Reviewerly/exHarmony huggingface dataset:
Multi-Stage Residual Fusion Network for LIDAR-Camera Road Detection

Only a few existing works exploit multiple modalities of data for road detection task in the context of autonomous driving. In this work, a deep learning based approach is developed to fuse LIDAR point cloud and camera image features over a bird's eye view representation. A two-stream fully-convolutional network is designed as encoder to extract general features of two types of data. Instead of limiting the fusion processing at a single stage or to a predefined extent, we propose a multi-stage residual fusion strategy to merge the feature maps in a residual learning fashion, and integrate the information at different network depth. Experiments conduct on KITTI road benchmark show that our proposed method has a significant improvement over single modality methods and other fusion approaches. And it is also among the top-performing algorithms.
Digital Society - Opportunity or Threat? Case Studies of Japan and the Czech Republic

Digital technologies, autonomous systems, and artificial intelligence are seen as mater-of-course parts of the world today. Japan, as one of the leading countries in technological innovations, is the most advanced in terms of approach to digital society. Japanese society and government have been working on a new concept of life where all the digital technologies will communicate through the internet. Society organised in this way is called Society 5.0 - super Smart society. In Japan, this concept is centred about seventeen sustainable goals (United Nations, 2018). Society 5.0 is a reaction to the fundamental technological revolutionary changes known today as Industry 4.0. Thus, Society 5.0 is the consequence of a technological revolution that will eventually affect not just the production but all parts of today's life as well.The new technologies evoke some fundamental philosophical and existential questions. How can technological advances improve the prosperity and functioning of society? What about the privacy and security of all the users? The present paper compares Japan and the Czech Republic from the perspective of digitalization of society based on three main aspects: government programs supporting digital technologies, number of technically educated experts and investments in science and research. The objective is to find out how much the Czech Republic has advanced towards preparations for digital society.
Two-Stream Network for Detecting Double Compression of H.264 Videos

Double compression (DC) involves compressing a video twice with the video codec. DC usually occurs when a compressed video is manipulated, because most videos are saved in the compression format after manipulating. Given this principle, if we can detect DC traces in videos, we can determine whether a video has been manipulated. Prior works have proposed methods for detecting DC of H.264 videos; however, they have limitations in that they can detect DC only for a limited dataset and set of compression parameters. To overcome these limitations, we propose a two-stream neural network that incorporates two components that analyze intra-coded frames and predictive frames. We explain why the proposed two-stream neural network can detect DC traces in videos more accurately than the prior method, and we demonstrate that the proposed network can detect DC videos that have various contents and parameters through extensive experiments.
FPO tree and DP3 algorithm for distributed parallel Frequent Itemsets Mining

Frequent Itemsets Mining is a fundamental mining model in Data Mining. It supports a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Many distributed parallel algorithms have been introduced to confront with very large-scale datasets of Big Data. However, the problems of running time and memory scalability still have not had adequate solutions for very large and "hard-to-mined" datasets. In this paper, we propose a distributed parallel algorithm named DP3 (Distributed PrePostPlus) which parallelizes the state-of-the-art algorithm PrePost+ and operates in Master-Slaves model. Slave machines mine and send local frequent itemsets and support counts to the Master for aggregations. In the case of tremendous numbers of itemsets transferred between the Slaves and Master, the computational load at the Master, therefore, is extremely heavy if there is not the support from our complete FPO tree (Frequent Patterns Organization) which can provide optimal compactness for light data transfers and highly efficient aggregations with pruning ability. Processing phases of the Slaves and Master are designed for memory scalability and shared-memory parallel in Work-Pool model so as to utilize the computational power of multi-core CPUs. We conducted experiments on both synthetic and real datasets, and the empirical results have shown that our algorithm far outperforms the well-known PFP and other three recently high-performance ones Dist-Eclat, BigFIM, and MapFIM.
SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks

In this paper, we propose SF-CNN, a fast convolutional neural network structure for JPEG image compression artifacts removal. Recently, Convolutional Neural Network (CNN)-based image restoration has shown great performance improvement. However, its heavy computational cost makes it difficult to apply to other uses such as high-level vision tasks. Since heavy computation arises from maintaining the spatial resolution of an input image, some works make a structure that is composed of spatial downsampling and upsampling operations. SF-CNN takes Spatial input and predicts residual Frequency using downsampling operations only. Since every 8×8 pixel is grouped and spatially invariant in the JPEG DCT domain, it is possible to down sample the input by a factor of 8 to reduce the computational cost. We show this simple structure is effective for compression artifacts removal. Our scalable baseline networks achieve results comparable to to the reference networks in reduced computations.
The Cost of Pie Charts

Visualization of proportions is a very common need and there are many techniques for it. The pie chart is popular among practitioners and general audience, but many prominent experts advise against using it. This paper reports an experiment where the pie chart is compared to stacked bar charts with a baseline condition of table. The aim is to study if the performance differences justify the bad reputation of pie charts. In the experiment the participants were requested to list out the elements of a visualization in descending order of size. The task time and the answer were recorded. The results show that the pie chart is slower and less accurate than the stacked bar chart, especially when the difference between the elements is small, but the participants find it slightly more pleasant to use. The participants also perceive the stacked bar chart as the most effective visualization.
Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Smart agriculture has emerged as a rich application domain for AI-driven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture's promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.
Deep Kinship Verification VIA Appearance-Shape Joint Prediction and Adaptation-Based Approach

Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variations between kinship pairs. In this work, we build a novel appearance and shape based deep learning pipeline. First we adopt the knowledge learned from general face recognition network to learn general facial features. Afterwards, we learn kinship oriented appearance and shape features from kinship pairs and combine them for the final prediction. We have evaluated the model performance on a widely used popular benchmark and demonstrated the superiority over the state-of-the-art.
Using convolutional neural networks to identify patient safety incident reports by type and severity

Abstract Objective To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. Materials and Methods A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from a statewide incident reporting system. Generalizability was evaluated using an independent hospital-level reporting system. CNN architectures were examined by varying layer size and hyperparameters. Performance was evaluated by F score, precision, recall, and compared to binary support vector machine (SVM) ensembles on 3 testing data sets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent = 6000/5950). Results A CNN with 6 layers was the most effective architecture, outperforming SVMs with better generalizability to identify incidents by type and severity. The CNN achieved high F scores (&amp;gt; 85%) across all test data sets when identifying common incident types including falls, medications, pressure injury, and aggression. When identifying common severity levels (medium/low), CNN outperformed SVMs, improving F scores by 11.9%–45.1% across all 3 test data sets. Discussion Automated identification of incident reports using machine learning is challenging because of a lack of large labelled training data sets and the unbalanced distribution of incident classes. The standard classification strategy is to build multiple binary classifiers and pool their predictions. CNNs can extract hierarchical features and assist in addressing class imbalance, which may explain their success in identifying incident report types. Conclusion A CNN with word embedding was effective in identifying incidents by type and severity, providing better generalizability than SVMs.
Labanotation Generation Based on Bidirectional Gated Recurrent Units with Joint and Line Features

Labanotation is an effective carrier for recording and displaying three-dimensional human movements. In the existing methods of Labanotation generation, the spatial characteristics are not fully considered. In addition, Long-term correlation of time series is not reflected. In this paper, we propose a novel method based on Bidirectional Gated Recurrent Units with Joint and Line features which can efficiently convert human movements into Labanotation. Firstly, Joint feature carries the location information; Line feature contains the direction information. These two types of features make full use of the correlation and co-occurrence between adjacent joints, thus embodying good spatial characteristics. Secondly, Bidirectional Gated Recurrent Units are applied to identify human movements. The unique gate-control structure of Bi-GRU can predict current status based on historical and future information. Thus, this method is good in timing modeling especially for long time series. The experimental results show that this method achieved an accuracy of 97.4%. It is higher than the state of the art, which demonstrating the effectiveness of our proposed method.
Fast Inpainting-Based Compression: Combining Shepard Interpolation with Joint Inpainting and Prediction

Inpainting-based compression has been suggested as a qualitative competitor to the JPEG family of transform-based codecs, specifically for high compression ratios. However, it also requires sophisticated interpolation, data optimisation and encoding tasks that are both slow and hard to implement. We propose a fast and simple alternative that combines Shepard interpolation with a novel joint inpainting and prediction approach. It represents the image by a fraction of its pixel values on a sparse regular subgrid that are selected by an efficient optimisation strategy. Experiments show that our codec is up to five orders of magnitude faster than traditional inpainting-based approaches. Qualitatively, it can surpass transform-based codecs, in particular for high compression ratios and cartoon-like images.
CiSA: An Inclusive Chatbot Service for International Students and Academics

