Miracle-dz/UniFAR-FTUs huggingface dataset:
Spotting Flares: The Vital Signs of the Viral Spread of Tweets Made During Communal Incidents

With the increasing use of Twitter for encouraging users to instigate violent behavior with hate and racial content, it becomes necessary to investigate the uniqueness in the dynamics of the spread of tweets made during violent communal incidents and the challenges they pose in early identification of potential viral content. In this article, we study the spread of the tweets made during several violent communal incidents along four major dimensions — the underlying follower network of the users, their structural and engagement characteristics, the cascades, and the cognitive aspects of the content, each of which plays a vital role in the spread of content. Using large public and collected data, we compare these features with tweets related to other subjects from several major domains, such as non-violent political events, celebrities, and technology, that contribute to a large fraction of the viral content over Twitter. We discover that while the spread of cascades and the users involved may provide strong early evidence of the viral content for several domains, the early phases of the spread of viral tweets related to violent communal incidents are characterized by cascades with protracted growth involving fringe or low-importance users, which would possibly make early prediction difficult. Our findings indicate that an interplay of certain network and cascade properties, together with the cognitive characteristics of tweets and the behavioral patterns of the engaging users, may provide stronger early indicators of the virality of this content.
Security Analysis of Zigbee Protocol Implementation via Device-agnostic Fuzzing

Zigbee is widely adopted as a resource-efficient wireless protocol in the IoT network. IoT devices from manufacturers have recently been affected due to major vulnerabilities in Zigbee protocol implementations. Security testing of Zigbee protocol implementations is becoming increasingly important. However, applying existing vulnerability detection techniques such as fuzzing to the Zigbee protocol is not a simple task. Dealing with low-level hardware events still remains a big challenge. For the Zigbee protocol, which communicates over a radio channel, many existing protocol fuzzing tools lack a sufficient execution environment. To narrow the gap, we designed Z-Fuzzer, a device-agnostic fuzzing tool for detecting security flaws in Zigbee protocol implementations. To simulate Zigbee protocol execution, Z-Fuzzer leverages a commercial embedded device simulator with pre-defined peripherals and hardware interrupt setups to interact with the fuzzing engine. Z-Fuzzer generates more high-quality test cases with code-coverage heuristics. We compare Z-Fuzzer with advanced protocol fuzzing tools, BooFuzz and Peach fuzzer, on top of Z-Fuzzer’s simulation platform. Our findings suggest that Z-Fuzzer can achieve greater code coverage in Z-Stack, a widely used Zigbee protocol implementation. Compared to BooFuzz and Peach, Z-Fuzzer found more vulnerabilities with fewer test cases. Three of them have been assigned CVE IDs with high CVSS scores (7.5~8.2).
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since most models learn one embedding per item. This leads to a large memory requirement (of storing one vector per item) and poor performance on sparse sessions with cold-start or unpopular items. Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for session-based recommendations. Our proposed method learns a transformation function from item metadata to embeddings, and is thus, item-ID free (i.e., does not need to learn one embedding per item). It integrates item metadata to learn shared representations of diverse item attributes. During inference, new or unpopular items will be assigned identical representations for the attributes they share with items previously observed during training, and thus will have similar representations with those items, enabling recommendations of even cold-start and sparse items. Additionally, M2TRec is trained in a multi-task setting to predict the next item in the session along with its primary category and subcategories. Our multi-task strategy makes the model converge faster and significantly improves the overall performance. Experimental results show significant performance gains using our proposed approach on sparse items on the two datasets.
Is it Violin or Viola? Classifying the Instruments’ Music Pieces using Descriptive Statistics

Classifying music pieces based on their instrument sounds is pivotal for analysis and application purposes. Given its importance, techniques using machine learning have been proposed to classify violin and viola music pieces. The violin and viola are two different instruments with three overlapping strings of the same notes, and it is challenging for ordinary people or even musicians to distinguish the sound produced by these instruments. However, the classification of musical instrument pieces was barely performed by prior research. To solve this problem, we propose a technique using descriptive statistics to reliably distinguish between violin and viola music pieces. Likewise, a similar technique on the basis of histogram is introduced alongside the main descriptive statistics approach. These approaches are derived based on the nature of the instruments’ strings and the range of their pieces. We also solve the problem in the current literature which divide the audio into segments for processing instead of managing the whole song. Thereby, we compile a dataset of recordings that comprises of violin and viola solo pieces from the Baroque, Classical, Romantic, and Modern eras. Experiment results suggest that our approach achieves high accuracy on solo pieces as compared to other methods with 0.97 accuracy on Baroque pieces.
Psychology-informed Recommender Systems Tutorial

