Delay (<i>ms</i>). by Alisson Assis Cardoso (7861175)

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5G basic time-frequency resource structure (<i>μ</i> = 0). by Alisson Assis Cardoso (7861175)

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Link utilization (%). by Alisson Assis Cardoso (7861175)

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Example of multiplicative cascades. by Alisson Assis Cardoso (7861175)

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Adaptive and on-batch estimation of <i>γ</i> for 2000 samples from the MAWI Trace aggregated in 1<i>ms</i>. by Alisson Assis Cardoso (7861175)

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Byte loss rate (%). by Alisson Assis Cardoso (7861175)

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Throughput per user (<i>Mbps</i>). by Alisson Assis Cardoso (7861175)

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Adaptive and on-batch estimation of <i>α</i> for 2000 samples from the MAWI Trace aggregated in 1<i>ms</i>. by Alisson Assis Cardoso (7861175)

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Numerology for the LTE Release 15 NSA [1]. by Alisson Assis Cardoso (7861175)

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Buffer occupancy (%). by Alisson Assis Cardoso (7861175)

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Bidirectional energy flow between energy districts and utility. by Waqar Ahmed (572588)

Assembly task flow. by Guohua Cao (697580)

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Python implementation from Symplectic decomposition from submatrix determinants by Jason L. Pereira (11598632)

“…Python implementation of the algorithm and demonstration of how to use the functions.…”

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</p><h2>Model Architecture</h2><p dir="ltr">The model is based on <code>pysentimiento/robertuito-base-uncased</code> with the following modifications:</p><ul><li>A dense classification layer was added over the base model</li><li>Uses input IDs and attention masks as inputs</li><li>Generates a multi-class classification with 5 hate categories</li></ul><h2>Dataset</h2><p dir="ltr"><b>HATEMEDIA Dataset</b>: Custom hate speech dataset with categorization by type:</p><ul><li><b>Labels</b>: 5 hate type categories (0-4)</li><li><b>Preprocessing</b>:</li><li>Null values ​​removed from text and labels</li><li>Reindexing and relabeling (original labels are adjusted by subtracting 1)</li><li>Exclusion of category 2 during training</li><li>Conversion of category 5 to category 2</li></ul><h2>Training Process</h2><h3>Configuration</h3><ul><li><b>Batch size</b>: 128</li><li><b>Epoches</b>: 5</li><li><b>Learning rate</b>: 2e-5 with 10% warmup steps</li><li><b>Early stopping</b> with patience=2</li><li><b>Class weights</b>: Balanced to handle class imbalance</li></ul><h3>Custom Metrics</h3><ul><li>Recall for specific classes (focus on class 2)</li><li>Precision for specific classes (focus on class 3)</li><li>F1-score (weighted)</li><li>AUC-PR</li><li>Recall at precision=0.6 (class 3)</li><li>Precision at recall=0.6 (class 2)</li></ul><h2>Evaluation Metrics</h2><p dir="ltr">The model is evaluated using:</p><ul><li>Macro recall, precision, and F1-score</li><li>One-vs-Rest AUC</li><li>Accuracy</li><li>Per-class metrics</li><li>Confusion matrix</li><li>Full classification report</li></ul><h2>Technical Features</h2><h3>Data Preprocessing</h3><ul><li><b>Tokenization</b>: Maximum length of 128 tokens (truncation and padding)</li><li><b>Encoding of labels</b>: One-hot encoding for multi-class classification</li><li><b>Data split</b>: 80% training, 10% validation, 10% testing</li></ul><h3>Optimization</h3><ul><li><b>Optimizer</b>: Adam with linear warmup scheduling</li><li><b>Loss function</b>: Categorical Crossentropy (from_logits=True)</li><li><b>Imbalance handling</b>: Class weights computed automatically</li></ul><h2>Requirements</h2><p dir="ltr">The following Python packages are required:</p><ul><li>TensorFlow</li><li>Transformers</li><li>scikit-learn</li><li>pandas</li><li>datasets</li><li>matplotlib</li><li>seaborn</li><li>numpy</li></ul><h2>Usage</h2><ol><li><b>Data format</b>:</li></ol><ul><li>CSV file or Pandas DataFrame</li><li>Required column name: <code>text</code> (string type)</li><li>Required column name: Data type label (integer type, 0-4) - optional for evaluation</li></ul><ol><li><b>Text preprocessing</b>:</li></ol><ul><li>Automatic tokenization with a maximum length of 128 tokens</li><li>Long texts will be automatically truncated</li><li>Handling of special characters, URLs, and emojis included</li></ul><ol><li><b>Label encoding</b>:</li></ol><ul><li>The model classifies hate speech into 5 categories (0-4)</li><li><code>0</code>: Political hatred: Expressions directed against individuals or groups based on political orientation.…”

A flow diagram to explain the algorithm named 2BDP (Biomarker Discovery Process at Binomial Decision Point) that we used to find the best fitting model to predict RRiF. by Nabarun Chakraborty (535772)

Impact of breath-detection algorithms on main MBW outcomes. by Marc-Alexander Oestreich (13961375)

Interaction and functionality of various modules in the proposed sentiment analysis system. by Hongtao Wang (570131)

Flow diagram of the reachable workspace. by Zesheng Wang (15244172)

Flow chart of UNNT. by Vineeth Gutta (18462574)