Reviewerly/exHarmony huggingface dataset:
Fair DMA Scheduler for Low-Latency Accelerator Offloading

Accelerators are increasingly being used on general-purpose servers to achieve higher computing performance than can be achieved by CPUs alone. Accelerators are expensive computing components and consume a lot of power, so one accelerator should be shared by multiple applications to save power consumption. However, there is a concern that transfer contention occurs when direct memory access (DMA) transfers are performed to accelerators simultaneously. In this paper, we propose a DMA scheduler that can mitigate the latency increase by DMA transfer contention. In the proposed method, all requests are divided into equally small units of data chunks, and DMA transfers are performed by selecting requests in a fair round-robin manner. This enables small requests that are sensitive to delay increase to be transferred in between transfers of preceding large requests, thus mitigating latency increase by DMA transfer contention. Fair transfer not only makes the proposed method versatile but also prevents excessive transfer latency for large requests. Through performance evaluation, we confirmed that the proposed method improves the transfer time of small requests by 60.0%–82.5%. We also found that the overhead impact of performing DMA transfers in small units is negligible. These results shows that the proposed method effectively reduces DMA transfer contention while having a small overhead impact on the overall accelerator offload system.
Online Handwriting Recognition using LSTM on Microcontroller and IMU Sensors

The trend toward the Internet of Things has led to a rapid increase in the amount of data that needs to be processed. Artificial intelligence (AI) can serve as a very helpful tool to extract or compress essential information of data. However, AI places high demands on a system’s hardware. This is not exactly in line with the strengths of embedded systems.This paper combines AI on embedded systems with the not-yet fully explored subject of online handwriting recognition (HWR). The main contribution is the deployment and real-time operation of AI on a microcontroller (MCU). Model architectures using long short-term memory (LSTM) cells and 1D convolutional neural networks (CNNs) are used to process live data from inertial measurement units (IMUs) sensors. The dataset used for training the AI models was recorded with a self-developed prototype. After training, the models are converted and deployed on a MCU. The conversion process includes quantization from a 32-bit floating-point to an 8-bit fixed-point datatype. The TensorFlow Lite Micro (TFLM) framework is used to run inference on the MCU. For predictions in real-time optimizations are applied to the framework, which results in running inference approx. 827 times faster. The optimized AI model implementation is then used to classify handwritten characters using the live data from the IMU sensors. This first approach has shown, that the separation of the symbols is necessary to be able to classify characters from live sensor data with high accuracy.
Discrete Event Simulation in Cloud-Edge Manufacturing Environments: Performance, Energy and Cost Trade-offs

Escalating the performance of Discrete Event Simulations (DESs) in manufacturing factory environ-ments plays a valuable role towards harnessing their real-time predictive and analytical capabilities. However, this attraction of performance-enhancement, whilst justified, has often caused the oversight of examination into how other notable metrics such as energy -efficiency and Total Cost of Ownership (TCO) affect the feasibility of DES management and implementation in factories. Hence, this work investigates how a line-balancing DES combination of performance, energy and TCO varies between low-resource edge devices, high-resource edge devices and remote High Performance Computing (HPC) clusters in the cloud. The findings demonstrate that, although an HPC cluster is noticeably more viable in terms of per-formance, it is not a consistently advantageous option for energy-efficiency and TCO. Alternately, we argue that a high-resource edge device displays itself as the preferred factory-appropriate hardware-choice to complement all three metrics-of-interest considered in this work.
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher

We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels [1] but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.
Math Chunking and Function Recognition using Deep Learning

In machine learning applications, mapping math knowledge from the series of tokens in a formula or expression to their linguistic semantic meaning remains an open area of research. One fundamental task towards that end is the chunking of a math equation/expression into meaningful math entities. It is the equivalent of sentence segmentation or chunking in natural language processing. Math chunking is quite broad and in a nascent stage in math linguistics. In this paper, we begin an exploration into this task using deep learning on a focused part of chunking, namely, recognition of functions (along with their arguments and parameters), in input equations. Specifically, we propose math-chunking models to identify a list of standard functions. We further develop an annotated dataset to train and evaluate our models. Our experimental results show that one of our proposed deep learning models, namely BiLSTM-CRF, can achieve rather high state-of-the-art performance on the mathematical formula chunking task.
An Effective Buffer Overflow Detection With Super Data-Flow Graphs

