Reviewerly/exHarmony huggingface dataset:
STAR-Lite: A light-weight scalable self-taught learning framework for older adults’ activity recognition

Recognizing activities for older adults is challenging as we observe a variety of activity patterns caused due to aging (e.g., limited dexterity, limb control, slower response time) or/and underlying health conditions (e.g., dementia). However, existing literature with deep learning methods has successfully recognized activities when the dataset contains high-quality annotations and is captured in a controlled environment. On the contrary, data captured in a real-world environment, especially with older adults exhibiting memory-related symptoms, varying psychological and mental health status, reliance on caregivers to perform daily activities, and unavailability of domain-specific annotators, makes obtaining quality data with annotations challenging; leaving us with limited labeled data and abundant unlabeled data. In this paper, we hypothesize that projecting the labeled data representations comprising a specific set of activities onto a new representation space characterized by the unlabeled data comprising activities beyond the limited activities in the labeled dataset would help us rely less on the annotated data to improve activity detection performance. Motivated by this, we propose STAR-Lite, a self-taught learning framework that involves a pre-training framework to prepare the new representation space considering activities beyond the initial labels in the labeled dataset. STAR-Lite projects the labeled data representations on the new representation space characterized by unlabeled data labels and learns higher-level representations of the labeled dataset while optimizing inter- and intra- class distances without explicitly using a computation hungry similarity-based approach. We demonstrate that our proposed approach, STAR-Lite (a) improves activity recognition performance in a supervised setting and (b) is feasible for real-world deployment. To enhance the feasibility of deploying STAR-Lite on devices with limited memory resources, we explore model compression techniques such as pruning and quantization and propose a novel layer-wise pruning-rate optimization technique that effectively compresses the network while preserving the model performance. The evaluation was performed using the Alzheimer’s Activity Recognition dataset (AAR) captured from 25 individuals living in a retirement community center with IRB approval (#Y18NR12035) using an in-house SenseBox infrastructure while concurrently assessing the clinical evaluation of the participants for dementia, and independent living. Our extensive evaluation reveals that STAR-Lite can detect activities with an F1-score of 85.12% despite 62% reduction in model size and 5% improvement of execution time on a resource constrained device.
AS-DMF: A Lightweight Malware Encrypted Traffic Detection Method Based on Active Learning and Feature Selection

The sharp increasing volume of encrypted traffic generated by malware brings a huge challenge to traditional payload-based malicious traffic detection methods. Solutions that based on machine learning and deep learning are becoming mainstream. However, the machine learning-based methods are limited by manual-design features, which have the problem of highly correlated multicollinearity. And both methods rely heavily on a large number of labeled samples, which needs lots of human effort. In this paper, we apply the active learning to the malicious encrypted traffic detection problem and propose AS-DMF framework. AS-DMF is a lightweight detection framework that combine the uncertainty sampling and density-based query strategy to query the informative and representative instances from the sample set and then train them in a detection (DMF) model. Moreover, we propose a feature selection mechanism which can select the meaningful features of traffic efficiently. Our comprehensive experiments on the real-word dataset indicate that AS-DMF achieves lightweighting at both feature and data levels with a high performance of 0.9460 mAcc.
Geochemical prospecting of polymetallic mineralization in Gimbi-Nejo area, West Ethiopia

It is strongly believed that tremendous resources of precious metals and oxides are hidden in the West Ethiopian region. The present work presents a significant test to conduct oriented geochemical mapping for forty-two elements combined with typical geological features in the Pan-African basement exposed area using stream sediment and rock samples. The stream sediments most likely contain some elements directly derived from the surrounding Precambrian to Tertiary bedded metamorphic and igneous intrusive rock units. The obtained data suggest high economic potentials for Au (2.14 ng/g), Cu (43.56 μg/g), Cr (365.1 μg/g), Ni (69.2 μg/g), Co (34.46 μg/g), V (204.1 μg/g), Ti (13278 μg/g), Mn (1472 μg/g), Fe2O3 (10.97 %), P (888 μg/g), Al2O3 (14.05 %), Ba (501.7 μg/g), Zr (309.5 μg/g), Sr (155.7 μg/g), and Nb (28.2 μg/g). In the composite geochemical anomalies maps, anomalous zones HS12, HS16, HS18, HS19, HS20, and HS28 suggest that transitional metals of intense differentiation with high average contents as compared to Chinese recommended values may be the favourable focus of exploration in the migmatitic gneiss (Cu ∼ 66.38 μg/g), granitic orthogneiss (Zr ∼ 426.9 μg/g), upper basalt/lower basalt (Nb ∼ 63.4 μg/g), meta-volcanic (Mn ∼ 2680 μg/g, Ti ∼ 29823 μg/g, V ∼ 365.6 μg/g, Fe2O3 ∼ 1629 %), meta-sedimentary formation (Au ∼ 5.3 ng/g), Abshala tectonic melange (Cr ∼ 2457.1 μg/g), and meta-ultrabasic rocks (Co ∼ 62.65 μg/g, Cu ∼ 66.38 μg/g, Ni ∼ 313.4 μg/g). In conclusion, fifteen ore-forming elements are distinctively abnormal in the area with sixty-four new discovered mineralized spots.
Neural Re-ranking for Multi-stage Recommender Systems

