Reviewerly/exHarmony huggingface dataset:
An Efficient Identification of Breast Cancer using Linear Discriminant Analysis on Histopathological Images

There is a significant amount of interest in machine learning, and scientists generally examine its application in every possible setting. Breast cancer is one of the diseases that have the highest mortality rate among women. As a result of a delay in the detection of malignant tissues, it is also known as the second biggest cause of mortality in the world. Postmenopausal symptoms, obesity, genes passed down from families, hormonal imbalance, and genetic abnormalities that are not expressed are the most prevalent risk factors for breast cancer. Even though breast cancer is being discovered at a later stage in many underdeveloped nations, death rates remain excessively high. Early detection is essential if we are to bring the mortality toll down and save more lives. Histopathological pictures are the types of data that are taken into consideration by the suggested method. In this article, we will concentrate mostly on distinguishing between benign and malignant forms of cancer. In this investigation, the diagnosis of breast cancer was accomplished by the application of linear discriminant analysis (LDA). The model is evaluated with the assistance of efficiency indicators such as accuracy, sensitivity, and specificity, as well as the F1-score.
A Format Based Smart Object Tracking Model for Enhance the Quality of Digital Images

In general, choosing a format for images is a very important factor when choosing a particular type of material in image searches. This is seen as one of the important findings about RAW and JPEG. However, the general majority of experts can imagine the difference between these two forms. JPEG is a common format for photos, and it's handy. If you send images over the web or print images, most often the files are saved in JPG format. However, questions about image integrity have piled up on JPEGs. Of course, this format is described as the most common image compression format and is important for most digital cameras. But this is technically a lossy transformation that degrades the original parameters of the images. This is where the main problem lies, which format to take and save the images creates the problem. In this paper, a smart format enhanced object tracking model was proposed to improve the attributes of the digital images. The camera is initially programmed to make the file smaller by discarding a few pixels. The compression will be more or less depending on the settings you choose. If the file size is set too large, the camera will reject the minimum data. If you need to fit as many views as possible, you set a lower resolution. The proposed model enhances the objects with high compression ratio and provides the better results.
IceBerg: Deep Generative Modeling for Constraint Discovery and Anomaly Detection

Automatic constraint discovery from a relational database is beneficial for domain experts in fraud detection and intelligent auditing. Its objective is to discover a set of inherent constraints underlying the database such that tuples violating them are considered anomalous. In this paper, we propose Ice-Berg as the first system to simultaneously detect anomalous tuples and discover the associated human-readable constraints. The backbone of IceBerg is a novel generative network, namely KD-VAE, that integrates Kernel Density estimation with Variational AutoEncoder. KD-VAE is expected to learn the distributions of normal tuples. We can perform anomalous data detection by calculating the likelihood that the tuple fits the distributions of normal tuples and abnormality interpretation by comparing the detected anomalous tuples with their generated normal counterparts. We empirically compare the proposed method with several state-of-the-art outlier detection methods on 13 real-world datasets. The results show that IceBerg outperforms its competitors in most cases, especially for complex datasets with high-dimensional features.
Fine-grained analysis of the transformer model for efficient pruning

In automatic speech recognition, deep learning models such as transformers are increasingly used for their high performance. However, they suffer from their large size, which makes it very difficult to use them in real contexts. Hence the idea of pruning them. Conventional pruning methods are not optimal and sometimes not efficient since they operate blindly without taking into account the nature of the layers or their number of parameters or their distribution. In this work, we propose to perform a fine-grained analysis of the transformer model layers in order to determine the most efficient pruning approach. We show that it is more appropriate to prune some layers than others and underline the importance of knowing the behavior of the layers to choose the pruning approach.
Security Enhanced Federated Learning Approach using Blockchain

In traditional machine learning approach, data gathered from all the edge devices are sent to centralized server for training and prediction of the output. In the centralized approach, user has to compromise on the data privacy and integrity in sharing their own data to centralized server. To overcome this issue federated machine learning approach was introduced, in which model and data are decentralized and the machine learning model will be trained on the data in local devices and parameters will be sent to cloud server for consensus change, enhancing the data privacy of the users. But still authentication of the nodes to cloud server and vice versa is a major concern to be addressed in federated machine learning as malicious nodes can impersonate as authenticated node and communicate to cloud server. In the proposed model, node authentication is implemented using Ethereum based blockchain with smart contracts thereby enhancing security of Federated machine learning approach. The efficiency of the node authentication is measured and compared with machine learning algorithms which achieves 99% accuracy.
Email Spam Classification Using Supervised Learning in Different Languages

