Reviewerly/exHarmony huggingface dataset:
Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models.Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations.We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge.We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian.While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior.We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models.Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.
Adversarial imitation learning with mixed demonstrations from multiple demonstrators

The aim of generative adversarial imitation learning (GAIL) is to allow an agent to learn an optimal policy from demonstrations via an adversarial training process. However, previous works have not considered a realistic setting for complex continuous control tasks such as robot manipulation, in which the available demonstrations are imperfect and possibly originate from different policies. Such a setting poses significant challenges for the application of the GAIL-related methods. This paper proposes a novel imitation learning (IL) algorithm, MD2-GAIL, to enable an agent to learn effectively from imperfect demonstrations by multiple demonstrators. Instead of training the policy from scratch, unsupervised pretraining is used to speed up the adversarial learning process. Confidence scores representing the quality of the demonstrations are utilized to reconstruct the objective function for off-policy adversarial training, making the policy match the optimal occupancy measure. Based on the Soft Actor Critic (SAC) algorithm, MD2-GAIL incorporates the idea of maximum entropy into the process of optimizing the objective function. Meanwhile, a reshaped reward function is adopted to update the agent policy to avoid falling into local optima.Experiments were conducted based on robotic simulation tasks, and the results show that our method can efficiently learn from the available demonstrations and achieves better performance than other state-of-the-art methods.
An Efficient Breast Cancer Analysis Technique by Using a Combination of HOG and Canny Edge Detection Techniques

Breast cancer is a common and frequent cancer among women all over the world and one of the major causes of cancer related deaths. Early and timely detection of breast cancer plays a key role for the proper treatment of the disease. Pathological diagnosis of such cancer is a very important part and if applied in an effective manner would save a lot of lives. The diagnosis includes the analysis of histopathological images of the cancer cells and it is a tedious task and requires a certain level of expertise. This paper proposes a machine learning approach to analyse the histopathological images of breast tissues and shows an improved technique for the detection of malignant (cancerous) cells. Feature extraction from images plays the central role in image processing. A combination of Histograms of oriented gradient (HOG) and Canny Edge detection technique is applied for extracting features which are then reduced by applying Principal component analysis. Classification algorithms like Support Vector Machine (SVM), Logistic Regression (LR) and Adaboost are used to train the proposed model. The experiment shows 94% correct detection of malignant or cancerous cases. Different types of comparisons are shown to identify the suitable method that can help pathologists in the process of breast cancer detection.
Multi-label image recognition with two-stream dynamic graph convolution networks

Recent studies use Graph Convolution Networks (GCN) to model label correlation for multi-label images because of the outstanding performance of GCN in relational modeling tasks. However, the traditional GCN has low generalization, and the current state-of-the-arts' accuracy is poor. Therefore, we propose a Two-Stream Dynamic Graph Convolution Network (2S-DGCN) to improve the performance of multi-label image recognition. In 2S-DGCN, we first obtain the Up Confidence Score of prediction categories (UCS), the content-aware category and the label discriminant vector by a Semantic Attention Module (SAM) and a Dynamic Graph Convolution Network (DGCN) in upstream. Then fed the new graph feature nodes reconstructed by lateral embedding the content-aware category and the label discriminant vector into a DGCN to produce the Down Confidence Score of prediction categories (DCS) in downstream. Finally, the Final Confidence Score of prediction categories (FCS) for multi-label image recognition is synthesized by fusing the UCS and DCS. Extensive experiments on the public multi-label benchmarks achieve mAPs of 85.6% on MS-COCO and 95.4% on VOC 2007. The results of compared experiment and visualization demonstrate that our method has better performance than the current state-of-the-art methods.
Towards Sovereign Games

Video games can be dynamic sovereign spaces for Indigenous representation and expression when the self-determination of Indigenous people is supported. Where games are concerned, self-determination involves the autonomy and right of Indigenous people to make key decisions regarding the process of how a game is developed as well as what that game entails. A game is sovereign when self-determination is a respected practice throughout all phases of development from conceptualization to distribution. In what ways can games be sovereign and what challenges may be faced? Utilizing comparative case studies of the self-determined games Lost Memories, Terra Nova, and When Rivers Were Trails, this research identifies possible approaches and challenges for sovereign games looking at development as well as the resulting designs.
Universal Active Learning via Conditional Mutual Information Minimization

Modern machine learning systems require massive amounts of labeled training data in order to achieve high accuracy rates which is very expensive in terms of time and cost. Active learning is an approach which uses feedback to only label the most informative data points and significantly reduce the labeling effort. Many heuristics for selecting data points have been developed in recent years which are usually tailored to a specific task and a general unified framework is lacking. In this work, a new information theoretic criterion is proposed based on a minimax log-loss regret formulation of the active learning problem. First, a Redundancy Capacity theorem for active learning is derived along with an optimal learner. This leads to a new active learning criterion which naturally induces an exploration - exploitation trade-off in feature selection and generalizes previously proposed heuristic criteria. The new criterion is compared analytically and via empirical simulation to other commonly used information theoretic active learning criteria. Next, the linear hyper-plane hypotheses class with possibly asymmetric label noise is considered. The achievable performance for the proposed criterion is analyzed using a new low complexity greedy algorithm based on the Posterior Matching scheme for communication with feedback. It is shown that for general label noise and bounded feature distribution, the new information theoretic criterion decays exponentially fast to zero.
A Fast Algorithm for Near-Duplicate Image Detection

Due to the popularity of digital image devices, the number of photos has increased dramatically. Because there are many photos with similar contents, the technology of detecting similar image is beneficial to the field of data storage, image retrieval and information security. This paper proposes an improved algorithm for near-duplicate image detection that not only utilizes the global feature of the color histogram, but also take into account of local complexity based on Entropy. It improves the accuracy of near-duplicate image detection without the increase of computing greatly. The experimental results on the image datasets indicate the effectiveness of the proposed algorithm.
3C-GAN: class-consistent CycleGAN for malaria domain adaptation model

Unpaired domain translation models with distribution matching loss such as CycleGAN are now widely being used to shift domain in medical images. However, synthesizing medical images using CycleGAN can lead to misdiagnosis of a medical condition as it might hallucinate unwanted features, especially if theres a data bias. This can potentially change the original class of the input image, which is a very serious problem. In this paper, we have introduced a modified distribution matching loss for CycleGAN to eliminate feature hallucination on the malaria dataset. In the context of the malaria dataset, unintentional feature hallucination may introduce a facet that resembles a parasite or remove the parasite after the translation. Our proposed approach has enabled us to shift the domain of the malaria dataset without the risk of changing their corresponding class. We have presented experimental evidence that our modified loss significantly reduced feature hallucination by preserving original class labels. The experimental results are better in comparison to the baseline (classic CycleGAN) that targets the translating domain. We believe that our approach will expedite the process of developing unsupervised unpaired GAN that is safe for clinical use.
Emotion recognition at the edge with AI specific low power architectures

Nowadays Deep Learning is applied in almost every research field and helps getting amazing results in a great number of challenging tasks. The main problem is that this kind of learning and consequently Neural Networks that can be defined deep, are resource intensive. They need specialized hardware to perform computation in a reasonable time. Many tasks are mandatory to be as much real-time as possible . It is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them “efficient and usable”. All these optimizations can help us to produce incredibly accurate and fast learning models. The paper reports a study in this direction for the challenging face detection and emotion recognition tasks.
Advanced extreme learning machine‐based ensemble classification scheme with enhanced data perturbation for human <scp>DNA</scp> sequences

Abstract The dramatic growth in machine learning has brought in significant features and quantified non‐linear associations in the data derived from sensitive medical datasets. The data should be preserved without influencing the associated classifications by applying a robust, effective and reliable data perturbation technique before enforcing ensemble classification. In this paper, an Integrated Condensation Scheme imposed Privacy Preserving Rotation‐based Data Perturbation and Ensemble Classification (ICS‐PPR‐DPEC) is proposed for ensuring privacy of such sensitive data. Condensation Algorithm‐based Data Perturbation is used for constructing homogenous groups determined from the distance between tuples. It also generates a rotation matrix for conducting perturbation that ensures higher data sensitivity protection before it is sent for classification. Advanced Extreme Learning Machine‐based Ensemble Classification Scheme includes kernel, norm‐optimized and regularized Extreme Learning Machine (ELM)‐based classifiers for attaining predominant classification accuracy in identifying human DNA sequences. This approach facilitates classification by constructing ensembles which are trained through randomly resampled ELM classifiers. It includes an objective function that systematically improves the accuracy and diversity among resulting ensembles. The experimental results of the proposed ICS‐PPR‐DPEC are found to be excellent in terms of classification Accuracy, Precision, Recall, and Kappa statistic when compared to the benchmarked techniques.
Facilitating Data Discovery for Large-scale Science Facilities using Knowledge Networks

