Reviewerly/exHarmony huggingface dataset:
Humor Detection in Product Question Answering Systems

Community question-answering (CQA) has been established as a prominent web service enabling users to post questions and get answers from the community. Product Question Answering (PQA) is a special CQA framework where questions are asked (and are answered) in the context of a specific product. Naturally, humorous questions are integral part of such platforms, especially as some products attract humor due to their unreasonable price, their peculiar functionality, or in cases that users emphasize their critical point-of-view through humor. Detecting humorous questions in such systems is important for sellers, to better understand user engagement with their products. It is also important to signal users about flippancy of humorous questions, and that answers for such questions should be taken with a grain of salt.
Do Transformers Need Deep Long-Range Memory?

Deep attention models have advanced the modelling of sequential data across many domains.For language modelling in particular, the Transformer-XL -a Transformer augmented with a long-range memory of past activations -has been shown to be state-ofthe-art across a variety of well-studied benchmarks.The Transformer-XL incorporates a long-range memory at every layer of the network, which renders its state to be thousands of times larger than RNN predecessors.However it is unclear whether this is necessary.We perform a set of interventions to show that comparable performance can be obtained with 6X fewer long range memories and better performance can be obtained by limiting the range of attention in lower layers of the network.
RDON: a rack-scale disaggregated data center network based on a distributed fast optical switch

Aiming to solve the low utilization and high operational cost in current data centers and the diversified resource requirements for different application types, we propose and investigate what we believe to be a novel rack-scale disaggregated optical data center network (RDON). Different types of hardware resources (i.e., CPU, memory, and storage) are deployed in standalone resource pools of the RDON. The RDON architecture encompasses nanosecond distributed fast optical switches for the low latency and high bandwidth interconnects between the computing and memory nodes, while electrical packet switches are utilized to support the traffic of latency insensitivity and low bandwidth between memory nodes and storage nodes. By replacing the onboard data bus with a network interconnection, RDON architecture can provide a fine granularity hardware resource and relieve the constraints of server-centric architecture. To assess the performance of the RDON under realistic scenarios, statistics of diverse applications are captured from the data center lab. The RDON is modeled based on the OMNeT++ discrete event simulator and compared with the server-centric architecture. Numerical results show that the performance of the RDON is closely coupled with the type of deployed application. Compared to the server-centric architecture, the RDON can achieve up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2.44</mml:mn> <mml:mo>×<!-- × --></mml:mo> </mml:mrow> </mml:math> faster running time when running a single application, whereas at most <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2.25</mml:mn> <mml:mo>×<!-- × --></mml:mo> </mml:mrow> </mml:math> is the fastest running time under coexisting diverse types of applications. Finally, the analysis of costs and power consumption show that, with respect to server-centric architecture, the RDON requires 21.5% extra capital cost but 46.7% less power consumption.
Voice-Indistinguishability: Protecting Voiceprint In Privacy-Preserving Speech Data Release

With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may hinder the collection and sharing of real-world speech data, which contain the speaker's identifiable information, i.e., voiceprint, which is considered a type of biometric identifier. Current studies on voiceprint privacy protection do not provide either a meaningful privacy-utility trade-off or a formal and rigorous definition of privacy. In this study, we design a novel and rigorous privacy metric for voiceprint privacy, which is referred to as voice-indistinguishability, by extending differential privacy. We also propose mechanisms and frameworks for privacy-preserving speech data release satisfying voice-indistinguishability. Experiments on public datasets verify the effectiveness and efficiency of the proposed methods.
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems

It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as with the necessity of approximations for the inference. In this work we take one of the simplest inference methods, a truncated max-product Belief Propagation, and add what is necessary to make it a proper component of a deep learning model: connect it to learning formulations with losses on marginals and compute the backprop operation. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs), allowing us to design a hierarchical model composing BP inference and CNNs at different scale levels. The model is applicable to a range of dense prediction problems, is well-trainable and provides parameter-efficient and robust solutions in stereo, flow and semantic segmentation.
Analysis on homomorphic technique for data security in fog computing

