Reviewerly/exHarmony huggingface dataset:
Siamese Network-Based System for Criminal Identification

To discourage criminal activities, the large number of CCTV installations throughout the country play a crucial role. Through this paper, we propose an AI-based solution that can leverage these devices to remotely identify and report absconding criminals. Using the one-shot learning approach, we present a face recognition algorithm that yields accurate results even with low training data. The Siamese Network architecture is used to verify if the face embeddings of the image detected is the same as that of the criminal. Two parallel neural networks are designed to take one input each- one being the detected face and the other being an embedding from the dataset. The outputs of the two networks are compared to predict whether the detected face is the same as the input face or not. This algorithm is further integrated with an automated model for updating the information of the recognized criminal into the database along with updating the appropriate law enforcement authorities about the last known whereabouts.
ARCaddy: Augmented Reality App Suite for Aircraft Maintenance

Aircraft Maintenance is a complex task requiring highly trained engineers. Facilitating Aircraft maintenance through providing proper tools and equipment is essential in ensuring good maintenance work. While there are a plethora of Augmented Reality applications, the best-suited applications for Aircraft Maintenance are to be selected and evaluated for their usefulness. In this paper, ARCaddy, an AR application suite consisting of a set of openly available AR applications is presented. The ARCaddy is packaged with relevant and tested AR applications. ARCaddy is evaluated for its usefulness by the feedback from the Aircraft Maintenance Engineers. ARCaddy app suite will help maintenance engineers to assist with handy tool kits for various use case in the field of aircraft maintenance.
International perspectives on academic English in turbulent times

The areas that have experienced the most impact include technical capability and readiness, pedagogic adaptations to online teaching, existing working patterns, and assessing learning. The need for rapid responses to the pandemic has demanded adapting existing, as well as adopting, new technological capabilities and pedagogic practices. Together with technological and pedagogical adaptations, teaching academic English in turbulent times also demands a higher degree of collaboration among all stakeholders. The responses summarised in this chapter exemplify the resourcefulness and creativity of academic English practitioners in responding to the challenges of teaching Academic English during the Covid-19 global pandemic. They also show their determination, dedication, and passion for the profession and, in particular, for their students to enable Academic English to continue, despite the turbulent times.
Application of Animation Character Intelligent Analysis Algorithm Based on Deep Learning

The exit situation of characters is unstable, and the image is easy to change with the scene. Therefore, in this paper, an intelligent analysis algorithm of animation characters based on SSD target detection is proposed, which is trained by data enhancement strategy, where the weight factor of positive and negative samples are adjusted by an optimization method. Finally, it analyzes the overall process of intelligent analysis ability service application of animation characters. The test results verify the effectiveness of the improved algorithm that the proposed method can optimize the training process of the model, which helps users to more conveniently understand all kinds of animation characters, effectively enhances the efficiency of information dissemination, and ultimately plays a role in promoting the development of animation industry.
Key determinants of corruption in the Middle East and North Africa

This study examines the effect of some economic and non-economic factors on corruption. It tests a number of hypotheses formulated around the impact of specific dimensions on increasing or reducing corruption in the Middle East and North Africa Region over a time series from 2008 to 2018. The objective of this study is to illuminate the impact of several independent factors (average income, economic freedom, education, income distribution and globalization) on corruption increase or decrease in the MENA region taking a few countries as a sample case. To reach this end, secondary data is employed and retrieved from secondary sources using published reports and indexes and official websites and databases maintained by various research institutions. The results are tested using panel data regression analysis as a statistical tool for data analysis and hypotheses testing. Findings indicate a significant effect of the presented economic and non-economic determinants on corruption in these sample countries except income distribution which shows an insignificance effect.
Remote sensing aircraft object detection algorithm based on memory CenterNet

