Reviewerly/exHarmony huggingface dataset:
Towards a Framework of Configuring and Evaluating ModSecurity WAF on Tomcat and Apache Web Servers

Open-source software has slowly infiltrated the enterprise space because the products tend to be cheaper, flexible, and secure in comparison to propriety products. However, open-source software incurs the cost of beavering to acquire professionals to, customize the product in meeting expectations, support fixes and in extending the product to a wide range of capabilities. ModSecurity is an open-source web application firewall (WAF) developed explicitly for Apache, and technically only listens to HTTP port 80. This study utilized the agility and flexibility property of open-source software to design a framework of configuring Apache module ModSecurity WAF to communicate with Tomcat server (which runs explicitly on HTTP port 8080). Furthermore, using suitable penetration testing methodology, this study investigates and compares the effectiveness of ModSecurity WAF in both Apache and Tomcat environments. ModSecurity WAF limitations were also investigated. In addition to providing a framework for configuring ModSecurity on tomcat server, this study provides an understanding of web application vulnerabilities, the techniques used to exploit them and the mitigation mechanisms to address them.
The Limit of Horizontal Scaling in Public Clouds

Public cloud users are educated to practice horizontal scaling at the application level, with the assumption that more processing capacity can be achieved by adding nodes into the server fleet. In reality, however, applications—even those specifically designed to be horizontally scalable—often face unpredictable scalability issues when running at scale. In this article, we study the limit of horizontal scaling in public clouds by identifying sources of such limitations and quantitatively measuring their impact on processing capacity. To this end, we develop ScaleBench as a distributed and parallel cloud-scale testing framework and propose a capacity degradation index (CDI) to describe the level of capacity degradation observed in our benchmark studies. We have conducted extensive experiments in four real public clouds to identify possible bottlenecks in compute, block storage, networking, and object storage. Further, we carry out large-scale experiments with a real-life video transcoding application on worker fleets with up to 3200 vCPU cores. Our experimental results provide the quantitative evidence on the limit of horizontal scaling in public clouds. This helps cloud users make better design decisions on horizontally scalable applications.
Improved covariant local feature detector

Local feature detection is a fundamental problem in computer vision. Recently, the research of local feature detection has been switched from handcrafted methods to learning based ones, especially deep learning based ones. A recent successful deep learning based feature detector is the covariant local feature detector that conducts keypoint detection by predicting the transformation of keypoints from nearby pixels. Although this method adopts a new detection framework compared to those methods by computing the keypoint's likelihood, it treats each pixel equally which may incorrectly detect unstable keypoints. On the other hand, other methods computing the keypoint probability could capture different evidence for keypoint detection as well as provide a natural weight for each prediction in the covariant detector. So, fusing information from other detectors into the covariant detector could improve its performance. Under this motivation, this paper proposes an improved covariant local feature detector by fusing feature information obtained from another detector, which is served as a confidence to guide the voting procedure when converting the predicted transformations into a meaningful score map for keypoint detection. In this way, the fused information can enhance the features that are considered to be good and weaken those unstable features. The proposed method is evaluated on four widely used benchmarks and consistent performance improvement over previous works is observed.
Toward insourcing-measurement in inbodied interaction design

column Share on Toward insourcing-measurement in inbodied interaction design Authors: m.c. schraefel University of Southampton University of SouthamptonView Profile , Aaron Tabor University of New Brunswick University of New BrunswickView Profile , Josh Andres IBM Research and Monash University IBM Research and Monash UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 2March - April 2020 pp 56–60https://doi.org/10.1145/3381340Published:25 February 2020Publication History 5citation674DownloadsMetricsTotal Citations5Total Downloads674Last 12 Months141Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Mobile based chatbot application for HIV/AIDS counseling using artificial intelligence markup language approach

Abstract Acquired Immune Deficiency Syndrome (AIDS) is a fearsome epidemic for modern society as the virus itself shortening the human life expectancy. Based on the report issued by the National committee of sexually transmitted diseases monitoring agency. In the 3rd quarter of 2017, Bali is the 4th most province with major HIV infection in the country. One of the foundations that focuses on HIV/AIDS assistance and prevention in Bali is Spirit Paramacitta Foundation. The problems faced by the Foundation are the limitations of the counselor and the reluctance of the community to consult. With the advancement of the information technologies and to support the goodwill of foundation is the basis of why this research exists. The goal of this research is to build a mobile-based chatbot application using Artificial Intelligence Markup Language (AIML), that will aid the foundation in giving people a reliable HIV/AIDS related information. AIML is used because it can make bots close to human capabilities. The application would be able to emulate the human conversation as good as the usual counseling session. The result of this research is chatbot has been able to help Spirit Paramacitta Foundation in counseling and disseminate information related to HIV/AIDS to the community.
CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement

We introduce cpces, a novel planner for the problem of deterministic conformant planning. cpces solves the problem by producing candidate plans based on a sample of the initial belief state, searching for counter-examples to these plans, and assigning these counter-examples to the sample, until a valid plan has been produced or the problem has been proved unfeasible. On top of providing a means to compute a conformant plan, the sample can also be understood as a justification for the plan being found, or relevant reasons why a plan cannot be found. We study the theoretical properties that cpces enjoys—correctness, completeness, and optimality—and how the several variants of cpces we describe differ in behaviour. Moreover, we establish a theoretical connection between the cpces framework and well-known concepts from the literature such as tags and width. With this connection we prove the worst case complexity for some variants of cpces. Finally, we show how cpces can be used in a more incremental fashion by learning sequencing of actions from the previous plan being found. Such a technique mimics the use of macro-operators, widely used in automated planning to speedup resolution. Our theoretical analysis is accompanied with a thorough experimental evaluation of the (many) possible incarnations of cpces. This not only proves our theoretical findings from an empirical perspective, but also shows that cpces is able to handle problems that have been traditionally hard to solve by the existing conformant planners, whilst remaining competitive over “easier” conformant planning problems. Importantly, cpces is able to prove many unsolvable conformant planning problems as such, extending substantially the reach of conformant planners.
A Time-Driven Workflow Scheduling Strategy for Reasoning Tasks of Autonomous Driving in Edge Environment

In various time-slots, the real-time reasoning tasks generated by autonomous vehicles are scheduled within tolerance time, which is the key problem to be solved in autonomous driving. Tasks are traditionally scheduled on the on-board unit (OBU), which leads to long completion time of tasks. Heuristic algorithms are widely used in task scheduling problems, which usually causes to premature convergence. Scheduling tasks in edge environment can effectively reduce completion time of tasks. In this paper, a workflow scheduling strategy was designed in edge environment according to the difference of reasoning tasks and the changes of edge nodes in various time-slots. Firstly, the model of Markov decision process (MDP) was built to describe the problem scenario, and the completion time of reasoning tasks was calculated by the workflow scheduling algorithm. Secondly, the Q-learning algorithm based on simulated anealing (SA-QL) was proposed to optimize the completion time of reasoning tasks. Finally, the performance of reinforcement learning algorithms based on simulated annealing (SA-RL) and particle swarm optimization (PSO) algorithm were comprehensively displayed from four perspectives: effectiveness, feasibility, exploration and convergence. The experimental results show that both SA-RL algorithms and PSO algorithm have good performance in feasibility and effectiveness. TD(0) algorithms have better performance of exploration and TD() algorithms have that of convergence.
Face Quality Assessment via Semi-supervised Learning

Face quality assessment, used for selecting a "good" subset from face images captured over multiple frames in uncontrolled conditions, plays a significant role in video-based face recognition. By removing the poor quality images, it can not only improve recognition performance but also reduce the computation cost. This paper proposes an end-to-end face quality assessment algorithm based on a semi-supervised learning framework. The contributions of the proposed method are threefold. (i) Making use of unlabeled data from target domain to fine-tune a neural network by a strategy of automatically updating labels. (ii) Combining prior knowledge with feature learning by using a set of characteristics as binary constraints. (iii) Proposing a light neural network model for training and predicting. Experiments demonstrate that our model can get much higher accuracy in face quality assessment task than the models trained with the same amount of labeled faces, meanwhile the complexity is lower. Experimental results also show that our method can improve the performance of face recognition by face selection.
Counterfeit Product Detection Analysis and Prevention as Well as Prepackage Coverage Assessment Using Machine Learning

There is no deny in the fact that duplicate product jeopardizes the luck of businesses worldwide over violating patent rights and causing immense commercial wound. Hence, it has to change into basic for firms to ensure their disgraces opposed to duplication. Existing technologies for electrical forged find encompass the applying of extra security looks prefer Watermark Technology to the emblem stock itself that raises the charges of one’s merchandise. In this paper, a reliable method for counterfeit prediction is proposed. This method can be used by customer to predict counterfeit for their daily need items available in the market. The item may be medicine, detergent or food packet. Now the development of new package of the product always comes with the risk of counterfeiting, sometimes that could affect our health, company reputation and goodwill. We have used the concept of invisible and visible watermarking present in item itself to provide authenticity of the product. This is a low cost solution that help enterprises and consumers identify the authenticity of products.
Black Box Search Space Profiling for Accelerator-Aware Neural Architecture Search

Neural Architecture Search (NAS) is a promising approach to discover good neural network architectures for given applications. Among the three basic components in a NAS system (search space, search strategy, and evaluation), prior work mainly focused on the development of different search strategies and evaluation methods. As most of the previous hardware-aware search space designs aimed at CPUs and GPUs, it still remains a challenge to design a suitable search space for Deep Neural Network (DNN) accelerators. Besides, the architectures and compilers of DNN accelerators vary greatly, so it is quite difficult to get a unified and accurate evaluation of the latency of DNN across different platforms. To address these issues, we propose a black box profiling-based search space tuning method and further improve the latency evaluation by introducing a layer adaptive latency correction method. Used as the first stage in our general accelerator-aware NAS pipeline, our proposed methods could provide a smaller and dynamic search space with a controllable trade-off between accuracy and latency for DNN accelerators. Experimental results on CIFAR-10 and ImageNet demonstrate our search space is effective with up to 12.7% improvement in accuracy and 2.2x reduction of latency, and also efficient by reducing the search time and GPU memory up to 4.35x and 6.25x, respectively.
Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection

In the last few years, surveillance cameras have been massively distributed both indoors and outdoors in public, due to security concerns creating a need to monitor unexpected actions or activities in a scene. An intelligent automated approach is highly required to detect anomalies from the scene as well, to save the time and cost required by laborers to detect the anomalies manually from monitor screens. In this research, we propose a deep learning-based approach to detect abnormal events and behavior from surveillance videos in crowd scenes. Thus, by using the keyframe extractor to localize the keyframes that contain important information from video frames. The selected keyframes are used to compute the optical flow values of magnitudes, orientations, and velocities of each keyframe to generate several 2D templates. Then, the obtained 2D templates are supplied to a pre-trained model 'AlexNet' to extract high-level features. The Neighborhood Component Analysis (NCA) feature selection method is applied to select the appropriate features, then use these features to generate a classification model via Support Vector Machine (SVM) classifier. Results are evaluated on several public datasets, along with a new dataset that we built it that contains different videos covering abnormal events and behaviors. The obtained results proved that the proposed method outperforms other methods.
Alternate Action Recommender System Using Recurrent Patterns of Smart Home Users

We present an alternate action recommender system for Internet of Things (IoT) smart home users. Our system takes the data of the IoT devices of the smart home users as input, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual user, and finally recommends the alternate possibilities of achieving the deviated actions from any of the active patterns. The derived active patterns are very specific to the users, which are based on their usage of the IoT devices in their smart home ecosystem, thus making the recommendations personalized. In our system, we also consider the context of the smart home environment through the IoT device state and criticality of the recommendations in the user's smart home system where these parameters play crucial role. We have designed and implemented our core algorithm by modifying the SMILE-ARM algorithm to better suit the IoT devices data of the smart home ecosystem and validated it using user trial methods and standard algorithm validation techniques.
Training Unity Machine Learning Agents using reinforcement learning method

This study aims to provide research of Machine Learning tools combined with Unity platform, to develop several Machine Learning Agents in various environments using multiple environment configurations and training scenarios. This work introduces a single competitive agent environment where the agent compete with simulated physics in a 3D world. Although the rules of the environment are generally basic, the trained agent learns a wide variety of interesting and complex solutions.
Dual-Attention Graph Convolutional Network

Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity. In this paper, we propose a dual-attention GCN to model the structural information of various texts as well as tackle the graph-invariant problem through embedding two types of attention mechanisms, i.e. the connection-attention and hop-attention, into the classic GCN. To encode various connection patterns between neighbour words, connection-attention adaptively imposes different weights specified to neighbourhoods of each word, which captures the short-term dependencies. On the other hand, the hop-attention applies scaled coefficients to different scopes during the graph diffusion process to make the model learn more about the distribution of context, which captures long-term semantics in an adaptive way. Extensive experiments are conducted on five widely used datasets to evaluate our dual-attention GCN, and the achieved state-of-the-art performance verifies the effectiveness of dual-attention mechanisms.
Tricks and Treats: Designing Technology to Support Mobility Assistance Dogs

Assistance dogs are a key intervention to support the autonomy of people with tetraplegia. Previous research on assistive technologies have investigated ways to, ultimately, replace their labour using technology, for instance through the design of smart home environments. However, both the disability studies literature and our interviews suggest there is an immediate need to support these relationships, both in terms of training and bonding. Through a case study of an accessible dog treats dispenser, we investigate a technological intervention responding to these needs, detailing an appropriate design methodology and contributing insights into user requirements and preferences.
Semantic Video Retrieval using Deep Learning Techniques

Content based video retrieval has been an active research area for many decades. Unlike tagged-based search engines which rely on user-assigned annotations to retrieve the desired content, content based retrieval systems match the actual content of video with the provided query to fetch the required set of videos. Thanks to the recent advancements in deep learning, the traditional pipeline of content based systems (pre-processing, segmentation, object classification, action recognition etc.) is being replaced by end-to-end trainable systems which are not only effective and robust but also avoid the complex processing in the conventional image based techniques. The present study exploits these developments to develop a semantic video retrieval system accepting natural language queries and retrieving the relevant videos. We focus on key individuals appearing in certain scenarios as queries in the current study. Persons appearing in a video are recognized by tuning FaceNet to our set of images while caption generation is exploited to make sense of the scenario within a given video frame. The outputs of the two modules are combined to generate a description of the frame. During the retrieval phase, natural language queries are provided to the system and the concept of word embeddings is employed to find similar words to those appearing in the query text. For a given query, all videos where the queried individuals and scenarios have appeared are returned by the system. The preliminary experimental study on a collection of 50 videos reported promising retrieval results.
Security requirements identification and prioritization for smart toys

A smart toy is a traditional toy (e.g., car, doll and stuffed pet) that can use sensors and cloud-based services to leverage data collection to learn user’s preferences and provide them with more personalized experiences. The high connectivity and the “intelligent” nature of smart toys are indeed appealing for children. Nevertheless, connected devices are easy targets of hacking and personal data exchanged with cloud services raises many security concerns, especially when the main target audience are vulnerable users, i.e., children. Many security breaches have been found in bestseller smart toys, and several security incidents have been reported. Many organizations (e.g., FBI, Consumers International and Mozilla) have raised alerts to parents pointing to the risks associated with such products. Many studies have been published trying to solve or mitigate security problems in smart toys, but they usually focus on specific aspects of the toy architecture and usage. In this paper, we show how we used the Microsoft SDL method to identify a comprehensive list of security issues based on specific regulations (e.g., COPPA, PIPPEDA, GDPR), threats based on surface attack analysis, and security requirements that address security issues and threats. We also present a method we adopt to prioritize the security requirements based on risk assessment, the AHP method and generic scenarios. Although the security requirements we identify and prioritize may not be sufficient to all cases, they are comprehensive enough to cover most scenarios. Furthermore, the rationale we provide of how to use the SDL process to identify and prioritize security requirements may be useful to tailor the requirements and the priority list to specific contexts.
Image analysis by fast improved radial <scp>harmonic‐Fourier</scp> moments algorithm

Abstract Radial harmonic‐Fourier moments (RHFMs) are popular for image reconstruction and invariant pattern recognition due to their properties of translation, scaling and rotation invariant. RHFMs possess lower computation complexity as compared to Zernike moments and Bessel‐Fourier moments. However, they always suffer from discontinuity, numerical instability near the center of image, and reconstruction error, especially have a rise for higher order of moments. In this paper, an improvement of radial harmonic‐Fourier moments (IRHFMs) is proposed for effectively avoiding the above‐mentioned problems.In this paper, a 2D fast Fourier transform algorithm also is applied to the image matrix to obtain the IRHFMs. Simulation experimental results demonstrate the proposed IRHFMs perform better than traditional RHFMs and other classic orthogonal moments including the latest image moments, for example, polar harmonic Fourier moments in terms of the image reconstruction capability and rotation invariant recognition accuracy in noise‐free and noisy conditions.