Reviewerly/exHarmony huggingface dataset:
Hierarchical text interaction for rating prediction

Traditional recommender systems encounter several challenges such as data sparsity and unexplained recommendation. To address these challenges, many works propose to exploit semantic information from review data. However, these methods have two major limitations in terms of the way to model textual features and capture textual interaction. For textual modeling, they simply concatenate all the reviews of a user/item into a single review. However, feature extraction at word/phrase level can violate the meaning of the original reviews. As for textual interaction, they defer the interactions to the prediction layer, making them fail to capture complex correlations between users and items. To address those limitations, we propose a novel Hierarchical Text Interaction model (HTI) for rating prediction. In HTI, we propose to model low-level word semantics and high-level review representations hierarchically. The hierarchy allows us to exploit textual features at different granularities. To further capture complex user–item interactions, we propose to exploit semantic correlations between each user–item pair at different hierarchies. At word level, we propose an attention mechanism specialized to each user–item pair, and capture the important words for representing each review. At review level, we mutually propagate textual features between the user and item, and capture the informative reviews. The aggregated review representations are integrated into a collaborative filtering framework for rating prediction. Experiments on five real-world datasets demonstrate that HTI outperforms state-of-the-art models by a large margin. Further case studies provide a deep insight into HTI’s ability to capture semantic correlations at different levels of granularities for rating prediction.
XMDD as Key Enabling Technology for Integration of Large scale eLearning Based on NRENs

National Research and Education Networks (NRENs) are developed to provide access and collaboration between educational organizations. Single-Sign-On (SSO) applications such as eduGAIN are used in the pan European research and education network (GÉANT) and Shibboleth is used in the Internet2 NRENs in the United States. However, the lack of data integration between many eLearning systems is still a challenge in the NREN domain. We address systematic data integration between large-scale eLearning systems by designing and prototyping an eLearning broker. The NREN eLearning architecture considers the strategies and policies of collaboration parties. We apply the eXtreme Model-Driven Development (XMDD) paradigm, using the DIME low-code development environment for data modeling, process modeling, interface modeling and prototyping. The motivating case study is the Afghanistan Research and Educational Network (AfgREN).
Students’ perceptions towards mobile learning in an English as a foreign language class

Emerging mobile pedagogy for English as a foreign language teaching makes portable devices a potentially beneficial tool for learning a foreign language. However, to date, there has been relatively little research on learning the relationships between Mobile-Assisted Language Learning and university students' perceptions of this innovative teaching approach. In this paper, some of the existing research on the topic of mobile learning is reviewed to connect language learning, educational technology, and perceptions of learners towards the use of mobile technology to support language learning. The 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -year students in the Germanic Philology Department at Sumy State University, Ukraine, were first exposed to a MALL experience, and then asked to express their thoughts on the incorporation of mobile devices into the language classroom. Research findings revealed overall positive attitudes and perceptions amongst the students surveyed. However, some technical and digital literacy challenges emerged during the intervention. Despite some constraints, the university students majoring in English indicated their readiness for mobile assisted learning. The learning/teaching materials elaborated for the study might be useful for practitioners and researchers in the field of mobile pedagogy for EFL teaching.
Emojis and the performance of humour in everyday electronically-mediated conversation

Abstract Emojis are little pictographs commonly added to electronic messages on several social media platforms. Besides being considered as a way to express emotions in electronically-mediated communication (EMC), similarly to ASCII emoticons, emojis are strictly involved in the performance of humour in everyday digital conversation. Drawing on a corpus of casual Whats­App dyadic chats, this paper analyses the contribution of emojis to humour in conversation. Results show that these pictographs not only help to signal the opening and closing of the play frame, but also to respond to humour, graphically reproducing laughter. For these purposes, the most common emojis employed by Whats­App users are the popular yellow smiling and laughing faces. Nevertheless, other pictographs are also involved in electronic humour, as less common emojis can be used in playful ways by themselves.
Performance Analysis of Optimization Algorithms GA, PSO, and ABC based on DWT-SVD watermarking in OpenCV Python Environment

In the recent years, security for multimedia data has under threat with high usage of Internet and image editing tools. In this paper, an Optimal SVD-DWT based Digital watermarking is employed to achieve copyright protection and prevent its illegal copy or reproduction. A survey on different optimization algorithms namely GA (Genetic Algorithm), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony) is carried to obtain optimized robustness and imperceptibility of watermarking algorithm employing performance metrics such as Peak Signal to Noise Ratio(PSNR), Structural Similarity Measurement (SSIM) and Normalized Cross Correlation(NCC). The experiments are conducted with python Anaconda. Also, the computational time taken by each optimization-algorithm is measured and compared. To remove the false positive in SVD based watermarking is prevented by employing DWT-SVD based hashing for authentication.
Enhancing College Chat Bot Assistant with the Help of Richer Human Computer Interaction and Speech Recognition

Chat bots are the expert systems that understands and responds to the query asked by users in their own language. Chat bot responds in conversation just like how a human interact with each other. It works as a virtual assistant and its accuracy is determined by finding correlation between user's queries and answers provided by chat bots. Implemented Chat bot provides two modes like text mode and audio mode for better user experience. In audio mode it facilitates an interactive way of answering through voice messages. During Institute's Academic Admission procedure there is a huge queue at the enquiry window. Situation is even more difficult for the parents who reside in different cities, states, and countries. The goal of this system is to provide a platform for student and parents to ask queries and clear doubts through simple English language text messages or audio commands. Students and parents will amalgamate with bot instead of making queue at enquiry desk to ask queries related to admission procedure.
Improvement of clear sky models for direct solar irradiance considering turbidity factor variable during the day

There are several clear sky models used to estimate the amount of received solar energy. Most of them rely on monthly constant turbidity factors which are site dependent. Comparing the results obtained with a clear sky model with data recorded in the period 2013–2016 in the Renewable Energy Systems and Recycling Research Centre in Brasov, Romania, resulted that the model overestimates in the warm season and underestimates in the cold season. To improve the clear sky model, time dependent interpolation polynomials are used instead of constant turbidity factor. Local direct solar irradiance measured each 10 min is used to create daily cubic spline functions using Schoenberg operator. These splines were used to obtain two interpolation polynomials for turbidity factor, for the warm and cold season respectively. The improved clear sky model forms were validated using the direct solar irradiance measured in 2017; the accuracy was improved by reducing the relative error from 8.12% to 4% in warm season and from 5.02% to 4.15% in cold season. As result, a better estimation of the received solar energy, with influence on the system design and financial benefits, is obtained. The methodology can be applied for any location with available measured data.
MagView: A Distributed Magnetic Covert Channel via Video Encoding and Decoding

Air-gapped networks achieve security by using the physical isolation to keep the computers and network from the Internet. However, magnetic covert channels based on CPU utilization have been proposed to help secret data to escape the Faraday-cage and the air-gap. Despite the success of such cover channels, they suffer from the high risk of being detected by the transmitter computer and the challenge of installing malware into such a computer. In this paper, we propose MagView, a distributed magnetic cover channel, where sensitive information is embedded in other data such as video and can be transmitted over the air-gapped internal network. When any computer uses the data such as playing the video, the sensitive information will leak through the magnetic covert channel. The "separation" of information embedding and leaking, combined with the fact that the covert channel can be created on any computer, overcomes these limitations. We demonstrate that CPU utilization for video decoding can be effectively controlled by changing the video frame type and reducing the quantization parameter without video quality degradation. We prototype MagView and achieve up to 8.9 bps throughput with BER as low as 0.0057. Experiments under different environment are conducted to show the robustness of MagView. Limitations and possible countermeasures are also discussed.
A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis' formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation-maximization (EM) algorithm has converged. However, Louis' formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis' formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis' formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis' formula with previously existing methods. Results from these presentations suggest that Louis' formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.
VERONICA: Expressive and Precise Concurrent Information Flow Security

Methods for proving that concurrent software does not leak its secrets has remained an active topic of research for at least the past four decades. Despite an impressive array of work, the present situation remains highly unsatisfactory. With contemporary compositional proof methods one is forced to choose between expressiveness (the ability to reason about a wide variety of security policies), on the one hand, and precision (the ability to reason about complex thread interactions and program behaviours), on the other. Achieving both is essential and, we argue, requires a new style of compositional reasoning. We present VERONICA, the first program logic for proving concurrent programs information flow secure that supports compositional, high-precision reasoning about a wide range of security policies and program behaviours (e.g. expressive declassification, value-dependent classification, secret-dependent branching). Just as importantly, VERONICA embodies a new approach for engineering such logics that can be re-used elsewhere, called decoupled functional correctness (DFC). DFC leads to a simple and clean logic, even while achieving this unprecedented combination of features. We demonstrate the virtues and versatility of VERONICA by verifying a range of example programs, beyond the reach of prior methods.
FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras

A 3D imaging and mapping system that can handle both multiple-viewers and dynamic-objects is attractive for many applications. We propose a freeform structured light system that does not rigidly constrain camera(s) to the projector. By introducing an optimized phase-coded aperture in the projector, we transform the projector pattern to encode depth in its defocus robustly; this allows a camera to estimate depth, in projector coordinates, using local information. Additionally, we project a Kronecker-multiplexed pattern that provides global context to establish correspondence between camera and projector pixels. Together with aperture coding and projected pattern, the projector offers a unique 3D labeling for every location of the scene. The projected pattern can be observed in part or full by any camera, to reconstruct both the 3D map of the scene and the camera pose in the projector coordinates. This system is optimized using a fully differentiable rendering model and a CNN-based reconstruction. We build a prototype and demonstrate high-quality 3D reconstruction with an unconstrained camera, for both dynamic scenes and multi-camera systems.
The simple essence of algebraic subtyping: principal type inference with subtyping made easy (functional pearl)

MLsub extends traditional Hindley-Milner type inference with subtyping while preserving compact principal types, an exciting new development. However, its specification in terms of biunification is difficult to understand, relying on the new concepts of bisubstitution and polar types, and making use of advanced notions from abstract algebra. In this paper, we show that these are in fact not essential to understanding the mechanisms at play in MLsub. We propose an alternative algorithm called Simple-sub, which can be implemented efficiently in under 500 lines of code (including parsing, simplification, and pretty-printing), looks more familiar, and is easier to understand. We present an experimental evaluation of Simple-sub against MLsub on a million randomly-generated well-scoped expressions, showing that the two systems agree. The mutable automaton-based implementation of MLsub is quite far from its algebraic specification, leaving a lot of space for errors; in fact, our evaluation uncovered several bugs in it. We sketch more straightforward soundness and completeness arguments for Simple-sub, based on a syntactic specification of the type system. This paper is meant to be light in formalism, rich in insights, and easy to consume for prospective designers of new type systems and programming languages. In particular, no abstract algebra is inflicted on readers.
Towards Correlated Queries on Trading of Private Web Browsing History

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. In this paper, we study the trading of multiple correlated queries on private web browsing history data. We propose TERBE, which is a novel trading framework for correlaTed quEries based on pRivate web Browsing historiEs. TERBE first devises a modified matrix mechanism to perturb query answers. It then quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. Through real-data based experiments, our analysis and evaluation results demonstrate that TERBE balances total error and privacy preferences well within acceptable running time, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.
A Blockchain-based Decentralized Electronic Marketplace for Computing Resources

Abstract We propose a framework for building a decentralized electronic marketplace for computing resources. The idea is that anyone with spare capacities can offer them on this marketplace, opening up the cloud computing market to smaller players, thus creating a more competitive environment compared to today’s market consisting of a few large providers. Trust is a crucial component in making an anonymized decentralized marketplace a reality. We develop protocols that enable participants to interact with each other in a fair way and show how these protocols can be implemented using smart contracts and blockchains. We discuss and evaluate our framework not only from a technical point of view, but also look at the wider context in terms of fair interactions and legal implications.
FPGA based Faster Implementation of MAC Unit in Residual Number System

In this fast-growing world, where everyone is in hurry, speed has become a critical factor even in the electronics world. It's not the answer but a fast answer is the need of the time. Artificial intelligence has touched almost all aspects of our lives. But the software implementation of machine learning algorithms has not been able to meet the expectations of solutions in nanoseconds, especially where neural networks with extensive calculations and arithmetic computations are involved. Multiplication and accumulation unit is an integral part of many signal processing and machine learning algorithms. Here we implement an arithmetic module based on distributed arithmetic imbibed with Residual Number System to prove the efficacy of hardware design over software implementation. The purpose is to exploit the parallelism property of FPGAs to accelerate the computations. The target device used is xc6vlx75t3ff484 from Virtex- 6 family in Xilinx. Simulations are tested in MATLAB and ModelSim with associated data to justify its feasibility.
Driver authentication by quantifying driving style using GPS only

Driver authentication that is verifying a driver's identity constitutes an important aspect of modern day automobile. A driver gets access to drive a car based on his identity. Identity is normally verified with smart card or possession of key. Also there exist identity verification based on fingerprint, passcode and image based approach using camera mounted inside a car. However such approach do not consider driving style based authentication. In this paper, we present an approach that uses personalized statistical feature set extracted from the global positioning system (GPS) data to authenticate a driver. Such personalized feature set reduces computation, improves interpretability of features and accuracy. Proposed method is further enriched by determining and using the most suitable machine learning technique. Our approach is tuned to increase accuracy, sensitivity and specificity. Overall mean area under receiver operating characteristic curve (AUC) obtained is 0.9 which implies the robustness of this technique. Primary contribution of the paper is personalized feature set for driver authentication. We provide performance comparison of several machine learning algorithms such as SVM, Random Forest, Naïve Bayes, MLP etc. for driver authentication.
Multi-Oriented Text Recognition and Classification in Natural Images using MSER

Text recognition is a vast field of research and experimentation under image processing domain. It is a process by which the system locates the area whichever any kind of text is present and to extract them. The extracted text must be converted to human readable form after several processing and to classify them into meaningful classes based on the content. The platform used here is MATLAB R2018a. Firstly, Pre-processing is done on the ICDAR 2017 dataset in order to remove noise content. Then Segmentation is done to get a rough idea of the textual content present. Needful features are extracted using MSER (Maximally stable extremal regions). The obtained result is then processed with Stroke width transform. Geometrical features of text are matched with the regions. Finally, all of the processed regions are merged to obtain the exact text and extract them with OCR (Optical Character Recognition). Classifying these into meaningful attributes makes more sense to the extracted text.
Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity

Neighborhood-based algorithms are some of the most promising memory-based collaborative filtering approaches for recommender systems.Many of these algorithms rely on a global similarity measure to select the most similar neighbors for rating prediction.However, these approaches may fail in capturing some meaningful relationships among users.In the real world, although users can show interest in a wide range of objects, they can express more interest in objects contained in a specific topic, which typically comprises a bulk of closely related objects.In this paper, we propose a local similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar properties.For this, we use a clustering method to find groups of similar objects.Then we create a user-based similarity model for each cluster, which we named Cluster-based Local Similarity (CBLS) model.Each similarity model relies on rating normalization and resource allocation techniques that are sensitive to the ratings assigned to objects contained in the cluster.We performed experiments using two clustering algorithms (affinity propagation and K-Means) and compared the results with other neighborhood-based collaborative filtering approaches.Our numerical results on three benchmark datasets (MovieLens 100k, MovieLens 1M, and Netflix) demonstrate that the proposed method is competitive and outperforms traditional and state-of-the-art collaborative filtering-based similarity models in terms of accuracy metrics like mean absolute error (MAE) and root-mean-square error (RMSE).
A Collaborative, Immersive Language Learning Environment Using Augmented Panoramic Imagery

Augmenting immersive technologies with AI for foreign language learning is a relatively unexplored, multidisciplinary, and complex research paradigm. The Cognitive and Immersive Room at Rensselaer Polytechnic Institute facilitates cultural and foreign language learning beyond a traditional classroom setting. Exploration of virtual environments and real-world renderings occur at human-scale by groups of students and teachers simultaneously, without the need for head-mounted displays. Students travel to true-to-life Panoramic Scenes to learn authentic cultural knowledge and practice vocabulary within the same surroundings they would use such knowledge while traveling abroad. Multimodal input allows students to engage with the system through gesture, voice, and spatial positioning, creating a dynamic language learning experience. This work underwent initial assessment in a six-week summer course, AI-Assisted Immersive Chinese, and preliminary qualitative results find a majority of surveyed students indicate the system is useful, engaging, and fun.