Reviewerly/exHarmony huggingface dataset:
HC4: A New Suite of Test Collections for Ad Hoc CLIR

HC4 is a new suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian, topics in English and in the document languages, and graded relevance judgments. New test collections are needed because existing CLIR test collections built using pooling of traditional CLIR runs have systematic gaps in their relevance judgments when used to evaluate neural CLIR methods. The HC4 collections contain 60 topics and about half a million documents for each of Chinese and Persian, and 54 topics and five million documents for Russian. Active learning was used to determine which documents to annotate after being seeded using interactive search and judgment. Documents were judged on a three-grade relevance scale. This paper describes the design and construction of the new test collections and provides baseline results for demonstrating their utility for evaluating systems.
Learning To Recognize Procedural Activities with Distant Supervision

In this paper we consider the problem of classifying fine-grained, multi-step activities (e.g., cooking different recipes, making disparate home improvements, creating various forms of arts and crafts) from long videos spanning up to several minutes. Accurately categorizing these activities requires not only recognizing the individual steps that compose the task but also capturing their temporal dependencies. This problem is dramatically different from traditional action classification, where models are typically optimized on videos that span only a few seconds and that are manually trimmed to contain simple atomic actions. While step annotations could enable the training of models to recognize the individual steps of procedural activities, existing large-scale datasets in this area do not include such segment labels due to the prohibitive cost of manually annotating temporal boundaries in long videos. To address this issue, we propose to automatically identify steps in instructional videos by leveraging the distant supervision of a textual knowledge base (wikiHow) that includes detailed descriptions of the steps needed for the execution of a wide variety of complex activities. Our method uses a language model to match noisy, automatically-transcribed speech from the video to step descriptions in the knowledge base. We demonstrate that video models trained to recognize these automatically-labeled steps (without manual supervision) yield a representation that achieves superior generalization performance on four downstream tasks: recognition of procedural activities, step classification, step forecasting and egocentric video classification.
Life-long Learning for Reasoning-based Semantic Communication

Semantic communication is an emerging paradigm that focuses on understanding and delivering semantics or meaning of messages. Most existing semantic communication solutions define semantic meaning as the labels of objects recognized from a given form of source signal, while ignoring intrinsic information that cannot be directly observed. Since the models for recognizing labels need to be pre-trained with labelled dataset, the total number of semantic objects are often limited by a fixed set. In this paper, we propose a novel reasoning-based semantic communication architecture in which the semantic meaning is represented by a graph-based knowledge structure in terms of semantic-entity, relationships, and reasoning rules. An embedding-based semantic interpretation framework is proposed to convert the high-dimensional graph-based representation of semantic meaning into a low-dimensional representation, which is efficient for channel transmission. We develop a novel inference function-based approach that can automatically infer hidden information such as missing entities and relations that cannot be directly observed from the message. Finally, we introduce a life-long model updating approach in which the receiver can learn from previously received messages and automatically update the rules for reasoning the hidden information when new unknown semantic entities and relations have been discovered. Extensive experiments are conducted based on a real-world knowledge database and numerical results show that our proposed solution achieves 76% interpretation accuracy of the hidden meaning at the receiver when some entities are missing in the transmitted message.
Make Reddit Great Again: Assessing Community Effects of Moderation Interventions on r/The_Donald

The subreddit r/The_Donald was repeatedly denounced as a toxic and misbehaving online community, reasons for which it faced a sequence of increasingly constraining moderation interventions by Reddit administrators. It was quarantined in June 2019, restricted in February 2020, and finally banned in June 2020, but despite precursory work on the matter, the effects of this sequence of interventions are still unclear. In this work, we follow a multidimensional causal inference approach to study data containing more than 15M posts made in a time frame of 2 years, to examine the effects of such interventions inside and outside of the subreddit. We find that the interventions greatly reduced the activity of problematic users. However, the interventions also caused an increase in toxicity and led users to share more polarized and less factual news. In addition, the restriction had stronger effects than the quarantine, and core users of r/The_Donald suffered stronger effects than the rest of users. Overall, our results provide evidence that the interventions had mixed effects and paint a nuanced picture of the consequences of community-level moderation strategies. We conclude by reflecting on the challenges of policing online platforms and on the implications for the design and deployment of moderation interventions.
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification

Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive human-annotated training data, which are often costly to obtain and suffer from a long-tailed label distribution (i.e., many labels occur only a few times in the training set). In this paper, we study LMTC under the zero-shot setting, which does not require any annotated documents with labels and only relies on label surface names and descriptions. To train a classifier that calculates the similarity score between a document and a label, we propose a novel metadata-induced contrastive learning (MICoL) method. Different from previous text-based contrastive learning techniques, MICoL exploits document metadata (e.g., authors, venues, and references of research papers), which are widely available on the Web, to derive similar document–document pairs. Experimental results on two large-scale datasets show that: (1) MICoL significantly outperforms strong zero-shot text classification and contrastive learning baselines; (2) MICoL is on par with the state-of-the-art supervised metadata-aware LMTC method trained on 10K–200K labeled documents; and (3) MICoL tends to predict more infrequent labels than supervised methods, thus alleviates the deteriorated performance on long-tailed labels.
MSSNet: Multi-Scale-Stage Network for Single Image Deblurring

Most traditional single image deblurring methods before deep learning adopt a coarse-to-fine scheme that estimates a sharp image at a coarse scale and progressively refines it at finer scales. While this scheme has also been adopted in several deep learning-based approaches, recently a number of single-scale approaches have been introduced showing superior performance to previous coarse-to-fine approaches in terms of quality and computation time. In this paper, we revisit the coarse-to-fine scheme and analyze the defects of previous coarse-to-fine approaches. Based on the analysis, we propose Multi-Scale-Stage Network (MSSNet), a novel deep learning-based approach to single image deblurring with our remedies to the defects. MSSNet adopts three remedies: stage configuration reflecting blur scales, an inter-scale information propagation scheme, and a pixel-shuffle-based multi-scale scheme. Our experiments show that our remedies can effectively resolve the defects of previous coarse-to-fine approaches and improve the deblurring performance.
No Free Lunch Theorem for Security and Utility in Federated Learning

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of semi-honest partners; on the other hand, a certain amount of information has to be exchanged among different parties for the sake of learning utility. Such a challenge calls for the privacy-preserving federated learning solution, which maximizes the utility of the learned model and maintains a provable privacy guarantee of participating parties’ private data. This article illustrates a general framework that (1) formulates the trade-off between privacy loss and utility loss from a unified information-theoretic point of view, and (2) delineates quantitative bounds of the privacy-utility trade-off when different protection mechanisms including randomization, sparsity, and homomorphic encryption are used. It was shown that in general there is no free lunch for the privacy-utility trade-off , and one has to trade the preserving of privacy with a certain degree of degraded utility. The quantitative analysis illustrated in this article may serve as the guidance for the design of practical federated learning algorithms.
Polite or Direct? Conversation Design of a Smart Display for Older Adults Based on Politeness Theory

Conversational interfaces increasingly rely on human-like dialogue to offer a natural experience. However, relying on dialogue involving multiple exchanges for even simple tasks can overburden users, particularly older adults. In this paper, we explored the use of politeness theory in conversation design to alleviate this burden and improve user experience. To achieve this goal, we categorized the voice interaction offered by a smart display application designed for older adults into seven major speech acts: request, suggest, instruct, comment, welcome, farewell, and repair. We identified face needs for each speech act, applied politeness strategies that best address these needs, and tested the ability of these strategies to shape the perceived politeness of a voice assistant in an online study (n = 64). Based on the findings of this study, we designed direct and polite versions of the system and conducted a field study (n = 15) in which participants used each of the versions for five days at their homes. Based on five factors merged from our qualitative findings, we identified four distinctive user personas—socially oriented follower, socially oriented leader, utility oriented follower, and utility oriented leader—that can inform personalized design of smart displays.
Sar-Shipnet: Sar-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-Resolution Feature Fusion