Buffer overflow vulnerabilities have been a severe threat to computer systems in the past few decades. Therefore, several static approaches have been proposed for automatic vul-nerability detection and fixing. Static approaches are efficient but require large memory space to maintain all necessary information at different program points for precision. Existing works are usually tradeoffs between throughput capacity and precision. In this paper, we propose a novel buffer overflow detection approach by performing the progressive data-flow evaluation on programs with their super data-flow graphs, which are expected to cover all real data-flow paths. For this purpose, we realized the super data-flow graph generation based on classic reaching-definition analysis, as well as the progressive data-flow evaluation approach. With progressive evaluation, we transform all necessary data-flow paths into SMT formulas and use an SMT solver to check whether these formulas satisfy certain vulnerable conditions. Finally, we evaluated our approach with Juliet Test Suites v1.3 and it detected all vulnerabilities with a very low false positive rate. Meanwhile, it also detected 8 known CVE vulnerabilities.
Improving Robustness: When and How to Minimize or Maximize the Loss Variance

We introduce distributional variance penalization, a strategy for learning with limited and/or mislabeled data. While minimizing the loss function currently stands as the training objective for many machine learning applications, it suffers from poor robustness. In this paper, we show that we can improve upon robustness issues by minimizing the average loss along with penalizing the variance. In particular, we expand on past studies of directly penalizing the variance which adjusts the weights of individual samples, resulting in improved robustness. However, the weights can take negative values and lead to unstable behavior. We introduce distributional variance penalization, which solves the issue of negative weights. Distributional variance penalization minimizes the expectation with respect to a distinct distribution that achieves a similar weighting scheme as direct variance penalization. We study the impact of both positive and negative variance penalization in the context of classification, and show that the generalization and the robustness against mislabeled data can be improved for a broad class of loss functions. Experimental results show that test accuracy improves by up to 20% compared to ERM when training with limited data or mislabeled data.
Exploring the Explicit Modelling of Bias in Machine Learning Classifiers: A Deep Multi-label ConvNet Approach *

This paper addresses the problem that many machine learning classifiers make decisions based on data that are biased and can therefore result in prejudiced decisions. For example, in education (which this paper focuses on) a student may be rejected from a course based on historical decisions in the data that only exist due to historical biases in society or due to the skewed sampling of the data. Other approaches to dealing with bias in data include resampling methods (to counter imbalanced samples) and dimensionality reduction (to focus only on relevant features to the classification task). In this paper, we explore issues of modelling bias explicitly so that we can identify the types of bias and whether they are accounting for inflated predictive accuracies. In particular, we compare graphical model approaches to building classifiers, that are transparent in how they make decisions, with two forms of Deep Multi-label Convolutional Neural Networks to investigate if models can be built that maximise accuracy and minimise bias. We carry out this comparison on student entry and performance data from a higher educational institution.
Attribute encryption based access control methods under airborne networks

In order to solve the problem of multiple information security risks and unclear intrusion paths under the airborne network, Firstly, this paper studies the cross-domain access of each security domain under the airborne network, Moreover, a access control method under the airborne network is proposed by combing attribute based encryption and the proposed method not only enables access control between different security domains under the airborne network, but also reduces the security risk of unauthorized access between different security domains under the airborne network. Finally, the simulation experiment proves that the implementation of access control function based on attribute encryption is feasible in the airborne network environment, and verifies the effectiveness of the attribute encryption-based technology in the airborne network and provides technical guarantee capability for the safe operation of air transportation, which can provide strong support for the wide application of attribute-based encryption technology.
Separating Flows in Encrypted Tunnel Traffic

In many scenarios like wireless Internet access or encrypted VPN tunnels, encryption is performed on a per-packet basis. While this encryption approach effectively protects the confidentiality of the transmitted payload, it leaves traffic patterns involving inter-arrival times and packet lengths observable, e.g., to eavesdroppers on the air interface. It is a widespread belief that by only observing interleaved packets of different parallel flows, analysis and classification of the corresponding traffic by an eavesdropper is very difficult or close to impossible.In this paper, we show that it is indeed possible to separate packets belonging to different flows purely from patterns observed in the interleaved packet sequence. We devise a novel deep recurrent neural network architecture that allows us to detect individual anomalous packets in a flow. Based on this anomaly detector, we develop an algorithm to find a separation into flows that minimizes the anomaly score indicated by our model. Our experimental results obtained with synthetically crafted flows and real-world network traces indicate that our approach is indeed able to separate flows successfully with high accuracy.Being able to recover a flow's packet sequence from multiple interleaved flows, we show with this paper that the common packet-level encryption might be insufficient in scenarios where high levels of privacy have to be achieved. On the defender's side, our approach constitutes a valuable tool in encrypted traffic analysis, but also contributes a novel neural network architecture in the field of network intrusion detection in general.
Extending Rapid Class Augmentation to a YOLOv3 Object Detection Architecture

This paper integrates and adapts the original eXtending Rapid Class Augmentation (XRCA) progressive learning framework into a YOLOv3 object detection architecture and documents hundreds of times faster learning rates compared with a progressive stochastic gradient descent (SGD) training approach on the 80-class COCO object detection data set. The approach uses a YOLOv3 architecture to extract the features upon which the various operational modes of XRCA act to progressively update the weights that predict new object confidences, object classes, and their bounding boxes. The new XRCA-YOLOv3 framework is shown to eliminate the well-known problem of ‘catastrophic forgetting’ that often occurs when a model’s prediction head is trained on only new data in a sequential manner. This ability is shown to work for both the YOLO confidence weights as well as its box-class weights and enables XRCA-YOLOv3 to achieve model augmentation training times that are 2 orders of magnitude faster than SGD progressive techniques, while also providing the ability to discard all previously seen training data without affecting the performance of future classes.
Towards Fairness and Interpretability: Clinical Decision Support for Acute Coronary Syndrome

Machine learning based prediction of acute coronary syndrome positive patients is becoming increasingly common. However, there is little to no research in terms of assessing fairness and interpretability of these methods. This study discusses methodologies to reduce fairness related disparities in acute coronary syndrome predictions made by machine learning algorithms for different demographics sections of the population. Additionally, existing interpretability technique called LIME is used to understand predictions made using fairness methodologies. Our analysis show that discussed methods have: (1) potential to reduce treatment disparities; (2) generalize well to different healthcare setting in the United States; and (3) reduce overall false negative rates by 0.34 and improve AUROC score by 0.11 when compared to algorithms like XGBoost.
Transfer Learning model for Social Emotion Prediction using Writers Emotions in Comments

Social emotion prediction is concerned with the prediction of the reader’s emotion when exposed to a text. In this paper, we propose a transfer learning approach to social emotion prediction, where the source task is writer’s emotion prediction, an area in which models are advanced due to the rich literature and availability of large and high-quality training datasets. We utilized a pre-trained writer’s emotion prediction model to predict the writer’s emotion in comments, then we aggregated the emotions and trained a classifier to predict social emotion for posts. Results show that pre-trained models for writer’s emotion prediction can improve the prediction of social emotion. Furthermore, we demonstrate that our proposed model outperforms popular models in terms of F1-score and performs similarly to the best model in terms of Acc@1.
CNNBooster: Accelerating CNN Inference with Latency-aware Channel Pruning for GPU

Channel pruning is one of the mainly used meth-ods in current network model compression. However, existing channel pruning methods lack effective hardware runtime latency guidance, making the reduction in model size not fully converted into inference latency reduction, which results in poor inference performance of the pruned network models. This paper proposes a CNN channel pruning framework, CNNBooster, that incorporates hardware runtime latency in-formation, whose two major contributions are as follows: First, CNNBooster automatically analyses the latency behavior of various CNNs with channel reduction on different GPU hard-ware platforms, which achieves efficient localization of pruna-ble coordinates. Second, CNNBooster uses a flexible grained latency-aware and param-aware pruning algorithm based on prunable coordinates to achieve a significant reduction in model inference latency and parameter size. We evaluate CNNBooster with three benchmark models (VGG16, ResNet18 and MobilenetV1) on three NVIDIA-series platforms (V100, RTX 2080 Ti and Jetson Nano). The experimental results show that CNNBooster can achieve a maximum 67.51 % latency re-duction and 1.4x performance improvement compare with current state-of-the-art works.
Deeper Bidirectional Neural Networks with Generalized Non-Vanishing Hidden Neurons

The new NoVa hidden neurons have outperformed ReLU hidden neurons in deep classifiers on some large image test sets. The NoVa or nonvanishing logistic neuron additively perturbs the sigmoidal activation function so that its derivative is not zero. This helps avoid or delay the problem of vanishing gradients. We here extend the NoVa to the generalized perturbed logistic neuron and compare it to ReLU and several other hidden neurons on large image test sets that include CIFAR-100 and Caltech-256. Generalized NoVa classifiers allow deeper networks with better classification on the large datasets. This deep benefit holds for ordinary unidirectional backpropagation. It also holds for the more efficient bidirectional backpropagation that trains in both the forward and backward directions.
On Selection of Optimal Kernel Function for Software Defect Prediction

