Reviewerly/exHarmony huggingface dataset:
Taming the Zoo: The Unified GraphIt Compiler Framework for Novel Architectures

We live in a new Cambrian Explosion of hardware devices. The end of conventional processor scaling has driven research and industry practice to explore a new generation of approaches. The old DNA of architecture design, including vectors, threads, shared or private memories, coherence or message passing, dataflow or von Neumann execution, are hybridized together in new and exciting ways. Each new architecture exposes a unique hardware-level API. Performance and energy efficiency are critically dependent on how well programs can use these APIs. One approach is to implement custom libraries for each new hardware architecture and application domain. A more scalable approach is to utilize a portable compiler infrastructure tailored to the application domain that makes it easy to generate efficient code for a diverse set of architectures with minimal porting effort.We propose the Unified GraphIt Compiler framework (UGC), which does exactly this for graph applications. UGC achieves portability with reasonable effort by decoupling the architecture-independent algorithm from the architecture-specific schedules and backends. We introduce a new domain-specific intermediate representation, GraphIR, that is key to this decoupling. GraphIR encodes high-level algorithm and optimization information needed for hardware-specific code generation, making it easy to develop different backends (GraphVMs) for diverse architectures, including CPUs, GPUs, and next-generation hardware such as Swarm and the HammerBlade manycore. We also build scheduling language extensions that make it easy to expose optimization decisions like load balancing strategies, blocking for locality, and other data structure choices. We evaluate UGC on five algorithms and 10 input graphs on these 4 distinct architectures and show that UGC enables implementing optimizations that can provide up to 53× speedup over programmer-generated straightforward implementations.
Using mobile phone data for epidemic response in low resource settings—A case study of COVID-19 in Malawi

Abstract The COVID-19 global pandemic has had considerable health impact, including sub-Saharan Africa. In Malawi, a resource-limited setting in Africa, gaining access to data to inform the COVID-19 response is challenging. Information on adherence to physical distancing guidelines and reducing contacts are nonexistent, but critical to understanding and communicating risk, as well as allocating scarce resources. We present a case study which leverages aggregated call detail records into a daily data pipeline which summarize population density and mobility in an easy-to-use dashboard for public health officials and emergency operations. From March to April 2021, we have aggregated 6-billion calls and text messages and continue to process 12 million more daily. These data are summarized into reports which describe, quantify, and locate mass gatherings and travel between subdistricts. These reports are accessible via web dashboards for policymakers within the Ministry of Health and Emergency Operations Center to inform COVID-19 response efforts and resource allocation.
Portfolio Search and Optimization for General Strategy Game-Playing

Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games. We first review existing portfolio techniques and propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm. Moreover, a series of variants are developed to solve problems in different aspects. We further analyze the performance of discussed agents in a general strategy game-playing task. For this purpose, we run experiments on three different game-modes of the Stratega framework. For the optimization of the agents' parameters and portfolio sets we study the use of the N-tuple Bandit Evolutionary Algorithm. The resulting portfolio sets suggest a high diversity in play-styles while being able to consistently beat the sample agents. An analysis of the agents' performance shows that the proposed algorithm generalizes well to all game-modes and is able to outperform other portfolio methods.
Cryptanalysis of the RSA variant based on cubic Pell equation

RSA (Rivest-Shamir-Adleman) cryptosystem is the most popular asymmetric key cryptographic algorithm used in computer science and information security. Recently, an RSA-like cryptosystem was proposed using a novel product that arises from a cubic field connected to the cubic Pell equation. The relevant key equation is ed≡1mod(p2+p+1)(q2+q+1) with N=pq. This RSA variant is claimed to be robust against the Wiener's attack and hence the bit-size of the private key could be shorter, namely d<N1/4. In this paper, we explore the further security analysis and investigate the potential small private exponent attack. We show that such RSA variant is particularly vulnerable to the lattice-based method. To be specific, we can carry out the lattice-based small private exponent attack if d<N2−2, which is less secure than the standard RSA. Furthermore, we conduct numerical experiments to verify the validity of the proposed attack.
Exercise recommendation algorithm based on improved collaborative filtering

The recommendation method based on collaborative filtering has some shortcomings in the field of exercise recommendation, such as lack of interpretability and rationality. The existing methods for students' cognitive diagnosis are too rough to measure students' mastery of knowledge point, and the measurement of students' ability has the disadvantage of hysteresis. This paper proposes an exercise recommendation method aimed at improving students' mastery of the specified knowledge point faster. For the designated student and the designated knowledge point, this method can choose the exercise that can help student improve the level of mastery of the knowledge point as fast as possible in all the exercises including the knowledge point, and recommend to the student. This method is based on the improved cognitive diagnosis method and Long Short-term Memory Networks LSTM, and recommends exercises for the target students that can improve the knowledge level of students similar to the target students. According to the experimental test, the exercises recommended by this method can help target students to improve their mastery of the target knowledge point to a greater extent under the condition of doing the same number of exercises.
MFuseNet: Robust Depth Estimation With Learned Multiscopic Fusion

We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation. Unlike multi-view stereo with images captured at unconstrained camera poses, the proposed system controls the motion of a camera to capture a sequence of images in horizontally or vertically aligned positions with the same parallax. In this system, we propose a new heuristic method and a robust learning-based method to fuse multiple cost volumes between the reference image and its surrounding images. To obtain training data, we build a synthetic dataset with multiscopic images. The experiments on the real-world Middlebury dataset and real robot demonstration show that our multiscopic vision system outperforms traditional two-frame stereo matching methods in depth estimation. Our code and dataset are available at https://sites.google.com/view/multiscopic.
EBSNN: Extended Byte Segment Neural Network for Network Traffic Classification

Network traffic classification is important to intrusion detection and network management. Most of existing methods are based on machine learning techniques and rely on the features extracted manually from flows or packets. However, with the rapid growth of network applications, it is difficult for these approaches to handle new complex applications. In this article, we design a novel neural network, the Extended Byte Segment Neural Network (EBSNN), to classify netwrk traffic. EBSNN first divides a packet into header segments and payload segments, which are then fed into encoders composed of the recurrent neural networks with the attention mechanism. Based on the outputs, another encoder learns the high-level representation of the whole packet. In particular, side-channel features are learned from header segments to improve the performance. Finally, the label of the packet is obtained by the softmax function. Furthermore, EBSNN can classify network flows by examining the first few packets. Thorough experiments on the real-world datasets show that EBSNN achieves better performance than the state-of-the-art methods in both the application identification task and the website identification task.
A New Data Augmentation Method for Time Series Wearable Sensor Data Using a Learning Mode Switching-Based DCGAN

This letter describes a new image augmentation method based on a DCGAN considering mode switching in terms of transient learning accuracy threshold on the problem of human gesture recognition through imaging of wearable sensor time-series data and a deep CNN structure. Because the discriminator in GANs learns faster than the generator, it is known that mode collapse occurs, in which only image modes biased to a specific image type are augmented among various image forms. In this study, to solve the mode collapse caused by the learning difficulty mismatch between networks, we add a learning mode switching layer between the generator and discriminator and receive feedback from both networks’ transient learning accuracies to switch the learning mode if predefined thresholds are exceeded. We confirm that the proposed approach balanced the learning rate between the generator and discriminator networks, resolved the mode collapse problem, and increased the test accuracy of a deep CNN trained with an augmented image set by approximately 20.35% compared to a conventional DCGAN. In addition, it showed better accuracy on a performance comparison with other improved DCGAN methods.
Sparse least trimmed squares regression with compositional covariates for high-dimensional data

High-throughput sequencing technologies generate a huge amount of data, permitting the quantification of microbiome compositions. The obtained data are essentially sparse compositional data vectors, namely vectors of bacterial gene proportions which compose the microbiome. Subsequently, the need for statistical and computational methods that consider the special nature of microbiome data has increased. A critical aspect in microbiome research is to identify microbes associated with a clinical outcome. Another crucial aspect with high-dimensional data is the detection of outlying observations, whose presence affects seriously the prediction accuracy.In this article, we connect robustness and sparsity in the context of variable selection in regression with compositional covariates with a continuous response. The compositional character of the covariates is taken into account by a linear log-contrast model, and elastic-net regularization achieves sparsity in the regression coefficient estimates. Robustness is obtained by performing trimming in the objective function of the estimator. A reweighting step increases the efficiency of the estimator, and it also allows for diagnostics in terms of outlier identification. The numerical performance of the proposed method is evaluated via simulation studies, and its usefulness is illustrated by an application to a microbiome study with the aim to predict caffeine intake based on the human gut microbiome composition.The R-package 'RobZS' can be downloaded at https://github.com/giannamonti/RobZS.Supplementary data are available at Bioinformatics online.
Attention-Enhanced CNN for Chinese Calligraphy Styles Classification

Chinese calligraphic style works are cultural treasures and have important research value. Compared with Chinese calligraphic fonts, the features of different styles are more difficult to identify for untrained eyes, so the classification of calligraphy style is an important and challenging task. To this end, we propose a simple and good-performance model for classification of Chinese calligraphic styles of regular font. This method is based on CNN which composed of four stacked convolutional blocks. In addition, as one of the successful attention mechanisms, Convolutional Block Attention Modules (CBAM) is inserted after the third and fourth convolution block respectively. Experiments show that the use of CBAM has a greater improvement to the baseline than the similar Squeeze-and-Excitation (SE) block, and the accuracy of the proposed model better than some well-known network architectures, reaching 98.5%. Finally, we visualize trained models by using the grad-CAM. The results indicate that the model shows different areas of interest for the same Chinese calligraphy characters of regular script written in different styles. Combined with the interpretation of the visualization results, we prove that the proposed model can recognize the discriminative feature of the calligraphy styles.
Cross-task feature alignment for seeing pedestrians in the dark

Pedestrian detection in low-light environments has been an extremely challenging task because of the serious degradation of color and texture information. In the latest research, multi-task learning is introduced into image relighting and pedestrian detection tasks, which improves the performance of detecting pedestrians in low-light environments significantly. However, many problems in the multi-task learning period, including the misalignment of scale and channel of features from image relighting and pedestrian detection tasks, remain unresolved, thereby resulting in insufficient feature representation. In this paper, we propose a novel cross-task feature alignment method to tackle the aforementioned problems. Specifically, the proposed method imposes four feature alignment (FA) layers before the feature fusing and sharing step in multi-task learning period to align the scale and channel of features across tasks and fine-tune feature representation iteratively. In addition, we design a novel multi-scale feature-enhanced detection network to further improve the performance of the detector. Experimental results from simulated and real-world scenarios prove that our method prominently boosts the ability to detect pedestrians in the dark.
Using Chatbots in Flipped Learning Online Sessions: Perceived Usefulness and Ease of Use

Many educators in higher education are using blended learning as an important component of their academic programs. Of the many forms of blended learning, the flipped learning approach, has attracted much attention from many universities, where students learn course materials typically through online video lectures before class so that they can do more active learning in the classroom. Its actual implementation, however, is often beset with challenges, with student disengagement in pre-class online activities being one major problem reported in many previous flipped learning studies. Students who fail to complete the pre-class tasks often have difficulty in performing the follow-up in-class discussions with the instructors and peers. This study, which is part of a larger research project on engaging student in flipped learning, explored the use of two types of chatbots in flipped learning online sessions: Quiz Chatbot, and Self-Regulated Learning Chatbot. We described in detail the implementations of the two chatbots, and evaluated the chatbots in terms of its perceived usefulness, and perceived ease of use. We also examined the extent of student behavioral engagement with the chatbots. Suggestions to improve the chatbots were discussed, along with recommendations for future research.
PGT: A Progressive Method for Training Models on Long Videos

Convolutional video models have an order of magnitude larger computational complexity than their counter-part image-level models. Constrained by computational resources, there is no model or training method that can train long video sequences end-to-end. Currently, the main-stream method is to split a raw video into clips, leading to incomplete fragmentary temporal information flow. Inspired by natural language processing techniques dealing with long sentences, we propose to treat videos as serial fragments satisfying Markov property, and train it as a whole by progressively propagating information through the temporal dimension in multiple steps. This progressive training (PGT) method is able to train long videos end-to-end with limited resources and ensures the effective transmission of information. As a general and robust training method, we empirically demonstrate that it yields significant performance improvements on different models and datasets. As an illustrative example, the proposed method improves SlowOnly network by 3.7 mAP on Charades and 1.9 top-1 accuracy on Kinetics with negligible parameter and computation overhead. Code is available at: https://github.com/BoPang1996/PGT.
Robust Visual Tracking Control Based on Adaptive Sliding Mode Strategy: Quadrotor UAV - Catamaran USV Heterogeneous System

