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Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework for lightweight and efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured […]

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Recognizing emotions from written text is a very important part of Natural Language Processing (NLP) and is commonly used for feeling or sentiment analysis or keeping track of someone’s mental health status. This study uses a readable emotion-detecting framework with a RoBERTa-base model that has been modified and trained specifically for the Emotions for NLP dataset and provides an accuracy of 0.924% and f1 score of 0.925%. The main contributions of this study are the use of four […]

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Whole-slide imaging has transformed histopathology into a data-intensive domain, with current approaches dominated by end-to-end deep learning that encode morphology implicitly within latent representations. This limits interpretability, reproducibility, and cross-dataset generalization. This review positions histomics as an intermediate phenotype representation layer that maps histological images to structured, multi-scale descriptors of tissue morphology, spatial organization, and architectural context. A unified taxonomy of histomic features across biological scales is presented, along with an analysis of artificial intelligence frameworks spanning classical […]

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While Large Language Models (LLMs) have shown great promise in transforming credit risk assess-ment, existing evaluation frameworks are almost exclusively concerned with general financial NLP tasks and neglect the specific reasoning needed by practitioners. To address this, we develop the Credit Context Log Understanding and Prediction Evaluation (CCLUPE) benchmark. Unlike the previous benchmarks, CCLUPE aims to capture and evaluate the intricate reasoning unique to each constituent of the Chinese credit market, where evaluations are heavily based on the […]

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The transition from smart to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart-intelligent city as an urban Cyber-Physical System across environmental, economic, and social sustainability pillars, metrics and their tradeoffs. A methodology based on Deep Reinforcement Learning and reward-shaping […]

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Image classification is one of the earliest and most fundamental approaches in computer vision (CV). In the literature, a wide variety of methods have been proposed, ranging from handcrafted feature-based methods to deep-learning-based approaches such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Each new method has been developed to address the shortcomings of previous methods and achieve higher performance. Image classification is a broad field of study in contemporary scientific research. In this study, image classification […]

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This paper proposes Functional Consciousness (FC) as a measurable architectural property: the observable capacity of a system to access and reason about internal representations of its own states. We introduce a computationally tractable metric on FC that operationalizes core tenets of major consciousness theories through self-models and their associated reasoning power, measured through informational richness and state-space expansion under inference. The resulting Functional Consciousness Score (FCS) is applied to benchmark systems with known internal structure, including a Waymo […]

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Quantum computing introduces a new class of cyber threats that challenge existing detection paradigms. While significant research has focused on post-quantum cryptography (PQC) and the long-term risk of cryptographic breakage, far less attention has been given to the detection of quantum-enabled attacks during the pre-cryptographically-relevant quantum (pre-CRQC) era. Current intrusion detection systems, network monitors, and cryptographic telemetry tools lack the conceptual models and operational indicators needed to identify adversaries who leverage quantum acceleration, quantum-optimized reconnaissance, or hybrid-mode downgrade […]

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For years, the way large language models handle inference has been stuck inside a box — literally. The high-bandwidth RDMA networks that make modern LLM serving work have confined both prefill and decode to the same datacenter, sometimes even the same rack. A team of researchers at Moonshot AI and Tsinghua University is making the case that this constraint is about to break down — and that the right architecture can already exploit that shift. The research team […]

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