Software defect prediction is used to identify the most likely defect-prone modules. This activity can help the quality assurance team to distribute limited resources efficiently as well as reduce the time and cost of the testing phase. Support Vector Machine (SVM) has been widely used to build software defect prediction models. Nevertheless, the accuracy of such method depends on multiple factors such as the type of SVM method, choice of kernel functions and dimensionality of the dataset. This paper continues along that research line and examines the impact and stability of four kernel functions and feature dimensionality reduction on the performance of SVM for software defect pre- diction. Comprehensive experiments have been conducted using four kernel functions, ten feature selection thresholds based on the Information gain algorithm, and seven public datasets. This has resulted in 280 experiments. The findings demonstrate that there is no kernel function that can show stable performance across different experimental settings. However, both Radial basis and Sigmoid kernel functions showed more stability than Polynomial and Linear kernel functions. In addition, changing the feature set from high to low dimensionality for each kernel function did not always improve the accuracy of SVM, which contradicts the findings that say using a small set of features can be as effective as using a large set of features.
Parallel Processing Techniques for Analyzing Large Video Files: a Deep Learning Based Approach

Videos are a popular type of media that require analysis to extract the information underlying the data in a timely manner. Often due to the very large size of such data and the involvement of computationally expensive operations, performing the analysis can take a significant amount of time. This paper presents techniques to speed up deep learning-based analysis to perform tasks like tracking objects and filtering video data by applying parallel processing techniques. The proposed approach and techniques leverage parallel processing on two levels: by using GPUs for analyzing individual frames and by distributing the processing load over a fleet of Executor nodes. Experiments with Apache Spark and TensorFlow-based prototypes built for handling various video analysis use cases were conducted on an Amazon EC2 cloud for various combinations of system and workload parameters. Insights into system performance including the reduction in processing time that accrues from applying the proposed parallel processing technique in each scenario are reported in the paper.
Uncertainty Prediction for Facial Action Units Recognition under Degraded Conditions

Facial action units (AUs) represent muscular activities, and their recognition from facial images can capture various psychological states, such as people’s interests as consumers and mental health states. However, degradation of conditions, such as occlusions by hand, often occurs and affects the accuracy of AUs recognition in the real world. Most existing studies on degraded conditions have adopted the approach using additional training images and advanced structures of neural networks to improve the robustness of AUs recognition from a degraded facial image. However, such an approach cannot deal with cases in which evidence of the AUs is completely or almost invisible. Therefore, we propose a novel method to address the degraded conditions by predicting the uncertainties of the AUs recognition caused by them. Our method interpolates the high-uncertainty data using surrounding data to reduce the influence of the degraded conditions, and visualizes the conditions causing the uncertainties to handle cases where the conditions are very poor and need to be improved. In the evaluation experiments, the public datasets BP4D+ and DISFA were modified to degrade them for testing. By evaluating the modified test data, we demonstrated that the maximum improvement with our method was 12% for BP4D+ and 17% for DISFA, and that our method can prevent the decrease in accuracy owing to degraded conditions. We also presented some visualization examples which demonstrate that our method can reasonably predict the conditions and uncertainties.
A Hybrid Model for QoS Prediction based on Improved Conditional Variational Autoencoder

As a nonfunctional attribute of Web services, Quality of Service (QoS) is a crucial criterion that researchers utilize to develop personalized service recommendation. The QoS history is thin and contains noisy data because of the network environment and other inevitable factors. In order to increase the effectiveness of Web service recommendation, it is essential to accurately predict the missing QoS values. Therefore, a model for QoS prediction based on improved Conditional Variational Autoencoder is proposed in order to improve the prediction accuracy of missing QoS. The Bias based Singular Value Decomposition (BiasSVD) is employed by the model to pre-populate the initial sparse QoS matrix with missing values. By improved Conditional Variational Autoencoder, the pre-filled matrix is learned and the QoS is reconstructed. Then, the users' plausible similar neighbors are filtered using the Pearson Correlation Coefficient and Independent Sample T-test. The missing QoS is predicted by combining the reconstruction matrix. Last but not least, the experimental results on the actual dataset WSDream demonstrate that the model improves the QoS prediction accuracy to considerable degree.