Reviewerly/exHarmony huggingface dataset:
LOVF: Layered Organic View Fusion for Click-through Rate Prediction in Online Advertising

Organic recommendation and advertising recommendation usually coexist on e-commerce platforms. In this paper, we study the problem of utilizing data from organic recommendation to reinforce click-through rate prediction in advertising scenarios from a multi-view learning perspective. We propose a novel method, termed LOVF (Layered Organic View Fusion). LOVF implements a multi-view fusion mechanism - for each advertising instance, LOVF derives deep representations layer-by-layer from the organic recommendation view and these deep representations are then fused into the corresponding vanilla representations of the advertising view. Extensive experiments across a variety of backbones demonstrate LOVF's generality, effectiveness and efficiency on a new real-world production dataset. The dataset encompasses data from both the organic recommendation and advertising scenarios. Notably, LOVF has been successfully deployed in the advertising recommender system of JD.com, which is one of the world's largest e-commerce platforms; online A/B testing shows that LOVF achieves impressive improvement on advertising clicks and revenue. Our code and dataset are available at https://github.com/adsturing/lovf for facilitating further research.
A Constraints-based Dynamic Time Warping Method for Gesture Recognition with Kinect

Gesture recognition is still actively researched in non-contact human-computer interaction (HCI), where the Dynamic Time Warping (DTW) algorithm is commonly employed. However, the computation load of traditional DTW algorithms during the matching phase presents a challenge for gesture recognition, especially when the number of reference gestures in the template library increases. In order to solve this problem, a Constraints-based Dynamic Time Warping (CDTW) method is proposed in this paper, including Global-Path Constraint, First-Frame Constraint, and Feature-Vector Constraint. These three constraints are expected to limit the region of the warping path, exclude several reference gestures from the template library, and reduce computation load directly with reduced elements of the feature vector. To verify the proposed CDTW method, Microsoft Kinect-based comparative experiments with the traditional DTW method have been carried out. Experimental results show that our CDTW method boosts the efficiency of gesture recognition with a 17% decrease in recognition time and a 3% increase in average recognition accuracy, compared to the traditional DTW algorithm.
LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment

Data collection and mining is a crucial bottleneck for cross-lingual information retrieval (CLIR). While previous works used machine translation and iterative training, we present a novel approach to cross-lingual pretraining called LAPCA (language-agnostic pretraining with cross-lingual alignment). We train the LAPCA-LM model based on XLM-RoBERTa and łexa that significantly improves cross-lingual knowledge transfer for question answering and sentence retrieval on, e.g., XOR-TyDi and Mr. TyDi datasets, and in the zero-shot cross-lingual scenario performs on par with supervised methods, outperforming many of them on MKQA.
Visual Servoing Gain Tuning by Sarsa: an Application with a Manipulator

This paper investigates a Sarsa-based visual servoing control gain tuning method and the application on a manipulator. For a typical visual servo controller, fixed control gains will not provide the best performance. Therefore, state action reward state action (SARSA) algorithm, one of learning-based methods from reinforcement learning (RL), is introduced to select control gains in every control step. The norm of the visual error is used to define the state space. The positive gain of the controller is discretized as the actions. A reward function is defined to evaluate the performance of every action. Both a numerical test and a robot experiment are carried out to validate the presented algorithm.
LENS: Predictive Diagnostics for Flexible and Efficient Assessments

The utility of assessment systems lies in their capacity to transform observations of student behavior into meaningful inferences about learning, knowledge, and skills. Common practice is to use latent variable models and produce scores on scales. However the simplicity of these psychometric models may filter out potentially valuable information present in student behavior. In particular, scale scores are not optimized to support granular instructional decisions. Machine learning offers promising alternatives, but proposed deep learning architectures are not ideally suited for operational testing conditions involving sparse data and shifting category labels for test questions.
The Use of SMS and Other Mobile Phone-based Messaging to Support Education at Scale: A Synthesis of Recent Evidence

Higher levels of device ownership and lower connectivity requirements are key reasons why mobile learning may have potential to support education at scale in low-income contexts. Interest in the use of mobile phones as an educational medium - particularly through the use of SMS or messaging apps - has been renewed recently, as a result of school closures prompted by the Covid-19 pandemic. As a result, the evidence base for educational interventions using SMS at scale has recently expanded. In this work-in-progress synthesis paper, we review recent research studies which have used SMS for education at scale, with findings published since the onset of the Covid-19 pandemic in 2020. We find that there has been a notable increase in studies which have used SMS to promote and support education at a large scale. In addition to its use as a medium for directly supporting learners, it has also been applied to promoting parental engagement and encouraging participation in formal schooling. The efficacy of interventions has been mixed, which highlights the need for nuance and further research as the field looks to understand which benefits of mobile learning could be beneficial to retain post-pandemic.
Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation

Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastrophic forgetting in both FS-XLT and MTL for all 6 languages in our preliminary experiments. To mitigate the issue, we propose a simple yet effective prompt learning approach that can preserve the multilinguality of multilingual pre-trained language model (mPLM) in FS-XLT and MTL by bridging the gap between pre-training and fine-tuning with Fixed-prompt LM Tuning and our hand-crafted prompts. Experimental results on all 6 languages in terms of both automatic and human evaluations demonstrate the effectiveness of our approach. Our code is available at https://github.com/JeremyLeiLiu/XLinguDial.
Improving Programming Q&amp;A with Neural Generative Augmentation

Knowledge-intensive programming Q&A is an active research area in industry. Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we propose ProQANS and its variants ReProQANS and ReAugProQANS to tackle programming Q&A. ProQANS is a neural search approach that leverages unlabeled data on the Internet (such as StackOverflow) to mitigate the cold-start problem. ReProQANS extends ProQANS by utilizing reformulated queries with a novel triplet loss. We further use an auxiliary generative model to augment the training queries, and design a novel dual triplet loss function to adapt these generated queries, to build another variant of ReProQANS termed as ReAugProQANS. In our empirical experiments, we show ReProQANS has the best performance when evaluated on the in-domain test set, while ReAugProQANS has the superior performance on the out-of-domain real programming questions, by outperforming the state-of-the-art model by up to 477% lift on the MRR metric respectively. The results suggest their robustness to previously unseen questions and its wide application to real programming questions.
Exploiting Ubiquitous Mentions for Document-Level Relation Extraction

Recent years have witnessed the transition from sentence-level to document-level in relation extraction (RE), with new formulation, new methods and new insights. Yet, the fundamental concept, mention, is not well-considered and well-defined. Current datasets usually use automatically-detected named entities as mentions, which leads to the missing reference problem. We show that such phenomenon hinders models' reasoning abilities. To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. We evaluate various methods and conduct thorough experiments to demonstrate the efficacy of our formula. Specifically, the results indicate that incorporating coreferences helps reduce the long-term dependencies, further improving models' robustness and generalization under adversarial and low-resource settings. The new dataset is made publicly available for future research.
Towards Scalable Vocabulary Acquisition Assessment with BERT

In this investigation we propose new machine learning methods for automated scoring models that predict the vocabulary acquisition in science and social studies of second grade English language learners, based upon free-form spoken responses. We evaluate performance on an existing dataset and use transfer learning from a large pre-trained language model, reporting the influence of various objective function designs and the input-convex network design. In particular, we find that combining objective functions with varying properties, such as distance among scores, greatly improves the model reliability compared to human raters. Our models extend the current state of the art performance for assessing word definition tasks and sentence usage tasks in science and social studies, achieving excellent quadratic weighted kappa scores compared with human raters. However, human-human agreement still surpasses model-human agreement, leaving room for future improvement. Even so, our work highlights the scalability of automated vocabulary assessment of free-form spoken language tasks in early grades.
News Popularity Beyond the Click-Through-Rate for Personalized Recommendations

Popularity detection of news articles is critical for making relevant recommendations for users and drive user engagement for maximum business value. Among several well-known metrics such as likes, shares, comments, Click-Through-Rate (CTR) has evolved as a default metric of popularity. However, CTR is highly influenced by the probability of news articles getting an impression, which in turn depends on the recommendation algorithm. Furthermore, it does not consider the age of the news articles, which are highly perishable and also misses out on human contextual behavioral preferences towards news. Here, we use the MIND dataset, open sourced by Microsoft to investigate the existing metrics of popularity and propose six new metrics. Our aim is to create awareness about the different perspectives of measuring popularity while discussing the advantages and disadvantages of the proposed metrics with respect to the human click behavior. We evaluated the predictability of the proposed metrics in comparison to CTR prediction. We further evaluated the utility of the proposed metrics through different test cases. Our results indicate that by using appropriate popularity metrics, we can reduce the initial news corpus (item set) by 50% and still could achieve 99% of the total clicks as compared to unfiltered news corpus based recommender systems. Similarly, our results show that we can reduce the effective number of articles recommended per impression that could improve user experience with the news platforms. The metrics proposed in this paper can be useful in other contexts, especially in recommenders with perishable items e.g. video reels or blogs.
Pose driven motion image generation aided by depth information