In order to solve the problem of poor detection accuracy of CenterNet in remote sensing aircraft with dense targets and complex backgrounds, an improved memory CenterNet object detection algorithm is proposed. First, the multiscale receptive field module and residual attention module are introduced to improve ResNet50, which is used as a new backbone feature extraction network. It has a strong feature extraction ability and detection speed at the same time. Different dilated ratios are used in the multiscale receptive field to expand the receptive field and extract multiscale features, and the multiscale target features are highlighted through the attention mechanism. Then the last set of residual networks in ResNet50 is deleted, and the residual attention network is introduced and stacked in the same way. The feature parameters of different channels in the residual attention network are calculated by two-dimensional cosine transform and weighted with the original feature map, and the target features are highlighted to enhance the ability to locate the target at the end of the network. Second, three times deconvolution is replaced by a memory feature fusion module. The shallow detail information and the deep semantic information are fully fused. The key information in the output feature map is further enriched, which is beneficial to the improvement of model detection performance. Finally, the experimental results show that the average accuracy of remote sensing aircraft dataset is 88.46%, which is 18.21% higher than that of the original algorithm and effectively improves the detection accuracy of remote sensing aircraft targets.
A Bayesian Hierarchical Model for Extracting Individuals’ Theory-Based Causal Knowledge

Abstract Extracting an individual’s scientific knowledge is essential for improving educational assessment and understanding cognitive tasks in engineering activities such as reasoning and decision-making. However, knowledge extraction is an almost impossible endeavor if the domain of knowledge and the available observational data are unrestricted. The objective of this paper is to quantify individuals’ theory-based causal knowledge from their responses to given questions. Our approach uses directed-acyclic graphs (DAGs) to represent causal knowledge for a given theory and a graph-based logistic model that maps individuals’ question-specific subgraphs to question responses. We follow a hierarchical Bayesian approach to estimate individuals’ DAGs from observations. The method is illustrated using 205 engineering students’ responses to questions on fatigue analysis in mechanical parts. In our results, we demonstrate how the developed methodology provides estimates of population-level DAG and DAGs for individual students. This dual representation is essential for remediation since it allows us to identify parts of a theory that a population or individual struggles with and parts they have already mastered. An addendum of the method is that it enables predictions about individuals’ responses to new questions based on the inferred individual-specific DAGs. The latter has implications for the descriptive modeling of human problem-solving, a critical ingredient in sociotechnical systems modeling.
Conversational Recommender System Using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) uses the best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Deep Reinforcement Learning has been used widely for games, robotics etc. Limited work has been done for applying DRL for Conversational Recommender System (CRS). Hence, this tutorial covers the application of DRL for CRS. We give conceptual introduction to Reinforcement Learning and Deep Reinforcement Learning and cover Deep Q-Network, Dyna, REINFORCE and Actor Critic methods. We then cover various real life case studies with increasing complexity starting from CRS, deep CRS, adaptivity, topic guided CRS, deep and large-scale CRSs. We plan to share pre-read for Reinforcement Learning and Deep Reinforcement learning so that participants can grasp the material well.
“My AI must have been broken”: How AI Stands to Reshape Human Communication

From autocomplete and smart replies to video filters and deepfakes, we increasingly live in a world where communication between humans is augmented by artificial intelligence. AI often operates on behalf of a human communicator by recommending, suggesting, modifying, or generating messages to accomplish communication goals. We call this phenomenon AI-Mediated Communication (or AI-MC) [1, 4]. While AI-MC has the potential of making human communication more efficient, it impacts other aspects of our communication in ways that are not yet well understood. Over the last three years, my collaborators and I have been documenting the impact of AI-MC on communication outcomes, language use, interpersonal trust, and more. The talk will outline early experimental findings from this work, mostly led by Cornell and Stanford graduate students Maurice Jakesch, Hannah Mieczkowski, and Jess Hohenstein. For example, the research shows that AI-MC involvement can result in language shifting towards positivity [2, 7]; impact the evaluation of others [2, 4]; change the extent to which we take ownership over our messages [6]; and shift assignment of blame for communication outcomes [3]. Given the impact of AI-MC on interpersonal evaluations, the talk will also cover our recent research examining the (mostly false) heuristics humans use when evaluating whether text was written by AI [5]. Overall, AI-MC raises significant practical and ethical concerns as it stands to reshape human communication, calling for new approaches to the development and regulation of these technologies.
Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders

Stitch Fix, an online personal shopping and styling service, creates a personalized shopping experience to meet any purchase occasion across multiple platforms. For example, a client who wants more one-on-one support in shopping for an outfit or look can request a stylist to curate a 'Fix', an assortment of 5 items; or they can browse their own personalized shop and make direct purchases in our 'Freestyle' experience. We know that personal style changes and evolves over time, so in order to provide the client with the most personalized and dynamic experience across platforms, it is important to recommend items based on our holistic and real-time understanding of their style across all of our platforms. This work introduces the Client Time Series Model (CTSM), a scalable and efficient recommender system based on Temporally-Masked Encoders (TME) that learns one client embedding across all platforms, yet is able to provide distinctive recommendations depending on the platform. An A/B test showed that our model outperformed the baseline model by 5.8% in terms of expected revenue.
Timely Personalization at Peloton: A System and Algorithm for Boosting Time-Relevant Content

At Peloton, we are challenged to not just surface relevant recommendations of fitness classes to our members, but also timely ones. As our fitness content library expands, we continually produce classes on certain themes which are most timely during a narrow time window. To address this challenge, we provide some control over our recommendations to external stakeholders, such as production and marketing teams. They enter timed boosts of certain classes during the windows they are relevant in. We have built out algorithms which take these desired classes and elevate the number of impressions for them, while preserving members' engagement with our recommendations. In this paper, we discuss the system, the algorithms and some results from a few A/B tests showing how boosting works in practice.
MM-FOOD: a high-dimensional index structure for efficiently querying content and concept of multimedia data

The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure’s construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large.
Evaluation Framework for Cold-Start Techniques in Large-Scale Production Settings

In recommender systems, cold-start issues are situations where no previous events (e.g., ratings), are known for certain users or items. Mitigating cold-start situations is a fundamental problem in almost any recommender system [3, 5]. In real-life, large-scale production systems, the challenge of optimizing the cold-start strategy is even greater. We present an end-to-end framework for evaluating and comparing different cold-start strategies. By applying this framework in Outbrain's recommender system, we were able to reduce our cold-start costs by half, while supporting both offline and online settings. Our framework solves the pain of benchmarking numerous cold-start techniques using surrogate accuracy metrics on offline datasets - coupled with an extensive, cost-controlled online A/B test. In this abstract, We'll start with a short introduction to the cold-start challenge in recommender systems. Next, we will explain the motivation for a framework for cold-start techniques. Lastly, we will then describe - step by step - how we used the framework to reduce our exploration by more than 50%.
The Effect of Feedback Granularity on Recommender Systems Performance

The main source of knowledge utilized in recommender systems (RS) is users' feedback. While the usage of implicit feedback (i.e. user's behavior statistics) is gaining in prominence, the explicit feedback (i.e. user's ratings) remain an important data source. This is true especially for domains, where evaluation of an object does not require an extensive usage and users are well motivated to do so (e.g., video-on-demand services or library archives).
Two-Layer Bandit Optimization for Recommendations

Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is not straightforward. One of the challenges we face is the existence of several competing content surfacing components, a phenomenon not unusual in large-scale recommender systems. This often leads to challenging scenarios, where improving the recommendations in one component can lead to performance degradation of another, i.e., "cannibalization". To address this problem we introduce an efficient two-layer bandit approach which is contextualized to user cohorts of similar taste. We mitigate cannibalization at runtime within a single multi-intent content surfacing platform by formalizing relevant offline evaluation metrics, and by involving the cross-component interactions in the bandit rewards. The user engagement in our proposed system has more than doubled as measured by online A/B testings.
Training and Deploying Multi-Stage Recommender Systems

Industrial recommender systems are made up of complex pipelines requiring multiple steps including feature engineering and preprocessing, a retrieval model for candidate generation, filtering, a feature store query, a ranking model for scoring, and an ordering stage. These pipelines need to be carefully deployed as a set, requiring coordination during their development and deployment. Data scientists, ML engineers, and researchers might focus on different stages of recommender systems, however they share a common desire to reduce the time and effort searching for and combining boilerplate code coming from different sources or writing custom code from scratch to create their own RecSys pipelines.
ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling

Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates. This can be overwhelming for systems and algorithms designed to train recommendation models in batches, given the continuous and potentially fast change of content, context and user preferences or intents. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably for long periods of time. Online models that continuously learn from such flows of data are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online.
Automate Page Layout Optimization: An Offline Deep Q-Learning Approach

The modern e-commerce web pages have brought better customer experience and more profitable services by whole page optimization at different granularity, e.g., page layout optimization, item ranking optimization, etc. Generating the proper page layout per customer's request is one of the vital tasks during the web page rendering process, which can directly impact customers' shopping experience and their decision-making. In this paper, we formulate the request-rendering interactions as a Markov decision process (MDP) and solve it by deep reinforcement learning (RL). Specifically, we present the design and implementation of applying offline Deep Q-Learning (DQN) to the contextual page layout optimization problem. Through the offline evaluation method, we demonstrate the effectiveness of the proposed framework, i.e., the RL agent has the potential to perform better than the baseline ranker by learning from the offline data set, e.g., the RL agent can improve the average cumulative rewards up to 36.69% comparing to the baseline ranker.
RecWork: Workshop on Recommender Systems for the Future of Work

As organizations increasingly digitize their business processes, the role of recommender systems in work environments is expanding. The goal of the RecWork workshop is closing the gap in recommender systems research for work environments in areas such as calendaring, productivity, community building, space planning, workforce development, and information routing. RecWork will bring together experts who will collaboratively synthesize a forward-looking research agenda for recommender systems in the workplace. The outcome will be captured through a white paper that will serve as the foundation for future RecWork workshops. These steps will help advance research in workplace recommenders and broaden the reach of the RecSys conference.
Who do you think I am? Interactive User Modelling with Item Metadata

Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as "connecting relevant content to interested users". Explanations have been found to help recommender systems achieve this goal by giving users a look under the hood that helps them understand why they are recommended certain items. Furthermore, explanations can be considered to be the first step towards interacting with the system. Indeed, for a user to give feedback and guide the system towards better understanding her preferences, it helps if the user has a better idea of what the system has already learned.
Predicting Deep Atmospheric Turbulence Using a Deep Echo State Neural Network

A Deep Echo State Network (DeepESN) is applied to deep atmospheric turbulence. This network demonstrates a satisfactory prediction performance. Current Brier Skill Scores are around 30/100. Final results will be reported at the conference.
Ranked Document Retrieval in External Memory

The ranked (or top- k ) document retrieval problem is defined as follows: preprocess a collection {T 1 ,T 2 ,… ,T d } of d strings (called documents) of total length n into a data structure, such that for any given query (P,k) , where P is a string (called pattern) of length p ≥ 1 and k ∈ [1,d] is an integer, the identifiers of those k documents that are most relevant to P can be reported, ideally in the sorted order of their relevance. The seminal work by Hon et al. [FOCS 2009 and Journal of the ACM 2014] presented an O(n) -space (in words) data structure with O(p+k log k) query time. The query time was later improved to O(p+k) [SODA 2012] and further to O(p/ log σn+k ) [SIAM Journal on Computing 2017] by Navarro and Nekrich, where σ is the alphabet size. We revisit this problem in the external memory model and present three data structures. The first one takes O(n) -space and answer queries in O(p/B + log B n + k/B+ log * (n/B) ) I/Os, where B is the block size. The second one takes O(n log * (n/B) ) space and answer queries in optimal O(p/B + log B n + k/B) I/Os. In both cases, the answers are reported in the unsorted order of relevance. To handle sorted top- k document retrieval, we present an O(n log (d/B)) space data structure with optimal query cost.
PeachPie: Mature PHP to CLI compiler

PeachPie is a compiler of PHP code into Common Language Infrastructure (CLI) assemblies, enabling direct interoperability between PHP and .NET projects. This work describes the overall PeachPie architecture and the implementation of its components, highlighting and explaining the differences from its predecessor Phalanger. PeachPie can compile both legacy proprietary PHP applications and modern PHP frameworks, porting them into CLI with the ability to integrate with other projects written in languages such as C#. This work might provide helpful insight for people performing static analysis of PHP, developing a custom compiler targeting CLI, or exploring possibilities of dynamic language compilation.