Communication has grown exponentially in recent times. Current generation envisions email as one of the fastest modes of communication for shorter and longer distances. The constant development of spam email attacks and the harmful go-aheads inherent in these attacks spread across a big variety of business, personal and social activities have created a requirement for an intelligent automated spam detection system. Attempts to facilitate malware, steal sensitive data, steal identity and cause financial and reputational damage are on the rise, putting customers' privacy in danger. Current solutions are rather imperfect considering the multifaceted functional scope of email, but they are considered. The proposed work examined the application of supervised learning to clustering spam and ham emails. With machine learning technology, tools suitable for various languages. Spam detection for Chinese, Russian, Arabic, Italian, French, German, and English emails is developed. Experimental results were compared in terms of different machine learning classification models to propose an optimal solution
CandyRL: A Hybrid Reinforcement Learning Model for Gameplay

Although Reinforcement Learning (RL) is becoming increasingly popular in gameplay, making a generalized RL model is still challenging. This paper presents CandyRL, a generalized RL solution for match-3 games, particularly Candy Crush Friends Saga. CandyRL rewards RL agents not only for achieving the game objectives but also for learning some intrinsic basic skills, which are not directly related to the game objectives. CandyRL also includes a hybrid model that combines several pre-trained agents to determine the actions. We propose two approaches to determine the weights of the pre-trained agents in the hybrid model to reflect their importance by using (i) a heuristic method and (ii) an RL-based approach. We show that the hybrid model outperforms all the pre-trained agents used as its building blocks through the experiments. Moreover, the RL-based approach of learning weights in the hybrid model shows better generalization than the other approaches as it performs better on unseen new game levels.
Cluster Management of Scientific Literature in HSTOOL

In this paper, we expand a methodology for horizon scanning of scientific literature to discover scientific trends. In this methodology, scientific articles are automatically clustered within a broadly defined field of research based on the topic. We develop a new method to allow an analyst to handle the large number of clusters that result from the automatic clustering of articles. The method is based on estimating an information-theoretical distance between all possible pairs of clusters. Each of the scientific articles has a probability distribution of affiliation over all possible clusters arising from the clustering process. Using these, we investigate possible pairwise mergers between all pairs of existing clusters and calculate the entropies of the probability distributions of all articles after each possible merger of two clusters. These entropies are visualized in a dendritic tree and a cluster graph. The merger with minimal total entropy is the proposed cluster pair to be merged.
TranSQL: A Transformer-based Model for Classifying SQL Queries

Domain-Specific Languages (DSL) are becoming popular in various fields as they enable domain experts to focus on domain-specific concepts rather than software-specific ones. Many domain experts usually reuse their previously-written scripts for writing new ones; however, to make this process straightforward, there is a need for techniques that can enable domain experts to find existing relevant scripts easily. One fundamental component of such a technique is a model for identifying similar DSL scripts. Nevertheless, the inherent nature of DSLs and lack of data makes building such a model challenging. Hence, in this work, we propose TRANSQL, a transformer-based model for classifying DSL scripts based on their similarities, considering their few-shot context. We build TRANSQL using BERT and GPT-3, two performant language models. Our experiments focus on SQL as one of the most commonly-used DSLs. The experiment results reveal that the BERT-based TRANSQL cannot perform well for DSLs since they need extensive data for the fine-tuning phase. However, the GPT-based TRANSQL gives markedly better and more promising results.
A Large Scale Document-Term Matching Method Based on Information Retrieval

With the increasing popularity of digital documents in the information era, large scale document-term matching is critical for document similarity tasks. However, the traditional automatic term matching method is mainly from the perspective of classification, which ignores the macroscopic and ambiguous characteristics of domain terms. This paper proposes a document-term matching method based on information retrieval (IR-DTM). We utilized an automatic data collection method, which can effectively alleviate the problem of insufficient data and greatly reduce the manual workload. Then, we realized semantic matching between macro terms and micro documents by extending the words in terms, thereby effectively improving the accuracy of document-term matching. Experimental results show that our method can perform effective automatic matching of domain terms and case documents. The effectiveness of IR-DTM is evaluated by R@1, R@5, and R@10. The R@10 percentage points on PolProv1.0, EcoProv1.0, and TechProv1.0 datasets can reach 84.6%, 92.5% and 93.2% respectively.
Can We Predict Consequences of Cyber Attacks?