Re-ranking is one of the most critical stages for multi-stage recommender systems (MRS), which re-orders the input ranking lists by modeling the cross-item interaction. Recent re-ranking methods have evolved into deep neural architectures due to the significant advances in deep learning. Neural re-ranking, therefore, has become a trending topic and many of the improved algorithms have demonstrated their use in industrial applications, enjoying great commercial success. The purpose of this tutorial is to explore some of the recent work on neural re-ranking, integrating them into a broader picture and paving ways for more comprehensive solutions for future research. In particular, we provide a taxonomy of current methods according to the objectives and training signals. We examine and compare these methods qualitatively and quantitatively, and identify some open challenges and future prospects.
Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation

Content-based news recommenders learn words that correlate with user engagement and recommend articles accordingly. This can be problematic for users with diverse political preferences by topic — e.g., users that prefer conservative articles on one topic but liberal articles on another. In such instances, recommenders can have a homogenizing effect by recommending articles with the same political lean on both topics, particularly if both topics share salient, politically polarized terms like "far right" or "radical left." In this paper, we propose attention-based neural network models to reduce this homogenization effect by increasing attention on words that are topic specific while decreasing attention on polarized, topic-general terms. We find that the proposed approach results in more accurate recommendations for simulated users with such diverse preferences.
Translating the Public Service Media Remit into Metrics and Algorithms

After multiple years of providing automated video recommendations in the ZDFmediathek, ZDF has established a solid ground for the usage of recommender systems. Being a Public Service Media (PSM) provider, our most important driver on this journey is our Public Service Media Remit (PSMR). We are committed to cultivate PSM values such as diversity, fairness, and transparency while providing fresh and relevant content. Therefore, it is important for us to not only measure the success of our recommender systems in terms of basic business Key Performance Indicators (KPIs) such as clicks and viewing minutes but also to ensure and to measure the achievement of PSM values. While speaking about PSM values, however, it is important to keep in mind that there is no easy way to directly measure values as such. In order to be able to measure their extent in a recommender system, we need to translate these values into public value metrics. However, not only the final results are essential for the PSMR. Additionally, it is highly important to establish transparency while working towards these results, that is, while defining the data, the algorithms, and the pipelines used in recommender systems. In our talk we will provide a deeper insight into how we approach this task with Model Cards and give an overview of some models, their Model Cards, and metrics that we are currently using for ZDFmediathek.
RepSys: Framework for Interactive Evaluation of Recommender Systems

Making recommender systems more transparent and auditable is crucial for the future adoption of these systems. Available tools typically present mostly errors of models aggregated over all test users, which is often insufficient to uncover hidden biases and problems. Moreover, the emphasis is primarily on the accuracy of recommendations but less on other important metrics, such as the diversity of recommended items, the extent of catalog coverage, or the opportunity to discover novel items at bestsellers' expense. In this work, we propose RepSys, a framework for evaluating recommender systems. Our work offers a set of highly interactive approaches for investigating various scenario recommendations, analyzing a dataset, and evaluating distributions of various metrics that combine visualization techniques with existing offline evaluation methods. RepSys framework is available under an open-source license to other researchers.
Heterogeneous Graph Representation Learning for multi-target Cross-Domain Recommendation

This paper discusses the current challenges in modeling real world recommendation scenarios and proposes the development of a unified Heterogeneous Graph Representation Learning framework for multi-target Cross-Domain recommendation (HGRL4CDR). A shared graph with user-item interactions from multiple domains is proposed as a way to provide an effective representation learning layer and unify the modelling of various heterogeneous data. A heterogeneous graph transformer network will be integrated to the representation learning model to prioritize the most important neighbours, and the proposed model would be able to capture complex information as well as adapt to dynamic changes in the data using matrix perturbation. Using the real world Amazon Review dataset, experiments would be conducted on multi-target cross domain recommendation.
Modeling User Repeat Consumption Behavior for Online Novel Recommendation