Large-scale multiuser scientific facilities, such as geographically distributed observatories, remote instruments, and experimental platforms, represent some of the largest national investments and can enable dramatic advances across many areas of science. Recent examples of such advances include the detection of gravitational waves and the imaging of a black hole's event horizon. However, as the number of such facilities and their users grow, along with the complexity, diversity, and volumes of their data products, finding and accessing relevant data is becoming increasingly challenging, limiting the potential impact of facilities. These challenges are further amplified as scientists and application workflows increasingly try to integrate facilities' data from diverse domains. In this paper, we leverage concepts underlying recommender systems, which are extremely effective in e-commerce, to address these data-discovery and data-access challenges for large-scale distributed scientific facilities. We first analyze data from facilities and identify and model user-query patterns in terms of facility location and spatial localities, domain-specific data models, and user associations. We then use this analysis to generate a knowledge graph and develop the collaborative knowledge-aware graph attention network (CKAT) recommendation model, which leverages graph neural networks (GNNs) to explicitly encode the collaborative signals through propagation and combine them with knowledge associations. Moreover, we integrate a knowledge-aware neural attention mechanism to enable the CKAT to pay more attention to key information while reducing irrelevant noise, thereby increasing the accuracy of the recommendations. We apply the proposed model on two real-world facility datasets and empirically demonstrate that the CKAT can effectively facilitate data discovery, significantly outperforming several compelling state-of-the-art baseline models.
STPAcc: Structural TI-Based Pruning for Accelerating Distance-Related Algorithms on CPU-FPGA Platforms

As a promising solution to boost the performance of distance-related algorithms (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges. In this work, we propose, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STPAcc</i> , an optimization framework based on structural triangle-inequality (TI)-based pruning (STP) for accelerating distance-related algorithms on CPU-FPGA platforms. STPAcc provides a domain-specific language to unify distance-related algorithms effectively, a structural TI-based pruning strategy to remove unnecessary distance computations, a coarse-grained workload partitioning and mapping strategy to fully exploit the potentials of the CPU-FPGA platform, and fine-grained hardware optimizations to further improve performance on the FPGA. Intensive experiments show that STPAcc designs achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$31.42\times $ </tex-math></inline-formula> speedup and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$99.63\times $ </tex-math></inline-formula> better energy efficiency on average over standard CPU-based implementations.
IoTGazePass: A New Password Scheme for IoT Applications

Authentication is one of the most important security aspects to protect user privacy in computers, internet environments and IoT applications. In this regard, the traditional textual-based password type is the most common means of authentication. However, this type of passwords has many disadvantages such as the hardness of remembering for the powerful passwords and easiness of guessing and cracking for the weak passwords. Moreover, it is also vulnerable to brute force, dictionary attacks and social engineering. Another possible alternative to the textual-based password is the graphical-based password. This type of passwords is partly driven by the fact that humans are able to remember and recognize images and colors better than texts. However, such type of passwords is more vulnerable to shoulder surfing attacks. In order to tackle the weaknesses of existing types of passwords, this paper proposes a new password scheme called IoTGazePass scheme, which is intended to be suitable for IoT applications, taking into the account that the new password must be very easy to remember, very difficult to guess, quicker for accessing and consumes minimum resources, as well as it should be random and changeable.
RECOIN

