Substantial scholarship has estimated the susceptibility of jobs to automation, but little has examined how job contents evolve in the information age as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs. Here we explore patterns and consequences of changes in occupational skill and characterize occupations and workers subject to the greatest re-skilling pressure. Recent work found that changing skill requirements are greatest for STEM occupations. Nevertheless, analyzing 167 million online job posts covering 727 occupations over the last decade, we find that re-skilling pressure is greatest for low-skilled occupations when accounting for distance between skills. We further investigate the differences in skill change across employer and market size, as well as social demographic groups, and find that these differences tend to widen the economic divide. Jobs from large employers and markets experienced less change relative to small employers and markets, and non-white workers in low-skilled jobs are most demographically vulnerable. We conclude by showcasing our model's potential to precisely chart job evolution towards machine-interface integration using skill embedding spaces.
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There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model training process is both time-intensive and energy-intensive, inevitably affecting the user's demand for application efficiency. Layer freezing, an efficient model training technique, has been proposed to improve training efficiency. Although existing layer freezing methods demonstrate the great potential to reduce model training costs, they still remain shortcomings such as lacking generalizability and compromised accuracy. For instance, existing layer freezing methods either require the freeze configurations to be manually defined before training, which does not apply to different networks, or use heuristic freezing criteria that is hard to guarantee decent accuracy in different scenarios. Therefore, there lacks a generic and smart layer freezing method that can automatically perform ``in-situation'' layer freezing for different networks during training processes. To this end, we propose a generic and efficient training framework (SmartFRZ). The core proposed technique in SmartFRZ is attention-guided layer freezing, which can automatically select the appropriate layers to freeze without compromising accuracy. Experimental results show that SmartFRZ effectively reduces the amount of computation in training and achieves significant training acceleration, and outperforms the state-of-the-art layer freezing approaches.
Communication with the goal of accurately conveying meaning, rather than accurately transmitting symbols, has become an area of growing interest. This paradigm, termed semantic communication, typically leverages modern developments in artificial intelligence and machine learning to improve the efficiency and robustness of communication systems. However, a standard model for capturing and quantifying the details of "meaning" is lacking, with many leading approaches to semantic communication adopting a black-box framework with little understanding of what exactly the model is learning. One solution is to utilize the conceptual spaces framework, which models meaning explicitly in a geometric manner. Though prior work studying semantic communication with conceptual spaces has shown promising results, these previous attempts involve hand-crafting a conceptual space model, severely limiting the scalability and practicality of the approach. In this work, we develop a framework for learning a domain of a conceptual space model using only the raw data with high-level property labels. In experiments using the MNIST and CelebA datasets, we show that the domains learned using the framework maintain semantic similarity relations and possess interpretable dimensions.
A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes, and helps a job seeker find relevant jobs from a list of job posts. However, since job seekers apply only to a few jobs, interaction records in resume-job datasets are sparse. Different from many prior work that use complex modeling techniques, we tackle this sparsity problem using data augmentations and a simple contrastive learning approach. ConFit first creates an augmented resume-job dataset by paraphrasing specific sections in a resume or a job post. Then, ConFit uses contrastive learning to further increase training samples from $B$ pairs per batch to $O(B^2)$ per batch. We evaluate ConFit on two real-world datasets and find it outperforms prior methods (including BM25 and OpenAI text-ada-002) by up to 19% and 31% absolute in nDCG@10 for ranking jobs and ranking resumes, respectively.
The traveling salesman (or salesperson) problem, short TSP, is a problem of strong interest to many researchers from mathematics, economics, and computer science. Manifold TSP variants occur in nearly every scientific field and application domain: engineering, physics, biology, life sciences, and manufacturing just to name a few. Several thousand papers are published on theoretical research or application-oriented results each year. This paper provides the first systematic survey on the best currently known approximability and inapproximability results for well-known TSP variants such as the "standard" TSP, Path TSP, Bottleneck TSP, Maximum Scatter TSP, Generalized TSP, Clustered TSP, Traveling Purchaser Problem, Profitable Tour Problem, Quota TSP, Prize-Collecting TSP, Orienteering Problem, Time-dependent TSP, TSP with Time Windows, and the Orienteering Problem with Time Windows. The foundation of our survey is the definition scheme T3CO, which we propose as a uniform, easy-to-use and extensible means for the formal and precise definition of TSP variants. Applying T3CO to formally define the variant studied by a paper reveals subtle differences within the same named variant and also brings out the differences between the variants more clearly. We achieve the first comprehensive, concise, and compact representation of approximability results by using T3CO definitions. This makes it easier to understand the approximability landscape and the assumptions under which certain results hold. Open gaps become more evident and results can be compared more easily.
Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. However, keeping the encoding time within an acceptable threshold for a smooth user experience is important to reduce the carbon footprint and energy consumption on encoding servers in video streaming applications. Toward this realization, we introduce an encoding latency-a ware dynamic resolution encoding scheme (LADRE) for adaptive video streaming applications. LADRE determines the encoding resolution for each target bitrate by utilizing a random forest-based prediction model for every video segment based on spatiotemporal features and the acceptable target latency. Experimental results show that LADRE achieves an overall average quality improvement of 0.58 dB PSNR and 0.43 dB XPSNR while maintaining the same bitrate, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 200 s segments using the VVenC encoder, when the encoding latency for each representation is set to remain below the 200 s threshold. This is accompanied by an 84.17 % reduction in overall encoding energy consumption.
Robotic coaches have been recently investigated to promote mental well-being in various contexts such as workplaces and homes. With the widespread use of Large Language Models (LLMs), HRI researchers are called to consider language appropriateness when using such generated language for robotic mental well-being coaches in the real world. Therefore, this paper presents the first work that investigated the language appropriateness of robot mental well-being coach in the workplace. To this end, we conducted an empirical study that involved 17 employees who interacted over 4 weeks with a robotic mental well-being coach equipped with LLM-based capabilities. After the study, we individually interviewed them and we conducted a focus group of 1.5 hours with 11 of them. The focus group consisted of: i) an ice-breaking activity, ii) evaluation of robotic coach language appropriateness in various scenarios, and iii) listing shoulds and shouldn'ts for designing appropriate robotic coach language for mental well-being. From our qualitative evaluation, we found that a language-appropriate robotic coach should (1) ask deep questions which explore feelings of the coachees, rather than superficial questions, (2) express and show emotional and empathic understanding of the context, and (3) not make any assumptions without clarifying with follow-up questions to avoid bias and stereotyping. These results can inform the design of language-appropriate robotic coach to promote mental well-being in real-world contexts.
Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dexterous hands -- especially on real robotic platforms -- due to the high dimensionality of the problem. In this work, we focus on the multi-fingered grasping task with the anthropomorphic hand of the iCub humanoid. We propose the RESidual learning with PREtrained CriTics (RESPRECT) method that, starting from a policy pre-trained on a large set of objects, can learn a residual policy to grasp a novel object in a fraction ($\sim 5 \times$ faster) of the timesteps required to train a policy from scratch, without requiring any task demonstration. To our knowledge, this is the first Residual Reinforcement Learning (RRL) approach that learns a residual policy on top of another policy pre-trained with DRL. We exploit some components of the pre-trained policy during residual learning that further speed-up the training. We benchmark our results in the iCub simulated environment, and we show that RESPRECT can be effectively used to learn a multi-fingered grasping policy on the real iCub robot. The code to reproduce the experiments is released together with the paper with an open source license.
The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy within the dairy sector in Ireland. An agent-based modelling approach is introduced to estimate the adoption rate. The model considers grid energy prices, revenue, costs, and maintenance expenses to calculate the probability of PV adoption. The ABM outputs estimate that by year 2022, 2.45% of dairy farmers have installed PV. This is a 0.45% difference to the actual PV adoption rate in year 2022. This validates the proposed ABM. The paper demonstrates the increasing interest in PV systems as evidenced by the rate of adoption, shedding light on the potential advantages of PV energy adoption in agriculture. This study possesses the potential to forecast future rates of PV energy adoption among dairy farmers. It establishes a groundwork for further research on predicting and understanding the factors influencing the adoption of renewable energy.
The new era of technology has brought us to the point where it is convenient for people to share their opinions over an abundance of platforms. These platforms have a provision for the users to express themselves in multiple forms of representations, including text, images, videos, and audio. This, however, makes it difficult for users to obtain all the key information about a topic, making the task of automatic multi-modal summarization (MMS) essential. In this paper, we present a comprehensive survey of the existing research in the area of MMS.
While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence). Factorization Machine provides a possible approach to learning element-wise interaction for recommender systems, but they are not directly applicable to our task due to the inability to model contexts and word sequences. In this work, we develop two Position-aware Factorization Machines which consider word interaction, context and position information. Such information is jointly encoded in a set of sentiment-oriented word interaction vectors. Compared to traditional word embeddings, SWI vectors explicitly capture sentiment-oriented word interaction and simplify the parameter learning. Experimental results show that while they have comparable performance with state-of-the-art methods for document-level classification, they benefit the snippet/sentence-level sentiment analysis.
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