Reviewerly/exHarmony huggingface dataset:
Predictors of Success in Information Security Policy Compliance

The purpose of this study is to build on the ISP compliance literature by creating a prediction model that includes four predictor variables, namely, leadership, supportive organizational culture, engagement, and role values with one dependent variable – ISP compliance. We then seek to find out which of the predictor variables are most influential in predicting ISP compliance. An instrument with 5 constructs was administered electronically to a diverse set of employees in the U.S.A. ranging in work experience from new hires to CEOs and from many industries. Collected data were analyzed using multiple regression analysis. The results showed that all predictor variables in the model were significant. Supportive organizational culture followed by role values offered the largest explanatory power of the four predictor variables. We discuss the implications of the findings and offer suggestions for future research opportunities.
Complexity of finite-variable fragments of propositional temporal and modal logics of computation

We prove that branching-time temporal logics CTL and CTL⁎ are polynomial-time embeddable into their single-variable fragments. It follows that satisfiability for CTL and CTL⁎, and therefore also for alternating-time temporal logics ATL and ATL⁎, in languages with one propositional variable is as algorithmically hard as satisfiability for the full logic: EXPTIME-complete for CTL and ATL, and 2EXPTIME-complete for CTL⁎ and ATL⁎. We discuss applicability of the technique used in the proofs to other modal and temporal logics, including propositional dynamic, epistemic, and temporal-epistemic logics, as well as many-dimensional and first-order modal and temporal logics.
An FPGA Overlay for CNN Inference with Fine-grained Flexible Parallelism

Increasingly, pre-trained convolutional neural networks (CNNs) are being deployed for inference in various computer vision applications, both on the server-side in the data centers and at the edge. CNN inference is a very compute-intensive task. It is a challenge to meet performance metrics such as latency and throughput while optimizing power. Special-purpose ASICs and FPGAs are suitable candidates to meet these power and performance budgets simultaneously. Rapidly evolving CNN architectures involve novel convolution operations such as point convolutions, depth separable convolutions, and so on. This leads to substantial variation in the computational structure across CNNs and layers within a CNN. Because of this, FPGA reconfigurability provides an attractive tradeoff compared to ASICs. FPGA-based hardware designers address the structural variability issue by generating a network-specific accelerator for a single network or a class of networks. However, homogeneous accelerators are network agnostic and often sacrifice throughput and FPGA LUTs for flexibility. In this article, we propose an FPGA overlay for efficient processing of CNNs that can be scaled based on the available compute and memory resources of the FPGA. The overlay is configured on the fly through control words sent by the host on a per-layer basis. Unlike current overlays, our architecture exploits all forms of parallelism inside a convolution operation. A constraint system is employed at the host end to find out the per-layer configuration of the overlay that uses all forms of parallelism in the processing of the layer, resulting in the highest throughput for that layer. We studied the effectiveness of our overlay by using it to process AlexNet, VGG16, YOLO, MobileNet, and ResNet-50 CNNs targeting a Virtex7 and a bigger Ultrascale+VU9P FPGAs. The chosen CNNs have a mix of different types of convolution layers and filter sizes, presenting a good variation in model size and structure. Our accelerator reported a maximum throughput of 1,200 GOps/second on the Virtex7, an improvement of 1.2 \( \times \) to 5 \( \times \) over the recent designs. Also, the reported performance density, measured in giga operations per second per KLUT, is 1.3 \( \times \) to 4 \( \times \) improvement over existing works. Similar speed-up and performance density is also observed for the Ultrascale+VU9P FPGA.
Inconsistent Defect Labels: Essence, Causes, and Influence