This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We broadly extract different types of SAR image features and raise the intriguing question that whether these extracted features are beneficial to (1) suppress data variations (e.g., complex land-sea backgrounds, scattered noise) of real-world SAR images, and (2) enhance the features of ships that are small objects and have different aspect (length-width) ratios, therefore resulting in the improvement of ship detection. To answer this question, we propose a SAR-ship detection neural network (call SAR-ShipNet for short), by newly developing Bidirectional Coordinate Attention (BCA) and Multi-resolution Feature Fusion (MRF) based on CenterNet. Moreover, considering the varying length-width ratio of arbitrary ships, we adopt elliptical Gaussian probability distribution in CenterNet to improve the performance of base detector models. Experimental results on the public SAR-Ship dataset show that our SAR-ShipNet achieves competitive advantages in both speed and accuracy.
Supervised Contrastive Learning for Product Matching

Contrastive learning has moved the state of the art for many tasks in computer vision and information retrieval in recent years. This poster is the first work that applies supervised contrastive learning to the task of product matching in e-commerce using product offers from different e-shops. More specifically, we employ a supervised contrastive learning technique to pre-train a Transformer encoder which is afterward fine-tuned for the matching task using pair-wise training data. We further propose a source-aware sampling strategy that enables contrastive learning to be applied for use cases in which the training data does not contain product identifiers. We show that applying supervised contrastive pre-training in combination with source-aware sampling significantly improves the state-of-the-art performance on several widely used benchmarks: For Abt-Buy, we reach an F1-score of 94.29 (+3.24 compared to the previous state-of-the-art), for Amazon-Google 79.28 (+ 3.7). For WDC Computers datasets, we reach improvements between +0.8 and +8.84 in F1-score depending on the training set size. Further experiments with data augmentation and self-supervised contrastive pre-training show that the former can be helpful for smaller training sets while the latter leads to a significant decline in performance due to inherent label noise. We thus conclude that contrastive pre-training has a high potential for product matching use cases in which explicit supervision is available.
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview

The increasing digitization and datification of all aspects of people’s daily life, and the consequent growth in the use of personal data, are increasingly challenging the current development and adoption of Machine Learning (ML). First, the sheer complexity and amount of data available in these applications strongly demands for ML algorithms that can be trained directly on complex structures, which can be naturally described by graphs. In fact, graphs inherently capture information about entities, their attributes, and relationships between them. Directly applying ML to graphs relieves domain experts and data scientists from the challenging and time-consuming problem of designing a suitable vector-based data representation used by classical ML techniques. Second, ML algorithms should not only be designed to achieve high technical and functional standards; as the automated decisions provided by these algorithms can have a relevant impact on people’s lives, their behavior has to be aligned with the values and principles of individuals and society. This demands for designing automated algorithms that we, as humans, can trust, fulfilling the requirements of fairness, robustness, privacy, and explainability. Third, designing effective ML algorithms requires skills and expertise developed at different levels. This substantially hinders the democratization and widespread availability of such technology for society at large, which in turn demands for improving the level of automatization and systematization of their design process, while also providing guarantees on their performance. For this reason, this paper provides an overview of the current works focused towards learning trustworthily, automatically, and with guarantees on graphs.
Natural Language Processing based Automated Essay Scoring with Parameter-Efficient Transformer Approach

Existing automated scoring models implement layers of traditional recurrent neural networks to achieve reasonable performance. However, the models provide limited performance due to the limited capacity to encode long-term dependencies. The paper proposed a novel architecture incorporating pioneering language models of the natural language processing community. We leverage pre-trained language models and integrate it with adapter modules, which use a bottle-neck architecture to reduce the number of trainable parameters while delivering excellent performance. We also propose a model by re-purposing the bidirectional attention flow model to detect adversarial essays. The model we put forward achieves state-of-the-art performance on most essay prompts in the Automated Student Assessment Prize data set. We outline the previous methods employed to attempt this task, and show how our model outperforms them.
Security Issues in IoT and Cloud Computing Service Models with Suggested Solutions

Cloud computing is a new model for providing computing services, where computing is delivered as a service over the Internet. Cloud computing is a very popular choice among SMEs because computing services are provided at much lower prices compared to their own IT infrastructure. In the cloud computing model, computing services and data storage are outsourced to cloud service providers. Customers do not have full control over applications and their data. Therefore, an added overhead of security risks comes along, and the security of data becomes the primary concern for cloud customers when considering cloud services. This paper explores various deployment models of cloud computing services and IoT (Internet of Things), figures out associated data security issues with them, and suggests a metric-based solution to assess security provided by cloud services.
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media

The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.
Uncertainty-aware twin support vector machines

There exist uncertain data in the real world due to some factors such as imprecise measurements and noise. Unlike deterministic data, the features of samples in uncertain data are often described by interval numbers or random vectors with probability density functions. In this paper we propose novel twin support vector machines (TSVMs) to handle uncertain data. In the proposed models which are referred to as uncertainty-aware TSVMs, each uncertain sample is modeled as a random vector with Gaussian distributions. To deal with the multi-dimensional integrals in the original models, we derive an interesting and important theorem which helps us transform the original models into the model involving one-dimensional integrals. The simplification of models makes the optimization problem tractable and the simplified models are solved by using the quasi-Newton optimization algorithm. The proposed decision rule allows us to classify uncertain samples with means and covariance matrices. In addition, we extend the proposed models to their kernel versions to capture the nonlinear structure of uncertain data. Experiments on a series of data sets have been performed to demonstrate that the proposed models gain better classification performance than some existing algorithms, especially for representing uncertain cross-plane problems.
The Effect of Virtual Reality Glasses and Stress Balls on Pain and Vital Findings During Transrectal Prostate Biopsy: A Randomized Controlled Trial

This study aimed to determine effect of virtual reality (VR) glasses and stress balls (SB) on patients' pain and vital signs during transrectal prostate biopsy.This experimental research was conducted using a pre or post-procedure randomized controlled model.The study population consisted of 96 male patients who were admitted to the TRUS Unit of the Urology Department of a state hospital in southeastern Turkey for prostate biopsy. The patients were divided into three groups by block randomization as 32 for VR, 32 for SB, and 32 for the control group. A Patient Information Form and the Visual Analogue Scale were used to collect data. In data analysis, number, percentage, mean, standard deviation, chi-square, dependent group t-test, and ANOVA tests were used.Comparing mean values of pain and vital signs between the groups, there was no statistically significant difference in terms of pre-test mean scores (P > .05). After Tukey advanced analysis, we found that VR and SB significantly decreased pain, diastolic blood pressure, pulse, and respiratory rate and significantly increased oxygen saturation (P < .05).We found that VR glasses and squeezing SBs during the transrectal prostate biopsy procedure reduced pain and positively affected vital signs.
Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model

Twitter's popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an increase in the number of people who express their views in demeaning ways to others. As a result, hate speech has piqued interest in the subject of sentiment analysis, which has developed various algorithms for detecting emotions in social networks using intuitive means. This paper proposes the deep learning model to classify the sentiments in two separate analyses. In the first analysis, the tweets are classified based on the hate speech against the migrants and the women. In the second analysis, the detection is performed using a deep learning model to organise whether the hate speech is performed by a single or a group of users. During the text analysis, word embedding is implemented using the combination of deep learning models such as BiLSTM, CNN, and MLP. These models are integrated with word embedding methods such as inverse glove (global vector), document frequency (TF-IDF), and transformer-based embedding.