Threat modeling is a process by which security designers and researchers analyze the security of a system against known threats and vulnerabilities. There is a myriad of threat intelligence and vulnerability databases that security experts use to make important day-to-day decisions. Security experts and incident responders require the right set of skills and tools to recognize attack consequences and convey them to various stakeholders. In this paper, we used natural language processing (NLP) and deep learning to analyze text descriptions of cyberattacks and predict their consequences. This can be useful to quickly analyze new attacks discovered in the wild, help security practitioners take requisite actions, and convey attack consequences to stakeholders in a simple way. In this work, we predicted the multilabels (availability, access control, confidentiality, integrity, and other) corresponding to each text description in MITRE’s CWE dataset. We compared the performance of various CNN and LSTM deep neural networks in predicting these labels. The results indicate that it is possible to predict multilabels using a LSTM deep neural network with multiple output layers equal to the number of labels. LSTM performance was better when compared to CNN models.
Pose Estimation for Future Prediction of Falling

In this paper, we propose the use of various deep learning and AI techniques to evaluate the detection and prediction of an individual falling taken from images or video. Falls are a serious issue within the medical sector. While falls are often associated with senior centers and retirement homes, falls are responsible for approximately 37.3 million injuries severe enough for medical attention. The goal of this paper is to utilize human pose estimation to detect and predict falls that may occur.
Imitation from Observation using RL and Graph-based Representation of Demonstrations

Teaching robots behavioral skills by leveraging examples provided by an expert, also referred to as Imitation Learning from Observation (IfO or ILO), is a promising approach for learning novel tasks without requiring a task-specific reward function to be engineered. We propose a RL-based framework to teach robots manipulation tasks given expert observation-only demonstrations. First, a representation model is trained to extract spatial and temporal features from demonstrations. Graph Neural Networks (GNNs) are used to encode spatial patterns, while LSTMs and Transformers are used to encode temporal features. Second, based on an off-the-shelf RL algorithm, the demonstrations are leveraged through the trained representation to guide the policy training towards solving the task demonstrated by the expert. We show that our approach compares favorably to state-of-the-art IfO algorithms with a 99% success rate and transfers well to the real world.
On the Trade-off Between Benefit and Contribution for Clients in Federated Learning in Healthcare

Federated Learning (FL) is a learning paradigm that allows clients to profit from the data that is available across multiple clients to train a joint model. As FL allows to train such a joint model without explicitly sharing data, but only sharing model updates, it has attained popularity in healthcare settings where patient data is subject to strict privacy policies and needs to be locally stored at each hospital or healthcare provider. A particular challenge for FL settings is data imbalance across clients, as it has been found to be detrimental to model performance and impact the influence of each client on the learning process. Unfortunately, the healthcare domain is particularly prone to such imbalanced data due to regional differences in disease management, prescription behavior etc. In this paper, we introduce the two novel metrics Benefit and Contribution to quantify to which degree individual clients benefit from participation in FL and how they contribute to its success, respectively. Therefore, we measure Benefit and Contribution with respect to four types of imbalances present in data at each client side. Our results show that both client Benefit and Contribution are influenced by data imbalance in such a way that high imbalance in data quantity, label distribution and feature distribution reduces or nullifies clients’ Benefit while increasing their Contribution. Thus, the most valuable clients within a cohort benefit the least from their participation, exposing a critical thread to the success of clinical FL cohorts by withdrawing participation.
Identifying Metering Hierarchies with Distance Correlation and Dominance Constraints

In this paper, we consider observations from a series of smart meters that are either completely or partially aggregated, and our aim is to estimate the metering hierarchy. We propose to estimate this important metadata through a novel adaptation of the Chow–Liu tree learning procedure. Our approach takes into account prior knowledge from a set of dominance conditions that are easily elicited from the consumption data. In addition to more traditional correlation-based approaches we also introduce a distance-correlation-based method for detecting edges. Synthetic experiments show the benefits of distance correlation and the dominance conditions in recovering tree structure. The paper concludes with a real-world application of the method to infer energy metering hierarchies in a library building.
Deep Neural Network Piration without Accuracy Loss

