Reviewerly/exHarmony huggingface dataset:
A Multitask Multimodal Ensemble Model for Sentiment- and Emotion-Aided Tweet Act Classification

Speech act classification determining the communicative intent of an utterance has been studied widely over the years as an independent task. This holds true for discussion in any for a, including social media platforms such as Twitter. However, the tweeter’s emotional state has a huge impact on its pragmatic content because communication is fundamentally characterized and mediated by direct emotions. Sentiment as a human behavior often has a strong relation to emotion, and one helps to understand the other better. We hypothesize that the association between emotion and sentiment will provide a clearer understanding of the tweeter’s state of mind, aiding the identification of tweet acts (speech acts in Twitter, TAs). As the first step, we create a new multimodal, emotion-TA, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EmoTA</i> dataset collected from the open-source Twitter dataset. To incorporate these multiple aspects, we propose a multitask ensemble adversarial learning framework for multimodal TA classification (TAC). In addition, we also incorporate a joint embedding network, with bidirectional constraints to capture and efficiently integrate the shared semantic relationships across modalities and learn generalized features across multiple tasks. Experimental results indicate that the proposed framework boosts the performance of the primary task, TAC, by benefiting from the two secondary tasks, i.e., sentiment and emotion analyses compared to its unimodal and single-task TAC variants.
The Effect of a Foveated Field-of-view Restrictor on VR Sickness

Virtual reality sickness typically results from visual-vestibular conflict. Because self-motion from optical flow is driven most strongly by motion at the periphery of the retina, reducing the user’s field-of-view (FOV) during locomotion has proven to be an effective strategy to minimize visual vestibular conflict and VR sickness. Current FOV restrictor implementations reduce the user’s FOV by rendering a restrictor whose center is fixed at the center of the head mounted display (HMD), which is effective when the user’s eye gaze is aligned with head gaze. However, during eccentric eye gaze, users may look at the FOV restrictor itself, exposing them to peripheral optical flow which could lead to increased VR sickness. To address these limitations, we develop a foveated FOV restrictor and we explore the effect of dynamically moving the center of the FOV restrictor according to the user’s eye gaze position. We conducted a user study (n=22) where each participant uses a foveated FOV restrictor and a head-fixed FOV restrictor while navigating a virtual environment. We found no statistically significant difference in VR sickness measures or noticeability between both restrictors. However, there was a significant difference in eye gaze behavior, as measured by eye gaze dispersion, with the foveated FOV restrictor allowing participants to have a wider visual scan area compared to the head-fixed FOV restrictor, which confined their eye gaze to the center of the FOV.
Practical homomorphic encryption over the integers for secure computation in the cloud

We present novel homomorphic encryption schemes for integer arithmetic, intended primarily for use in secure single-party computation in the cloud. These schemes are capable of securely computing arbitrary degree polynomials homomorphically. In practice, ciphertext size and running times limit the polynomial degree, but this appears sufficient for most practical applications. We present four schemes, with increasing levels of security, but increasing computational overhead. Two of the schemes provide strong security for high-entropy data. The remaining two schemes provide strong security regardless of this assumption. These four algorithms form the first two levels of a hierarchy of schemes, and we also present the general cases of each scheme. We further elaborate how a fully homomorphic system can be constructed from one of our general cases. In addition, we present a variant based upon Chinese Remainder Theorem secret sharing. We detail extensive evaluation of the first four algorithms of our hierarchy by computing low-degree polynomials. The timings of these computations are extremely favourable by comparison with even the best of existing methods and dramatically outperform many well-publicised schemes. The results clearly demonstrate the practical applicability of our schemes.
Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks

Background: In making the deep neural network, activation functions play an important role. But the choice of activation functions also affects the network in term of optimization and to retrieve the better results. Several activation functions have been introduced in machine learning for many practical applications. But which activation function should use at hidden layer of deep neural networks was not identified. Objective: The primary objective of this analysis was to describe which activation function must be used at hidden layers for deep neural networks to solve complex non-linear problems. Methods: The configuration for this comparative model was used by using the datasets of 2 classes (Cat/Dog). The number of Convolutional layer used in this network was 3 and the pooling layer was also introduced after each layer of CNN layer. The total of the dataset was divided into the two parts. The first 8000 images were mainly used for training the network and the next 2000 images were used for testing the network. Results: The experimental comparison was done by analyzing the network by taking different activation functions on each layer of CNN network. The validation error and accuracy on Cat/Dog dataset were analyzed using activation functions (ReLU, Tanh, Selu, PRelu, Elu) at number of hidden layers. Overall the Relu gave best performance with the validation loss at 25th Epoch 0.3912 and validation accuracy at 25th Epoch 0.8320. Conclusion: It is found that a CNN model with ReLU hidden layers (3 hidden layers here) gives best results and improve overall performance better in term of accuracy and speed. These advantages of ReLU in CNN at number of hidden layers are helpful to effectively and fast retrieval of images from the databases.
Role of Agents to Enhance the Security and Scalability in Cloud Environment

Cloud computing is a novel paradigm that changes the industry viewpoint of inventing, developing, deploying, scaling, updating, maintaining, and paying for applications and the infrastructure on which they are deployed. Due to dynamic nature of cloud computing it is quite easy to increase the capacity of hardware or software, even without investing on purchases of it. This feature of cloud computing is named as scalability which is one of the main concern in cloud environment. This chapter presents the architecture of scalability by using mobile agents. It also highlights the other main issues prevailing in cloud paradigm. Further it presents the hybrid architecture for data security which is also the one of major concern of it. This chapter mainly highlights the solution for scalability and security.
Propagating Facial Prior Knowledge for Multitask Learning in Face Super-Resolution

Existing face hallucination methods always achieve improved performance through regularizing the model with facial prior. Most of them always estimate facial prior information first and then leverage it to help the prediction of the target high-resolution face image. However, the accuracy of prior estimation is difficult to guarantee, especially for the low-resolution face image. Once the estimated prior is inaccurate or wrong, the following face super-resolution performance is unavoidably influenced. A natural question that arises: how to incorporate facial prior effectively and efficiently without prior estimation? To achieve this goal, we propose to learn facial prior knowledge at training stage, but test only with low-resolution face image, which can overcome the difficulty of estimating accurate prior. In addition, instead of estimating facial prior, we directly explore the potential of high-quality facial prior in the training phase and progressively propagate the facial prior knowledge from the teacher network (trained with the low-resolution face/high-quality facial prior and high-resolution face image pairs) to the student network (trained with the low-resolution face and high-resolution face image pairs). Quantitative and qualitative comparisons on benchmark face datasets demonstrate that our method outperforms the state-of-the-art face super-resolution methods. The source codes of the proposed method will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wcy-cs/KDFSRNet</uri> .
Performance Analysis of Blockchain-Enabled Data-Sharing Scheme in Cloud-Edge Computing-Based IoT Networks

Blockchain and cloud-edge computing techniques are promising technologies for next-generation, secure, and privacy-preserving data-sharing systems. By integrating these technologies, the data demands of many data users, such as research institutes, hospitals, manufacturers, etc., can be met. Despite this promising integration, it is yet to be understood how the vulnerability and uncertainties of wireless communication links between the data producers, blockchain systems, cloud-edge computing-based platforms, and data users, as well as unstable validation parameters can affect the overall performance of such blockchain-enabled data-sharing systems. In this article, we considered a collaborative data-sharing scheme, where multiple data providers and data users collaborate to accomplish data-sharing tasks through the proposed blockchain and cloud-edge computing schemes. We considered the spatial distribution of data providers and data users to follow the independent homogeneous Poisson point process, while the transactions generation rate at each node was also modeled using an independent Bernoulli process. We then obtained analyses for some selected performance metrics of interest and evaluate the performance of the system. The obtained results showed that the proposed analyses can be useful in investigating the performance of any blockchain-enabled data-sharing system. This will aid successful deployments of effective data-sharing systems.
Scalable spin squeezing in a dipolar Rydberg atom array

The standard quantum limit bounds the precision of measurements that can be achieved by ensembles of uncorrelated particles. Fundamentally, this limit arises from the non-commuting nature of quantum mechanics, leading to the presence of fluctuations often referred to as quantum projection noise. Quantum metrology relies on the use of non-classical states of many-body systems to enhance the precision of measurements beyond the standard quantum limit1,2. To do so, one can reshape the quantum projection noise—a strategy known as squeezing3,4. In the context of many-body spin systems, one typically uses all-to-all interactions (for example, the one-axis twisting model4) between the constituents to generate the structured entanglement characteristic of spin squeezing5. Here we explore the prediction, motivated by recent theoretical work6–10, that short-range interactions—and in particular, the two-dimensional dipolar XY model—can also enable the realization of scalable spin squeezing. Working with a dipolar Rydberg quantum simulator of up to N = 100 atoms, we demonstrate that quench dynamics from a polarized initial state lead to spin squeezing that improves with increasing system size up to a maximum of −3.5 ± 0.3 dB (before correcting for detection errors, or roughly −5 ± 0.3 dB after correction). Finally, we present two independent refinements: first, using a multistep spin-squeezing protocol allows us to further enhance the squeezing by roughly 1 dB, and second, leveraging Floquet engineering to realize Heisenberg interactions, we demonstrate the ability to extend the lifetime of the squeezed state by freezing its dynamics. In the dipolar XY model, quench dynamics from a polarized initial state lead to spin squeezing that improves with increasing system size, and two refinements show further enhanced squeezing and extended lifetime of the squeezed state by freezing its dynamics.
Stability of clinical prediction models developed using statistical or machine learning methods

Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model-building strategy, including the sample size, number of predictors, and analysis method (e.g., regression or machine learning). We raise the concern that many models are developed using small datasets that lead to instability in the model and its predictions (estimated risks). We define four levels of model stability in estimated risks moving from the overall mean to the individual level. Through simulation and case studies of statistical and machine learning approaches, we show instability in a model's estimated risks is often considerable, and ultimately manifests itself as miscalibration of predictions in new data. Therefore, we recommend researchers always examine instability at the model development stage and propose instability plots and measures to do so. This entails repeating the model-building steps (those used to develop the original prediction model) in each of multiple (e.g., 1000) bootstrap samples, to produce multiple bootstrap models, and deriving (i) a prediction instability plot of bootstrap model versus original model predictions; (ii) the mean absolute prediction error (mean absolute difference between individuals' original and bootstrap model predictions), and (iii) calibration, classification, and decision curve instability plots of bootstrap models applied in the original sample. A case study illustrates how these instability assessments help reassure (or not) whether model predictions are likely to be reliable (or not), while informing a model's critical appraisal (risk of bias rating), fairness, and further validation requirements.
Guidelines for Artificial Intelligence Containment

The past few years have seen a remarkable amount of attention on the long-term future of artificial intelligence (AI). Icons of science and technology such as Stephen Hawking (Cellan-Jones, 2014), Elon Musk (Musk, 2014), and Bill Gates (Gates, 2015) have expressed concern that superintelligent AI may wipe out humanity in the long run. Stuart Russell, coauthor of the most-cited textbook of AI (Russell & Norvig, 2003), recently began prolifically advocating (Dafoe & Russell, 2016) for the field of AI to take this possibility seriously. AI conferences now frequently have panels and workshops on the topic. There has been an outpouring of support from many leading AI researchers for an open letter calling for greatly increased research dedicated to ensuring that increasingly capable AI remains “robust and beneficial,” and gradually a field of “AI safety” is coming into being (Pistono & Yampolskiy, 2016; Yampolskiy, 2016, 2018; Yampolskiy & Spellchecker, 2016). Why all this attention?
Immersive human–computer interactive virtual environment using large-scale display system

Large-scale display system with immersive human–computer interaction (HCI) is an important solution for virtual reality (VR) systems. In contrast to the traditional human–computer interactive VR system that requires the user to wear heavy VR headsets for visualization and data gloves for HCI, the proposing method utilizes a large-scale display screen (with or without 3D glasses) to visualize the virtual environment and a bare-handed gesture recognition solution to receive user instructions. The entire framework is named as an immersive HCI system. Through a virtual 3D interactive rectangular parallelepipe, we establish the correspondence between the virtual scene and the control information. A bare-handed gesture recognition method is presented based on extended genetic algorithm. An arm motion estimation method is designed based on the fuzzy predictive control theory. Experimental results showed that the proposed method has lower error rates than most existing solutions with acceptable recognition frequency rates.
Bulk Savings for Bulk Transfers: Minimizing the Energy-Cost for Geo-Distributed Data Centers

With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic distribution and large delay-tolerance of such inter-data-center bulk data transfers provide cloud service providers opportunities to optimize the operating cost. Most existing studies on inter-data-center bulk data transfers focus on minimizing the network bandwidth cost. However, the energy-cost of the bulk data transfers, which also accounts for a large proportion of operating cost in the data centers, still remains unexplored. This is an important problem, especially in the multi-electricity-market environment, where the electricity price exhibits both spatial and temporal diversities. In this paper, we systematically study the problem of how to route and schedule inter-data-center bulk data transfers to minimize the energy-cost for geo-distributed data centers. We model this problem as a min-cost multi-commodity flow problem and develop an efficient two-stage optimization method to solve it. Extensive evaluations with real-life inter-data-center network and electricity prices show that our method brings significant energy-cost savings over existing bulk data transfer methods.
Hybrid quantum repeater for qudits

We present a "hybrid quantum repeater" protocol for the long-distance distribution of atomic entangled states beyond qubits. In our scheme, imperfect noisy entangled pairs of two qudits, i.e., two discrete-variable $d$-level systems, each of, in principle, arbitrary dimension $d$, are initially shared between the intermediate stations of the channel. This is achieved via local, sufficiently strong light-matter interactions, involving optical coherent states and their transmission after these interactions, and optical measurements on the transmitted field modes, especially (but not restricted to) efficient continuous-variable homodyne detections ("hybrid" here refers to the simultaneous exploitation of discrete and continuous variable degrees of freedom for the local processing and storage of entangled states as well as their non-local distribution, respectively). For qutrits we quantify the light-matter entanglement that can be effectively shared through an elementary lossy channel, and for a repeater spacing of up to 10 km we show that the realistic (lossy) qutrit entanglement is even larger than any ideal (loss-free) qubit entanglement. After including qudit entanglement purification and swapping procedures, we calculate the long-distance entangled-pair distribution rates and the final entangled-state fidelities for total communication distances of up to 1280 km. With three rounds of purification, entangled qudit pairs of near-unit fidelity can be distributed over 1280 km at rates of the order of, in principle, 100 Hz.
Authenticated Data Redaction with Fine-Grained Control

Redactable signatures, a branch of malleable homomorphic signatures for editing, have wide applications in online interactions, from privacy enhancing to bandwidth saving. Recent research tends to apply this technique to solve the issue of authenticated data redaction in electronic health records (EHRs) systems, social networks, smart grid, etc. However, most of existing schemes are vulnerable to unauthorized arbitrary redaction or additional redaction. Redaction control is a crucial mechanism to restrict the actions that legitimate users can perform in sensitive systems, as well as constrain unauthorized manipulations from any user. In this paper, we propose a novel and generalized approach for constructing redactable signature scheme with fine-grained redaction control (RSS-FGRC), which allows the signer to specify a flexible and expressive redaction control policy to regulate the redaction operation of redactors. We analyse the security, efficiency and functionality of our new construction by comparing with other related works. The analysis results show that the performance of our construction has significant advantages over others, from the aspects of security and efficiency.
Bug Reports for Desktop Software and Mobile Apps in GitHub: What's the Difference?

As the number of desktop software and mobile apps is rapidly increasing, resolving bugs is a key task. Developers often examine bug reports (BRs) in software repositories to get hints for fixing the bugs. The quality of BRs is important. Pieter Hooimeijer and Westley Weimer created a descriptive model for the lifetime of a BR.
Personalized assessment model for alphabets learning with learning objects in e-learning environment for dyslexia

Internet has been source of knowledge for decades. The pool of information cannot be sustained in absence of the network of networks. Internet has many useful applications in commercial, social and educational areas. In today's scenario, e-learning is also one of the useful applications in the world of Internet. The medium of e-learning has achieved advancement in various fields such as adaptive e-learning systems. The branch of computer science with psycholinguistics has done tremendous job in providing technical solutions to learners. However, learning disorders on the platform of e-learning still require lots of research. Therefore, this paper provides a personalized assessment model for alphabet learning with learning objects for children's who face dyslexia. The cognitive inclination of dyslexic learner has been determined using assessment model. This paper studies the cognitive potential of dyslexic learner and has built a personalized e-learning platform to alleviate their alphabetical problems.
Simulating Dirac Hamiltonian in curved space-time by split-step quantum walk

Dirac particle represents a fundamental constituent of our nature. Simulation of Dirac particle dynamics by a controllable quantum system using quantum walks will allow us to investigate the non-classical nature of dynamics in its discrete form. In this work, starting from a modified version of one-spatial dimensional general inhomogeneous split-step discrete quantum walk we derive an effective Hamiltonian which mimics a single massive Dirac particle dynamics in curved $(1+1)$ space-time dimension coupled to $U(1)$ gauge potential---which is a forward step towards the simulation of the unification of electromagnetic and gravitational forces in lower dimension and at the single particle level. Implementation of this simulation scheme in simple qubit-system has been demonstrated. We show that the same Hamiltonian can represent $(2+1)$ space-time dimensional Dirac particle dynamics when one of the spatial momenta remains fixed. We also discuss how we can include $U(N)$ gauge potential in our scheme, in order to capture other fundamental force effects on the Dirac particle. The emergence of curvature in the two-particle split-step quantum walk has also been investigated while the particles are interacting through their entangled coin operations.