Motion transfer which can be used as drive technology of the interaction between users and virtual roles has been a research hotspot in recent years. It is essentially a deformation process of human appearances, consequently motion transfer is typically regarded as a pose-guided image generation task which can be solved by a GAN based framework. However, the real motions occur in 3D space and the image generation in 2D plane inevitably lacks the depth information guidance of the original motions, which will result in the confusion of depths for different body parts. Besides, the adversarial loss of GAN presents weak constraints on the silhouette details. In this paper, we propose a two-stage GAN based model to make up for the defect of lacking depth information and improve the accuracy of the generated silhouette details. In stage-I, we propose a silhouette attention GAN with a silhouette consistency loss to generate the depth maps of target people. This not only brings the depth information of the original motions in 3D space but also aligns the body regions with reliable silhouettes for the following person image generation. In stage-II, we propose a context-enhanced GAN with the target poses and depth maps generated in the first stage as input to generate the final motion images. The generated results have reliable depth information and accurate silhouettes, demonstrating the effectiveness of the proposed model.
Optimizing Reciprocal Rank with Bayesian Average for improved Next Item Recommendation

Next item recommendation is a crucial task of session-based recommendation. However, the gap between the optimization objective (Binary Cross Entropy) and the ranking metric (Mean Reciprocal Rank) has not been well-explored, resulting in sub-optimal recommendations. In this paper, we propose a novel objective function, namely Adjusted-RR, to directly optimize Mean Reciprocal Rank. Specifically, Adjusted-RR adopts Bayesian Average to adjust Reciprocal Rank loss with Normal Rank loss by creating position-aware weights between them. Adjusted-RR is a plug-and-play objective that is compatible with various models. We apply Adjusted-RR on two base models and two datasets, and experimental results show that it makes a significant improvement in the next item recommendation.
On Answer Position Bias in Transformers for Question Answering

Extractive Transformer-based models for question answering (QA) are trained to predict the start and end position of the answer in a candidate paragraph. However, the true answer position can bias these models when its distribution in the training data is highly skewed. That is, models trained only with the answer at the beginning of the paragraph will perform poorly on test instances with the answer at the end. Many studies have focused on countering answer position bias but have yet to deepen our understanding of how such bias manifests in the main components of the Transformer. In this paper, we analyze the self-attention and embedding generation components of five Transformer-based models with different architectures and position embedding strategies. Our analysis shows that models tend to map position bias in their attention matrices, generating embeddings that correlate the answer and its biased position, ultimately compromising model generalization.
Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives

The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion). These models are predominantly trained with standard point-wise classification objectives. The existing body of work exhibits two main shortcomings: (1) despite general design homogeneity, direct comparisons between models are hindered by varying evaluation datasets and protocols; (2) it leaves alternative model designs and training objectives vastly unexplored. In this work, we present a unified framework for news recommendation, allowing for a systematic and fair comparison of news recommenders across several crucial design dimensions: (i) candidate-awareness in user modeling, (ii) click behavior fusion, and (iii) training objectives. Our findings challenge the status quo in neural news recommendation. We show that replacing sizable user encoders with parameter-efficient dot products between candidate and clicked news embeddings (late fusion) often yields substantial performance gains. Moreover, our results render contrastive training a viable alternative to point-wise classification objectives.
An effective cryptanalysis of <scp>DES</scp> for secure communication using hybrid cryptanalysis and deep neural network

Summary In today's world, information security plays a key role in data storage and communication owing to the modern evolution of digitized data interchange in electronic mode. Cryptography is a widely preferred technique for securing transmitted information by transforming the original text into cipher text. A few cryptographic techniques have the inability to protect the data which are vulnerable to a distinct class of attacks. Therefore, a reliable cryptographic technique is necessitated for enlarging information security. In this paper, a new hybrid cryptanalysis (HCA) model has been proposed to acquire optimal cryptanalysis of data encryption standard (DES). It combines linear cryptanalysis (LCA) and neural cryptanalysis (NCA) for enhancing the performance of the cryptographic system with minimal time complexity. Primarily, the HCA model employs LCA to break cryptographic codes by analyzing linear approximations of the cryptographic algorithm. NCA is then applied with the aid of a deep neural network for identifying patterns in larger datasets. These patterns can be further utilized to break encryption. The efficacy of the proposed HCA model will be assessed through the assessment of three different datasets. The results manifest that the accuracy of the proposed model is increased up to 97.66% and the time needed to break encryption can be reduced.
Depth Map Super-Resolution Reconstruction Based on Multi-Channel Progressive Attention Fusion Network