In the present study, we developed a chatbot service termed ‘CiSA’ (Chatbot for International Students and Academics) to enable international students and academics to effectively acquire essential information regarding their academic and campus life. To investigate the pain points and needs of our target user group, user research consisting of interviews and surveys was conducted. Based on the key findings from qualitative analysis, the concept was further refined. A flowchart was constructed to illustrate the designed conversations, while the user interface components were determined to express the responses of the chatbot. The design was further prototyped using Google’s DialogFlow, and implemented in Facebook Messenger. Finally, the interviews with the target users were conducted to verify the effectiveness, satisfaction, and extensibility of the service. The outcome of this study provides a powerful way to support the facilitation of communication and social inclusion by using this conversational agent. Furthermore, by focusing on enhancing the accessibility, the research contributes towards the practical understanding of the investigation of its service design for chatbot aimed at inclusivity.
Privacy and Security in Internet-Connected Cameras

The Internet of Things (IoT) enables us to sense and share information of real-world events, including potentially privacy-sensitive information about the users' choices and behaviors. In this paper we focus on the security and privacy problems of Internet-connected cameras. We study two cameras: a consumer camera marketed as a baby monitor, and a surveillance camera marketed for enterprise (physical) security. We show how a generic algorithm can be used to infer actions recorded by the camera, even if the traffic is encrypted, and we also show how both cameras have security vulnerabilities that allow a remote attacker to gain access to the video frames captured by the camera. We also discuss new findings such as the fact that one camera has multiple vendors and domains that connect to a single cloud system supported by a single company, which is a trend we have previously seen in other IoT devices with one company designing the core-functionality of the device and then multiple vendors selling the device under their own brand name and developing different mobile applications for them.
Securing Malware Cognitive Systems against Adversarial Attacks

The cognitive systems along with the machine learning techniques have provided significant improvements for many applications. However, recent adversarial attacks, such as data poisoning, evasion attacks, and exploratory attacks, have shown to be able to either cause the machine learning methods to misbehave, or leak sensitive model parameters. In this work, we have devised a prototype of a malware cognitive system, called DeepArmour, which performs robust malware classification against adversarial attacks. At the heart of our method is a voting system with three different machine learning malware classifiers: random forest, multi-layer perceptron, and structure2vec. In addition, DeepArmour applies several adversarial countermeasures, such as feature reconstruction and adversarial retraining to strengthen the robustness. We tested DeepArmour on a malware execution trace dataset, which has 12, 536 malware in five categories. We are able to achieve 0.989 accuracy with 10-fold cross validation. Further, to demonstrate the ability of combating adversarial attacks, we have performed a white-box evasion attack on the dataset and showed how our system is resilient to such attacks. Particularly, DeepArmour is able to achieve 0.675 accuracy for the generated adversarial attacks which are unknown to the model. After retraining with only 10% adversarial samples, DeepArmour is able to achieve 0.839 accuracy.
A scalable decision-tree-based method to explain interactions in dyadic data

Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, the existing methods have poor explainability, a quality that is becoming essential in certain applications. We describe an explainable and scalable method that, operating on dyadic datasets, obtains an easily interpretable high-level summary of the relationship between entities. To do this, we propose a quality measure, which can be configured to a level that suits the user, that factors in the explainability of the model. We report experiments that confirm better results for the proposed method over alternatives, in terms of both explainability and accuracy. We also analyse the method's capacity to extract relevant actionable information and to handle large datasets.
TU-VDN: Tripura University Video Dataset at Night Time in Degraded Atmospheric Outdoor Conditions for Moving Object Detection

Even though thermal infrared images captured during night time are available in some publicly available datasets, such images acquisitioned in adverse weather conditions such as low light, dust, rain, fog etc. are not reported as yet to the best of our knowledge. Because of these deficiencies, object detection techniques applicable in weather affected night thermal infrared images have a very limited reporting in literature. In the present scope, we discussed the acquisition, creation, design, and ground truth annotation of a new video dataset consisting of nearly 60 videos representing 4 atmospheric conditions: low light, dust, rain, fog, named as Tripura University Video Dataset at Night time (TU-VDN) in adverse weather conditions, suitable for this purpose. The objective is to provide a night video dataset containing moving objects with annotated ground truth in the image frame sequences. Using TU-VDN a comparative study is made between the results of ten existing state-of-the-art moving object segmentation methods.
Environment Monitoring for Anomaly Detection System Using Smartphones

Currently, the popularity of smartphones with networking capabilities equipped with various sensors and the low cost of the Internet have opened up great opportunities for the use of smartphones for sensing systems. One of the most popular applications is the monitoring and the detection of anomalies in the environment. In this article, we propose to enhance classic road anomaly detection methods using the Grubbs test on a sliding window to make it adaptive to the local characteristics of the road. This allows more precision in the detection of potholes and also building algorithms that consume less resources on smartphones and adapt better to real conditions by applying statistical outlier tests on current threshold-based anomaly detection methods. We also include a clustering algorithm and a mean shift-based algorithm to aggregate reported anomalies on data to the server. Experiments and simulations allow us to confirm the effectiveness of the proposed methods.
Analysis of the Factors Causing Lazy Students to Study Using the ELECTRE II Algorithm

Abstract This study aims to search for factors causing lazy students at STIKOM Tunas Bangsa. Data obtained from student questionnaire results with a sample of 20 students. The attributes used are 4, namely body condition (A1), lack of learning motivation (A2), lecturer influence (A3) and environment (A4). The method used in the study is ELECTRE II. From the results of the study, the condition factor of the body (A1) is the first alternative to get the first rank with a pure concordance value of 1.8571 and a pure discordance value of -2. Followed by the influence factor of lecturer (A3) on the second rank with pure concordance value of 1.1905 and pure discordance value of -1.6935. With this analysis, it is expected that it can contribute to universities to pay attention to the lazy symptoms of lectures that can hinder students’ success and pay attention to students based on ELECTRE II calculation analysis on the causes of lazy lectures so as to improve the educational process.
Semi-automatic mapping of source code using naive Bayes

The software industry has not adopted continuous use of static architecture conformance checking. One hindrance is the needed mapping from source code elements to elements of the architecture. We present a novel approach of generating and combining dependency and semantic information extracted from an initial set of mapped source code files. We use this to train a Naive Bayes classifier that is then used to map the remainder of the source code files. We compare this approach with the HuGMe technique on six open source projects with known mappings. We find that our approach provides an average performance improvement of 0.22 and an average precision and recall F1-score improvement of 0.26 in comparison to HuGMe.
Aladdin: Optimized Maximum Flow Management for Shared Production Clusters

The rise in popularity of long-lived applications (LLAs), such as deep learning and latency-sensitive online Web services, has brought new challenges for cluster schedulers in shared production environments. Scheduling LLAs needs to support complex placement constraints (e.g., to run multiple containers of an application on different machines) and larger degrees of parallelism to provide global optimization. But existing schedulers usually suffer severe constraint violations, high latency and low resource efficiency. This paper describes Aladdin, a novel cluster scheduler that can maximize resource efficiency while avoiding constraint violations: (i) it proposes a multidimensional and nonlinear capacity function to support constraint expressions; (ii) it applies an optimized maximum flow algorithm to improve resource efficiency. Experiments with an Alibaba workload trace from a 10,000-machine cluster show that Aladdin can reduce violated constraints by as mush as 20%. Meanwhile, it improves resource efficiency by 50% compared with state-of-the-art schedulers.
Quantum Machine Learning Tensor Network States

Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar tools---called tensor network methods---form the backbone of modern numerical methods used to simulate many-body physics and have a further range of applications in machine learning. Finding and contracting tensor network states is a computational task which quantum computers might be used to accelerate. We present a quantum algorithm which returns a classical description of a rank-$r$ tensor network state satisfying an area law and approximating an eigenvector given black-box access to a unitary matrix. Our work creates a bridge between several contemporary approaches, including tensor networks, the variational quantum eigensolver (VQE), quantum approximate optimization (QAOA), and quantum computation.