Reviewerly/exHarmony huggingface dataset:
Improving sequence-to-sequence learning via optimal transport

Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word given the previous ground-truth partial sentence. This procedure focuses on modeling local syntactic patterns, and may fail to capture long-range semantic structure. We present a novel solution to alleviate these issues. Our approach imposes global sequence-level guidance via new supervision based on optimal transport, enabling the overall characterization and preservation of semantic features. We further show that this method can be understood as a Wasserstein gradient flow trying to match our model to the ground truth sequence distribution. Extensive experiments are conducted to validate the utility of the proposed approach, showing consistent improvements over a wide variety of NLP tasks, including machine translation, abstractive text summarization, and image captioning.
Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection

Vicky Zayats, Mari Ostendorf. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Improving employees' cyber security awareness

In 2019, employees still aren't convinced about their company's vulnerability to cybercrimes. While immensely frustrating for employers, employees’ disposition on this matter aligns with the general public's unawareness or failure to address data security. Employees still aren't convinced about their company's vulnerability to cybercrime, and this aligns with the general public's unawareness or failure to address data security. Employees are the primary vulnerability at a company, regardless of the size or scale. To reduce the risk they pose to your company's security, consider creative ways to engage and encourage employees to proactively contribute to your cyber security. Starting from the top of your organisation, design a cyber security policy and compliance plan that educates and incentivises employees to learn and follow security best practices, says Grayson Kemper of Clutch.
Quantum adiabatic machine learning by zooming into a region of the energy surface

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, a novel algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the ROC curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge

Text-to-image generation, i.e. generating an image given a text description, is a very challenging task due to the significant semantic gap between the two domains. Humans, however, tackle this problem intelligently. We learn from diverse objects to form a solid prior about semantics, textures, colors, shapes, and layouts. Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more and more details. In this paper, and inspired by this process, we propose a novel text-to-image method called LeicaGAN to combine the above three phases in a unified framework. First, we formulate the multiple priors learning phase as a textual-visual co-embedding (TVE) comprising a text-image encoder for learning semantic, texture, and color priors and a text-mask encoder for learning shape and layout priors. Then, we formulate the imagination phase as multiple priors aggregation (MPA) by combining these complementary priors and adding noise for diversity. Lastly, we formulate the creation phase by using a cascaded attentive generator (CAG) to progressively draw a picture from coarse to fine. We leverage adversarial learning for LeicaGAN to enforce semantic consistency and visual realism. Thorough experiments on two public benchmark datasets demonstrate LeicaGAN's superiority over the baseline method. Code has been made available at https://github.com/qiaott/LeicaGAN.
TiPoint

Smartglasses mostly rely on hardware interfaces such as touch-pad and buttons, which are often cumbersome and counter-intuitive to use. Furthermore, smartglasses feature cheap and low-power hardware preventing the use of advanced pointing techniques. To overcome these issues, we introduce TiPoint, a freehand mid-air interaction technique. TiPoint uses the monocular camera embedded in smartglasses to detect the user's hand, enabling intuitive and non-intrusive interaction. We introduce a light-weight algorithm for fingertip detection, which is especially suited for the limited hardware specifications and the short battery life time of smartglasses. Our evaluation shows that TiPoint as a mid-air non-intrusive interface delivers a better experience for users and smart glasses interactions, with users completing typical tasks 1.82 times faster than when using the original hardware.
Text, Image, Data, Interaction: Understanding Information Visualization

Interactive visualizations of data are increasingly addressed to everyday users with a range of interests and goals. As a medium of communication, information visualization (or infovis) is a rich and relevant object of study for writing researchers and teachers as we endeavor to understand and help our students gain proficiency with new digital literacies and practices. This article proposes a framework that engages four key elements in information visualizations—text, image, data, and interaction—with the goal of better understanding how information visualizations communicate, especially with mainstream audiences.
Effects of Gamified Augmented Reality in Public Spaces

Advancements in smartphone technology have resulted in the proliferation of Augmented Reality (AR) applications and games. Researchers have acknowledged the great potential of AR applications to enhance entertainment and improve learning experiences. In this study, we examined the potential effects of gamified AR in public places. We developed ARQuiz, an AR-based quiz game, for a public exhibition space and conducted a user study with respondents via survey (N = 176; 55.68% female, mean age 35.94 and SD = 11.89) and face-to-face interview (N = 28; 57.14% female, mean age 31.07 and SD = 7.42). We analyzed the relationship between perceived application usefulness, perceived application enjoyment, perceived exhibition enjoyment, and perceived quiz enjoyment. In addition, we examined perceived sociability before and after the quiz, quiz score, and user behavior in the exhibition space. The results indicate that visitors who enjoyed playing the ARQuiz game enjoyed the exhibition more, obtained better quiz results and felt more social after visiting the exhibition. Furthermore, the ARQuiz was regarded as a possible platform for improving visitors' learning and overall experiences in public exhibitions. Although some players expressed concerns about the privacy and intrusiveness of AR, our results indicate that a well-designed AR game may boost the overall satisfaction of an exhibition visit and increase players' sociabilitys.
Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics

Image metrics based on Human Visual System (HVS) play a remarkable role in the evaluation of complex image processing algorithms.However, mimicking the HVS is known to be complex and computationally expensive (both in terms of time and memory), and its usage is thus limited to a few applications and to small input data.All of this makes such metrics not fully attractive in real-world scenarios.To address these issues, we propose Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature (mean-opinion-score). DIQM can emulate existing visual metrics efficiently, reducing the computational costs by more than an order of magnitude with respect to existing implementations.
Sequential measurement-device-independent entanglement detection by multiple observers

Violation of a Bell inequality certifies that the underlying state must be entangled in a device-independent way, although there exist some entangled states which do not violate such an inequality. However, for every entangled state, it is possible to find a Hermitian operator called an entanglement witness that can detect entanglement through some local measurements in a device-dependent method, but implementation of wrong measurements may lead to fake detection of entanglement. To avoid such difficulties, measurement-device-independent entanglement witness (MDI-EW) based on a semiquantum nonlocal game was proposed, which is not only robust against wrong measurements but also against a specific kind of lossy detectors. We employ here a measurement-device-independent entanglement witness to detect entanglement in a scenario where half of an entangled pair is possessed by a single observer while the other half is with multiple observers performing unsharp measurements, sequentially, independently, and preserving entanglement as much as possible. Interestingly, we find that the numbers of successful observers who can detect entanglement, measurement-device-independently, both with equal and unequal sharpness parameters of the noisy measurements, are greater than that obtained with standard and Bell-inequality-based entanglement detection methods, reflecting its robustness. The entanglement contents of the sequentially shared states are also analyzed. Unlike other scenarios, our investigations also reveal that in this measurement-device-independent situation, states having entanglement in proximity to maximal remain entangled until there are two sequential observers, even if they measure sharply.
A bibliometric approach to finding fields that co-evolved with information technology

Abstract Among the declining industries, for example music industry, some have been revived by information technology (IT). At the same time, in academic fields, some have expected co-evolutions between IT and other fields to cause the resurgence of either field. In this research, the clustering of citation networks with 14,438 academic papers resulted in the identification of 28 academic fields in the areas “Computer Science” or “Information Science and Library Science.” Co-evolutions between these 28 fields and citing fields to the 28 fields were evaluated by an investigation of contents; a methodology to search co-evolutions was also proposed. This paper proposes that pairs of academic fields (with both high correlation and high dissimilarity) co-evolve, and some co-evolving pairs of academic fields were found. This research contributes to the discovery of the co-evolution between academic fields.
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e. the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with an auxiliary task for state prediction and best practices for multi-agent learning, SAD establishes a new state of the art for 2-5 players on the self-play part of the Hanabi challenge.
An Approach for Bengali Text Summarization using Word2Vector

Text Summarization is one of the mentionable research areas of Natural language processing. Several approaches have already been developed in this concern. Such as - Abstractive approach and extractive approach. Most recent recurrent neural network methods are producing much better results. Several mentionable research has already been discussed for English language summarizer, but a few have already done for the Bengali language. There are so many prerequisites for data analysis purpose-word2vector is one of them. Understanding the vector representation of any text leads the way to identify the key main points of that specific text and helps to measure the relationship of that text with other texts in similarity/dissimilarity [11]. Generated matrix using word2vector can easily applicable for identifying top-ranked sentence/words, either domain specific or in general form. In this paper, a word2vector approach has been discussed in the context of text summarization for the Bengali language.
Improving Classification Model's Performance Using Linear Discriminant Analysis on Linear Data

Classification is a supervised learning technique for predicting the class of given data points. Before doing classification, it is essential to build a classification model using classification algorithms. There are several classification algorithms which can be used for prediction. Linear Discriminant Analysis (LDA) is used for reducing the dimensionality of datasets. This paper represents how LDA improves different classification model's performance.
Model Optimization Boosting Framework for Linear Model Hash Learning

