As neural networks have become more powerful, there has been a rising desire to deploy them in the real world; however, the power and accuracy of neural networks is largely due to their depth and complexity, making them difficult to deploy, especially in resource-constrained devices. Neural network quantization has recently arisen to meet this demand of reducing the size and complexity of neural networks by reducing the precision of a network. With smaller and simpler networks, it becomes possible to run neural networks within the constraints of their target hardware. This paper surveys the many neural network quantization techniques that have been developed in the last decade. Based on this survey and comparison of neural network quantization techniques, we propose future directions of research in the area.

Billions of live-streaming viewers share their opinions on scenes they are watching in real-time and interact with the event, commentators as well as other viewers via text comments. Thus, there is necessary to explore viewers' comments with scenes in E-sport live-streaming events. In this paper, we developed CS-lol, a new large-scale dataset containing comments from viewers paired with descriptions of game scenes in E-sports live-streaming. Moreover, we propose a task, namely viewer comment retrieval, to retrieve the viewer comments for the scene of the live-streaming event. Results on a series of baseline retrieval methods derived from typical IR evaluation methods show our task as a challenging task. Finally, we release CS-lol and baseline implementation to the research community as a resource.

Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. As units of knowledge in a specific field of expertise, extracted terms are not only beneficial for several terminographical tasks, but also support and improve several complex downstream tasks, e.g., information retrieval, machine translation, topic detection, and sentiment analysis. ATE systems, along with annotated datasets, have been studied and developed widely for decades, but recently we observed a surge in novel neural systems for the task at hand. Despite a large amount of new research on ATE, systematic survey studies covering novel neural approaches are lacking. We present a comprehensive survey of deep learning-based approaches to ATE, with a focus on Transformer-based neural models. The study also offers a comparison between these systems and previous ATE approaches, which were based on feature engineering and non-neural supervised learning algorithms.

With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.

While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions.

Abstract--Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, and inter-connectivity with appliances for monitoring purposes. Hence, the importance of SGs as a research field is increasing with every passing year. This paper focuses on novel features and applications of smart grids in domestic and industrial sectors. Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization to study the efforts made up till date for maximized energy management and cost minimization in SGs. Many counter Smart grids refers to an electric grid that delivers the attack solutions such as secure data collectors, broadcast authentication, electricity from utility (power generator sources/company) to and secure DoS-resistant broadcast authentication the users (residential/industrial). A simple smart grid connection protocols have been studied to secure the data collection and is shown in Figure 1, with bi-directional communication coping the demands of users in efficient ways [9], [10]. The process of electricity other challenges are faced by both utility and users (energy delivery is capable of monitoring, modeling, controlling, data supply and energy demand) such as energy management, filtering, and data processing with help of number of intelligent cost efficiency, reducing power losses, and reducing pollutant features such as Artificial Intelligence (AI) or Computational emissions [11], [12]. The aforementioned challenges can be Intelligence (CI) as shown in Figure 2. SGs allow users to addressed using optimization techniques in SGs to maximize schedule the appliances depending upon pricing hours and the profit (for both users and utility) by managing electricity its demand that helps in saving energy, increasing reliability, distribution and reducing emissions. Furthermore, SGs support Optimization in SGs is employed to find the conditions with bidirectional power line communications such as Home Area maximum benefits while (at the same time) minimizing the Network (HAN) or Wide Area Network (WAN), and wireless electricity wastage and cost [13]. Hence, optimization problem communications such as ZigBee, 6LowPAN, Z-wave, IoT in SGs is defined as a scenario (i.e., an objective function) that networks, etc. [3]-[6]. For future work, we aim to expand our research for other optimization algorithms (i.e., ABC, ACO). Our contributions in this paper are: fluenced by a set of variables and/or constraints.

Estimating the effect of a treatment on an outcome is a fundamental problem in many fields such as medicine [33, 34, 61], public policy [20] and more [2, 37]. For example, doctors might be interested in how a treatment, such as a drug, affects the recovery of patients [18], economists may be interested in how a job training program affects employment prospectives [35], and advertisers may want to model the average effect an advertisement has on sales [36]. However, individuals may react differently to the treatment of interest, and knowing only the average treatment effect in the population is insufficient. For example, a drug may have adverse effects on some individuals but not others [61], or a person's education and background may affect how much they benefit from job training [35, 50]. Measuring the extent to which different individuals react differently to treatment is known as heterogeneous treatment effect (HTE) estimation. Traditionally, HTE estimation has been done through subgroup analysis [9, 19]. However, this can lead to cherry-picking since the practitioner is the one who identifies subgroups for estimating effects. Recently, there has been more focus on data-driven estimation of heterogeneous treatment effects by letting the data identify which features are important for treatment effect estimation using machine learning techniques [28, 39, 61, 69]. A straightforward approach is to create interaction terms between all covariates and use them in a regression [6].

Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need for a large number of samples in high dimension and the difficulty to handle data in distinct spaces. In this review, we detail several recently proposed approaches to mitigate these issues. We insist in particular on unbalanced OT, which compares arbitrary positive measures, not restricted to probability distributions (i.e. their total mass can vary). This generalization of OT makes it robust to outliers and missing data. The second workhorse of modern computational OT is entropic regularization, which leads to scalable algorithms while lowering the sample complexity in high dimension. The last point presented in this review is the Gromov-Wasserstein (GW) distance, which extends OT to cope with distributions belonging to different metric spaces. The main motivation for this review is to explain how unbalanced OT, entropic regularization and GW can work hand-in-hand to turn OT into efficient geometric loss functions for data sciences.

Without consciousness, Princeton neuroscientist Michael Graziano warns in a new essay published by The Wall Street Journal, artificial intelligence-powered chatbots are doomed to be dangerous sociopaths that could pose a real danger to human beings. With the rise of chatbots like ChatGPT, powerful systems that can imitate the human mind to an impressive degree, AI tools have become more accessible than ever before. But those algorithms will glibly fib about anything that suits their purpose. To make align them with our values, Graziano thinks, they're going to need consciousness. "Consciousness is part of the tool kit that evolution gave us to make us an empathetic, prosocial species," Graziano writes.

Data privacy and decentralised data collection has become more and more popular in recent years. In order to solve issues with privacy, communication bandwidth and learning from spatio-temporal data, we will propose two efficient models which use Differential Privacy and decentralized LSTM-Learning: One, in which a Long Short Term Memory (LSTM) model is learned for extracting local temporal node constraints and feeding them into a Dense-Layer (LabelProportionToLocal). The other approach extends the first one by fetching histogram data from the neighbors and joining the information with the LSTM output (LabelProportionToDense). For evaluation two popular datasets are used: Pems-Bay and METR-LA. Additionally, we provide an own dataset, which is based on LuST. The evaluation will show the tradeoff between performance and data privacy.