Reviewerly/exHarmony huggingface dataset:
A Pseudo Label-Wise Attention Network for Automatic ICD Coding

Automatic International Classification of Diseases (ICD) coding is defined as a kind of text multi-label classification problem, which is difficult because the number of labels is very large and the distribution of labels is unbalanced. The label-wise attention mechanism is widely used in automatic ICD coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different ICD codes. However, the label-wise attention mechanism is redundant and costly in computing. In this paper, we propose a pseudo label-wise attention mechanism to tackle the problem. Instead of computing different attention modes for different ICD codes, the pseudo label-wise attention mechanism automatically merges similar ICD codes and computes only one attention mode for the similar ICD codes, which greatly compresses the number of attention modes and improves the predicted accuracy. In addition, we apply a more convenient and effective way to obtain the ICD vectors, and thus our model can predict new ICD codes by calculating the similarities between EMR vectors and ICD vectors. Our model demonstrates effectiveness in extensive computational experiments. On the public MIMIC-III dataset and private Xiangya dataset, our model achieves the best performance on micro F1 (0.583 and 0.806), micro AUC (0.986 and 0.994), P@8 (0.756 and 0.413), and costs much smaller GPU memory (about 26.1% of the models with label-wise attention). Furthermore, we verify the ability of our model in predicting new ICD codes. The interpretablility analysis and case study show the effectiveness and reliability of the patterns obtained by the pseudo label-wise attention mechanism.
Efficient Incremental Computation of Aggregations over Sliding Windows

Computing aggregation over sliding windows, i.e., finite subsets of an unbounded stream, is a core operation in streaming analytics. We propose PBA (Parallel Boundary Aggregator), a novel parallel algorithm that groups continuous slices of streaming values into chunks and exploits two buffers, cumulative slice aggregations and left cumulative slice aggregations, to compute sliding window aggregations efficiently. PBA runs in O(1) time, performing at most 3 merging operations per slide while consuming O(n) space for windows with n partial aggregations. Our empirical experiments demonstrate that PBA can improve throughput up to 4X while reducing latency, compared to state-of-the-art algorithms.
Echocardiography image enhancement using texture-cartoon separation

Due to the speckled nature of cardiac ultrasound imaging, it is not easy to process and extract useful information directly from the acquired image. In this work, we have proposed a method to reduce the effect of speckle artifacts through the decomposition of echocardiography images into cartoon and texture components. The first component (i.e., cartoon image) contains image structures containing smooth areas and sharp edges, and the texture component is mainly composed of highly oscillating and repetitive patterns. To decompose the image into these two subcomponents, convolutional sparse coding has been utilized as a solid tool for solving the decomposition optimization function. The significant advantage of using convolutional sparse coding, compared to classical sparse coding methods, is image quality enhancement due to not using the block coding, making the classic solutions computationally feasible. The original image has been masked with the cartoon part leading to suppress speckle artifacts which result in image quality enhancement. Besides, it has been shown that using this speckle reduction scenario, considerable accuracy enhancement of the segmentation task can be achieved, compared to segmentation of the original image. Numerical results provide acceptable reasons to prove the efficiency of the proposed algorithm. Resulting echocardiography videos show a mean segmentation enhancement of 15.98 for Hausdorff distance (in pixels) and 0.0632 for the Dice similarity coefficient.
The financial impacts of information systems security breaches on publicly traded companies: reactions of different sectors

Purpose Today, information systems and technology provides a wide set of tools for companies to increase the efficiency of their businesses. Although technology offers many benefits to businesses, it also brings risks as the information systems security breaches. Security breaches and their financial impact is a constant concern of the researchers and practitioners. This paper explores information systems breaches and their financial impacts on the publicly traded companies in different sectors. Design/methodology/approach After a comprehensive data collection process, data from 192 events are analyzed by employing Event Study Methodology and a comparison of the results between the four highly affected sectors (Consumer Goods, Technology, Financial and Communications) is presented. The abnormal returns on the prices of stocks after the events are calculated with the Market Model. Also, the results of the Market Adjusted Model and Mean Adjusted Model are presented to support the results. Findings While information systems security breaches have a significant negative impact on the Financials and the Technology sectors for all the event windows in the study ([−5, 0], [−5, 1], [−5, 5], and [−5, 10]), the significant negative impact is observed only on the [−5, 5] and [−5, 10] event windows for the Consumer Goods sector. No significant negative impact is observed in the Communications sector, in fact, the cumulative abnormal returns are positive for this sector. Originality/value The contribution of this paper to provide evidence about the financial impacts of the information systems breaches for businesses in different sectors. While there are studies that have previously focused on the information systems breaches and their financial impacts on businesses, to the best of our knowledge, this is the first study that compares this effect between the four highly impacted sectors. With a relatively larger sample size and broader event windows than the past studies in the literature, statistical evidence is provided to managers to justify their investments in information security and build preventive measures to secure the market value of their firms.
Lavs: A Lightweight Audio-Visual Saliency Prediction Model

Audio information is essential for guiding human attention and visual perception, which has been verified by many comprehensive psychological studies. However, the audio modality has been rather neglected in modeling visual attention, most of the current visual attention models heavily depend on visual information. Additionally, current existing high-performing visual attention models rely on deeper convolution neural networks (CNNs), benefiting from their extraordinary feature learning ability but incurring high computational cost. To this end, we propose a novel lightweight audio-visual saliency (LAVS) model to efficiently address the problem of fixation prediction in videos. To the best of our knowledge, our proposed model constitutes the first attempt to exploit a lightweight network and combines the visual and audio cues to perform saliency estimation in videos. Specifically, our proposed model consists of four modules, which are spatial-temporal visual saliency estimation module, audio features extraction module, source sound localization module, and audio-visual saliency fusion module. Extensive experiments across datasets validate the effectiveness and real-time performance of the proposed LAVS model, which outperforms the other state-of-the-art methods.
Comparative Case Study of Machine Learning Classification Techniques Using Imbalanced Credit Card Fraud Datasets

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Machine Learning-Assisted Channel Estimation in Massive MIMO Receiver

We propose a new Machine Learning (ML) approach to channel estimation (CE) in Massive Multiple Input Multiple Output (MIMO) receivers. The algorithm employs a recurrent neural network (RNN) for iterative channel tap search and nonlinear de-noising of their amplitudes in the time domain. Our method outperforms the sparse minimum mean square error (MMSE) CE that is based on time-domain windowing and a further linear denoising of channel taps within the window. Simulation results are presented for user speed of 5km/h in non-line-of-sight scenarios of the 5G QuaDRiGa 2.0 channel. The results are provided in both antenna and beamspace domains of the 64 antennas receiver.
One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order

Neural Architecture Search (NAS), which aims at automatically designing neural architectures, recently draw a growing research interest. Different from conventional NAS methods, in which a large number of neural architectures need to be trained for evaluation, the one-shot NAS methods only have to train one supernet which synthesizes all the possible candidate architectures. As a result, the search efficiency could be significantly improved by sharing the supernet’s weights during the candidate architectures’ evaluation. This strategy could greatly speed up the search process but suffer a challenge that the evaluation based on sharing weights is not predictive enough. Recently, pruning the supernet during the search has been proven to be an efficient way to alleviate this problem. However, the pruning direction in complex-structured search space remains unexplored. In this paper, we revisited the role of path dropout strategy, which drops the neural operations instead of the neurons, in supernet training, and several interesting characters of the supernet trained with dropout are found. Based on the observations, a Hierarchically-Ordered Pruning Neural Architecture Search (HOPNAS) algorithm is proposed by dynamically pruning the supernet with a proper pruning direction. Experimental results indicate that our method is competitive with state-of-the-art approaches on CIFAR10 and ImageNet.
Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

Abstract This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.
SM‐Detector: A security model based on BERT to detect SMiShing messages in mobile environments

Summary The growing use of SMS by businesses to communicate with their customers has made attackers more interested in smishing attacks. Smishing is a security attack that involves sending a fake SMS message in order to steal the personal credentials of mobile users. This kind of attack has become a serious cyber‐security issue and has caused great financial losses for both people and businesses. In this article we propose a hybrid security model called “SM‐Detector” aiming to detect smishing messages in mobile environments. To increase the efficiency of “SM‐Detector,” we have combined three different detection methods: (i) identification of malicious URLs, (ii) identification of suspected words, phone numbers and emails with regular expression analysis, and (iii) classification of messages using BERT‐based algorithms to distinguish spam messages. “SM‐Detector” also includes a mobile application allowing the user to check their SMS and report smishing messages. Its strength is that it can deal with mixed text messages written in Arabic or English. The experimental evaluation conducted on English and Arabic datasets showed a remarkable accuracy of 99.63%.
A Real-time Object Detection Solution and Its Application in Transportation

Object Detection Algorithms is widely used in transportation. With YOLOv3 however, it is impossible to achieve real-time detection. This paper made some adjustments to YOLOv3, and proposed a new light-scale model named MobileNetv1_yolov3lite. In our MobileNetv1_yolov3lite, we use MobileNetv1 instead of Darknet53 as our backbone network, and we use a newly proposed module yolov3lite for feature fusion. These adjustments achieve significant increases in detecting speed, and can achieve real-time detection. However, it suffers from accuracy loss. In order to improve detecting accuracy, we further modify loss function as well as training methods, which contributes to a higher accuracy.
Deep Inclusion Relation-aware Network for User Response Prediction at Fliggy

User response prediction plays a crucial role in many applications (e.g. search ranking and personalized recommendation) at online travel platforms. Although existing methods have made a great success by focusing on feature interaction or user behaviors, they cannot synthetically exploit item inclusion relations describing relationships of an item including or being included by another one, which are important components among travel items. To this end, in this paper, we propose a novel Deep Inclusion Relation-aware Network (DIRN) for user response prediction by synthetically exploiting inclusion relations among travel items. Specifically, on the item graph constructed with inclusion relations, we first leverage a node embedding approach to learn the item graph-based embedding. Then, we design Representation-based Interest Layer and Relation Path Interest Layer to extract user latent interest with user behaviors in two ways. Representation-based Interest Layer models the item-to-item similarity based on item representations containing the graph-based embedding with an attention mechanism and obtains user temporal interest by summing up representations of interacted items with similarities. Relation Path Interest Layer measures item-to-item realistic associations to extract user interest with inclusion relation paths. Offline experiments on a real-world data from Fliggy clearly validate the effectiveness of DIRN. Furthermore, DIRN has been successfully deployed online in search ranking at Fliggy and achieves significant improvement.
CoLEAP: Cooperative Learning-Based Edge Scheme With Caching and Prefetching for DASH Video Delivery

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.
Imbalanced Fault Diagnosis Based on Particle Swarm Optimization and Sparse Auto-Encoder

Imbalanced fault diagnosis becomes increasingly im-portant as the number of fault samples is relatively small in practical situations. Sparse auto-encoder(SAE) has been well addressed in fault diagnosis while it is not suitable for imbalanced fault diagnosis. Cost sensitive learning can be utilized to extend the sparse auto-encoder to cost sensitive sparse auto-encoder(CS-SAE). However, the class weights assigned to different classes are usually unknown in practice. So we propose to use particle swarm optimization(PSO) to optimize the class weights for cost sensitive sparse auto-encoder(PSO-CSSAE). The experiments have shown that the proposed approach consistently outperforms the state of the art on Tennesse Eastman(TE) dataset.
Structure-Attribute-Based Social Network Deanonymization With Spectral Graph Partitioning

Online social networks have gained tremendous popularity and have dramatically changed the way we communicate in recent years. However, the publishing of social network data raises more and more privacy concerns. To protect user privacy, social networking data are usually anonymized before being released. Nevertheless, existing anonymization techniques do not have sufficient protection effects. A large number of deanonymization attacks have arisen, and they mainly make use of either network topology or node attribute information to successfully reidentify anonymized users. In this article, we model a social network as a structure-attribute network (SAN) integrating the structural characteristics and the attribute information associated with social network users. A novel similarity measurement of social network nodes is proposed by considering the structural similarity and attribute similarity. A two-phase scheme is then designed to perform deanonymization by first dividing a social network (graph) into smaller subgraphs based on spectral graph partitioning and then applying the proposed deanonymization algorithm on each matched subgraph pair. We simulate the deanonymization attack with extensive experiments on three real-world datasets, and the experimental results demonstrate that our approach can improve the accuracy and time complexity of deanonymization compared with the state of the art.
Smart material to build mail spam filtering technique using Naive Bayes and MRF methodologies

In this Communication Era, E-mail services play a very important role. These E-mails are threatened by unsolicited messages called as spam messages. This issue of spam E-mail is addressed by several Filtration techniques. Still, a complete solution has yet been found to this problem. To overcome the disadvantages of various filtering techniques, a Hybrid technique is created by combining the Naive Bayes Algorithm and the Markov Random Field. The Naive Bayes algorithm is used to determine the prevalence and configuration of values in a dataset and to perform a basic probabilistic classification operation in this proposed hybrid technique. The naive Bayes technique employs the Bayes theorem to determine if an e-mail is spam or not. Then, the Hidden Markov Field is used to simulate the statistical action of spam. To create function vectors, an arriving message (email) must be broken down into features. These selected features can be weighted to compensate for inter-word dependence in the learning algorithms. This is accomplished by redefining the good and useful features to include both independent tokens and subgroups of sequential tokens, as well as by adjusting the grouping window's duration. By requiring smaller weightings for shorter gatherings of simultaneous tokens, the inter-word dependency of linguistic structure can be compensated. As a result, the shortcomings of both algorithms are overcome. Additionally, the findings demonstrate that this hybrid approach has resulted in an efficient spam detection solution in terms of accuracy and time consumption.
Can We Replace Real-World With Synthetic Data in Deep Learning-Based ADAS Algorithm Development?

Deep learning models require vast amounts of annotated training data. Gathering and annotating the data from the real world is an expensive and time-consuming process. Thus, synthetically generated data are being researched more and more. This article tries to answer the question of whether and to what extent synthetically generated data can help in developing the advanced driver-assistance system algorithms for autonomous vehicles, for the object detection task based on deep learning. Two state-of-the-art deep learning object detectors were trained on various combinations of real-world and synthetic data. A total of 12 detectors were tested on real-world test images. Results show that synthetic data can contribute to better detector performance until a certain ratio between real-world and synthetic data is reached.
CATFPN: Adaptive Feature Pyramid With Scale-Wise Concatenation and Self-Attention

It is a typical problem in the field of object detection to simultaneously detect objects with large scale variation in one image. Recently proposed state-of-the-art object detectors generally learn pyramidal feature representation to deal with the scale variation, which has been proved effective via various feature pyramid networks. However, the majority of the feature pyramid networks based on heuristic feature fusion strategies may be suboptimal, as excess human guidance will restrict the self-learning of deep neural networks. An adaptive feature pyramid is bound to provide a significant performance boost. In this paper, we propose a novel feature pyramid network named CATFPN that consists of Scale-Wise Feature Concatenation (SWFC) module and Global Context (GC) block. The SWFC module evenly distributes semantic features for each feature layer and the GC block introduces a self-attention mechanism. As a feature pyramid network, the CATFPN can be applied to any detector based on multi-scale features. We adopt the CATFPN in typical RetinaNet and Faster R-CNN detector models, without bells and whistles, achieving 1.1% AP and 0.7% AP improvements over FPN on the MS COCO benchmark, respectively. Our competitive performance reported on the test-dev subset of COCO achieves 42.3% AP.