Reviewerly/exHarmony huggingface dataset:
Collaborative security risk estimation in agile software development

Purpose Today, agile software development teams in general do not adopt security risk-assessment practices in an ongoing manner to prioritize security work. Protection Poker is a collaborative and lightweight software security risk-estimation technique that is particularly suited for agile teams. Motivated by a desire to understand why security risk assessments have not yet gained widespread adoption in agile development, this study aims to assess to what extent the Protection Poker game would be accepted by agile teams and how it can be successfully integrated into the agile practices. Design/methodology/approach Protection Poker was studied in capstone projects, in teams doing a graduate software security course and in sessions with industry representatives. Data were collected via questionnaires, observations and group interviews. Findings Results show that Protection Poker has the potential to be adopted by agile teams. Key benefits include good discussions on security and the development project, along with increased knowledge and awareness. Challenges include ensuring efficient use of time and gaining impact on the end product. Research limitations/implications Using students allowed easy access to subjects and an ability to collect rich data over time, but at the cost of generalizability to professional settings. Results from interactions with professionals supplement the data from students, showing similarities and differences in their opinions on Protection Poker. Originality/value The paper proposes ways to tackle the main obstacles to the adoption of the Protection Poker technique, as identified in this study.
RATS

Recent approaches to tiled 360° adaptive bitrate video streaming present significant bandwidth savings at little risk of stalling when only parts of the video, e.g., the current and predicted viewport, are transferred in high quality while the rest of the 360° video tiles are transferred in a lower quality. While this is currently feasible for video on demand scenarios, it poses a difficult problem for 360° live streaming as naive methods produce a considerable overhead owing to the lack of tiling support in existing hardware encoders.
AiAds

Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it's crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers. In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser's campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several of key challenges. Finally, online A/B test and long-term grouping experiment demonstrate the advancement and effectiveness of this system.
Towards a distributed token based payment system using blockchain technology

This paper presents a distributed payment system based on payment tokens using Blockchain technology; these payment tokens protect the consumer from identity theft by unauthorized usage of his payment card details (PAN,CVV), due to mismanagements from other parties of the payment system. Furthermore, a private Blockchain consortium consolidates the security and privacy of the proposed payment system; this consortium consists of a private Blockchain called Bank Authority that acts as the interoperability domain, in addition to two other private Blockchains, each serves as the acquirer Bank and the issuer Bank, respectively, the security of this payment system is reinforced by introducing special nodes that verify and validate transactions, in order to detect and block fraud attempts. Moreover, to keep the payment system confidentiality and protect the economic interests of all the Banks in the system, only the transaction hash is recorded in the consortium's Blockchain and only the Banks involved in a transaction will have access to its details.
Mining Algorithm Roadmap in Scientific Publications

The number of scientific publications is ever increasing. The long time to digest a scientific paper posts great challenges on the number of papers people can read, which impedes a quick grasp of major activities in new research areas especially for intelligence analysts and novice researchers. To accelerate such a process, we first define a new problem called mining algorithm roadmap in scientific publications, and then propose a new weakly supervised method to build the roadmap. The algorithm roadmap describes evolutionary relation between different algorithms, and sketches the undergoing research and the dynamics of the area. It is a tool for analysts and researchers to locate the successors and families of algorithms when analyzing and surveying a research field. We first propose abbreviated words as candidates for algorithms and then use tables as weak supervision to extract these candidates and labels. Next we propose a new method called Cross-sentence Attention NeTwork for cOmparative Relation (CANTOR) to extract comparative algorithms from text. Finally, we derive order for individual algorithm pairs with time and frequency to construct the algorithm roadmap. Through comprehensive experiments, our proposed algorithm shows its superiority over the baseline methods on the proposed task.
The Modeling and Simulation of Data Clustering Algorithms in Data Mining with Big Data

Big Data is a popular cutting-edge technology nowadays. Techniques and algorithms are expanding in different areas including engineering, biomedical, and business. Due to the high-volume and complexity of Big Data, it is necessary to conduct data pre-processing methods when data mining. The pre-processing methods include data cleaning, data integration, data reduction, and data transformation. Data clustering is the most important step of data reduction. With data clustering, mining on the reduced data set should be more efficient yet produce quality analytical results. This paper presents the different data clustering methods and related algorithms for data mining with Big Data. Data clustering can increase the efficiency and accuracy of data mining.
A Human-Inspired Recognition System for Pre-Modern Japanese Historical Documents

Recognition of historical documents is a challenging problem due to the noised, damaged characters, and background. However, in Japanese historical documents, not only contains the mentioned problems, pre-modern Japanese characters were written in cursive and are connected. Therefore, character segmentation-based methods do not work well. This leads to the idea of creating a new recognition system. In this paper, we propose a human-inspired document reading system to recognize multiple lines of pre-modern Japanese historical documents. During the reading, people employ eyes movement to determine the start of a text line. Then, they move the eyes from the current character/word to the next character/word. They can also determine the end of a line or skip a figure to move to the next line. The eyes movement integrates with visual processing to operate the reading process in the brain. We employ attention-based encoder-decoder to implement this recognition system. First, the recognition system detects were to start a text line. Second, the system scans and recognize character by character until the text line is completed. Then, the system continues to detect the start of the next text line. This process is repeated until reading the whole document. As results, the system is successful to recognize multiple lines, connected and cursive characters without performing character/line segmentation. Besides, we also employ a coverage model which stores the history of eyes movement to predict the next movement more precisely. We tested our human-inspired recognition system on the pre-modern Japanese historical document provided by the PRMU Kuzushiji competition. The results of the experiments demonstrate the superiority and effectiveness of our proposed system by achieving Sequence Error Rate of 9.87% and 53.81% on level 2 and level 3 of the dataset, respectively. These results outperform to any other systems participated in the PRMU Kuzushiji competition.
Social Attentive Deep Q-network for Recommendation

While deep reinforcement learning has been successfully applied to recommender systems, it is challenging and unexplored to improve the performance of deep reinforcement learning recommenders by effectively utilizing the pervasive social networks. In this work, we develop a Social Attentive Deep Q-network (SADQN) agent, which is able to provide high-quality recommendations during user-agent interactions by leveraging social influence among users. Specifically, SADQN is able to estimate action-values not only based on the users' personal preferences, but also based on their social neighbors' preferences by employing a particular social attention layer. The experimental results on three real-world datasets demonstrate that SADQN significantly improves the performance of deep reinforcement learning agents that overlook social influence.
Comparing CNN and Human Crafted Features for Human Activity Recognition

Deep learning techniques such as Convolutional Neural Networks (CNNs) have shown good results in activity recognition. One of the advantages of using these methods resides in their ability to generate features automatically. This ability greatly simplifies the task of feature extraction that usually requires domain specific knowledge, especially when using big data where data driven approaches can lead to anti-patterns. Despite the advantage of this approach, very little work has been undertaken on analyzing the quality of extracted features, and more specifically on how model architecture and parameters affect the ability of those features to separate activity classes in the final feature space. This work focuses on identifying the optimal parameters for recognition of simple activities applying this approach on both signals from inertial and audio sensors. The paper provides the following contributions: (i) a comparison of automatically extracted CNN features with gold standard Human Crafted Features (HCF) is given, (ii) a comprehensive analysis on how architecture and model parameters affect separation of target classes in the feature space. Results are evaluated using publicly available datasets. In particular, we achieved a 93.38% F-Score on the UCI-HAR dataset, using 1D CNNs with 3 convolutional layers and 32 kernel size, and a 90.5% F-Score on the DCASE 2017 development dataset, simplified for three classes (indoor, outdoor and vehicle), using 2D CNNs with 2 convolutional layers and a 2×2 kernel size.
Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

In this letter, we propose an adaptive risk-based replanning strategy in the context of multirobot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and human motion prediction uncertainty. The performance of our method is studied through an extensive suite of experiments of increasing complexity. Results obtained using both a high-fidelity simulator and real robots confirm that this strategy outperforms a nonadaptive replanning strategy in all cases with respect to the chosen social metrics. First, the overall performance of the team depends on its replanning strategy, and second on the available information about the humans. Although an adaptive replanning strategy with global perception leads to the best performance, it is computationally expensive and infeasible in some real applications. Local perception shows comparable results as long as updates of relevant human poses affecting a task's risk are available within the execution time of that task. Conversely, the nonadaptive replanning strategy is shown to have degraded results with global perception as decisions in this case can be based on outdated information that lead to invalid plans.
Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking in surgical video

Vision-based tracking in an important component for building computer assisted interventions in minimally invasive surgery as it facilitates estimation of motion for instruments and anatomical targets. Tracking-by-detection algorithms are widely used for visual tracking, where the problem is treated as a classification task and a tracking target appearance model is updated over time using online learning. In challenging conditions, like surgical scenes, where tracking targets deform and vary in scale, the update step is prone to include background information in model appearance or to lack the ability to estimate change of scale, which degrades the performance of classifier. In this paper, we propose a Patch-based Adaptive Weighting with Segmentation and Scale (PAWSS) tracking framework that tackles both scale and background problems. A simple but effective colour-based segmentation model is used to suppress background information and multi-scale samples are extracted to enrich the training pool, which allows the tracker to handle both incremental and abrupt scale variations between frames. Experimentally, we evaluate our approach on Online Tracking Benchmark (OTB) dataset and Visual Object Tracking (VOT) challenge datasets, showing that our approach outperforms recent state-of-the-art trackers, and it especially improves successful rate score on OTB dataset, while on VOT datasets, PAWSS ranks among the top trackers while operating at real-time frame rates. Focusing on the application of PAWSS to surgical scenes, we evaluate on MICCAI 2015 challenge instrument tracking challenge and in vivo datasets, showing that our approach performs the best among all submitted methods and also has promising performance on in vivo surgical instrument tracking.
An Ontology-Driven Approach to Automating the Process of Integrating Security Software Systems

A wide variety of security software systems need to be integrated into a Security Orchestration Platform (SecOrP) to streamline the processes of defending against and responding to cybersecurity attacks. Lack of interpretability and interoperability among security systems are considered the key challenges to fully leverage the potential of the collective capabilities of different security systems. The processes of integrating security systems are repetitive, time-consuming and error-prone; these processes are carried out manually by human experts or using ad-hoc methods. To help automate security systems integration processes, we propose an Ontology-driven approach for Security OrchestrAtion Platform (OnSOAP). The developed solution enables interpretability, and interoperability among security systems, which may exist in operational silos. We demonstrate OnSOAP's support for automated integration of security systems to execute the incident response process with three security systems (Splunk, Limacharlie, and Snort) for a Distributed Denial of Service (DDoS) attack. The evaluation results show that OnSOAP enables SecOrP to interpret the input and output of different security systems, produce error-free integration details, and make security systems interoperable with each other to automate and accelerate an incident response process.
Image Registration in Medical Robotics and Intelligent Systems: Fundamentals and Applications

Medical image registration, by transforming two or more sets of imaging data into one coordinate system, plays a central role in medical robotics and intelligent systems from diagnostics and surgical planning to real‐time guidance and postprocedural assessment. Recent advances in medical image registration have made a significant impact in orthopedic, neurological, cardiovascular, and oncological applications.The recent literature in medical image registration is reviewed, providing a discussion of their fundamentals and applications. Within each section, the registration techniques are introduced, classifying each method based on their working mechanisms, and discussing their benefits and limitations are discussed. Recently, machine learning has had an important impact on the field of image registration, yielding novel methods and unprecedented speed. The validation of registration methods, however, remains a challenge due to the lack of reliable ground truth. Medical image registration will continue to make significant impacts in the area of advanced medical imaging, as the fusion/combination of multimodal images and advanced visualization technology become more widespread.
Improved local search for graph edit distance

The graph edit distance (GED) measures the dissimilarity between two graphs as the minimal cost of a sequence of elementary operations transforming one graph into another. This measure is fundamental in many areas such as structural pattern recognition or classification. However, exactly computing GED is NP-hard. Among different classes of heuristic algorithms that were proposed to compute approximate solutions, local search based algorithms provide the tightest upper bounds for GED. In this paper, we present K-REFINE and RANDPOST. K-REFINE generalizes and improves an existing local search algorithm and performs particularly well on small graphs. RANDPOST is a general warm start framework that stochastically generates promising initial solutions to be used by any local search based GED algorithm. It is particularly efficient on large graphs. An extensive empirical evaluation demonstrates that both K-REFINE and RANDPOST perform excellently in practice.
Big Stream Processing Systems: An Experimental Evaluation

As the world gets more instrumented and connected, we are witnessing a flood of digital data generated from various hardware (e.g., sensors) or software in the format of flowing streams of data. Real-time processing for such massive amounts of streaming data is a crucial requirement in several application domains including financial markets, surveillance systems, manufacturing, smart cities, and scalable monitoring infrastructure. In the last few years, several big stream processing engines have been introduced to tackle this challenge. In this article, we present an extensive experimental study of five popular systems in this domain, namely, Apache Storm, Apache Flink, Apache Spark, Kafka Streams and Hazelcast Jet. We report and analyze the performance characteristics of these systems. In addition, we report a set of insights and important lessons that we have learned from conducting our experiments.
Rule based intelligent system verbalizing mathematical notation

An adaptive and adaptable multi-purpose math-to-speech translation system is proposed in the paper. Along with the detailed presentation and design approach for the core math-to-speech translation system, exemplary output and tests are discussed. A scripting extension, providing flexibility of the system and enabling the user to adjust the output translations to his/hers preferences is incorporated into the presented solution. Some unit-tests and adaptable versions of translation rule sets are elaborated and evaluated by means of standard measures of machine translation quality. Designed as a flexible self contained solution, the presented system may be applied for various purposes such as e-learning audio presentation systems, educational tools for non-native speakers or visually impaired people, and may be easily adjusted to different national languages.
Clouded data: Privacy and the promise of encryption

Personal data is highly vulnerable to security exploits, spurring moves to lock it down through encryption, to cryptographically ‘cloud’ it. But personal data is also highly valuable to corporations and states, triggering moves to unlock its insights by relocating it in the cloud. We characterise this twinned condition as ‘clouded data’. Clouded data constructs a political and technological notion of privacy that operates through the intersection of corporate power, computational resources and the ability to obfuscate, gain insights from and valorise a dependency between public and private. First, we survey prominent clouded data approaches (blockchain, multiparty computation, differential privacy, and homomorphic encryption), suggesting their particular affordances produce distinctive versions of privacy. Next, we perform two notional code-based experiments using synthetic datasets. In the field of health, we submit a patient’s blood pressure to a notional cloud-based diagnostics service; in education, we construct a student survey that enables aggregate reporting without individual identification. We argue that these technical affordances legitimate new political claims to capture and commodify personal data. The final section broadens the discussion to consider the political force of clouded data and its reconstitution of traditional notions such as the public and the private.
Technical Mechanics of a Trans-Border Waste Flow Tracking Solution Based on Blockchain Technology

When it comes to waste management and waste tracking, the main objectives are: prevent and reuse as much waste as possible. In the last decade, there has been a strong shift from the disposal of waste to recycling thereof. Within the EU, EU member states are required to establish waste prevention programs through the "Waste Framework EU Directive" which attempts to turn the EU into a recycling society. Similar endeavors also exist in other countries and regions. With globalization, the disposal of waste became a lucrative business case, often without transparency whether the waste has been disposed of properly and according to regulations or directives installed by each country. Waste flow management is the basis of sustainable waste prevention and recycling. Short-term solutions and financial profit accelerated malpractice in waste disposal and recycling. The currently used systems cannot track waste across borders in a transparent, but data-protected and tamper-proof way. Therefore, solutions to address these concerns have to be found and implemented. Blockchain enables new approaches, especially in ecosystems with distrust in every participating stakeholder, which is the case in waste flow tracking. We introduce and discuss a novel solution for waste tracking, on the basis of blockchain technology and smart contracts, that fulfills the aforementioned requirements.