Given a user's historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in. Online novel recommendation is important but underexplored. In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days. We have two observations about online novel recommendation for new users. First, repeat novel consumption of new users is a common phenomenon. Second, interactions between users and novels are informative. To accurately predict whether a user will reconsume a novel, it is crucial to characterize each interaction at a fine-grained level. Based on these two observations, we propose a neural network for online novel recommendation, called NovelNet. NovelNet can recommend the next novel from both the user's consumed novels and new novels simultaneously. Specifically, an interaction encoder is used to obtain accurate interaction representation considering fine-grained attributes of interaction, and a pointer network with a pointwise loss is incorporated into NovelNet to recommend previously-consumed novels. Moreover, an online novel recommendation dataset is built from a well-known online novel reading platform and is released for public use as a benchmark. Experimental results on the dataset demonstrate the effectiveness of NovelNet 1.
An Improved Parallel Prefix Sums Algorithm

The prefix sums problem for an array [Formula: see text] is to compute all sums [Formula: see text], [Formula: see text]. In this paper, we introduce an improvement for the best previous algorithm that runs in [Formula: see text] time using [Formula: see text] processors on a Sum Concurrent Read Concurrent Write, Parallel Random Access Machine (Sum-CRCW PRAM). The improvements include (1) reducing the total number of operations and (2) reducing the amount of storage required by the algorithm.
FAccTRec 2022: The 5th Workshop on Responsible Recommendation

The 5th Workshop on Responsible Recommendation (FAccTRec 2022) was held in conjunction with the 16th ACM Conference on Recommender Systems on September, 2022 at Seattle, USA, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.
Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative Filtering

Collaborative Filtering (CF) is a class of methods widely used to support high-quality Recommender Systems (RSs) across several industries [6]. Studies have uncovered distinct advantages and limitations of CF in many real-world applications [5, 9]. Besides the inability to address the cold-start problem, sensitivity to data sparsity is among the main limitations recurrently associated with this class of RSs. Past work has extensively demonstrated that data sparsity critically impacts CF accuracy [2, 3, 4]. The proposed talk revisits the relation between data sparsity and CF from a new perspective, evincing that the former also impacts the fairness of recommendations. In particular, data sparsity might lead to unfair bias in domains where the volume of activity strongly correlates with personal characteristics that are protected by law (i.e., protected attributes). This concern is critical for RSs deployed in domains such as the recruitment domain, where RSs have been reported to automate or facilitate discriminatory behaviour [7]. Our work at SEEK deals with recommender algorithms that recommend jobs to candidates via SEEK's multiple channels. While this talk focuses on our perspective of the problem in the job recommendation domain, the discussion is relevant to many other domains where recommenders potentially have a social or economic impact on the lives of individuals and groups.
Dual Attentional Higher Order Factorization Machines

Numerous problems of practical significance such as clickthrough rate (CTR) prediction, forecasting, tagging and so on, involve complex interaction of various user, item and context features. Manual feature engineering has been used in the past to model these combinatorial features but it requires domain expertise and becomes prohibitively expensive as the number of features increases. Feedforward neural networks alleviate the need for manual feature engineering to a large extent and have shown impressive performance across multiple domains due to their ability to learn arbitrary functions. Despite multiple layers of non-linear projections, neural networks are limited in their ability to efficiently model functions with higher order interaction terms. In recent years, Factorization Machines and its variants have been proposed to explicitly capture higher order combinatorial interactions. However not all feature interactions are equally important, and in sparse data settings, without a suitable suppression mechanism, this might result into noisy terms during inference and hurt model generalization. In this work we present Dual Attentional Higher Order Factorization Machine (DA-HoFM), a unified attentional higher order factorization machine which leverages a compositional architecture to compute higher order terms with complexity linear in terms of maximum interaction degree. Equipped with sparse dual attention mechanism, DA-HoFM summarizes interaction terms at each layer, and is able to efficiently select important higher order terms. We empirically demonstrate effectiveness of our proposed models on the task of CTR prediction, where our model exhibits superior performance compared to the recent state-of-the-art models, outperforming them by up to 6.7% on the logloss metric.
Personalizing Benefits Allocation Without Spending Money

