Reviewerly/exHarmony huggingface dataset:
SERC: Syntactic and Semantic Sequence based Event Relation Classification

Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are closely related and influence each other. So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. We use the syntactic structure of the text for identifying temporal and causal relations between two events from the text. We extract parts-of-speech tag sequence, dependency tag sequence and word sequence from the text. We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features. Evaluation of our model on four popular datasets yields promising results for temporal and causal relation classification.
Session-typed concurrent contracts

Abstract In sequential languages, dynamic contracts are usually expressed as boolean functions without externally observable effects, written within the language. We propose an analogous notion of concurrent contracts for languages with session-typed message-passing concurrency. Concurrent contracts are partial identity processes that monitor the bidirectional communication along channels and raise an alarm if a contract is violated. Concurrent contracts are session-typed in the usual way and must also satisfy a transparency requirement, which guarantees that terminating compliant programs with and without the contracts are observationally equivalent. We illustrate concurrent contracts with several examples. We also show how to generate contracts from a refinement session-type system and show that the resulting monitors are redundant for programs that can statically be seen to be well-typed.
Exploring the Collaboration Possibilities of Distributed Making for Storytelling Using 3D Printing Pens

As digital making tools, such as 3D printers and laser cutters, become more affordable and easier to use by non-experts, new opportunities arise for geographically distributed designers to use them collaboratively and at a distance. In this exploratory study, we investigated the possibilities of a 3D printing pen for collaboratively creating multimodal stories around the themes of ecology and climate change for elementary school-aged children. Our interdisciplinary team of four students met regularly over a video link and used shared documents and 3D pens to create a multimodal adventure book, with audio and tactile elements, that we then used as a design probe to elicit feedback from experienced community science educators working with children about the possibilities of using hands-on distributed collaborative techniques in the future. We found that using this approach is promising for facilitating creative verbal and visual collaboration at a distance, and using tactile and audio representations with narrative elements provides new ways to reflect on complex topics, such as climate change.
Efficient Search in a Panoramic Image Database for Long-term Visual Localization

In this work, we focus on a localization technique that is based on image retrieval.In this technique, database images are kept with GPS coordinates and the geographic location of the retrieved database image serves as an approximate position of the query image.In our scenario, database consists of panoramic images (e.g.Google Street View) and query images are collected with a standard field-of-view camera in a different time.While searching the match of a perspective query image in a panoramic image database, unlike previous studies, we do not generate a number of perspective images from the panoramic image.Instead, taking advantage of CNNs, we slide a search window in the last convolutional layer belonging to the panoramic image and compute the similarity with the descriptor extracted from the query image.In this way, more locations are visited in less amount of time.We conducted experiments with state-of-the-art descriptors and results reveal that the proposed sliding window approach reaches higher accuracy than generating 4 or 8 perspective images.
Image Tampering Localization Using Unified Two-Stream Features Enhanced with Channel and Spatial Attention

Image tampering localization has attracted much attention in recent years. To differentiate between tampered and pristine image regions, many methods have increasingly leveraged the powerful feature learning ability of deep neural networks. Most of these methods operate on either spatial image domain directly or residual image domain constructed with high-pass filtering, while some take inputs from both domains and fuse the features just before making decisions. Though they have achieved promising performance, the gain of integrating feature representations of different domains is overlooked. In this paper, we show that learning a unified feature set is beneficial for tampering localization. In the proposed method, low-level features are firstly extracted from two input streams: one is a spatial image, and the other is a high-pass filtered residual image. The features are then separately enhanced with channel attention and spatial attention, and are subsequently subjected to an early-fusion to form a unified feature representation. The unified features play an important role under an adapted Mask R-CNN framework, achieving more accurate pixel-level tampering localization. Experimental results on five tampered image datasets have shown the effectiveness of the proposed method. The implementation is available at https://github.com/media-sec-lab/AEUF-Net.
VisuaLeague: Visual Analytics of Multiple Games

One of the most popular eSports (electronic sports) game types practiced is the Multiplayer Online Battle Arena (MOBA) genre, represented by one of the most popular competitive games, League of Legends (LoL). As in many traditional sports, to improve player and team performance, players and coaches analyze all the game events, such as, the positions and trajectories of the players, representing their movements, events and actions they performed during the game (spatial and temporal data). This paper presents VisuaLeague, a visualization tool for analysis of LoL matches for single player, teams, professional matches, and multiple games. The tool offers interaction with the visualizations, filtering and aggregation of data, and clustering to solve the common problems presented in analysis with voluminous amount of data, like cluttering and overlapping. We evaluated VisuaLeague through a user study covering the various types of analysis with two professional coaches. Results indicate that the tool was overall intuitive, useful, efficient and innovative and coaches show a particular interest in the analysis of professional training matches and multiple games as those provide visualizations that often lack in common tools, specially, regarding spatio-temporal data.
Application of An Encrypted Data Search Across Mobile Clouds

Cloud storage provides a convenient and more storage at low cost, but data privacy is a major concept that prevents users from storing files on the cloud. One way of improving the data privacy is to encrypt the files before sending them onto the cloud and decrypt the files when they are downloaded. However, data encryption is a difficult task for the mobile devices, and data retrieval process is a complicated communication between the data user and cloud. With limited capacity and a limited battery life of mobile phones, these issues may introduce heavy overhead to computing and more power consumption for mobile device users, which makes the encrypted search over mobile cloud very difficult and as a challenge task for the user. In this paper, we have proposed, TEES, in which with more bandwidth and better energy efficient encrypted search over a mobile cloud. The proposed architecture removes the computation from mobile devices to the cloud, and hence we further can optimize the communications of the mobile clients and the cloud.
Polynomial Image-Based Rendering for non-Lambertian Objects

Non-Lambertian objects present an aspect which depends on the viewer's position towards the surrounding scene. Contrary to diffuse objects, their features move non-linearly with the camera, preventing rendering them with existing Depth Image-Based Rendering (DIBR) approaches, or to triangulate their surface with Structure-from-Motion (SfM). In this paper, we propose an extension of the DIBR paradigm to describe these non-linearities, by replacing the depth maps by more complete multi-channel “non-Lambertian maps”, without attempting a 3D reconstruction of the scene. We provide a study of the importance of each coefficient of the proposed map, measuring the trade-off between visual quality and data volume to optimally render non-Lambertian objects. We compare our method to other state-of-the-art image-based rendering methods and outperform them with promising subjective and objective results on a challenging dataset.
How Usability and User Experience Vary Among the Basic m-Commerce, AR and VR Based User Interfaces of Mobile Application for Online Shopping

Now a days, augmented reality (AR) and virtual reality (VR) based user interfaces allow the consumers to buy product over the internet using browser or mobile application. A limited number of mobile application use the AR, VR, or the both technologies simultaneously. Again, usability and user experience of using AR and VR based user interfaces to buy/sell product online were not well explored. Therefore, the objectives of this research are to design and develop a mobile application using the AR and VR technologies and to explore the usability and user experience of using the basic m-commerce, AR and VR based user interfaces of mobile application for shopping product (furniture) online. To attain these objectives, a mobile application was designed and developed for selling/buying product (furniture) online that includes the facilities of interacting with both AR and VR based user interfaces. The application provides the attractive features like viewing a specific product directly in user’s own space by augmenting the 3D model and allow users to design virtual room in their own preferable way by placing the selected furniture and other products in a VR environment. The developed system was evaluated with 60 participants through the System Usability Scale (SUS) method to explore how usability and user experience vary among different options (basic m-commerce system, AR, and VR based user interfaces) of the developed application. The study results showed that better user experience and usability were observed for AR based shopping followed by using the basic m-commerce feature. The VR showed less usability and user experiences since the users were not well familiar with the uses of VR and required much training to learn how to interact using VR in mobile application.
Pyramidal Layered Scene Inference with Image Outpainting for Monocular View Synthesis

Generating novel views from a single input is a challenging task that requires the prediction of occluded and non-visible content. Nevertheless, it is an interesting and active area of research due to its several applications such as entertainment. In this work, we propose an end-to-end architecture for monocular view synthesis based on the layered scene inference (LSI) method. The LSI uses layered depth images that can represent complex scenes with a reduced number of layers. To improve the LSI predictions, we develop two new strategies: (i) a pyramidal architecture that learns LDI predictions for different resolutions of the input and (ii) an image outpainting for filling the missing information at the LDI borders. We evaluate our method on the KITTI dataset, and show that the proposed versions outperform the baseline.
Blockchain Technology: A Fundamental Overview

In November 2008, Satoshi Nakamoto announced the concept of blockchain in a whitepaper about digital payment approach called Bitcoin. Since then, several data-driven domains have identified the potential of using this technology to provide a secure and a trusted platform for transactions processing. In fact, these domains have recognized blockchain as a revolutionized platform of data sharing and data storage. Blockchain technology can be presented as a protocol or software, which offers a secure transfer and storage of exclusive instances of values via the internet and without the need for third-party intermediation. From the functional perspective, blockchain can serve as an open, distributed, and immutable ledger that records transactions in a verifiable and permanent fashion. To understand the revolutionary consideration of blockchain technology, a fundamental overview of the technology is required. Accordingly, this chapter will give an explanation of blockchain concept, its fundamental features, components, categories, and architectures. To emphasize the importance of utilizing this technology in several data-driven domains, a section that addresses the usage of blockchain technology will be presented. Moreover, this chapter will highlight certain aspects related to the future utilization of blockchain technology from Artificial Intelligent (AI) and digital privacy implementations.
Measuring the Effect of Categorical Encoders in Machine Learning Tasks Using Synthetic Data

Most of the datasets used in Machine Learning (ML) tasks contain categorical attributes. In practice, these attributes must be numerically encoded for their use in supervised learning algorithms. Although there are several encoding techniques, the most commonly used ones do not necessarily preserve possible patterns embedded in the data when they are applied inappropriately. This potential loss of information affects the performance of ML algorithms in automated learning tasks. In this paper, a comparative study is presented to measure how the different encoding techniques affect the performance of machine learning models. We test 10 encoding methods, using 5 ML algorithms on real and synthetic data. Furthermore, we propose a novel approach that uses synthetically created datasets that allows us to know a priori the relationship between the independent and the dependent variables, which implies a more precise measurement of the encoding techniques' impact. We show that some ML models are affected negatively or positively depending on the encoding technique used. We also show that the proposed approach is more easily controlled and faster when performing experiments on categorical encoders.
Quantum Algorithm for Dyck Language with Multiple Types of Brackets

We consider the recognition problem of the Dyck Language generalized for multiple types of brackets. We provide an algorithm with quantum query complexity \(O(\sqrt{n}(\log n)^{0.5k})\), where n is the length of input and k is the maximal nesting depth of brackets. Additionally, we show the lower bound for this problem which is \(\varOmega (\sqrt{n}c^{k})\) for some constant c.Interestingly, classical algorithms solving the Dyck Language for multiple types of brackets substantially differ from the algorithm solving the original Dyck language. At the same time, quantum algorithms for solving both kinds of the Dyck language are of similar nature and requirements.
Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/s.
<i>Ta</i>as an emergent language practice of audience design in CMC

Abstract This study examines the use of ungendered third person Chinese pronoun ta in digital first-and-third person voiced discourses (i.e. small stories). The study asks what implications the script choice ta , as opposed to gendered 他 ta ‘he’ and 她 ta ‘she’, has for audience design and the facilitation of character empathy. The study draws on 131 digital texts from celebrity verified accounts on social media platform Sina Weibo in October 2015. From a Discourse Analytical perspective focused on deixis relative to the notion of empathy in storytelling, the study investigates emergent practices which involve the orthographic manipulation of gender. The study proposes that ta is an interpersonal resource whose deictic properties as a non-standard spelling are exploited as a property of audience design to facilitate an appeal to empathy. This facilitation is advanced by the script choice which offers a wider scope of reference, and thus targets a wider audience.
CADRE

Large bibliographic data sets hold the promise of revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Providing high-quality data services for large network datasets such as the Microsoft Academic Graph, which contains more than two billion citation links, poses significant difficulties for universities. Data systems based on the property graph model are capable of delivering efficient graph query services for large networks. However, real-life queries often combine multiple types of data models. To satisfy the needs of different user groups, we developed and deployed a cloud-based data system consisting of scalable graph and text-indexed query engines. For non-expert users, the property graph model also presents a technological barrier. To alleviate the steep learning curve, we designed an intuitive graphical user interface for query-building. For advanced users, a scalable notebook service in our platform provides a more flexible computing environments where the query results can be further analyzed. These systems form the data-backbone of the Collaborative Archive and Data Research Environment (CADRE), which provides efficient and high-quality bibliographic data services to eleven large public universities in North America.
Security and Forensics Exploration of Learning-based Image Coding

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG-AI) and MJoint Video Experts Team's (JVET) deep neural networks (DNN) based video coding.These codecs in fact represent a new type of media format.As a dire consequence, traditional media security and forensic techniques will no longer be of use.This paper proposes an initial study on the effectiveness of traditional watermarking on two stateof-the-art learning based image coding.Results indicate that traditional watermarking methods are no longer effective.We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.
Dual Learning for Query Generation and Query Selection in Query Feeds Recommendation

Query feeds recommendation is a new recommended paradigm in mobile search applications, where a stream of queries need to be recommended to improve user engagement. It requires a great quantity of attractive queries for recommendation. A conventional solution is to retrieve queries from a collection of past queries recorded in user search logs. However, these queries usually have poor readability and limited coverage of article content, and are thus not suitable for the query feeds recommendation scenario. Furthermore, to deploy the generated queries for recommendation, human validation, which is costly in practice, is required to filter unsuitable queries. In this paper, we propose TitIE, a query mining system to generate valuable queries using the titles of documents. We employ both an extractive text generator and an abstractive text generator to generate queries from titles. To improve the acceptance rate during human validation, we further propose a model-based scoring strategy to pre-select the queries that are more likely to be accepted during human validation. Finally, we propose a novel dual learning approach to jointly learn the generation model and the selection model by making full use of the unlabeled corpora under a semi-supervised scheme, thereby simultaneously improving the performance of both models. Results from both offline and online evaluations demonstrate the superiority of our approach.
Fairness and communication-based semantics for session-typed languages

Polarized SILL is a programming language that combines functional programming with session-typed message-passing concurrency. It features general recursion; code and channel transmission; and synchronous and asynchronous communication. To reason about Polarized SILL programs, we develop the first program equivalence framework based on observable communications. We give meaning to Polarized SILL programs using an observed communication semantics (OCS). Our OCS is the first to support general recursion and code transmission. We then develop a communication-based testing equivalences framework and show that one of the equivalences captured by our framework coincides with barbed congruence, the canonical notion of process equivalence. Polarized SILL's operational semantics is specified using a multiset rewriting system. We introduce fairness for multiset rewriting systems to ensure that our OCS is well-defined in the presence of non-terminating processes, and use fairness properties to simplify reasoning about processes. This work lays the foundation for observational reasoning for session-typed languages with general recursion.