Depth maps captured by traditional consumer-grade depth cameras are often noisy and low-resolution. Especially when upsampling low-resolution depth maps with large upsampling factors, the resulting depth maps tend to suffer from vague edges. To address these issues, we propose a multi-channel progressive attention fusion network that utilizes a pyramid structure to progressively recover high-resolution depth maps. The inputs of the network are the low-resolution depth image and its corresponding color image. The color image is used as prior information in this network to fill in the missing high-frequency information of the depth image. Then, an attention-based multi-branch feature fusion module is employed to mitigate the texture replication issue caused by incorrect guidance from the color image and inconsistencies between the color image and the depth map. This module restores the HR depth map by effectively integrating the information from both inputs. Extensive experimental results demonstrate that our proposed method outperforms existing methods.
Prisma : A Tierless Language for Enforcing Contract-client Protocols in Decentralized Applications

Decentralized applications (dApps) consist of smart contracts that run on blockchains and clients that model collaborating parties. dApps are used to model financial and legal business functionality. Today, contracts and clients are written as separate programs—in different programming languages—communicating via send and receive operations. This makes distributed program flow awkward to express and reason about, increasing the potential for mismatches in the client-contract interface, which can be exploited by malicious clients, potentially leading to huge financial losses. In this article, we present Prisma , a language for tierless decentralized applications, where the contract and its clients are defined in one unit and pairs of send and receive actions that “belong together” are encapsulated into a single direct-style operation, which is executed differently by sending and receiving parties. This enables expressing distributed program flow via standard control flow and renders mismatching communication impossible. We prove formally that our compiler preserves program behavior in presence of an attacker controlling the client code. We systematically compare Prisma with mainstream and advanced programming models for dApps and provide empirical evidence for its expressiveness and performance.
Effects of Luminance Contrast on Depth Perception in Optical See-Through Augmented Reality

ABSTRACTABSTRACTAugmented reality (AR) technology presents the rendered virtual object in the real world whose depth cues can assist the depth estimation of the virtual object. For many AR applications, the successful interaction between the real world and the virtual object relies on the accuracy of depth perception. In the past, the research of depth perception in AR mainly focused on the impact of virtual object properties, including the size, color, and shading model. In this study, an experiment has been conducted to investigate the influence of ambient luminance, virtual object luminance, and shading model which is a luminance-related parameter on depth perception using an optical see-through head-mounted display. Results show that a higher luminance contrast between the real scene and the virtual object leads to a lower accuracy of depth perception in AR.KEYWORDS: Augmented realitydepth perceptionreal environmentvirtual objectluminance contrast Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key Research and Development Program of China under Grant [2020YFC1523103]; the National Natural Science Foundation of China [under Grant 62205018, 62002018, 61960206007].
Open Science in Italy: Lessons Learned En Route to Opening Scholarship

Following the analysis of the slow uptake of open science in Italy, we identify four main lessons learned that may be useful to scholars and research policy makers engaged in promoting the uptake of open science culture and practices in their own countries.
Natural Semantic Metalanguage Analysis of English Speech Act Verb ‘Inform’

Generally, every language has a lexicon that is grouped into classified verbs. Each verb can be broken down into some specific subtypes. Particularly in English, verbs are semantically divided into several subtypes. Verbs such as tell, ask, inform, offer, forgive, and others are belonging to the types of verbs of speech or speech acts. This study aims to analyze natural semantic metalanguage analysis of English speech act verb of inform. In collecting data is using observational method. Therefore, the theory of natural semantic metalanguage (NSM) is used to analyze the data taken from the American Corpus (COCA). By way of explication, the particular lexicon is explained thoroughly to reveal the subtle meaning of semantic features without any bias. The research argues that the four subtypes of the speech act verb "inform" demonstrate varying semantic forms and structures. The distinctions in lexicon become evident within their common semantic domain. Moreover, the four associated lexicons with the verb "inform" possess unique semantic traits and serve specific purposes within speech acts.