Reviewerly/exHarmony huggingface dataset:
Offensiveness Detection in Hinglish Code-Switched Language

Detecting offensive content is one of the most sought-after tasks with the increased online presence. Though it is a challenging task on its own due to the short text, spell variations, and emojis, it is even more challenging for code-switched languages. Many Indian users are habituated to use Hinglish (Hindi and English) on almost all social media platforms. Hinglish has no grammar rules, it is very difficult to infer the meaning for the current machine translation tools. We have built an offensive language detection system that deals with code-switched languages, by fine-tuning the pre-trained BERT on the HOT (Hinglish Offensive Tweets) dataset with a fully connected network as a task-specific layer. We used sentiment score and the number of bad words as two additional features to build the classifier. By using weighted cross-entropy loss in our model, we could improve the F1 Score to 0.902 while the current state-of-the-art model on this dataset has only 0.895.
Personalizing Retrieval-Based Dialogue Agents

The development of various kinds of interactive assistants at present is highly in demand. In this field, one critical problem is the personalization of these dialog assistants seeking to increase user loyalty and involvement in a conversation, which may be a competitive advantage for enterprises employing them. This paper presents a study of retrieve models for a personalized dialogue agent. To train models the Persona Chat and Toloka Persona Chat Rus datasets are used. The study found the most effective models among the retrieval models, learning strategies. Also, to solve one of the major limitations of the personalization of dialogue assistants—the lack of large data sets with dialogues containing person characteristics—a text data augmentation method was developed that preserves individual speech patterns and vocabulary.
Reflections on “Rock, Paper, Scissors”: Communicating Science to the Public through a Demonstrator

Communicating science to the public is increasingly important. Demonstrators are a valuable and established tool for communication in technology research and development. However, their role in communicating current science and technology to the public has not received much attention neither in research nor practice. This paper reflects on the design and usage of the demonstrator “Rock, Paper, Scissors”, which we developed to communicate current advances in Human-Robot Interaction to public audiences. We discuss two years of “Rock, Paper, Scissors” in action and its evolution within this period. We conclude with an outlook to future work regarding technology development and evaluation of science communication.
LegisLatio: A visualization Tool for Legislative Roll-call Vote Data

Appropriate communication and understanding of political data are key to achieve a healthy dialogue between civil society and political institutions. This is particularly important for data generated by bodies that most directly represent citizens, such as congresses, senates, parliaments, and other forms of legislatures. Visualization tools have the potential to support the exploration and understanding of such data and make it more accessible and appealing to the general public. With this vision, we present LegisLatio, an interactive visualization tool that enables open-ended analysis of legislative roll-call vote data from multi-party electoral systems. We describe the design of the tool and illustrate its effectiveness with data generated by the Ecuadorian legislature. We also discuss the findings of a qualitative observation that hints at LegisLatio’s potential to promote citizen engagement and participation.
Rethinking Zero-shot Action Recognition: Learning from Latent Atomic Actions

To avoid time-consuming annotating and retraining cycle in applying supervised action recognition models, Zero-Shot Action Recognition (ZSAR) has become a thriving direction. ZSAR requires models to recognize actions that never appear in training set through bridging visual features and semantic representations. However, due to the complexity of actions, it remains challenging to transfer knowledge learned from source to target action domains. Previous ZSAR methods mainly focus on mitigating representation variance between source and target actions through integrating or applying new action-level features. However, the action-level features are coarse-grained and make the learned one-to-one bridge fragile to similar target actions. Meanwhile, integration or application of features usually requires extra computation or annotation. These methods didn't notice that two actions with different names may still share the same atomic action components. It enables humans to quickly understand an unseen action given bunch of atomic actions learned from seen actions. Inspired by this, we propose Jigsaw Network (JigsawNet) which recognizes complex actions through unsupervisedly decomposing them into combinations of atomic actions and bridging group to group relationships between visual features and semantic representations. To enhance the robustness of learned group-to-group bridge, we propose Group Excitation (GE) module to model intra-sample knowledge and Consistency Loss to enforce the model learn from inter-sample knowledge. Our JigsawNet achieves state-of-the-art performance on three benchmarks and surpasses previous works with noticeable margins.
Deep Cross-Layer Collaborative Learning Network for Online Knowledge Distillation

Recent online knowledge distillation (OKD) methods focus on capturing rich and useful intermediate information by performing multi-layer feature learning. Existing works only consider intermediate layer feature maps between the same layers and ignore valuable information across layers, which results in the lack of appropriate cross-layer supervision in detail and the process of learning. Besides, this manner provides insufficient supervision information to supervise the learning of student, since it fails to construct a qualified teacher. In this work, we propose a Deep Cross-layer Collaborative Learning network (DCCL) for OKD, which efficiently exploits fruitful knowledge of peer student models by keeping appropriate intermediate cross-layer supervision. Specifically, each student gradually integrates its own features at different layers for feature matching, so as to effectively utilize features in low and high levels for learning more composite knowledge. Moreover, we assign a collaborative knowledge learning strategy, in which a qualified teacher is established via fusing the features of last convolution layers for enhancing high-level representation. In this way, all student models continuously transfer the rich teacher’s internal representation as well as capture its dynamic growth process, and in turn assist the learning of the fusion teacher to further supervise students. In the experiments, our proposed DCCL has shown great generalization ability with various backbone models on CIFAR-100, Tiny ImageNet and ImageNet, and also demonstrated superior performance against mainstream OKD works. Our code is available here: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nanxiaotong/DCCL</uri> .
A Hybrid Federated Learning Framework With Dynamic Task Allocation for Multi-Party Distributed Load Prediction

In distributed load prediction problems, training datasets on load consumption and load-related features are scattered within various districts and parties. To fully exploit the underlying patterns of these decentralized data, federated learning is needed as a privacy-preserving distributed load prediction scheme. However, current federated learning frameworks address either the horizontal or vertical separation of data, and tend to overlook the case where both are present. To this end, we propose a hybrid federated learning framework for multi-party distributed load prediction. We seamlessly integrate horizontal and vertical federated learning to address the hybrid scenario where features are scattered within local heterogeneous parties and samples are scattered within homogeneous districts. Moreover, we develop an optional dynamic task allocation scheme to improve fairness among parties and boost training efficiency. A follow-up case study is presented to justify the necessity of adopting the proposed framework. The advantages of the framework in convergence of training, losslessness, data security, accuracy, generalization performance, information fairness and computational efficiency are also theoretically and/or experimentally confirmed.
Correlation Filtering Tracking Method Based on Unsupervised Domain Adaptive

With the rapid development of society, target tracking has become increasingly integrated into people’s lives. It has become an urgent requirement to improve tracker robustness and success rate as much as possible in order to successfully overcome complex tracking challenges. To address the issue of fast filter model degradation in existing correlation filtering tracking algorithms, we propose a new tracking model based on the ADTrack algorithm and the domain invariant projection algorithm. Improve tracking performance even more. Using various datasets, the performance of the proposed method is demonstrated to be superior to that of state-of-the-art algorithms.
Analysis Regarding The Learning-To-Learn Process In The Implementation Of A Meta-Supervised Algorithm For Few-Shot Learning

Currently, in the efforts of consolidating models based on theories that simulate cognitive and learning human processes, the Meta-Learning paradigm (MeL) has emerged in the Machine Intelligence area. MeL has been applied in combination with other approaches for Machine Learning and Deep-Learning, for instance, to solve new tasks with few samples in the few-shot learning approach. However, to consider MeL as a well-defined learning paradigm aimed at successfully simulating learning human processes, it is essential to find a from to evaluate its learning-to-learn process (LTLP). Metrics reported in the literature to evaluate the LTLP are insufficient and shallow to understand the results reported by MeL-based models. The most common metric used to evaluate and compare MeL-based models is the metric accuracy. Therefore, this work proposes the use of the Centered Kernel Alignment (CKA) metric to evaluate the LTLP of MeL-based models for few-shot learning. The CKA metric helps to quantify how much of the meta-knowledge and meta-experience learned in the meta-training of the meta-models are used to solve new tasks, contributing to the understanding of the LTLP. A MeL methodology is implemented using 9 prior-models in the meta-training of meta-models. In the experiments of the meta-models, the CKA metric is used to analyze similarities between the meta-features obtained by each prior-model. Besides, a proposed meta-supervised algorithm uses the CKA metric to select the meta-parameters to solve new tasks in the base-domain. The results showed CKA scores of 0.674 and 0.799 (these scores mean the level in which the new base-tasks to solve are different from the meta-learned tasks), and accuracies of 92.27% and 99.73%, for solving the new base-tasks using the CUB and Mini-ImageNet datasets, respectively.
Workload Aware Cost-Based Partial Loading of Raw Data for Limited Storage Resources

Modern-day applications generate a large amount of data stored in raw formats. The traditional way of data processing requires the entire raw data files to be loaded into a database management system DBMS. Although traditional DBMS offers optimized query execution on loaded data, the loading process itself is time-consuming. On the other hand, raw data query processing engines suffer from slow query execution times QET even though loading time gets minimized. The paper presents Workload and Storage Aware Cost-based raw data partitioning WSAC technique to improve the QET of frequent queries by loading only target data into the DBMS. The technique considers query workload, storage budget, and loading cost of attributes. WSAC outputs three partitions: (1) Memory budget partition, (2) Remaining workload partition, (3) Unused data partition of the dataset table. Only the first partition is loaded in the DBMS, and two other partitions stay in raw format. The technique is demonstrated using the Sloan Digital Sky Survey SDSS dataset. WSAC improves the storage budget utilization by 5%. It also reduces the workload execution time WET by 95.37% as compared to the original.
Multi-View Auto-Calibration Method Based on Human Pose Estimation

This paper contributes a novel automatic calibration method of a multi-camera system based on human joints. The main difficulty of this problem is the lack of enough known world coordinate points and corresponding two-dimensional image points. Most previous methods address this difficulty by using customized calibration tools, which is tedious due to a lot of manual intervention. In this work, we use human joints as the corresponding points between cameras to deal with this problem. In addition, the proposed method does not need a customized calibration tool, but only requires a person to walk in the calibrated scene, which can reduce the calibration cost greatly. Moreover, the experimental results of binocular and four-camera system indicate that the proposed method outperforms the state of-the-art methods in the case of small public market for both binocular cameras and multi-camera cameras.
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning

How to effectively handle label noise has been one of the most practical but challenging tasks in Deep Neural Networks (DNNs). Recent popular methods for training DNNs with noisy labels mainly focus on directly filtering out samples with low confidence or repeatedly mining valuable information from low-confident samples. However, they cannot guarantee the robust generalization of models due to the ignorance of useful information hidden in noisy data. To address this issue, we propose a new effective method named as LaCoL (Latent Contrastive Learning) to leverage the negative correlations from the noisy data. Specifically, in label space, we exploit the weakly-augmented data to filter samples and adopt classification loss on strong augmentations of the selected sample set, which can preserve the training diversity. While in metric space, we utilize weakly-supervised contrastive learning to excavate these negative correlations hidden in noisy data. Moreover, a cross-space similarity consistency regularization is provided to constrain the gap between label space and metric space. Extensive experiments have validated the superiority of our approach over existing state-of-the-art methods.
Recognition of Modern Modulated Waveforms with Applications to ABMS and VDATS Test Program Set Development

A convenient and well-performing Automatic Modulation Recognition technique for discrimination of numerous modern modulated waveforms found in commercial as well as military communication systems applicable to the new Air Force ABMS system as well as VDATS TPS development is presented. It involves generating complex feature vectors composed of high-order direct cumulant, cyclostationary and Fourier of wavelet transform features created with the help of Principal Component Analysis and variance data compression. Twelve modulated waveforms are used to evaluate the performance of the expanded feature vectors: eight commercial modulated waveforms [Quaternary Amplitude Shift Keying (QASK), Quaternary Frequency Shift Keying (QFSK), Quaternary Phase Shift Keying (QPSK), 16-Point Quadrature Amplitude Modulation (QAM-4,4), Gaussian Minimum Shift Keying (GMSK), Frequency Quadrature Amplitude Modulation (FQAM), Filter Bank Multi Carrier (FBMC) and Universal Filtered Multi Carrier (UFMC)], (Cosine) Binary Offset Carrier - BOC(1,1) - waveforms used in the European Galileo Navigation System and three waveforms utilized in defense military systems [Quaternary Linear Frequency Modulation (QLFM), Quaternary Pulse Width and Pulse Position Modulations (QPWM and QPPM)]. Generated complex feature vectors are categorized by a neural network to compare with corresponding library feature patterns.
Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation

The problem of image change detection via every-day indoor robot navigation is explored from a novel perspective of the self-attention technique. Detecting semantically non-distinctive and visually small changes remains a key challenge in the robotics community. Intuitively, these small non-distinctive changes may be better handled by the recent paradigm of the attention mechanism, which is the basic idea of this work. However, existing self-attention models require significant retraining cost per domain, so it is not directly applicable to robotics applications. We propose a new self-attention technique with an ability of unsupervised on-the-fly domain adaptation, which introduces an attention mask into the intermediate layer of an image change detection model, without modifying the input and output layers of the model. Experiments, in which an indoor robot aims to detect visually small changes in everyday navigation, demonstrate that our attention technique significantly boosts the state-of-the-art image change detection model. Our datset is available at https://github.com/KojiTakeda00/Small_object_change_detection
Blind Recognition of Convolutional Codes: A Matrix Transformation-Aided Deep Learning Approach

This paper focuses on the blind recognition of convolutional codes, a significant research problem in cognitive radios and signal interception. The existing methods based on deep learning (DL) usually directly take the received sequence as the input of a network, of which the recognition accuracy for high-rate convolutional codes is often poor. An identification framework called MT-CNN, combining matrix transformation with convolutional neural networks (CNN), is proposed in this paper. We offer a novel matrix transformation algorithm of which the result can highlight the differences between different encoders. Our proposed MT-CNN method adopts a feature fusion strategy, employing the codeword matrix and its feature map obtained through matrix transformation as the network's input. Simulations show that the proposed approach could provide significant improvements compared to existing methods, especially for the convolutional codes with a high rate.
Protecting On-Chip Data Access Against Timing-Based Side-Channel Attacks on Multicores

Shared hardware resources in multicore processors enable massive performance gains but bring security challenges. Adversaries have successfully leaked on-chip data using timing-based side-channel attacks (SCA) targeting shared caches, network-on-chip, and memory controller hardware. Existing mitigation schemes protect on-chip data against attacks targeting a single hardware resource, while leaving the additional channels unprotected. This paper implements multi-level mitigation schemes that protect on-chip data access against timing-based SCA targeting multiple shared hardware resources. It evaluates the performance implications of individual and multi-level mitigation schemes using security-critical graph and machine learning workloads. The multi-level mitigation schemes are shown to have ~2% overall performance implication that primarily depends on the cache behavior of the workloads.
Optimizing Energy Consumption of Rotor UAV by Path Planning

Rotor Unmanned Aerial Vehicles (UAVs) have been widely used in daily life due to their portability, flexibility, and low cost. However, the limited endurance of rotor UAV becomes the bottleneck in various applications. Optimization of UAV energy consumption by path planning is the primary energy consumption optimization. The energy consumption of rotor UAVs depends not only on the flight distance but also on the speed and the number of turns they take. In this paper, we formulate the rotor UAV path planning as a traveling salesman problem to optimize rotor UAV energy consumption. We propose an improved sine and cosine algorithm based on opposition-based learning (SCAO) to solve the optimization problem.
MD-Net: Multi-Detector for Local Feature Extraction

Establishing a sparse set of keypoint correspondences between images is a fundamental task in many computer vision pipelines. Often, this translates into a computationally expensive nearest neighbor search, where every keypoint descriptor at one image must be compared with all the descriptors at the others. In order to lower the computational cost of the matching phase, we propose a deep feature extraction network capable of detecting a predefined number of complementary sets of keypoints at each image. Since only the descriptors within the same set need to be compared across the different images, the matching phase computational complexity decreases with the number of sets. We train our network to predict the keypoints and compute the corresponding descriptors jointly. In particular, in order to learn complementary sets of keypoints, we introduce a novel unsupervised loss which penalizes intersections among the different sets. Additionally, we propose a novel descriptor-based weighting scheme meant to penalize the detection of keypoints with non-discriminative descriptors. With extensive experiments we show that our feature extraction network, trained only on synthetically warped images and in a fully unsupervised manner, achieves competitive results on 3D reconstruction and re-localization tasks at a reduced matching complexity.
Automated Text Summarization and Topic Detection on News Aggregation System Using BART and SVM

With a large amount of news consumed by the public, it is impossible to digest all the available news. This paper developed an automated text summarization and topic detection algorithm for news articles, allowing the public to read summarized news without losing the essential points of the news. The algorithm will then be used to build and develop a system that has news aggregation technology. First, the system will scrape news articles from various sources, then topic detection and text summarization will be applied to each article before finally being displayed. The methodology used in this research can be divided into data gathering, topic detection, text summarization, and system development. The result of this research shows that the Support Vector Machine performed exceptionally well in topic detection tasks, better than other supervised learning algorithms used in this research, whereas Bidirectional and Auto-Regressive Transformer (BART) with the appropriate parameters performed relatively well in text summarization. To conclude, topic detection and automated text summarization can both be combined and used to develop a news aggregation system, with Support Vector Machine and BART both performing well in their respective tasks.
Detection and Identification of Surface Cover in Coalbed Methane Enrichment Area Based on Spectral Unmixing

Based on the existing geological data and a large amount of research data in the study area, the article compared and analyzed the difference in hyperspectral reflectance between the potential enrichment area and the reference area. The use of Sentinel-2 multispectral images has been investigated to construct the surface recognition model of coalbed methane enrichment areas from two aspects: vegetation covered area and bare areas. Firstly, semi-automatic endmember extraction is implemented by adopting a more pratical spectral un-mixing method. And the vegetation research area and min-eral research area are separated accordingly. In the vegetation research area, the rich red edge information of Sentinel-2 is used to construct an anomaly recognition model. While in bare soil areas, advanced information processing technology is used to extract weak mineral alteration anomalies. The re-sults are validated with field data.