The future of work is a hot topic among businesses now and has been since the shift to hybrid work. While processes and personnel undergo significant transformations, business and IT leaders must focus intensely on the digital employee experience (DEX) when analyzing and deploying any operational change to the status quo of their current processes. The digital employee experience (DEX) interprets how digital workplace strategies impact employees' overall day-to-day experience. An enterprise's workforce is the bedrock of resilient and sustainable operational shifts. Measurable success cannot be achieved without employees on the same page.

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items. Due to the security and privacy concerns, it is more practical to perform adversarial attacks under the black-box setting, where the architecture/parameters and training data of target systems cannot be easily accessed by attackers. However, generating high-quality fake user profiles under black-box setting is rather challenging with limited resources to target systems. To address this challenge, in this work, we introduce a novel strategy by leveraging items' attribute information (i.e., items' knowledge graph), which can be publicly accessible and provide rich auxiliary knowledge to enhance the generation of fake user profiles. More specifically, we propose a knowledge graph-enhanced black-box attacking framework (KGAttack) to effectively learn attacking policies through deep reinforcement learning techniques, in which knowledge graph is seamlessly integrated into hierarchical policy networks to generate fake user profiles for performing adversarial black-box attacks. Comprehensive experiments on various real-world datasets demonstrate the effectiveness of the proposed attacking framework under the black-box setting.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a neutral emotion. The other emotions - bad, good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish, French, Italian, and other languages. Third, the findings from source tracking showed that Twitter for Android was associated with 35.2% of tweets. It was followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other sources. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, and other domains. Finally, to support similar research in this field, we have developed a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

Through generations, the field of artificial intelligence has persevered and become a hugely significant part of modern life. Of the myriad technological advances of the 20th and 21st centuries, one of the most influential is undoubtedly artificial intelligence (AI). From search engine algorithms reinventing how we look for information to Amazon's Alexa in the consumer sector, AI has become a major technology driving the entire tech industry forward into the future. According to a study from Grand View Research, the global AI industry was valued at $93.5 billion in 2021. AI as a force in the tech industry exploded in prominence in the 2000s and 2010s, but AI has been around in some form or fashion since at least 1950 and arguably stretches back even further than that.

Nitin Rakesh, a distinguished leader in the IT services industry, is the Chief Executive Officer and Director of Mphasis. For centuries, banks demonstrated expertise in keeping, lending and saving money. In return, customers followed a bank's regulations. This included how banks stipulated interest rates for lending, identified creditworthy cohorts and facilitated banking transactions. Fast-forward to 2020, and banks are now viewed under the same lens as customer-facing organizations like movie theatres, restaurants and hotels.

Recommender Systems are a subclass of machine learning systems that employ sophisticated information filtering strategies to reduce the search time and suggest the most relevant items to any particular user. Hybrid recommender systems combine multiple recommendation strategies in different ways to benefit from their complementary advantages. Some hybrid recommender systems have combined collaborative filtering and content-based approaches to build systems that are more robust. In this paper, we propose a hybrid recommender system, which combines Alternating Least Squares (ALS) based collaborative filtering with deep learning to enhance recommendation performance as well as overcome the limitations associated with the collaborative filtering approach, especially concerning its cold start problem. In essence, we use the outputs from ALS (collaborative filtering) to influence the recommendations from a Deep Neural Network (DNN), which combines characteristic, contextual, structural and sequential information, in a big data processing framework. We have conducted several experiments in testing the efficacy of the proposed hybrid architecture in recommending smartphones to prospective customers and compared its performance with other open-source recommenders. The results have shown that the proposed system has outperformed several existing hybrid recommender systems.

Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that will further lead to a cold-start issue. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. In this paper, we attempt to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa, and experiment on whether the CF recommender has been further enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm. Experimental result shows that the recommender enhanced with the sentiment ratings predicted by the fine-tuned RoBERTa has the best performance, and achieved 30.7% overall gain by comparing MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning paradigm, although superior to traditional fine-tune paradigm in pure sentiment analysis, fail to further improve the CF recommender.

Sundar Pichai, Google CEO, stated that AI's impact on human development would be greater than the impact of electricity or fire. Although it may sound lofty, AI's potential is evident from its use to discover space, combat climate change, and develop cancer treatments. Although it is difficult to visualize the impact of machines making quicker and more accurate decisions than humans, one thing is certain: 2022 will see breakthroughs and trends in AI and ML. All over the globe, artificial technology trends are generating much buzz. This technology has revolutionized nearly every sector of various industries. The automation of business workflows has made it possible for industries to thrive since artificial intelligence. Every business, from small-scale start-ups to established businesses, wants to reap the benefits of this incredible technology.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The combination of Wi-Fi 6 and 5G mobility, combined with an increasingly wired and mobile world of internet of things (IoT) technology, promises to bring billions more devices onto networks in the coming years. This will have a profound impact on workplaces of the future, in ways that go far beyond the clear trends of remote employees and hybrid workforces. The world is entering a place where many people can seamlessly connect with fellow workers virtually from any location, with the workplace becoming more intelligent and hoteling becoming the norm. Examples include the ability to schedule a desk similar to seats at the movies or a flight, as well as the ability to crowdsource the temperature in the office.

While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks. To demonstrate our hypothesis, we propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnerable elements and substituting them with adversarial items. In both untargeted and targeted attack scenarios, we observe significant performance deterioration using the proposed profile pollution algorithm. Motivated by such observations, we design an efficient adversarial defense method called Dirichlet neighborhood sampling. Specifically, we sample item embeddings from a convex hull constructed by multi-hop neighbors to replace the original items in input sequences. During sampling, a Dirichlet distribution is used to approximate the probability distribution in the neighborhood such that the recommender learns to combat local perturbations. Additionally, we design an adversarial training method tailored for sequential recommender systems. In particular, we represent selected items with one-hot encodings and perform gradient ascent on the encodings to search for the worst case linear combination of item embeddings in training. As such, the embedding function learns robust item representations and the trained recommender is resistant to test-time adversarial examples. Extensive experiments show the effectiveness of both our attack and defense methods, which consistently outperform baselines by a significant margin across model architectures and datasets.