With rich visual data, such as images, becoming readily associated with items, visually-aware recommendation systems (VARS) have been widely used in different applications. Recent studies have shown that VARS are vulnerable to item-image adversarial attacks, which add human-imperceptible perturbations to the clean images associated with those items. Attacks on VARS pose new security challenges to a wide range of applications such as e-Commerce and social networks where VARS are widely used. How to secure VARS from such adversarial attacks becomes a critical problem. Currently, there is still a lack of systematic study on how to design secure defense strategies against visual attacks on VARS. In this paper, we attempt to fill this gap by proposing an adversarial image reconstruction and detection framework to secure VARS. Our proposed method can simultaneously (1) secure VARS from adversarial attacks characterized by local perturbations by image reconstruction based on global vision transformers; and (2) accurately detect adversarial examples using a novel contrastive learning approach. Meanwhile, our framework is designed to be used as both a filter and a detector so that they can be jointly trained to improve the flexibility of our defense strategy to a variety of attacks and VARS models. We have conducted extensive experimental studies with two popular attack methods (FGSM and PGD). Our experimental results on two real-world datasets show that our defense strategy against visual attacks is effective and outperforms existing methods on different attacks. Moreover, our method can detect adversarial examples with high accuracy.

Learning and remembering to use APIs are difficult. Several techniques have been proposed to assist developers in using APIs. Most existing techniques focus on recommending the right API methods to call, but very few techniques focus on recommending API arguments. In this paper, we propose ARIST, a novel automated argument recommendation approach which suggests arguments by predicting developers' expectations when they define and use API methods. To implement this idea in the recommendation process, ARIST combines program analysis (PA), language models (LMs), and several features specialized for the recommendation task which consider the functionality of formal parameters and the positional information of code elements (e.g., variables or method calls) in the given context. In ARIST, the LMs and the recommending features are used to suggest the promising candidates identified by PA. Meanwhile, PA navigates the LMs and the features working on the set of the valid candidates which satisfy syntax, accessibility, and type-compatibility constraints defined by the programming language in use. Our evaluation on a large dataset of real-world projects shows that ARIST improves the state-of-the-art approach by 19% and 18% in top-1 precision and recall for recommending arguments of frequently-used libraries. For general argument recommendation task, i.e., recommending arguments for every method call, ARIST outperforms the baseline approaches by up to 125% top-1 accuracy. Moreover, for newly-encountered projects, ARIST achieves more than 60% top-3 accuracy when evaluating on a larger dataset. For working/maintaining projects, with a personalized LM to capture developers' coding practice, ARIST can productively rank the expected arguments at the top-1 position in 7/10 requests.

Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However, user interactions can be affected by noise stemming from account sharing, inconsistent preferences, or accidental clicks. To address this issue, we (i) propose a new evaluation protocol that takes multiple future items into account and (ii) introduce a novel relevance-aware loss function to train a SRS with multiple future items to make it more robust to noise. Our relevance-aware models obtain an improvement of ~1.2% of NDCG@10 and 0.88% in the traditional evaluation protocol, while in the new evaluation protocol, the improvement is ~1.63% of NDCG@10 and ~1.5% of HR w.r.t the best performing models.

Women seeking a relationship have revealed which messages a prospective partner should definitely not send on dating apps. Researchers led by Purdue University, Indiana, found that among 275 participants -- mostly female -- starting a conversation with a sexually explicit message was the biggest turn-off, especially for people looking for a long-term relationship. Conversely, someone whose initial message included a greeting and a question was more likely to get a positive response. It comes as a separate group of scientists also revealed that tall men would prioritize tall women for relationships, but see short ones as just a fling. Women seeking a long-term relationship on dating apps find a sexually explicit opening line surprising and a violation, according to new research.

The saying goes that'mother knows best', and now struggling singletons can enlist their mum's help finding love online. Dating app, Badoo, now lets you add video clips from your family members to your dating profile. The unusual tool is part of Badoo's new'Family-Approved' feature, which allows users to show that they've called on their family to help them land a date. Remy Le Fèvre, Global Head of Brand Engagement and Influence at Badoo, said: 'Here at Badoo, we're all about making sure singles feel confident putting their best foot forward when dating, and for many of us, that means getting a little help from the people that love them most. 'Family-Approved is here to help singles feel good right from the start of their dating journey, whilst also showing potential matches when profiles have had the trusted green light from their loved ones - which could be a fun icebreaker when starting a conversation!'

There are so many good smart displays out there that it can be hard to choose which one to buy. Right now, one of our favorites, Amazon's second-generation Echo Show 8, is running down to $75 from $130 -- a 42 percent discount and just $5 off its lowest price. There are other available options on sale, like an adjustable stand or Blink Mini, but expect to pay a little extra for those. The Echo Show 8 is part speaker, part tablet, with TV shows and movies available from streamers like Netflix, Hulu and, of course, Prime Video. These come alongside music from Spotify, Apple Music and Amazon Music.

Advances in voice technology and voice user interfaces (VUIs) -- such as Alexa, Siri, and Google Home -- have opened up the potential for many new types of interaction. However, despite the potential of these devices reflected by the growing market and body of VUI research, there is a lingering sense that the technology is still underused. In this paper, we conducted a systematic literature review of 35 papers to identify and synthesize 127 VUI design guidelines into five themes. Additionally, we conducted semi-structured interviews with 15 smart speaker users to understand their use and non-use of the technology. From the interviews, we distill four design challenges that contribute the most to non-use. Based on their (non-)use, we identify four opportunity spaces for designers to explore such as focusing on information support while multitasking (cooking, driving, childcare, etc), incorporating users' mental models for smart speakers, and integrating calm design principles.

Recommender systems have found significant commercial success but still struggle with integrating new users. Since users often interact with content in different domains, it is possible to leverage a user's interactions in previous domains to improve that user's recommendations in a new one (multi-domain recommendation). A separate research thread on knowledge graph enhancement uses external knowledge graphs to improve single domain recommendations (knowledge graph enhancement). Both research threads incorporate related information to improve predictions in a new domain. We propose in this work to unify these approaches: Using information from interactions in other domains as well as external knowledge graphs to make predictions in a new domain that would be impossible with either information source alone. We apply these ideas to a dataset derived from millions of users' requests for content across three domains (videos, music, and books) in a live virtual assistant application. We demonstrate the advantage of combining knowledge graph enhancement with previous multi-domain recommendation techniques to provide better overall recommendations as well as for better recommendations on new users of a domain.

Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To make progress, we studied GitHub Copilot, a code-recommendation system used by millions of programmers daily. We developed CUPS, a taxonomy of common programmer activities when interacting with Copilot. Our study of 21 programmers, who completed coding tasks and retrospectively labeled their sessions with CUPS, showed that CUPS can help us understand how programmers interact with code-recommendation systems, revealing inefficiencies and time costs. Our insights reveal how programmers interact with Copilot and motivate new interface designs and metrics.

Purchases you make through the links below may earn us and our publishing partners a commission. Air quality is a major concern throughout the country with the Canadian wildfires still blazing. With all the reports of smoke choking the fresh air in the Northeast, it's understandable to be concerned about how fresh the airflow in your home is. Fortunately, we found an Amazon deal on a Reviewed-approved air quality monitor to help you know exactly how safe it is to take deep breaths in your own four walls. Right now, the online shopping giant is offering its own smart air quality monitor for $54.99.