Unless you're really trying to save space on the floor, there's little reason to not opt for a self-emptying robot vacuum these days. Automated vacuuming likely means more frequent vacuuming, and more frequent vacuuming means faster dustbin filling. And at this point, that shouldn't be your problem. Automatic dustbin emptying has become such a mainstream robot vacuum feature that a ton of self-emptying robot vacs are solidly in the sub- 500 range -- and even more models join that club when there's a big sale event. The latest one happens to line up perfectly with spring cleaning season: Amazon's second Big Spring Sale is happening from March 25 and March 31. And Amazon is extremely well-versed in robot vacuum deals.

SAVE 210: As of March 28, get the Eufy Robot Vacuum C10 for 269.99, down from its usual price of 479.99. Tired of trying to keep the carpets clean with daily, fruitless vacuuming? Leave that chore behind and employ a robot to do it. And not one that just sweeps and picks nothing up. One that can deep clean your floors and give you some peace of mind that they're actually getting scrubbed.

SAVE 400: As of March 28, the Ecovacs Deebot N30 Omni robot vacuum is on sale for 399.99 during the Amazon Big Spring Sale. As fire as my favorite robot vacuum deal at Amazon's Big Spring Sale is, 800 is still kind of steep for a robot vacuum at the end of the day. For those looking to spend less, consider the similar Ecovacs Deebot N30 Omni. After a combination of a steep flat discount and a casual 100 coupon tacked on, you can secure many of the same high-end specs of my beloved Roborock Qrevo Master for 399.99. First things first: A robot vacuum and mop combo that not only self-empties, but self-washes and dries its own mopping pads is a rare find under 400.

As of March 28, the PetSafe ScoopFree SmartSpin automatic litter box has a 59.99 coupon during Amazon's Big Spring Sale, bringing it down to 339.96. Self-emptying litter boxes of the coveted egg shape typically require setting 600 to 800 aside -- a steep ask for the average person when other recurring cat supplies keep adding up. But there's an affordable hidden gem amongst the Litter-Robots and Leo's Loo Toos of the world. The PetSafe ScoopFree SmartSpin is already much more approachable at 399.95 when it's not on sale, but Amazon's Big Spring Sale has delivered a nice little 59.99 coupon to tack on. Now 339.96, you could technically buy two ScoopFree SmartSpins for less than the price of a single Litter-Robot 4. That's also 60 cheaper than the next-best automatic litter box deal live during this sale, the Petkit PuraMax 2 for 399.99.

SAVE 145: As of March 28, the Dreame D10 Plus robot vacuum and mop combo is down to just 254.99 at Amazon during the Big Spring Sale. The price of a new robot vacuum that also mops can come with a bit of sticker shock. If you just want something that can tidy up your floors and you're not picky, we suggest opting for something more affordable, like this Dreame D10 Plus. Usually pretty reasonable at 399.99, you can pick up the Dreame D10 Plus for even cheaper during Amazon's Big Spring Sale. It even beats its Black Friday price by 5.

Garmin has new AI features for its fitness app, but you'll have to pay to access them. On Thursday, the smartwatch maker popular with sports and fitness enthusiasts announced Garmin Connect Plus, a new paid subscription tier for its Garmin Connect smartphone app. The main selling point for the app's premium version, which shows health and fitness data, is a suite of new AI features. The features, dubbed Active Intelligence, provide "personalized insights and suggestions throughout the day based on health and activity data," according to the press release. The more subscribers use Garmin Connect Plus, "insights will become more tailored to them and their goals," the announcement continued.

Elon Musk's xAI artificial intelligence firm has acquired Musk's X – the social media platform formerly known as Twitter – for 33bn, marking the latest twist in the billionaire's rapid consolidation of power. The all-stock deal announced on Friday combines two of Musk's multiple portfolio companies, which also include automaker Tesla and SpaceX, and potentially eases Musk's ability to train his AI model known as Grok. Musk announced the transaction in a post on X, saying: "The combination values xAI at 80bn and X at 33bn ( 45B less 12B debt)." "xAI and X's futures are intertwined," he wrote. "Today, we officially take the step to combine the data, models, compute, distribution and talent."

X owner Elon Musk announced late Friday that the social media site had been sold ... to xAI, Musk's own Artificial Intelligence company. "xAI and X's futures are intertwined," Musk posted on X. "Today, we officially take the step to combine the data, models, compute, distribution and talent ... this will allow us to build a platform that doesn't just reflect the world but actively accelerates human progress." The AI company is now valued at 80 billion, with X valued at 33 billion, which includes 12 million in debt. Musk purchased X, then known as Twitter, for 44 billion in October 2022. What does this mean to the X user?

Starting in February 2024, the HKUST Library further extended the scope of AI literacy to AI utilization, which focuses on fostering student involvement in utilizing state-of-the-art technologies in the projects that initiated by the Library, named "Digital Scholarship (DS) CoLab". A key focus of the DS CoLab scheme has been on cultivating talents and enabling students to utilize advanced technologies in practical context. It aims to reinforce the library's role as a catalyst and hub for fostering multidisciplinary collaboration and cultivate the "can do spirit" among university members. The Library offers 1-2 projects per year for students to engage with advanced technologies in practical contexts while supporting the Library in tackling challenges and streamlining operational tasks. The tool that introduced in this paper was mainly developed by two of the authors, Sherry Yip Sau Lai and Berry Han Liuruo, as part-time student helpers under one of our DS CoLab scheme in the 2024 Spring Semester (February to May 2024). This paper details the complete journey from ideation to implementation of developing a Chinese Named-Entity Recognition (NER) Tool from the group up within one semester, from the initial research and planning stages to execution and come up a viable product. The collaborative spirit fostered by this project, with students playing a central role, exemplifies the power and potential of innovative educational models that prioritize hands-on learning with student involvement.

Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sharing. In FL, a global model is trained iteratively on local datasets residing on individual devices, each contributing to the model's improvement. However, the heterogeneous nature of these local datasets, stemming from diverse user behaviours, device capabilities, and data distributions, poses a significant challenge. The inherent heterogeneity in federated learning gives rise to various issues, including model performance discrepancies, convergence challenges, and potential privacy concerns. As the global model progresses through rounds of training, the disparities in local data quality and quantity can impede the overall effectiveness of federated learning systems. Moreover, maintaining fairness and privacy across diverse user groups becomes a paramount concern. To address this issue, this paper introduces a novel FL framework, ClusterGuardFL, that employs dissimilarity scores, k-means clustering, and reconciliation confidence scores to dynamically assign weights to client updates. The dissimilarity scores between global and local models guide the formation of clusters, with cluster size influencing the weight allocation. Within each cluster, a reconciliation confidence score is calculated for individual data points, and a softmax layer generates customized weights for clients. These weights are utilized in the aggregation process, enhancing the model's robustness and privacy. Experimental results demonstrate the efficacy of the proposed approach in achieving improved model performance in diverse datasets.