Efficient hashing techniques have attracted extensive research interests in both storage and retrieval of high-dimensional data, such as images and videos. In existing hashing methods, a linear model is commonly utilized owing to its efficiency. To obtain better accuracy, linear-based hashing methods focus on designing a generalized linear objective function with different constraints or penalty terms that consider the inherent characteristics and neighborhood information of samples. Differing from existing hashing methods, in this study, we propose a self-improvement framework called Model Boost (MoBoost) to improve model parameter optimization for linear-based hashing methods without adding new constraints or penalty terms. In the proposed MoBoost, for a linear-based hashing method, we first repeatedly execute the hashing method to obtain several hash codes to training samples. Then, utilizing two novel fusion strategies, these codes are fused into a single set. We also propose two new criteria to evaluate the goodness of hash bits during the fusion process. Based on the fused set of hash codes, we learn new parameters for the linear hash function that can significantly improve the accuracy. In general, the proposed MoBoost can be adopted by existing linear-based hashing methods, achieving more precise and stable performance compared to the original methods, and adopting the proposed MoBoost will incur negligible time and space costs. To evaluate the proposed MoBoost, we performed extensive experiments on four benchmark datasets, and the results demonstrate superior performance.
ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation

6D object pose estimation is a prerequisite for many applications. In recent years, monocular pose estimation has attracted much research interest because it does not need depth measurements. In this work, we introduce ConvPoseCNN, a fully convolutional architecture that avoids cutting out individual objects. Instead we propose pixel-wise, dense prediction of both translation and orientation components of the object pose, where the dense orientation is represented in Quaternion form. We present different approaches for aggregation of the dense orientation predictions, including averaging and clustering schemes. We evaluate ConvPoseCNN on the challenging YCB-Video Dataset, where we show that the approach has far fewer parameters and trains faster than comparable methods without sacrificing accuracy. Furthermore, our results indicate that the dense orientation prediction implicitly learns to attend to trustworthy, occlusion-free, and feature-rich object regions.
Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis

PURPOSE Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored. METHODS We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels. RESULTS Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations. CONCLUSION Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.
Transfer of motor skill between virtual reality viewed using a head-mounted display and conventional screen environments

Abstract Background Virtual reality viewed using a head-mounted display (HMD-VR) has the potential to be a useful tool for motor learning and rehabilitation. However, when developing tools for these purposes, it is important to design applications that will effectively transfer to the real world. Therefore, it is essential to understand whether motor skills transfer between HMD-VR and conventional screen-based environments and what factors predict transfer. Methods We randomized 70 healthy participants into two groups. Both groups trained on a well-established measure of motor skill acquisition, the Sequential Visual Isometric Pinch Task (SVIPT), either in HMD-VR or in a conventional environment (i.e., computer screen). We then tested whether the motor skills transferred from HMD-VR to the computer screen, and vice versa. After the completion of the experiment, participants responded to questions relating to their presence in their respective training environment, age, gender, video game use, and previous HMD-VR experience. Using multivariate and univariate linear regression, we then examined whether any personal factors from the questionnaires predicted individual differences in motor skill transfer between environments. Results Our results suggest that motor skill acquisition of this task occurs at the same rate in both HMD-VR and conventional screen environments. However, the motor skills acquired in HMD-VR did not transfer to the screen environment. While this decrease in motor skill performance when moving to the screen environment was not significantly predicted by self-reported factors, there were trends for correlations with presence and previous HMD-VR experience. Conversely, motor skills acquired in a conventional screen environment not only transferred but improved in HMD-VR, and this increase in motor skill performance could be predicted by self-reported factors of presence, gender, age and video game use. Conclusions These findings suggest that personal factors may predict who is likely to have better transfer of motor skill to and from HMD-VR. Future work should examine whether these and other predictors (i.e., additional personal factors such as immersive tendencies and task-specific factors such as fidelity or feedback) also apply to motor skill transfer from HMD-VR to more dynamic physical environments.
Emojis and Law: contextualized flexibility of meaning in cyber communication

Because some sentiments simply cannot be verbalized, the written message lacks rendered intonation and the embodied dynamics of expressed feeling. With the rise of emails and other means of digital communication, the emotive shortcomings of new communication channels are even more acute even while they become emotively more expansive. The color, form, and context of emojis in visual communication may lead to more kinds of (mis)interpretation. Although emojis might be presented as a tool for clarification of the communicative intent, it is still unknown whether visual add-ons also introduce more uncertainty or flexibility in language. As a result, emojis contribute to a digital cultural shift in message transmission that also reflects an evolving hermeneutic of legal communication and communicative intention. In a growing number of legal cases, emojis serve as evidence, or as facts which reveal asserted, yet unsaid information and implied meaning. This study depicts the flexible nature of emojis in e-communication, and so analyzes applications in various legal cases to demonstrate the way emojis, as negative emotional influencers, affect the understanding of online communication and decision-making in courts.