A deep neural network (DNN) classifier is often viewed as the intellectual property of a model owner due to the huge resources required to train it. To protect intellectual property, the model owner can embed a watermark into the DNN classifier (called target classifier) such that it outputs pre-determined labels (called trigger labels) for pre-determined inputs (called trigger inputs). Given the black-box access to a suspect classifier, the model owner can verify whether the suspect classifier is pirated version of its classifier by first querying the suspect classifier for trigger inputs and then checking whether the predicted labels match with the trigger labels. Many studies showed that an attacker can pirate the target classifier (called pirated classifier) via retraining or fine-tuning the target classifier to remove its watermark. However, they sacrifice the accuracy of the pirated classifier, which is undesired for critical applications such as finance and healthcare. In our work, we propose a new attack without sacrificing the accuracy of the pirated classifier for in-distribution testing inputs while preventing the detection from the model owner. Our idea is that an attacker can detect the trigger inputs in the inference stage of the pirated classifier. In particular, given a testing input, we let the pirated classifier return a random label if the input is detected as a trigger input. Otherwise, the pirated classifier predicts the same label as the target classifier. We evaluate our attack on benchmark datasets and find that our attack can effectively identify the trigger inputs. Our attack reveals that the intellectual property of a model owner can be violated with existing watermarking techniques, highlighting the need for new techniques.
Deep Contrastive Anomaly Detection for Airline Ancillaries Prediction

The increasing range of ancillary products offered by airlines is making existing, static frameworks obsolete. The changing expectations of customers have created a need for more dynamic and reactive offers. In order to tailor an offer to an individual journey, it is possible to leverage similar journeys and the observed outcomes using a semi-supervised approach. In this paper, a multi-stage deep learning framework, namely Deep Ancillaries Prediction (DAP), is developed to understand personalised demand for airline ancillaries and enhance pricing strategies. DAP solves the overlapping distribution problem and class imbalances observed in real-world airline datasets. The framework incorporates a contrastive learning module to learn richer feature embeddings and an autoencoder for semi-supervised learning into one framework, and outperforms current ancillary prediction systems. The modules can be trained separately and hence, are suitable for an online learning setting. This framework is designed to be transferable to different prediction tasks. Significant performance enhancements are attained compared to the current state-of-the-art algorithms.
Bad Citrus: Reducing Adversarial Costs with Model Distances

Recent work by Jia et al. [1], showed the possibility of effectively computing pairwise model distances in weight space, using a model explanation technique known as LIME. This method requires query-only access to the two models under examination. We argue this insight can be leveraged by an adversary to reduce the net cost (number of queries) of launching an evasion campaign against a deployed model. We show that there is a strong negative correlation between the success rate of adversarial transfer and the distance between the victim model and the surrogate used to generate the evasive samples. Thus, we propose and evaluate a method to reduce adversarial costs by finding the closest surrogate model for adversarial transfer.
Edge utilization in graph convolutional networks for graph classification

Graph convolutional neural networks are designed to apply convolutional operations directly on non-Euclidean structure graph data, generating orderly arranged matrix representations of graphs. However, only node features are fully exploited even though edge features may also play an important role in some domains such as chemoinformatics. In this paper, we proposed two new approaches of utilizing edge features on graph convolutional neural networks, Feature embedding adjacent matrix and Reverse graph. Methodologies of basic graph convolutional neural networks only tend to propagate node features to neighbor nodes along edges by convolutional operations. By applying Feature embedding adjacent matrix, edge features are synthesized into node features and also propagated to neighbor nodes during propagation process. Reverse graph approach builds a special auxiliary graph to propagate edge features to neighbor edges. Therefore, a synthetical presentation including both edge features and node features is built. Experiments demonstrated our new approaches improve graph classification accuracies, especially on data sets with low accuracies on basic GCNs.
Understanding Linguistic Variations in Neutral and Strongly Opinionated Reviews

Reviews with a user rating close to the center of the rating scale are often referred to as neutral reviews and are prevalent in consumer feedback. By leveraging annotated data, implicit characteristics of neutral reviews can be learned for a better prediction. In case of the absence of annotated data, often, unsupervised lexicon-based approaches are employed. Nevertheless, word-level sentiment and hand-crafted aggregation rules of lexicon-based are usually inadequate for distinguishing neutral reviews. Therefore, in this study, we try to find additional distinguishing signals for identifying neutral reviews. We investi-gate a number of attributes, such as the frequency of contrasting conjunctions, extreme opinions, intensifiers, modifiers, and negation, to discover distinctive elements in neutral reviews. We find that some linguistic features, such as contrasting conjunctions and mitigators can provide additional signals that may help to distinguish neutral reviews across multi-domain datasets. Our analysis and findings deliver insights for developing effective unsupervised methods for discerning different types of reviews.