate the applicability, an agent is trained to perform a walk in the search space by selecting a candidate neighbor solution at each step. Specifically, we target the floorplanning problem, where a neighbor solution is generated through perturbing the sequence pair encoding of a floorplan. Experimental results demonstrate the efficacy of the acquired heuristics as well as the potential of automatic heuristic design.
Real-time vehicle detection and counting based on YOLO and DeepSORT

Intelligent vehicle detection and counting are becoming increasingly important in the field of highway and transport infrastructure management. Traditional methods based on image information have shown several limitations. Especially in real-world environment conditions, real-time detection, classification and counting each type of vehicle are still a big challenge. The main purpose of this study is to develop an adaptive model that combine YOLOv4 and DeepSORT. The new model can detect object with high accuracy and fast calculation time by taking the benefits of tracking with a focus on simple, effective algorithms. Experiment results have shown that our proposed approach outperforms the original one at least 11% of AP and 12% of AP50 for most field scenarios of our dataset at a real-time speed of ~32 FPS.
Adaptive Task Sampling for Meta-learning

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct few-shot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. In this paper, we propose an adaptive task sampling method to improve the generalization performance. Unlike instance based sampling, task based sampling is much more challenging due to the implicit definition of the task in each episode. Therefore, we accordingly propose a greedy class-pair based sampling method, which selects difficult tasks according to class-pair potentials. We evaluate our adaptive task sampling method on two few-shot classification benchmarks, and it achieves consistent improvements across different feature backbones, meta-learning algorithms and datasets.