The label quality of defect data sets has a direct influence on the reliability of defect prediction models. In this paper, we conduct a systematic study of inconsistent defect labels in multi-version-project defect data sets, i.e., many instances having the same source code but different labels over multiple versions of a software project. First, we report the phenomena of inconsistent labels by real examples and analyze their essence in the context of defect prediction. Then, we uncover the causes that lead to the occurrence of inconsistent labels for the representative label collection approaches. Finally, we investigate the actual influence of inconsistent labels on defect prediction models. We find that inconsistent labels in general exist in six multi-version-project defect data sets (either widely used or the most up-to-date in the literature) collected by diverse label collection approaches. In particular, inconsistent labels in a training data set significantly reduce the prediction performance of a model, while inconsistent labels in a test data set can lead to a considerable evaluation bias on the real performance. Therefore, we recommend that: on the one hand, researchers leverage our findings to make targeted methodological improvements on existing defect label collection approaches to reduce the generation of inconsistent labels; on the other hand, practitioners detect and exclude inconsistent labels in defect data sets to avoid their potential negative influence on defect prediction.
LiDAR-camera-system-based 3D object detection with proposal selection and grid attention pooling

3D object detection is an important module for autonomous driving. A LiDAR camera optical system is suitable for accurate object detection, for it provides both 3D structure and 2D texture features. However, as LiDAR and a camera have different sensor properties, it is challenging to generate effective fusion features. Motivated by this, we propose, to the best of our knowledge, a novel LiDAR-camera based 3D object detection method. First, proposal selection is presented to utilize accurate 2D proposals predicted from RGB images to improve the quality of 3D proposals. It contains a (i) proposal addition and (ii) proposal filter. To increase the recall rate, the proposal addition generates extra 3D proposals via back-projecting 2D proposals on LiDAR depth. The proposal filter removes unrelated 3D proposals by matching 2D proposals with intersection-over-union thresholds. Then, considering the LiDAR mechanism, grid attention pooling is employed to estimate weights of grid points from LiDAR and image features to generate salient pooling features. Comparisons and ablation studies demonstrate that the proposed method achieves better performance and benefits the advanced application of a LiDAR camera system.
Using a Machine Learning Model for Malicious URL Type Detection

The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.
Self-Supervised Motion Perception for Spatiotemporal Representation Learning

In this study, we propose a novel pretext task and a self-supervised motion perception (SMP) method for spatiotemporal representation learning. The pretext task is defined as video playback rate perception, which utilizes temporal dilated sampling to augment video clips to multiple duplicates of different temporal resolutions. The SMP method is built upon discriminative and generative motion perception models, which capture representations related to motion dynamics and appearance from video clips of multiple temporal resolutions in a collaborative fashion. To enhance the collaboration, we further propose difference and convolution motion attention (MA), which drives the generative model focusing on motion-related appearance, and leverage multiple granularity perception (MG) to extract accurate motion dynamics. Extensive experiments demonstrate SMP's effectiveness for video motion perception and state-of-the-art performance of self-supervised representation models upon target tasks, including action recognition and video retrieval. Code for SMP is available at github.com/yuanyao366/SMP.
Investigating External Audit Records to Detect Fraudulent Firms Employing Various Machine Learning Methods

Fraudulent firm detection is a vital issue for the development of the economy worldwide. Auditing is a review process of financial statements that controls abnormal activities in the organization. Machine learning methods provide a faster solution to identify the fraudulent firm more precisely. The objective of this work is to propose a machine learning model that can investigate, predict fraudulent cases and generate more stable classification results. Therefore, we gathered audit data from the Kaggle repository and explored its characteristics. Then, highly collinear features have been reduced from this dataset. After that, various feature transformation methods were implemented and generated transformed datasets. Later, we employed several state-of-the-art classifiers into these datasets and investigated the performance of the classifier. Logistic regression shows the highest accuracy and F-measure with a confidence interval (98.6%±0.9%) for the squared transformed, and XGBoost provides the highest area under the curve with confidence interval (98.4%±0.9%) for the box-cox transformed dataset.
Deep Order-Preserving Learning With Adaptive Optimal Transport Distance

We consider a framework for taking into consideration the relative importance (ordinality) of object labels in the process of learning a label predictor function. The commonly used loss functions are not well matched to this problem, as they exhibit deficiencies in capturing natural correlations of the labels and the corresponding data. We propose to incorporate such correlations into our learning algorithm using an optimal transport formulation. Our approach is to learn the ground metric, which is partly involved in forming the optimal transport distance, by leveraging ordinality as a general form of side information in its formulation. Based on this idea, we then develop a novel loss function for training deep neural networks. A highly efficient alternating learning method is then devised to alternatively optimise the ground metric and the deep model in an end-to-end learning manner. This scheme allows us to adaptively adjust the shape of the ground metric, and consequently the shape of the loss function for each application. We back up our approach by theoretical analysis and verify the performance of our proposed scheme by applying it to two learning tasks, i.e. chronological age estimation from the face and image aesthetic assessment. The numerical results on several benchmark datasets demonstrate the superiority of the proposed algorithm.
UAV Dynamic Tracking Algorithm Based on Deep learning

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay; 2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability; 3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system.
Deep Reconstruction of 3-D Human Poses From Video

Deep learning advances have made it possible to recoverfull 3-D meshes of human models from individual images. However, the extension of this notion to videos for recovering temporally coherent poses is still underexplored. A major challenge in this direction is the lack of appropriately annotated video data for learning the desired computational models. The existing human pose datasets only provide 2-D or 3-D skeleton joint annotations, whereas the datasets are also insufficiently recorded in constrained environments. We first contribute a technique to synthesize monocular action videos with rich 3-D annotations that are suitable for learning computational models for full mesh 3-D human pose recovery. Compared to the existing methods that simply “texture map” clothes onto the 3-D human pose models, our approach incorporates Physics-based realistic cloth deformations with human body movements. The generated videos cover a large variety of human actions, poses, and visual appearances, while the annotations record accurate human pose dynamics and human body surface information. Our second major contribution is an end-to-end trainable recurrent neural network for full pose mesh recovery from monocular videos. Using the proposed video data and a long short-term memory recurrent structure, our network explicitly learns to model the temporal coherence in videos and imposes geometric consistency over the recovered meshes. We establish the effectiveness of the proposed model with quantitative and qualitative analysis using the proposed and benchmark datasets.
Blockchain Security Using Merkle Hash Zero Correlation Distinguisher for the IoT in Smart Cities

Internet of Things (IoT) data is one of the most important assets in business models for offering various ubiquitous and brilliant services. The IoT is provided with the advantage of susceptibility that cybercriminals and other malicious users. Even though smart cities are intended to extend productivity and efficiency, residents and authorities face risks when they avoid cybersecurity. The conventional blockchain methods were introduced to ensure the secure management and examination of the smart city big data. But, the blockchains are found to have computationally high costs, and failed to improve the security, not adequate resource-constrained IoT devices have been designated for smart cities. In order to address these issues, the proposed novel blockchain model called blockchain secured Merkle hash zero correlation distinguisher (BSMH-ZCD) is suitable for IoT devices within the cloud infrastructure. The objective of the BSMH-ZCD method is to enhance security and reduce the run time and computational overhead. Initially, the Merkle hash tree is used to create the hash value with every transaction. Next, the zero correlation distinguisher is applied to perform the data encryption and decryption operation for the ARX block for obtaining proficient secure data access in the IoT devices. Experimental assessment of the proposed BSMH-ZCD method and existing methods are carried out by using the taxi driver data set and Novel Corona Virus 2019 data set with different factors, such as running time, computational complexity, and security with respect to a number of blocks and executions. By using the taxi driver data set, the experimental results reveal that the BSMH-ZCD method performs better with a 19% improvement in security, 20% reduction of computational complexity, and 29% faster running time for IoT compared to existing works.
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition

The sensor-based human activity recognition (HAR) in mobile application scenarios is often confronted with sensor modalities variation and annotated data deficiency.Given this observation, we devised a graph-inspired deep learning approach toward the sensor-based HAR tasks, which was further used to build a deep transfer learning model toward giving a tentative solution for these two challenging problems.Specifically, we present a multi-layer residual structure involved graph convolutional neural network (ResGCNN) toward the sensor-based HAR tasks, namely the HAR-ResGCNN approach.Experimental results on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective at capturing the characteristics of actions with comparable results compared to other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%, respectively).More importantly, the deep transfer learning experiments using the ResGCNN model show excellent transferability and few-shot learning performance.The graph-based framework shows good meta-learning ability and is supposed to be a promising solution in sensor-based HAR tasks.
Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis

Abstract Reinforcement learning is a promising strategy for automatically training policies for challenging control tasks. However, state-of-the-art deep reinforcement learning algorithms focus on training deep neural network (DNN) policies, which are black box models that are hard to interpret and reason about. In this chapter, we describe recent progress towards learning policies in the form of programs. Compared to DNNs, such programmatic policies are significantly more interpretable, easier to formally verify, and more robust. We give an overview of algorithms designed to learn programmatic policies, and describe several case studies demonstrating their various advantages.
Hand Gesture and Arm Movement Recognition for Multimodal Control of a 3-DOF Helicopter

This paper presents the application of hand gestures and arm movements to control a dual rotor testbench. A multimodal control method is developed for a 3-degrees-of-freedom (DOF) tandem helicopter based on surface electromyography sensors and an inertial measurement unit (IMU) included in the Myo Armband sensor. The recognition system can classify five different hand gestures which are used for switching between flight modes and generating set point values for the helicopter. The 3-DOF helicopter testbench is fully designed and implemented as a low cost alternative for assessing the effectiveness of flight controls for unmanned aerial vehicles. The position of the helicopter is regulated by a cascade-dual-PID control scheme that allows a fast switching between a gesture mode and an IMU mode. Experimental results show the effectiveness of using hand gesture recognition and arm movement for controlling an aerial test bench with a fast and accurate response.
Privacy-Preserving k-Means Clustering over Blockchain-Based Encrypted IoMT Data

The data analyst aspires to train their ML model, such as k-means with the IoMT data. However, the data owners are not confident enough to share their private data with the data analyst for security and privacy concerns. This study proposes privacy-preserving k-means based on Paillier. All transactions are recorded in a distributed, immutable ledger for authenticity and secure data sharing among the participants. Three medical datasets are used and performance analysis exhibits that secure k-means achieves accuracies of 94.95%, 81.88%, and 78.10% on BCWD, HDD, and DD dataset, where standard techniques provide 96.60%, 81.00%, and 77.00%, respectively. On the other hand, secure k-means takes 2200 s, 1500 s, and 2605 s, where the standard method takes 3357 s, 2534 s, and 3709 s on BCWD, HDD, and DD datasets, respectively. Therefore, secure k-means can protect the privacy of the data owners, achieves almost comparable accuracy to the conventional methods, and outperforms them in time consumption.
Self-Supervised Multi-Category Counting Networks for Automatic Check-Out

The practical task of Automatic Check-Out (ACO) is to accurately predict the presence and count of each product in an arbitrary product combination. Beyond the large-scale and the fine-grained nature of product categories as its main challenges, products are always continuously updated in realistic check-out scenarios, which is also required to be solved in an ACO system. Previous work in this research line almost depends on the supervisions of labor-intensive bounding boxes of products by performing a detection paradigm. While, in this paper, we propose a Self-Supervised Multi-Category Counting (S2MC2) network to leverage the point-level supervisions of products in check-out images to both lower the labeling cost and be able to return ACO predictions in a class incremental setting. Specifically, as a backbone, our S2MC2 is built upon a counting module in a class-agnostic counting fashion. Also, it consists of several crucial components including an attention module for capturing fine-grained patterns and a domain adaptation module for reducing the domain gap between single product images as training and check-out images as test. Furthermore, a self-supervised approach is utilized in S2MC2 to initialize the parameters of its backbone for better performance. By conducting comprehensive experiments on the large-scale automatic check-out dataset RPC, we demonstrate that our proposed S2MC2 achieves superior accuracy in both traditional and incremental settings of ACO tasks over the competing baselines.
End-to-end Handwritten Chinese Paragraph Text Recognition Using Residual Attention Networks

Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a longterm focus in the field of pattern recognition research.The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets.To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function.The novel residual attention gate block is more helpful in extracting essential features and making the training of deep convolutional neural networks more effective.In addition, we introduce the operations of batch bilinear interpolation which implement the mapping of two dimension text representation to one dimension text line representation without any position information of characters or text lines, and greatly reduce the labeling workload in preparing training datasets.In experimental, the proposed method is verified with two widely adopted handwritten Chinese text datasets, and achieves competitive results to the current state-of-the-art methods.Without using any position information of characters and text line, an accuracy rate of 90.53% is obtained in CASIA-HWDB test set.