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Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses. Our methodological proposal is two-fold: (1) We improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. (2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images. We supply a software package that enables researchers to use these methods directly and thereby ensure the broad applicability of the proposed framework in ecological practice. We show that our tuning strategy improves predictive performance. We demonstrate how the AL pipeline reduces the amount of pre-labeled data needed to achieve a specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance. We conclude that the combination of tuning and AL increases predictive performance substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America.

How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Specifically, which design elements lead to perceptions of visualization as an image or as information? Understanding how design elements contribute to viewers perceiving a visualization more as an image or information will help designers decide which elements to include to achieve their communication goals. For this study, we annotated 500 visualizations and analyzed the responses of 250 online participants, who rated the visualizations on a bilinear scale as image or information. We then conducted an in-person study (n = 101) using a free recall task to examine how the image/information ratings and design elements impact memory. The results revealed several interesting findings: Image-rated visualizations were perceived as more aesthetically appealing, enjoyable, and pleasing. Information-rated visualizations were perceived as less difficult to understand and more aesthetically likable and nice, though participants expressed higher positive sentiment when viewing image-rated visualizations and felt less guided to a conclusion. We also found different patterns among participants that were older. Importantly, we show that visualizations internalized as images are less effective in conveying trends and messages, though they elicit a more positive emotional judgment, while informative visualizations exhibit annotation focused recall and elicit a more positive design judgment. We discuss the implications of this dissociation between aesthetic pleasure and perceived ease of use in visualization design.

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For the Rician fading model, since the multi-path components cannot be estimated, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses. To ensure reliable performance, it's essential to accurately identify and localize these issues using service monitoring metrics. Given the complexity and scale of modern cloud systems, this task can be challenging and may require extensive expertise and resources beyond the capacity of individual humans. Some existing methods tackle this problem by analyzing each metric independently to detect anomalies. However, this could incur overwhelming alert storms that are difficult for engineers to diagnose manually. To pursue better performance, not only the temporal patterns of metrics but also the correlation between metrics (i.e., relational patterns) should be considered, which can be formulated as a multivariate metrics anomaly detection problem. However, most of the studies fall short of extracting these two types of features explicitly. Moreover, there exist some unlabeled anomalies mixed in the training data, which may hinder the detection performance. To address these limitations, we propose the Relational- Temporal Anomaly Detection Model (RTAnomaly) that combines the relational and temporal information of metrics. RTAnomaly employs a graph attention layer to learn the dependencies among metrics, which will further help pinpoint the anomalous metrics that may cause the anomaly effectively. In addition, we exploit the concept of positive unlabeled learning to address the issue of potential anomalies in the training data. To evaluate our method, we conduct experiments on a public dataset and two industrial datasets. RTAnomaly outperforms all the baseline models by achieving an average F1 score of 0.929 and Hit@3 of 0.920, demonstrating its superiority.

Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription-based computing services anytime, anywhere, on a pay-as-you-go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world-wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy-efficient and environmentally sustainable while continuing to deliver high-quality services. In this paper, we propose a vision for learning-centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this paper, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy-efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability.

Most studies of reflecting intelligent surfaces (RISs)-assisted visible light communication (VLC) systems have focused on the integration of RISs in the channel to combat the line-of-sight (LoS) blockage and to enhance the corresponding achievable data rate. Some recent efforts have investigated the integration of liquid crystal (LC)-RIS in the VLC receiver to also improve the corresponding achievable data rate. To jointly benefit from the previously mentioned appealing capabilities of the RIS technology in both the channel and the receiver, in this work, we propose a novel indoor VLC system that is jointly assisted by a mirror array-based RIS in the channel and an LC-based RIS aided-VLC receiver. To illustrate the performance of the proposed system, a rate maximization problem is formulated, solved, and evaluated. This maximization problem jointly optimizes the roll and yaw angles of the mirror array-based RIS as well as the refractive index of the LC-based RIS VLC receiver. Moreover, this maximization problem considers practical assumptions, such as the presence of non-users blockers in the LoS path between the transmitter-receiver pair and the user's random device orientation (i.e., the user's self-blockage). Due to the non-convexity of the formulated optimization problem, a low-complexity algorithm is utilized to get the global optimal solution. A multi-user scenario of the proposed scheme is also presented. Furthermore, the energy efficiency of the proposed system is also investigated. Simulation results are provided, confirming that the proposed system yields a noteworthy improvement in data rate and energy efficiency performances compared to several baseline schemes.

Deep learning-based person identification and verification systems have remarkably improved in terms of accuracy in recent years; however, such systems, including widely popular cloud-based solutions, have been found to exhibit significant biases related to race, age, and gender, a problem that requires in-depth exploration and solutions. This paper presents an in-depth analysis, with a particular emphasis on the intersectionality of these demographic factors. Intersectional bias refers to the performance discrepancies w.r.t. the different combinations of race, age, and gender groups, an area relatively unexplored in current literature. Furthermore, the reliance of most state-of-the-art approaches on accuracy as the principal evaluation metric often masks significant demographic disparities in performance. To counter this crucial limitation, we incorporate five additional metrics in our quantitative analysis, including disparate impact and mistreatment metrics, which are typically ignored by the relevant fairness-aware approaches. Results on the Racial Faces in-the-Wild (RFW) benchmark indicate pervasive biases in face recognition systems, extending beyond race, with different demographic factors yielding significantly disparate outcomes. In particular, Africans demonstrate an 11.25% lower True Positive Rate (TPR) compared to Caucasians, while only a 3.51% accuracy drop is observed. Even more concerning, the intersections of multiple protected groups, such as African females over 60 years old, demonstrate a +39.89% disparate mistreatment rate compared to the highest Caucasians rate. By shedding light on these biases and their implications, this paper aims to stimulate further research towards developing fairer, more equitable face recognition and verification systems.

In the realm of Tiny AI, we introduce "You Only Look at Interested Cells" (YOLIC), an efficient method for object localization and classification on edge devices. Seamlessly blending the strengths of semantic segmentation and object detection, YOLIC offers superior computational efficiency and precision. By adopting Cells of Interest for classification instead of individual pixels, YOLIC encapsulates relevant information, reduces computational load, and enables rough object shape inference. Importantly, the need for bounding box regression is obviated, as YOLIC capitalizes on the predetermined cell configuration that provides information about potential object location, size, and shape. To tackle the issue of single-label classification limitations, a multi-label classification approach is applied to each cell, effectively recognizing overlapping or closely situated objects. This paper presents extensive experiments on multiple datasets, demonstrating that YOLIC achieves detection performance comparable to the state-of-the-art YOLO algorithms while surpassing in speed, exceeding 30fps on a Raspberry Pi 4B CPU. All resources related to this study, including datasets, cell designer, image annotation tool, and source code, have been made publicly available on our project website at //kai3316.github.io/yolic.github.io

Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems. Based on a scoping review of computing research $(n=172)$, we present an applied taxonomy of sociotechnical harms to support a more systematic surfacing of potential harms in algorithmic systems. The final taxonomy builds on and refers to existing taxonomies, classifications, and terminologies. Five major themes related to sociotechnical harms - representational, allocative, quality-of-service, interpersonal harms, and social system/societal harms - and sub-themes are presented along with a description of these categories. We conclude with a discussion of challenges and opportunities for future research.

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements.