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This dataset comprises user-generated content from Stack Overflow, including post bodies, post tags, and user engagement metrics such as upvotes and downvotes. The data was collected from the stack exchange explorer based on user defined categories and other criteria like reputation and badges as explained in our work. It was collected to support research in technology and emotion analysis, focusing on understanding user interactions and sentiments within online communities.
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This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.
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Clean energy technologies, encompassing renewable resources like solar, wind, and hydropower, are essential in the global effort to reduce greenhouse gas emissions and combat climate change. As the globe prepares to transition away from fossil fuels, understanding the factors and parameters influencing the penetration of clean energy into existing energy markets has become a critical step. Controversies surrounding the environmental impacts of renewable technologies, variability in market structures, and economic pressures on clean energy companies can complicate this transition.
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Cloud computing has become a relatively new paradigm for the delivery of compute re-
sources, with key management services (KMS) playing a crucial role in securely handling cryptographic
operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, in-
cluding SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryp-
tion/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction
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The Smart Home Device Dataset consists of 5000 samples collected at an hourly interval starting from January 2022, representing consumer electronics and IoT-enabled devices in a home automation environment. Each entry is associated with a unique device ID, ensuring identification of distinct devices. The dataset captures real-time sensor readings, including temperature variations (18°C to 30°C), power consumption levels (10W to 500W), and user activity states (Active, Idle, or Sleep), which provide contextual insights into device operation.
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Solar Insecticidal Lamps Internet of Things (SIL-IoTs) is an advanced agricultural IoT system integrating solar insecticidal lamps with wireless sensor networks. It attracts pests with light, then kills them with high-voltage metal grids. Equipped with wireless communication modules and environmental sensors, SIL-IoTs can collect and transmit field data, including pest counts (discharge pulse counts, insect-killing sound pulse counts), environmental data (air temperature, humidity, light intensity, equipment box temperature), and operational status.
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The BNS (Bharatiya Nyay Sanhita) dataset is a comprehensive collection of legal texts which was web-scraped.. It consists of chapters and their respective sections, capturing detailed legal content relevant to the recently introduced BNS framework in India. This dataset was gathered using a Python-based web scraping script leveraging Selenium WebDriver, ensuring accuracy and completeness. Available in CSV formats, the dataset facilitates ease of access for legal research, natural language processing (NLP) tasks, and AI-based legal assistance applications.
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The PhishFOE Dataset is a comprehensive dataset designed for phishing URL detection using machine learning techniques. The dataset contains 101,083 URLs, with labeled features extracted from both the URL structure and HTML content of webpages. It provides insights into key characteristics that distinguish phishing websites from legitimate ones.
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Total Samples: 101,063
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Label:
0
for Legitimate,1
for Phishing
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Dataset was created for the purposes of exploring time distortion with non-ideal near-field conditions. A 90 Hz square wave is played at 100dBA through a bookshelf speaker with the port removed. The recordings were captured at 5 separate axial distances (From 2" to 17", following inverse square law), and at three levels of resistive loading (No added resistance, 1.5 ohm, 3 ohm). The DC resistance of the speaker was measured at 6.9 ohms. To avoid overtraining, captures were recorded on a moving dynamic microphone.
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