Abstract The fog computing model has given new trends of networking with different types of devices providing services at the end‐user point. It inherits most advanced features of cloud computing with localization rather than centralization like a cloud. It builds a platform for the Internet of things of different standards. Similar to the cloud, it is prone to privacy and security threat while sharing resources and services at the network edge. When the request for support is with more sensitive data, for example, as in business or research area, then fog devices face many potential threats resulting in leakage of data. The existing cloud environment provides data sharing service to the legal requestor in a highly secured manner using cryptographic encryption techniques. The uploaded data undergoes encryption and decryption, at the sending and receiving end only on providing the private key. Even more, security can be achieved by further computation on encrypted data. As an extension to fog computing, the data communication between fog nodes‐fog nodes and fog node‐cloud center is done with encryption/decryption for ensuring confidentiality. Homomorphic encryption is a cryptographic technique which allows performing computations on encrypted data without decryption so that the original message need not be disclosed to intermediates (servers) who are the only service provider and not a data user. Our work is motivated by the flaw of security issues in fog computing platforms, which involve heterogeneous devices.
An Efficient Data Duplication System based on Hadoop Distributed File System

HDFS [Hadoop Distributed File System] a part of Apache Hadoop to store large data set consistently. HDFS is used for process Massive-Scale Data in parallel and it ensures accessibility of facts by replicating data to different nodes. Still, the repetition policy of HDFS doesn't think about the name of knowledge. The recognition of the files tends to alter over time. Hence, maintaining a fixed replication issue can affect the storage efficiency of HDFS. An Efficient Data Duplication System Based on HDFS, is proposed which consider the reputations of the records set aside in HDFS before replication. The proposed technique successfully reduces storage consumption by up to 45% without moving the accessibility and fault recognition in HDFS. □
Feature Selection Using Approximated High-Order Interaction Components of the Shapley Value for Boosted Tree Classifier

The Shapely value originates from the cooperative game theory and has been widely used in solving machine learning problems due to its high interpretability and consistency. The typical Shapley value evaluates the importance score of a feature as its average marginal contribution to a fully parameterized model under all possible feature combinations; as a result, its value includes the influences from both selected features and unselected ones. To better separate the corresponding sources, it is suggested to decompose the Shapley value into high-order interaction effect components such that the influences from each individual feature can be effectively untangled. A feature's contribution is decomposed into distinct interaction components, and each component corresponds to a joint contribution resulting from a particular feature combination. The feature ranking is therefore evaluated with respect to the selected feature subset and calculated as the total incremental contribution by summing up its corresponding decomposed interaction values. In this study, a computationally efficient model-dependent greedy search algorithm on the high-order interaction components is proposed to solve an optimal subset selection problem with hundreds of time-lagged interrelated input features. Our algorithm extends a recently developed low-order polynomial time method for calculating the interaction component values. The empirical analysis demonstrates that the proposed method always outperforms other methods that are based on the typical Shapley value, the gain or the split count criteria, in terms of in-sample representativeness and out-of-sample forecasting performance for handling a problem with hundreds of time-lagged input features.
Vision-based sign language recognition system: A Comprehensive Review

Communication is an essential activity for a human being to express their feeling, ideas, work together, and for the overall development of society. A person with a hearing disability uses sign language for communication and this language develops naturally within them. However, it is not commonly understood by non-signer community and hence, there remains a communication gap between these two communities. To bridge this gap, a computer vision based recognition system can be used. Many researchers have been worked on this field to facilitate the ease of communication between these two communities. In this paper, the basic concept of sign language recognition system and review of its existing techniques along with their comparison presented. The main objective of presenting this survey is to highlight the importance of vision based method with a specific focus on sign language.
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection

Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion. Unlike previous methods that have many restrictions such as requiring camera response function, sensor noise model, and a stream of preview images with different exposures (not accessible in some scenarios e.g. mobile applications), we propose a novel deep neural network to automatically select exposure bracketing, named EBSNet, which is sufficiently flexible without having the above restrictions. EBSNet is formulated as a reinforced agent that is trained by maximizing rewards provided by a multi-exposure fusion network (MEFNet). By utilizing the illumination and semantic information extracted from just a single auto-exposure preview image, EBSNet enables to select an optimal exposure bracketing for multi-exposure fusion. EBSNet and MEFNet can be jointly trained to produce favorable results against recent state-of-the-art approaches. To facilitate future research, we provide a new benchmark dataset for multi-exposure selection and fusion.
A Verifiable Semantic Searching Scheme by Optimal Matching Over Encrypted Data in Public Cloud

Semantic searching over encrypted data is a crucial task for secure information retrieval in public cloud. It aims to provide retrieval service to arbitrary words so that queries and search results are flexible. In existing semantic searching schemes, the verifiable searching does not be supported since it is dependent on the forecasted results from predefined keywords to verify the search results from cloud, and the queries are expanded on plaintext and the exact matching is performed by the extended semantically words with predefined keywords, which limits their accuracy. In this paper, we propose a secure verifiable semantic searching scheme. For semantic optimal matching on ciphertext, we formulate word transportation (WT) problem to calculate the minimum word transportation cost (MWTC) as the similarity between queries and documents, and propose a secure transformation to transform WT problems into random linear programming (LP) problems to obtain the encrypted MWTC. For verifiability, we explore the duality theorem of LP to design a verification mechanism using the intermediate data produced in matching process to verify the correctness of search results. Security analysis demonstrates that our scheme can guarantee verifiability and confidentiality. Experimental results on two datasets show our scheme has higher accuracy than other schemes.
Camera Trace Erasing

Camera trace is a unique noise produced in digital imaging process. Most existing forensic methods analyze camera trace to identify image origins. In this paper, we address a new low-level vision problem, camera trace erasing, to reveal the weakness of trace-based forensic methods. A comprehensive investigation on existing anti-forensic methods reveals that it is non-trivial to effectively erase camera trace while avoiding the destruction of content signal. To reconcile these two demands, we propose Siamese Trace Erasing (SiamTE), in which a novel hybrid loss is designed on the basis of Siamese architecture for network training. Specifically, we propose embedded similarity, truncated fidelity, and cross identity to form the hybrid loss. Compared with existing anti-forensic methods, SiamTE has a clear advantage for camera trace erasing, which is demonstrated in three representative tasks.
Inhibition-related N2 and P3: Indicators of visually induced motion sickness (VIMS)

Visually induced motion sickness (VIMS) leads to psychophysiological discomfort and has an adverse effect on executive function. Response inhibition, which can inhibit inappropriate behavior to adapt to changing environments, is an important aspect of executive function. The present study investigated the changes in response inhibition following VIMS using event-related potentials (ERPs). In a two-choice oddball task, ERPs were recorded at baseline (pretest) and after a 40-min virtual reality (VR) training session (posttest). The results showed a larger deviant-N2 amplitude, smaller deviant-P3 amplitude and delayed deviant-P3 latency after the VR training. Our findings suggest that VIMS is characterized in part by impaired response inhibition and that inhibition-related N2 and P3 can be used as electrophysiological indices of response inhibition in the assessment of VIMS.
Standing at One’s Post: Post-Qualitative Inquiry as Ethical Enactment

In this article, I advocate for a reading of the “post” as a type of watchful cartographic practice that bears productive similarity to Foucault’s project of building a “history of the present.” I then consider the implications of such mapping practices for post-qualitative inquiry, which I understand as a type of ethical enactment desperately needed given the inequities and systems of exploitation that govern our contemporary moment. I end with a call for post-qualitative inquirers to articulate an overt ethical orientation toward change.
Popularity prediction on vacation rental websites

In the personal house renting scenario, customers usually make quick assessments based on previous customers' reviews, which makes such reviews essential for the business. If the house is assessed as popular, a Matthew effect will be observed as more people will be willing to book it. Due to the lack of definition and quantity assessment measures, however, it is difficult to make a popularity evaluation and prediction. To solve this problem, the concept of house popularity is well defined in this paper. Specifically, the house popularity is decided by inter-event timeand rating score at the same time. To make a more effective prediction over these two correlated variables, a dual-gated recurrent unit (DGRU) is employed. Furthermore, an encoder-decoder framework with DGRU is proposed to perform popularity prediction. Empirical results show the effectiveness of the proposed DGRU and the encoder-decoder framework in two-correlated sequences prediction and popularity prediction, respectively.
Abstracting gradual references

Gradual typing is an effective approach to integrate static and dynamic typing, which supports the smooth transition between both extremes via the imprecision of type annotations. Gradual typing has been applied in many scenarios such as objects, subtyping, effects, ownership, typestates, information-flow typing, parametric polymorphism, etc. In particular, the combination of gradual typing and mutable references has been explored by different authors, giving rise to four different semantics—invariant, guarded, monotonic and permissive references. These semantics were specially crafted to reflect different design decisions with respect to precision and efficiency tradeoffs. Since then, progress has been made in the formulation of methodologies to systematically derive gradual counterparts of statically-typed languages, but these have not been applied to study mutable references. In this article, we explore how the Abstracting Gradual Typing (AGT) methodology, which has been shown to be effective in a variety of settings, applies to mutable references. Starting from a standard statically-typed language with references, we systematically derive with AGT a novel gradual language, called λREF˜. We establish the properties of λREF˜; in particular, it is the first gradual language with mutable references that is proven to satisfy the gradual guarantee. We then compare λREF˜ with the main four existing approaches to gradual references, and show that the application of AGT does justify one of the proposed semantics: we formally prove that the treatment of references in λREF˜ corresponds to the guarded semantics, by presenting a bisimilation with the coercion semantics of Herman et al. In the process, we uncover that any direct application of AGT yields a gradual language that is not space-efficient. We consequently adjust the dynamic semantics of λREF˜ to recover space efficiency. We then show how to extend λREF˜ to support both monotonic and permissive references as well. Finally, we provide the first proof of the dynamic gradual guarantee for monotonic references. As a result, this paper sheds further light on the design space of gradual languages with mutable references and contributes to deepening the understanding of the AGT methodology.
A Supervised Learning Approach for Safety Event Precursor Identification in Commercial Aviation

Growth in the aviation industry coupled with advancements in technology and data science techniques have led commercial airlines to more data-driven analysis for both operational efficiency and improved safety practices. Multiple sources of safety-related information are now available to airline safety teams for evaluation post-flight. Various techniques currently exist that leverage large data sets to identify anomalous flight behavior. While many of these techniques provide valuable insight into safety-related events as triggered by various parameter exceedances, few are capable of leveraging available data to identify precursors to triggered events. Event precursors are often difficult to detect due to the high dimensionality of data sets and the heterogeneous nature of the information captured. Therefore a robust and repeatable methodology is needed to identify flight parameter subsets from the available data sources that can accurately predict safety related events prior to occurrence. These parameters can provide insights on precursors to events and enable proactively avoiding them. In this paper, a methodology is developed using supervised machine learning techniques to assess and track critical parameters leading up to safety events in the approach and landing phase. Time-series data obtained from commercial aviation operations is collected, analyzed, and pre-processed. This data is then used in conjunction with safety event definitions to train a model for classifying the data into event and non-event flights. The parameter significance from these trained models is used to understand and isolate parameters that lead to the clear separation between event and non-event flights. Preliminary results from implementation of this methodology on commercial airline flight data identify critical parameters at multiple altitudes and their progression for unstable approach events, which can be used in defining precursors.
Correlated Features Synthesis and Alignment for Zero-shot Cross-modal Retrieval

The goal of cross-modal retrieval is to search for semantically similar instances in one modality by using a query from another modality. Existing approaches mainly consider the standard scenario that requires the source set for training and the target set for testing share the same scope of classes. However, they may not generalize well on zero-shot cross-modal retrieval (ZS-CMR) task, where the target set contains unseen classes that are disjoint with the seen classes in the source set. This task is more challenging due to 1) the absence of the unseen classes during training, 2) inconsistent semantics across seen and unseen classes, and 3) the heterogeneous multimodal distributions between the source and target set. To address these issues, we propose a novel Correlated Feature Synthesis and Alignment (CFSA) approach to integrate multimodal feature synthesis, common space learning and knowledge transfer for ZS-CMR. Our CFSA first utilizes class-level word embeddings to guide two coupled Wassertein generative adversarial networks (WGANs) to synthesize sufficient multimodal features with semantic correlation for stable training. Then the synthetic and true multimodal features are jointly mapped to a common semantic space via an effective distribution alignment scheme, where the cross-modal correlations of different semantic features are captured and the knowledge can be transferred to the unseen classes under the cycle-consistency constraint. Experiments on four benchmark datasets for image-text retrieval and two large-scale datasets for image-sketch retrieval show the remarkable improvements achieved by our CFAS method comparing with a bundle of state-of-the-art approaches.