Recommender systems are essential tools to support human decision-making in online information spaces. Many state-of-the-art recommender systems adopt advanced machine learning techniques to model and predict user preferences from behavioral data. While such systems can provide useful and effective recommendations, their algorithmic design commonly neglects underlying psychological mechanisms that shape user preferences and behavior. In this tutorial, we offer a comprehensive review of the state of the art and progress in psychology-informed recommender systems, i.e., recommender systems that incorporate human cognitive processes, personality, and affective cues into recommendation models, along with definitions, strengths and weaknesses. We show how such systems can improve the recommendation process in a user-centric fashion. With this tutorial, we aim to stimulate more ideas and discussion with the audience on core issues of this topic such as the identification of suitable psychological models, availability of datasets, or the suitability of existing performance metrics to evaluate the efficacy of psychology-informed recommender systems. Besides, we present takeaways to recommender systems practitioners how to build psychology-informed recommender systems. Previous versions of this tutorial were presented, among others, at The ACM Web Conference 2022 and the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR) 2022.
Learning Recommendations from User Actions in the Item-poor Insurance Domain

While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community.
Hands-on Reinforcement Learning for Recommender Systems - From Bandits to SlateQ to Offline RL with Ray RLlib

Reinforcement learning (RL) is gaining traction as a complementary approach to supervised learning for RecSys due to its ability to solve sequential decision-making processes for delayed rewards. Recent advances in offline reinforcement learning, off-policy evaluation, and more scalable, performant system design with the ability to run code in parallel, have made RL more tractable for the RecSys real time use cases. This tutorial introduces RLlib [9], a comprehensive open-source Python RL framework built for production workloads. RLlib is built on top of open-source Ray [8], an easy-to-use, distributed computing framework for Python that can handle complex, heterogeneous applications. Ray and RLlib run on compute clusters on any cloud without vendor lock. Using Colab notebooks, you will leave this tutorial with a complete, working example of parallelized Python RL code using RLlib for RecSys on a github repo.
Towards the Evaluation of Recommender Systems with Impressions

In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study’s goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain.
Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?

Recommender systems are central to online information consumption and user-decision processes, as they help users find relevant information and establish new social relationships. However, recommenders could also (unintendedly) help propagate misinformation and increase the social influence of the spreading it. In this context, we study the impact of friend recommender systems on the social influence of misinformation spreaders on Twitter. To this end, we applied several user recommenders to a COVID-19 misinformation data collection. Then, we explore what-if scenarios to simulate changes in user misinformation spreading behaviour as an effect of the interactions in the recommended network. Our study shows that recommenders can indeed affect how misinformation spreaders interact with other users and influence them.
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.
Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations

Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For smaller data sets, an exhaustive search is possible; However, when the data size and model complexity increase, the number of configuration evaluations becomes the main computational bottleneck. A promising paradigm for tackling this type of problem is surrogate-based optimization. The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the output (target) space; the data for this model are obtained by evaluating the main learning engine, which is, for example, a factorization machine-based model. By learning to approximate the hyperparameter-target relation, the surrogate (machine learning) model can be used to score large amounts of hyperparameter configurations, exploring parts of the configuration space beyond the reach of direct machine learning engine evaluation. Commonly, a surrogate is selected prior to optimization initialization and remains the same during the search. We investigated whether dynamic switching of surrogates during the optimization itself is a sensible idea of practical relevance for selecting the most appropriate factorization machine-based models for large-scale online recommendation. We conducted benchmarks on data sets containing hundreds of millions of instances against established baselines such as Random Forest- and Gaussian process-based surrogates. The results indicate that surrogate switching can offer good performance while considering fewer learning engine evaluations.
TorchRec: a PyTorch Domain Library for Recommendation Systems

Recommendation Systems (RecSys) comprise a large footprint of production-deployed AI today. The neural network-based recommender systems differ from deep learning models in other domains in using high-cardinality categorical sparse features that require large embedding tables to be trained. In this talk we introduce TorchRec, a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production. In this talk we cover the building blocks of the TorchRec library including modeling primitives such as embedding bags and jagged tensors, optimized recommender system kernels powered by FBGEMM, a flexible sharder that supports a veriety of strategies for partitioning embedding tables, a planner that automatically generates optimized and performant sharding plans, support for GPU inference and common modeling modules for building recommender system models. TorchRec library is currently used to train large-scale recommender models at Meta. We will present how TorchRec helped Meta’s recommender system platform to transition from CPU asynchronous training to accelerator-based full-sync training.
Quality prediction in a smart factory: a real case study

The Industry 4.0 concept refers to new production patterns that include new technologies, manufacturing elements, and workforce organizations. It creates highly efficient production systems that change production processes, reduce production costs and improve product quality. Quality 4.0 is an evolution of Industry 4.0, which is a modification of traditional quality control charts. In this paper, our motivation is to improve manufacturing processes as we monitor product’s quality by improving the percentage of correctly manufactured products thereby achieving efficiency. A four-layer decision-making architecture is proposed where different models and techniques are applied and a comparative study is achieved on real industrial case study: 1) data exploration layer, 2) feature engineering layer, 3) modeling layer, in which three categories of time series forecasting algorithms are experimented: statistical model (ARIMA), machine learning models (Random forest and XGBOOST) and deep learning models (Stacked LSTM and Transformer-based model), and finally 4) interpretation layer. The transformer-based model scored the best. With the classification model’s interpretation, we deducted the recommended values to monitor the product’s quality in order to reach relatively zero defects.
Personalizing Benefits Allocation Without Spending Money: Utilizing Uplift Modeling in a Budget Constrained Setup

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.
Direct Evaluation of the Sensitivity of the Nonlinear Critical Point to Geometrical Imperfections

A generalized path‐following method is proposed for the direct evaluation of the critical point sensitivity to geometrical imperfections expressed as combination of given shapes. After a finite element discretization, the critical point is defined by a system of nonlinear algebraic equations imposing equilibrium and critical condition according to the null vector method. An arc‐length equation is added to follow a path of critical points in the imperfection space. The solution of the extended system by a Newton scheme requires a Jacobian involving also the derivatives of the critical condition with respect to the FE DOFs and the derivatives of all the problem equations with respect to the imperfection parameters. An analytical and efficient computation of the Jacobian is achieved by a solid‐shell model and a strain‐based model of the imperfection. In addition, a mixed stress‐displacement iterative scheme is devised for a highly efficient and robust solution. Finally, the derivatives of the critical point with respect to the imperfection parameters are also obtained analytically and can be used to generate a gradient‐based critical path for a quick search of the worst‐case imperfection. A compressed cold‐formed lipped channel beam is analyzed as a case study.
The Sw-5b NLR immune receptor induces earlier transcriptional changes in response to thrips-mediated inoculation of Tomato spotted wilt orthotospovirus compared to mechanical inoculation

The nucleotide-binding leucine-rich repeat (NLR) class of immune receptor, Sw-5b confers resistance to Tomato spotted wilt orthotospovirus (TSWV). Although Sw-5b is known to activate immunity upon recognition of the NSm of TSWV, we know very little about the downstream events that lead to resistance. Here, we investigated the early transcriptomic changes that occur in response to both mechanical and thrips-mediated inoculation of TSWV using near-isogenic resistant and susceptible tomato lines. Interestingly, the Sw-5b induces earlier transcriptional changes in response to thrips-mediated inoculation compared to mechanical inoculation of TSWV. A subset of the differentially expressed genes (DEGs) observed at 12 and 24 hours post thrips-mediated inoculation of TSWV was only present at 72 hours post mechanical inoculation. Although some DEGs were shared between thrips and mechanical inoculation at 72 hours postinfection, many DEGs were specific to either thrips-mediated or mechanical inoculation of TSWV. In response to thrips-mediated inoculation, an NLR immune receptor, cysteine-rich receptor-like kinase, G-type lectin S-receptor-like kinases, and transcription factors such as the ethylene response factor 1 and the calmodulin-binding protein 60 were induced. Whereas, in response to mechanical inoculation, fatty acid desaturase 2-9, cell death genes, DCL2b, RIPK/PBL14-like, and transcription factors such as ERF017 and WRKY75 were differentially expressed. Our findings reveal novel insights into Sw-5b responses specific to the method of TSWV inoculation. Given that TSWV is transmitted in nature primarily by the thrips, the DEGs we have identified provide a foundation for understanding the mechanistic roles of these genes in the Sw-5b-mediated resistance.
Amyloidogenic Propensity of Self-Assembling Peptides and their Adjuvant Potential for use as DNA Vaccines

De novo designed peptides that self-assemble into cross-β rich fibrillar biomaterials have been pursued as an innovative platform for the development of adjuvant- and inflammation-free vaccines. However, they share structural properties similar to amyloid species implicated in neurodegenerative diseases, which has been a long-standing concern for their translation. Here, we comprehensively characterize the amyloidogenic character of the amphipathic self-assembling cross-β peptide KFE8, compared to pathological amyloid and amyloid-like proteins α-synuclein (α-syn) and TDP-43. Further, we developed plasmid-based DNA vaccines with the KFE8 backbone serving as a fibrillizing scaffold for delivery of a GFP model antigen. We find that expression of tandem repeats of KFE8 is non-toxic and can be efficiently cleared by autophagy. We also demonstrate that synthetic KFE8 nanofibers do not cross-seed amyloid formation of α-syn in mammalian cells compared to α-syn preformed fibrils. In mice, vaccination with plasmids encoding the KFE32-GFP fusion protein elicited robust immune responses, inducing production of significantly higher levels of anti-GFP antibodies compared to soluble GFP or α-syn tagged GFP. Antigen-specific CD8+T cells were also detected in the spleens of vaccinated mice and cytokine profiles from antigen recall assays indicate a balanced Th1/Th2 response. These findings illustrate that cross-β-rich peptide nanofibers have distinct properties from those of pathological amyloidogenic proteins, and are an attractive platform for the development of DNA vaccines with self-adjuvanting properties and improved safety profiles.