This paper presents a robust visual control for the tracking of a catamaran USV from a quadrotor UAV via image-based visual servoing. A virtual camera approach is used for the extraction of the image features from the target, allowing the design of the rotorcraft position controller based on a class of adaptive sliding mode strategy, providing robustness against bounded external disturbances and uncertainties. The speed and heading control for the vessel is also designed by the same adaptive algorithm, which is in close-loop with a line-of-sight-based guidance law. Simulation results show the feasibility and performance of the proposed visual tracking control in a cooperation UAV-USV scheme in the presence of disturbances.
A Coq proof of the correctness of X25519 in TweetNaCl

We formally prove that the C implementation of the X25519 key-exchange protocol in the TweetNaCl library is correct. We prove both that it correctly implements the protocol from Bernstein's 2006 paper, as standardized in RFC 7748, as well as the absence of undefined behavior like arithmetic overflows and array out-of-bounds errors. We also formally prove, based on the work of Bartzia and Strub, that X25519 is mathematically correct, i.e., that it correctly computes scalar multiplication on the elliptic curve Curve25519. The proofs are all computer-verified using the Coq theorem prover. To establish the link between C and Coq we use the Verified Software Toolchain (VST).
M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments

We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.
CatChatter: Acoustic Perception for Mobile Robots

There are many examples in nature of animals using acoustics to understand and navigate the world around them. Inspired by this, we train an image-to-image translation network to learn the mapping from recorded chirps and echos from an environment to a 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> depth map of the environment. This work is focused on expanding on the capabilities of BatVision in a number of ways. We first propose various methods for data augmentation to help the model generalise on less data. We also propose changes to the model architecture to improve performance and training stability. Finally, we investigate the feasibility of 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> scene reconstruction by using more microphones and lidar based 3D SLAM data as ground truth for training the model.
Artificial Intelligence Auxiliary Algorithm for Wushu Routine Competition Decision Based on Feature Fusion

The developments of modern science and technology have significantly promoted the progress of sports science. Advanced technological methods have been widely used in sports training, which has not only improved the scientific level of training but also promoted the continuous growth of sports technology and competition results. Competitive Wushu routine is an important part of Chinese Wushu. The development trend of competitive Wushu routine affects the development of the whole Wushu movement. To improve the training effect of the Wushu routine using artificial intelligence, this paper employed fuzzy information processing and feature extraction technology to analyze the visual features in the process of Wushu competition. The deep neural network-based region segmentation method was employed for implicit feature extraction to examine the shape, texture, and other image features of Wushu routines and improve the recognition performance. The proposed feature extraction model achieved the highest average accuracy of 93.98% accuracy as compared to other contemporary algorithms. Finally, the model was evaluated to validate the superior performance of the proposed method in improving the decision-making ability and effective instruction ability of the martial arts routine competition.
Enforcing current-state opacity through shuffle and deletions of event observations

In this paper, we deal with current-state opacity, and propose an Opacity-Enforcer that is able to change, in an appropriate way, the order of observation of event occurrences in the system, and also to delete event observations, so as to mislead the Intruder to never be sure if the current state of the system is a secret state. We then present two necessary and sufficient conditions for the feasibility of current-state opacity enforceability (CSOE), i.e., if a current-state opacity enforcer that shuffles and deletes events can be synthesized for the system, and present an automaton-based verification test for CSOE and an algorithm to build the automaton that realizes the proposed opacity enforcement. We also present a protocol to mitigate the negative effect of opacity enforcement on the capability of a legitimate recipient to accurately estimate the current state of the system in the case when the information is also intended to be sent to some receiver that needs to be aware of the system evolution.
Weighted Voting in Physical Layer Authentication for Industrial Wireless Edge Networks

Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a preprocessing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and, thus, this article presents a solution to enhance the access classification accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information based authentication using a single sample which includes sample segmentation, grouping, and weighted voting and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices’ sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods.