Reviewerly/exHarmony huggingface dataset:
Ontological Foundations of Modelling Security Policies for Logical Analytics

Modelling of knowledge and actions in AI has advanced over the years but it is still a challenging topic due to the infamous frame problem, the inadequate formalization and the lack of automation. Some problems in cyber security such as logical vulnerability, risk assessment, policy validation etc. still require formal approach. In this paper we present the foundations of a new formal framework to address these challenges. Our approach is based on three-level formalisation: ontological, logical and analytical levels. Here we are presenting the first two levels which allow to model the security policies and provide a practical solution to the frame problem by efficient utilization of parameters as side effects. Key concepts are the situations, actions, events and rules. Our framework has potential use for analysis of a wide range of transactional systems within the financial, commercial and business domains and further work will include analytical level where we can perform vulnerability analysis of the model.
Relations between Cognitive Biases and Some Concepts of Information Behavior

Abstract Information behavior, as a part of human behavior, has many aspects, including a cognitive aspect. Cognitive biases, one of the important issues in psychology and cognitive science, can play a critical role in people’s behaviors and their information behavior. This article discusses the potential relationships between some concepts of human information behavior and cognitive biases. The qualitative research included semistructured face-to-face interviews with 25 postgraduate students who were at the writing-up stage of their research. The participants were selected using a purposeful sampling process. Interviews were analyzed using the coding technique of classic grounded theory. The research framework was the Eisenberg and Berkowitz information behavior model. The relationships that are discussed in this article include those between the principle of least effort on the one hand and availability bias and ambiguity aversion on the other; value-sensitive design and reactance; willingness to return and availability bias; library anxiety and ambiguity aversion, status quo bias, and stereotypical bias; information avoidance and selective perception, confirmation bias, stereotypical bias, and conservatism bias; information overload and information bias; and finally, filtering and attentional bias.
ChronoCache: Predictive and Adaptive Mid-Tier Query Result Caching

The performance of data-driven, web-scale client applications is sensitive to access latency. To address this concern, enterprises strive to cache data on edge nodes that are closer to users, thereby avoiding expensive round-trips to remote data centers. However, these geo-distributed approaches are limited to caching static data. In this paper we present ChronoCache, a mid-tier caching system that exploits the presence of geo-distributed edge nodes to cache database query results closer to users. ChronoCache transparently learns and leverages client application access patterns to predictively combine query requests and cache their results ahead of time, thereby reducing costly round-trips to the remote database. We show that ChronoCache reduces query response times by up to 2/3 over prior approaches on multiple representative benchmark workloads. representative benchmark workloads.
Towards Integrated Classification Lexicon for Handling Unknown Words in Chinese-Vietnamese Neural Machine Translation

In Neural Machine Translation (NMT), due to the limitations of the vocabulary, unknown words cannot be translated properly, which brings suboptimal performance of the translation system. For resource-scarce NMT that have small-scale training corpus, the effect is amplified. The traditional approach of amplifying the scale of the corpus is not applicable, because the parallel corpus is difficult to obtain in a resource-scarce setting; however, it is easy to obtain and utilize external knowledge, bilingual lexicon, and other resources. Therefore, we propose classification lexicon approach for processing unknown words in the Chinese-Vietnamese NMT task. Specifically, three types of unknown Chinese-Vietnamese words are classified and their corresponding classification lexicon are constructed by word alignment, Wikipedia extraction, and rule-based methods, respectively. After translation, the unknown words are restored by lexicon for post-processing. Experiment results on Chinese-Vietnamese, English-Vietnamese, and Mongolian-Chinese translations show that our approach significantly improves the accuracy and the performance of NMT especially in a resource-scarce setting.
Blockchain-based Automatic Indemnification Mechanism Based on Proof of Violation for Cloud Storage Services

In this paper we propose a blockchain-based automatic indemnification mechanism. Clients and service providers (SPs) exchange signed messages according to a predefined protocol that enables clients to utilize service-provider services with simultaneous cryptographic proof of service. When a client has discovered an SP to have violated a service level agreement based on cryptographic proof, then they can obtain cryptocurrency as a compensation by raising objections to a smart contract on a public blockchain. When an objection has been judged successful by programs located in the smart contract, a portion of the deposit (cryptocurrency in the smart contract) will be transferred to the account of the objection-raiser. In this way, the need to establish and run a customer service center with employees for processing such claims and refunds is obviated. Moreover, the traditional third-party trust is no longer needed. Focusing on services for cloud-storage service, we propose protocols and implementation of an indemnification system in the Ethereum. The feasibility of the proposed application has been demonstrated through pilot implementation and testing.
GraphAE: Adaptive Embedding across Graphs

Recently, learning embedding of nodes in graphs has attracted increasing research attention. There are two main kinds of graph embedding methods, i.e., the transductive embedding methods and the inductive embedding methods. The former focuses on directly optimizing the embedding vectors, and the latter tries to learn a mapping function for the given nodes and features. However, few works focus on applying the learned model from one graph to another, which is a pervasive idea in Computer Version or Natural Language Processing. Although some of the graph neural networks (GNNs) present similar motivation, none of them considers the graph bias among graphs. In this paper, we present an interesting graph embedding problem called Adaptive Task (AT), and propose a unified framework for this adaptive task, which introduces two types of alignment to learn adaptive node embedding across graphs. Then, based on the proposed framework, a novel graph adaptive embedding network is designed to address the adaptive task. Extensive experimental results demonstrate that our model significantly outperforms the state-of-the-art methods.
A Manifold Laplacian Regularized Semi-Supervised Sparse Image Classification Method With a Variant Trace Lasso Norm

Since the cost of labeling data is getting higher and higher, we hope to make full use of the large amount of unlabeled data and improve image classification effect through adding some unlabeled samples for training. In addition, we expect to uniformly realize two tasks, namely the clustering of the unlabeled data and the recognition of the query image. We achieve the goal by designing a novel sparse model based on manifold assumption, which has been proved to work well in many tasks. Based on the assumption that images of the same class lie on a sub-manifold and an image can be approximately represented as the linear combination of its neighboring data due to the local linear property of manifold, we proposed a sparse representation model on manifold. Specifically, there are two regularizations, i.e., a variant Trace lasso norm and the manifold Laplacian regularization. The first regularization term enables the representation coefficients satisfying sparsity between groups and density within a group. And the second term is manifold Laplacian regularization by which label can be accurately propagated from labeled data to unlabeled data. Augmented Lagrange Multiplier (ALM) scheme and Gauss Seidel Alternating Direction Method of Multiplier (GS-ADMM) are given to solve the problem numerically. We conduct some experiments on three human face databases and compare the proposed work with several state-of-the-art methods. For each subject, some labeled face images are randomly chosen for training for those supervised methods, and a small amount of unlabeled images are added to form the training set of the proposed approach. All experiments show our method can get better classification results due to the addition of unlabeled samples.
MirrorPad: Mirror on Touchpad for Direct Pen Interaction in the Laptop Environment

There are needs for pen interaction on a laptop, and the market sees many pen-enabled laptop products. Many of these laptops can be transformed into tablets, when pen interaction is needed. In a real situation, however, a workflow often requires both keyboard and pen interactions, and such a convertible feature may not be effective. In this study, we introduce MirrorPad, a novel interface device contained in a laptop for direct pen interaction. It is both a normal touchpad and a viewport for pen interaction with a mirrored region on the screen. We report findings and decisions obtained from the design iterations that we conducted with users to refine MirrorPad toward the final design. In the user study, MirrorPad showed the same performance as that of the laptop configuration during keyboard interaction and a performance similar to that of the tablet configuration during pen interaction. The user study results confirmed that MirrorPad effectively supports a workflow, which requires interspersed keyboard and pen interactions, thereby achieving its initial goal.
Continuously Adaptive Similarity Search

Similarity search is the basis for many data analytics techniques, including k-nearest neighbor classification and outlier detection. Similarity search over large data sets relies on i) a distance metric learned from input examples and ii) an index to speed up search based on the learned distance metric. In interactive systems, input to guide the learning of the distance metric may be provided over time. As this new input changes the learned distance metric, a naive approach would adopt the costly process of re-indexing all items after each metric change. In this paper, we propose the first solution, called OASIS, to instantaneously adapt the index to conform to a changing distance metric without this prohibitive re-indexing process. To achieve this, we prove that locality-sensitive hashing (LSH) provides an invariance property, meaning that an LSH index built on the original distance metric is equally effective at supporting similarity search using an updated distance metric as long as the transform matrix learned for the new distance metric satisfies certain properties. This observation allows OASIS to avoid recomputing the index from scratch in most cases. Further, for the rare cases when an adaption of the LSH index is shown to be necessary, we design an efficient incremental LSH update strategy that re-hashes only a small subset of the items in the index. In addition, we develop an efficient distance metric learning strategy that incrementally learns the new metric as inputs are received. Our experimental study using real world public datasets confirms the effectiveness of OASIS at improving the accuracy of various similarity search-based data analytics tasks by instantaneously adapting the distance metric and its associated index in tandem, while achieving an up to 3 orders of magnitude speedup over the state-of-art techniques.
Blockchain Technology Transforms E-Commerce for Enterprises

Blockchain is an emerging technology and gaining an attraction in businesses. The use of Blockchain technology will ease the processes of businesses and industries. It can be characterized as one of the most important predominant topics nowadays. The issues in Blockchain like privacy, security, and risk are always debatable. It has already transformed many individuals’ lifestyle and companies due to inordinate influence on industries and businesses. The features of blockchain technology guarantee to be more reliable and also expedite the services. It is important to consider the privacy and security issues in this research paper. It also discusses the challenges behind the innovative technology to transform the enterprises. There are several studies focuses on utilizing the blockchain data structure in various B2B and B2C applications. This paper includes the various aspects of trust has been examined at different dimensions. The dimensions of trust can be studied are based at different levels such as foundation, performance, process and purpose. Furthermore, It can delve the model that how the Blockchain technology can adapt in obtaining the trust level by incorporating various factors such as Security, Privacy, Brand Name, Usability, Reliability, Availability, Reputation, Third Party, Risk, Quality, Look and Feel. The use of block chain can transform ecommerce as there are many processes that can be automated and blockchain implementation can help the ecommerce application in the better way as they are working currently.
Marviq: Quality-Aware Geospatial Visualization of Range-Selection Queries Using Materialization

We study the problem of efficient spatial visualization on a large data set stored in a database using SQL queries with ad-hoc range conditions on numerical attributes, for example, a spatial scatterplot of taxi pickup events in New York between 1/1/2015 and 3/10/2015. We present a novel middleware-based technique called Marviq. It divides the selection-attribute domain into intervals, and precomputes and stores a visualization for each interval. These results are called MVS and stored as tables in the database. We can compute an exact visualization for a request by accessing MVS and retrieving additional records from the base table. To further reduce the latter time, we present algorithms for using MVS to compute an approximate visualization that satisfies a user-specified similarity threshold. We show a family of functions with certain properties that can use this technique. We present an improvement by dividing the MVS intervals into smaller intervals and materializing low-resolution visualization for these intervals. We report the results of an extensive evaluation of Marviq, including a user study, and show its high performance in both space and time.
Supporting an Iterative Conversation Design Process

Conversation design is an essential step in building a chatbot. Much like visual user interface design, conversation design benefits from prototyping and user testing to allow for conversation exploration and improvement. However, it can be overwhelming to quickly iterate on the conversation design as the iterative process requires not only designing a conversation but also building and testing a working chatbot equipped with the conversation. We developed ProtoChat, a prototype system that supports an iterative conversation design by allowing designers to (1) prototype conversations, (2) test the conversations with the crowd, and (3) review and analyze the crowdsourced conversation data. Results of an exploratory study with four conversation designers show that the designers successfully iterated on their conversation design by reviewing how the crowd followed the conversation, which provided insights into concrete action items for improving their conversation design.
Personal Space in Play: Physical and Digital Boundaries in Large-Display Cooperative and Competitive Games

As multi-touch displays grow in size and shrink in price, they are more commonly used as gaming devices. When co-located users play games on a single, large display, establishing and maintaining their physical and digital territories poses a social challenge to their interaction. To gain insight into the mechanisms of establishing and maintaining users' physical and digital territories, we analyze territorial interactions in cooperative and competitive multiplayer gameplay. Participants reported weighing each game interaction based on perceived intent to determine how socially acceptable they deemed each behaviour. In light of our observations, we contribute and discuss implications for the design of multi-user, large display, co-located, touchscreen games that consider display properties, digital and physical space, permeability of boundaries, and asymmetry of play to create interactions between players.
ARLooper: Collaborative Audiovisual Experience with Mobile Devices in a Shared Augmented Reality Space

ARLooper is an augmented reality mobile interface that enables the user to record sound, visualize it as a 3D waveform, play and control the waveform in an AR space. By employing the shared AR features of iOS ARKit, it also supports a collaborative audiovisual environment in which multiple users can interact with each other through the real-time synchronization of activities such as sound recording, playing, and manipulating. For this collaborative experience design, it uses user ID colors to distinguish the ownership status of the waveforms. This paper provides the design and technical overview of ARLooper in addition to the background of the research.
Big Data security and privacy techniques

Nowadays Information technologies have caused irreversible changes in many parts of our society. This development reached its climax with the advent of 'Big Data', whose economic and social benefits should not be neglected. However, 'Big Data' inherently threatens security as well as the founding pillars of personal data law, and for this reason many techniques are invented in parallel with the development of big data.
Automated Content Warnings for Sensitive Posts

The inclusion of content warnings for sensitive topics on webpages contributes to creating a psychologically safe internet for all users. Yet the pervasiveness of these warnings is limited by their reliance on content creators and hosts. Rather than placing the sole responsibility of content moderation on content creators and hosts, our system shows a strong proof-of-concept for automatically generating content warnings on the user's side by utilizing keyword identification, sentiment analysis, and online intervention user interface principles. We designed our system as a Chrome extension and evaluated it by testing its accuracy on a dataset of websites with and without sensitive content, and performing a user-interaction lab study. With promising future areas of research such as the ability to personalize thresholds and customize content warnings for specific user needs, this research is a step towards a psychologically safer internet for everyone.
An active learning ensemble method for regression tasks

Active learning is a typical approach for learning from both labeled and unlabeled examples aiming to build efficient and accurate predictive models at minimum expense under an expert's guidance. Since there is a lack of labeled data in many scientific fields whilst, at the same time, the labeling cost of unlabeled data is typically high in terms of time and expenditure, active learning has grown rapidly over recent years with great success. This is reflected in various studies providing insights and analyzing several active learning methods, especially in the case of classification tasks, whereas, there is only a limited number of studies concerning the implementation of active learning methods for regression ones. Within this context, the present paper sets out to put forward a pool-based active learning regression algorithm employing the query by committee strategy to evaluate the informativeness of unlabeled examples. The experimental results on a plethora of benchmark datasets demonstrate the efficiency of the proposed method, since it prevails over the baseline active learning approach applying the random sampling strategy, as well as familiar supervised methods.
Robust Visual Object Tracking With Multiple Features and Reliable Re-Detection Scheme

In recent years, correlation filter based trackers have seen widespread success because of their high efficiency and robustness. However, a single feature based tracker cannot deal with complex scenes such as serious occlusion, motion blur and illumination variation. In this paper, we develop a novel tracking method combining color feature, Hog feature and motion feature. The motion feature is estimated between adjacent frames by large displacement optical flow. Besides, in order to cope with boundary effect existing in traditional correlation filter based trackers, an adaptive cosine window is introduced in our method, which can highlight the target region, suppress the background region and enlarge search region. Meanwhile, a novel judge scheme combining Hog correlation response and color response is adopted to evaluate the reliability of tracking result. Finally, inverse sparse representation is presented to locate coarse positions of target in case of tracking failures. Extensive experiments on five famous tracking benchmarks including OTB100, TColor-128, UAVDT, UAV123 and VOT2016 demonstrate our proposed method outperform other sate-of-the-art methods in terms of robustness and accuracy.
Medical imaging security and forensics: a systematic literature review

Abstract Nowadays, medical imaging techniques are not just only used for diagnosis and prognosis of diseases but can also be used as evidence in court. Medical imaging in forensics is a very specialized field in which radiological techniques are used to aid pathologists to determine the cause of death or to identify the remains. Recently, due to tremendous free images and videos, altering programs on the Internet have made altering images and recordings very simple. Approving the trustworthiness of images or recordings and identifying any endeavor of fabrication without utilization of dynamic legal procedure is a very challenging task nowadays. Therefore security and forensics in medical imaging are coming into sight, which is a major challenge to the researchers. With the development in the field of advanced imaging, a few security and protection issues have been highlighted. Therefore this chapter discusses the validity of the advanced imaging, clarifies the existence cycle of the digital images and furthermore different activities that can be performed on it. Right off the bat, we present types of imaging modalities, tools for proving image authenticity, and the application of digital imaging techniques to several forensic subdisciplines is discussed. At last, we recognize and examine a few essential open research difficulties as future research challenges.
Techniques in De-Blurring Image

Abstract Images nowadays are an integral part of our lives, whether in scientific applications or social networks and where there is an image, the concept of image blurring may occur. Blurred is a major cause of image degradation and reduces image quality. Blur occurs due to air noise as well as incorrect camera setup. Along with the blur effects, the noise also spoils the captured image. Deblurring is the operation for removing blur or noise and restoring the underlying high-quality image. Blur can be different types like Motion Blur, Gaussian Blur, and Average Blur.