Modern e-commerce platforms make use of promotional offers such as discounts and rewards to encourage customers to complete purchases. While offering the promotions has a great effect on the sales, it also generates a monetary loss. By utilizing causal machine learning and optimization, our team at Booking.com was able to personalize the promotions allocation to customers, while efficiently controlling the spend within a given budget. In this talk we'll share the personalized promotion assignment techniques, such as uplift modeling and constrained optimization, which helped us to predict the outcomes of discounts offering and allocate them efficiently. This solution allowed us to unlock promotional campaigns to bring more value to the customers and grow our business.
Recommending for a multi-sided marketplace with heterogeneous contents

Many online personalization platforms today are recommending heterogeneous contents in a multi-sided marketplace consisting of consumers, merchants and other partners. For a recommender system to be successful in these contexts, it faces two main challenges. First, each side in the marketplace has different and potentially conflicting utilities. Recommending for a multi-sided marketplace therefore entails jointly optimizing multiple objectives with trade-offs. Second, the off-the-shelf recommendation algorithms are not applicable to the heterogeneous content space, where a recommendation item could be an aggregation of other recommendation items. In this work, we develop a general framework for recommender systems in a multi-sided marketplace with heterogeneous and hierarchical contents. We propose a constrained optimization framework with machine learning models for each objective as inputs, and a probabilistic structural model for users' engagement patterns on heterogeneous contents. Our proposed structural modeling approach ensures consistent user experience across different levels of aggregation of the contents, and provides levels of transparency to the merchants and content providers. We further develop an efficient optimization solution for ranking and recommendation in large-scale online systems in real time. We implement the framework at Uber Eats, one of the largest online food delivery platforms in the world and a three-sided marketplace consisting of eaters, restaurant partners and delivery partners. Online experiments demonstrate the effectiveness of our framework in ranking heterogeneous contents and optimizing for the three sides in the marketplace. Our framework has been deployed globally as the recommendation algorithm for Uber Eats' homepage.
Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’22)

The constant increase in the amount of data and information available on the Web has made the development of systems that can support users in making relevant decisions increasingly important. Recommender systems (RSs) have emerged as tools to address this task. RSs use the preferences expressed by a user, either explicitly or implicitly, to filter the available information and proactively suggest items that might be of interest to him or her. Although in early works about the topic there was a strong interest in ways to make such systems proactive, user-friendly, and persuasive, over time they became increasingly focused on the algorithmic component solely. However, this trend is gradually being reversed and always more attention is nowadays placed also on Human Decision Making models that focus on supporting the end user in understanding what is being proposed through RSs by using dynamic and persuasive interfaces. A recommender system should be based on valuable strategies for proactively guiding users to items that match their preferences and therefore should put attention on how it is possible to make this process trustable, pleasant, and user-friendly. Such systems, moreover, should take into account psychological, cognitive and emotional aspects to enable personalization that is appropriate not only to the context of use but also to the psychological reactions of the end user. The workshop provides a venue for works that invest in the design of recommender systems which consider users' experience during the interaction, as well as for works that explore the implications of human-computer interactions with different theories of human decision-making. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys'22, review its history, and discuss the most important topics considered at the workshop.
Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales

Conversational recommender systems offer a way for users to engage in multi-turn conversations to find items they enjoy. For users to trust an agent and give effective feedback, the recommender system must be able to explain its suggestions and rationales. We develop a two-part framework for training multi-turn conversational recommenders that provide recommendation rationales that users can effectively interact with to receive better recommendations. First, we train a recommender system to jointly suggest items and explain its reasoning via subjective rationales. We then fine-tune this model to incorporate iterative user feedback via self-supervised bot-play. Experiments on three real-world datasets demonstrate that our system can be applied to different recommendation models across diverse domains to achieve state-of-the-art performance in multi-turn recommendation. Human studies show that systems trained with our framework provide more useful, helpful, and knowledgeable suggestions in warm- and cold-start settings. Our framework allows us to use only product reviews during training, avoiding the need for expensive dialog transcript datasets that limit the applicability of previous conversational recommender agents.
GIS and Database Management for Mining Exploration

The mining sector has several tools which permits mining exploration works to be done in the most efficient and time conscious way as possible. This chapter is focus on Geographic Information Systems (GIS) and Database and aims to show how this combined approach can help in mineral exploration. It is all about building a database and GIS system capable first of all to assess if a geological/geographical data taken from the field is valid or wrong and to effectively manage mining data in a GIS designed to positively influence any mining exploration project from its earliest stages to the end of the project. We focus on Meiganga area where we have been involved.