The recent proposed Deformable Convolutional Networks (DCNs)greatly enhance the performance of conventional Convolutional Neural Networks (CNNs) on vision recognition tasks by allowing flexible input sampling during inference runtime. DCNs introduce an additional convolutional layer for adaptive sampling offset generation, followed by a bilinear interpolation (BLI) algorithm to integerize the generated non-integer offset values. Finally, a regular convolution is performed on the loaded input pixels. Compared with conventional CNNs, DCN demonstrated significantly increased computational complexity and irregular input-dependentmemory access patterns, making it a great challenge for deploying DCNs onto edge devices for real-time computer vision tasks. In this work, we propose RECOIN, a processing-in-memory (PIM) architecture, which supports DCN inference on resistive memory (ReRAM)crossbars, thus making the first DCN inference accelerator possible. We present a novel BLI processing engine that leverage both row-and column-oriented computation for in-situ BLI calculation. Amapping scheme and an address converter are particular designed to accommodate the intensive computation and irregular data access. We implement the DCN inference in a 4-stage pipeline and evaluate the effectiveness of RECOIN on six DCN models. Experimental results show RECOIN achieves respectively 225×and 17.4×improvement in energy efficiency compared to general-purpose CPU and GPU. Compared to two state-of-the-art ASIC accelerators, RECOIN achieve 26.8× and 20.4× speedup respectively.
All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers

Combining Natural Language with Vision represents a unique and interesting challenge in the domain of Artificial Intelligence. The AI City Challenge Track 5 for Natural Language-Based Vehicle Retrieval focuses on the problem of combining visual and textual information, applied to a smart-city use case. In this paper, we present All You Can Embed (AYCE), a modular solution to correlate single-vehicle tracking sequences with natural language. The main building blocks of the proposed architecture are (i) BERT to provide an embedding of the textual descriptions, (ii) a convolutional backbone along with a Transformer model to embed the visual information. For the training of the retrieval model, a variation of the Triplet Margin Loss is proposed to learn a distance measure between the visual and language embeddings. The code is publicly available at https://github.com/cscribano/AYCE_2021.
The Impacts of Sentiments and Tones in Community-Generated Issue Discussions

The diverse community members who contribute to the discussions on issue tracking systems of open-source software projects often exhibit complex affective states such as sentiments and tones. These affective states can significantly influence the effectiveness of the issue discussions in elaborating the initial ideas into actionable tasks that the development teams need to address. In this paper, we present an extended empirical study to investigate the impacts of sentiments and tones in community-generated issue discussions. We created and validated a large dataset of sentiments and tones in the issues posts and comments created by diverse community members in three popular open source projects. Our analysis results drew a complex picture of the relationships between, on the one hand, the sentiments and tones in the issue discussions, and on the other hand, various discussion and development-related measures such as the discussion length and the issue resolution time. We also found that when factors such as the issue poster roles and the issue types were controlled, sentiments and tones had varied associations with the measures. Insights gained from these findings can support open source community members in making and moderating effective issue discussions and guide the design of tools to better support community engagement.
Improving Sentiment Classification in Low-Resource Bengali Language Utilizing Cross-Lingual Self-supervised Learning

One of the barriers of sentiment analysis research in low-resource languages such as Bengali is the lack of annotated data. Manual annotation requires resources, which are scarcely available in low-resource languages. We present a cross-lingual hybrid methodology that utilizes machine translation and prior sentiment information to generate accurate pseudo-labels. By leveraging the pseudo-labels, a supervised ML classifier is trained for sentiment classification. We contrast the performance of the proposed self-supervised methodology with the Bengali and English sentiment classification methods (i.e., methods which do not require labeled data). We observe that the self-supervised hybrid methodology improves the macro F1 scores by 15%–25%. The results infer that the proposed framework can improve the performance of sentiment classification in low-resource languages that lack labeled data.
Real time anomalies detection in crowd using convolutional long short-term memory network

Violence is a critical social problem and demands to evaluate through computer vision approaches. At present, the incidences of violent actions get grown in the community, particularly in public places due to several economic and social causes. Moreover, our society’s populations are increasing day by day and it is challenging to keep citizens within limits as well as monitoring human activities in crowd is too hard. Thus, government organizations including local bodies, require examining such occurrences through smart surveillance. In this research, a lightweight computational architecture has been presented to classify non-violent and violent activities. A model has been proposed to extract time-based features using smart devices, high-speed wireless networks and cloud servers to classify real-time human activities. For this purpose, a deep learning-based model is employed to detect violent activities and assist the stakeholders in exposing such activities in real-time. Convolutional long short-term memory (Conv-LSTM) is employed to extend fully connected LSTM (FC-LSTM) to capture the frame and detect violent actions. The proposed model accomplished 95.16% validation accuracy using a standard crowd anomaly dataset.