Reviewerly/exHarmony huggingface dataset:
Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture

Abstract Breast cancer is one of the deadly diseases in women that have raised the mortality rate of women. An accurate and early detection of breast cancer using mammogram images is still a complex task. Hence, this article proposes a novel breast cancer detection model, which included five major phases: (a) preprocessing, (b) segmentation, (c) feature extraction, (d) feature selection, and (e) classification. The input mammogram image is initially preprocessed using contrast limited adaptive histogram equalization (CLAHE) and median filtering. The preprocessed image is then subjected to segmentation via the region growing algorithm. Subsequently, geometric features, texture features and gradient features are extracted from the segmented image. Since the length of the feature vector is large, it is essential to select the optimal features. Here, the selection of optimal features is done by a hybrid optimization algorithm. Once the optimal features are selected, they are subjected to the classification process involving the neural network (NN) classifier. As a novelty, the weight of NN is selected optimally to enhance the accuracy of diagnosis (benign and malignant). The optimal feature selection as well as the weight optimization of NN is accomplished by merging the Lion algorithm (LA) and particle swarm optimization (PSO), named as velocity updated lion algorithm (VU‐LA). Finally, a performance‐based evaluation is carried out between VU‐LA and the existing models like, whale optimization algorithm (WOA), gray wolf optimization (GWO), firefly (FF), PSO, and LA.
Human Activity Recognition From Multi-modal Wearable Sensor Data Using Deep Multi-stage LSTM Architecture Based on Temporal Feature Aggregation

Activity recognition from wearable sensors is a promising field of research with a wide variety of applications to track human activity from distant positions. In this paper, a multi-stage long short term memory (LSTM) based deep neural network is proposed to integrate multimodal features from numerous sensors for activity recognition. In the first stage, for separately extracting effective temporal features from each sensor, an individual stack of LSTM layers are introduced on each sensor data. Afterward, extracted features from numerous sensors are aggregated maintaining their temporal dependency. Finally, for joint optimization of the aggregated multimodal features, a global feature optimizer network is proposed consisting of multiple LSTM layers followed by series of densely connected layers that extracts the global features through the fusion of multimodal features. Extensive experimentations on a publicly available dataset provide very satisfactory performance with an average F1 score of 83.9%.
Target recognition of sport athletes based on deep learning and convolutional neural network

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.
SECONDO: A Platform for Cybersecurity Investments and Cyber Insurance Decisions

This paper represents the SECONDO framework to assist organizations with decisions related to cybersecurity investments and cyber-insurance. The platform supports cybersecurity and cyber-insurance decisions by implementing and integrating a number of software components. SECONDO operates in three distinct phases: (i) cyber-physical risk assessment and continuous monitoring; (ii) investment-driven optimized cyber-physical risk control; and (iii) blockchain-enabled cyber-insurance contract preparation and maintenance. Insurers can leverage SECONDO functionalities to actively participate in the management of cyber-physical risks of a shipping company to reduce their insured risk.
Plain

Unilateral legal contracts, such as terms of service, play a substantial role in modern digital life. However, few read these documents before accepting the terms within, as they are too long and the language too complicated. We propose the task of summarizing such legal documents in plain English, which would enable users to have a better understanding of the terms they are accepting. We propose an initial dataset of legal text snippets paired with summaries written in plain English. We verify the quality of these summaries manually, and show that they involve heavy abstraction, compression, and simplification. Initial experiments show that unsupervised extractive summarization methods do not perform well on this task due to the level of abstraction and style differences. We conclude with a call for resource and technique development for simplification and style transfer for legal language.
GA3C Reinforcement Learning for Surgical Steerable Catheter Path Planning

Path planning algorithms for steerable catheters, must guarantee anatomical obstacles avoidance, reduce the insertion length and ensure the compliance with needle kinematics.The majority of the solutions in literature focuses on graph based or sampling based methods, both limited by the impossibility to directly obtain smooth trajectories.In this work we formulate the path planning problem as a reinforcement learning problem and show that the trajectory planning model, generated from the training, can provide the user with optimal trajectories in terms of obstacle clearance and kinematic constraints.We obtain 2D and 3D environments from MRI images processing and we implement a GA3C algorithm to create a path planning model, able to generalize on different patients anatomies.The curvilinear trajectories obtained from the model in 2D and 3D environments are compared to the ones obtained by A* and RRT* algorithms.Our method achieves state-of-the-art performances in terms of obstacle avoidance, trajectory smoothness and computational time proving this algorithm as valid planning method for complex environments.
KVaC: Key-Value Commitments for Blockchains and Beyond

As blockchains grow in size, validating new transactions becomes more and more resource intensive. To deal with this, there is a need to discover compact encodings of the (effective) state of a blockchain—an encoding that allows for efficient proofs of membership and updates. In the case of account-based cryptocurrencies, the state can be represented by a key-value map, where keys are the account addresses and values consist of account balance, nonce, etc. We propose a new commitment scheme for key-value maps whose size does not grow with the number of keys, yet proofs of membership are of constant-size. In fact, both the encoding and the proofs consist of just two and three group elements respectively (in groups of unknown order like class groups). Verifying and updating proofs involves just a few group exponentiations. Additive updates to key values enjoy the same level of efficiency too. Key-value commitments can be used to build dynamic accumulators and vector commitments, which find applications in group signatures, anonymous credentials, verifiable databases, interactive oracle proofs, etc. Using our new key-value commitment, we provide the most efficient constructions of (sub)vector commitments to date.
Property Invariant Embedding for Automated Reasoning

Automated reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.
An artificial intelligence based model for evaluation of college students’ ability and characteristics through teaching evaluation

The teaching quality is the core of sustainable development in colleges and universities. The constructing scientific and reasonable teaching quality evaluation system is the key of teaching quality evaluation. This paper takes the quality of graduates as the core content, establishes a results-oriented teaching quality evaluation system in colleges and universities, and finds that the academic level of graduates and the level of competition in career selection are two quantitative dimensions reflecting the quality of graduates. Based on this, this paper establishes the evaluation model of the developmental potential index and gives the reference of the norm of teaching effect evaluation for self-evaluation. Finally, this paper discusses the components of graduate quality and the way to construct the evaluation system.
CAT: A CTC-CRF Based ASR Toolkit Bridging the Hybrid and the End-to-End Approaches Towards Data Efficiency and Low Latency

In this paper, we present a new open source toolkit for speech recognition, named CAT (CTC-CRF based ASR Toolkit). CAT inherits the data-efficiency of the hybrid approach and the simplicity of the E2E approach, providing a full-fledged implementation of CTC-CRFs and complete training and testing scripts for a number of English and Chinese benchmarks. Experiments show CAT obtains state-of-the-art results, which are comparable to the fine-tuned hybrid models in Kaldi but with a much simpler training pipeline. Compared to existing non-modularized E2E models, CAT performs better on limited-scale datasets, demonstrating its data efficiency. Furthermore, we propose a new method called contextualized soft forgetting, which enables CAT to do streaming ASR without accuracy degradation. We hope CAT, especially the CTC-CRF based framework and software, will be of broad interest to the community, and can be further explored and improved.
Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning

Spoken language understanding is typically based on pipeline architectures including speech recognition and natural language understanding steps. These components are optimized independently to allow usage of available data, but the overall system suffers from error propagation. In this paper, we propose a novel training method that enables pretrained contextual embeddings to process acoustic features. In particular, we extend it with an encoder of pretrained speech recognition systems in order to construct end-to-end spoken language understanding systems. Our proposed method is based on the teacher-student framework across speech and text modalities that aligns the acoustic and the semantic latent spaces. Experimental results in three benchmarks show that our system reaches the performance comparable to the pipeline architecture without using any training data and outperforms it after fine-tuning with ten examples per class on two out of three benchmarks.
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation

Speech is one of the most effective means of communication and is full of information that helps the transmission of utterer's thoughts. However, mainly due to the cumbersome processing of acoustic features, phoneme or word posterior probability has frequently been discarded in understanding the natural language. Thus, some recent spoken language understanding (SLU) modules have utilized end-to-end structures that preserve the uncertainty information. This further reduces the propagation of speech recognition error and guarantees computational efficiency. We claim that in this process, the speech comprehension can benefit from the inference of massive pre-trained language models (LMs). We transfer the knowledge from a concrete Transformer-based text LM to an SLU module which can face a data shortage, based on recent cross-modal distillation methodologies. We demonstrate the validity of our proposal upon the performance on Fluent Speech Command, an English SLU benchmark. Thereby, we experimentally verify our hypothesis that the knowledge could be shared from the top layer of the LM to a fully speech-based module, in which the abstracted speech is expected to meet the semantic representation.
Traffic Sign Detection And Recognition Using TensorFlow’ s Object Detection API With A New Benchmark Dataset

In this study TensorFlow's Object Detection API used in Turkey traffic sign detection and recognition of the aid intended traffic signs. A dataset suitable for TensorFlow's Object Detection API has been prepared for traffic signs by using photographs in different traffic and weather conditions. A web-based data tagging application has been developed to prepare this dataset. This dataset was trained by selecting the appropriate deep learning model and results were obtained on the test data. Although the training data are insufficient, the results on the test images are satisfactory.
Randomized Gram--Schmidt Process with Application to GMRES

A randomized Gram--Schmidt algorithm is developed for orthonormalization of high-dimensional vectors or QR factorization. The proposed process can be less computationally expensive than the classical Gram--Schmidt process while being at least as numerically stable as the modified Gram--Schmidt process. Our approach is based on random sketching, which is a dimension reduction technique consisting in estimation of inner products of high-dimensional vectors by inner products of their small efficiently computable random images, so-called sketches. In this way, an approximate orthogonality of the full vectors can be obtained by orthogonalization of their sketches. The proposed Gram--Schmidt algorithm can provide computational cost reduction in any architecture. The benefit of random sketching can be amplified by performing the nondominant operations in higher precision. In this case the numerical stability can be guaranteed with a working unit roundoff independent of the dimension of the problem. The proposed Gram--Schmidt process can be applied to Arnoldi iteration and results in new Krylov subspace methods for solving high-dimensional systems of equations or eigenvalue problems. Among them we chose the randomized GMRES method as a practical application of the methodology.
What shapes feature representations? Exploring datasets, architectures, and training

In naturalistic learning problems, a model's input contains a wide range of features, some useful for the task at hand, and others not. Of the useful features, which ones does the model use? Of the task-irrelevant features, which ones does the model represent? Answers to these questions are important for understanding the basis of models' decisions, as well as for building models that learn versatile, adaptable representations useful beyond the original training task. We study these questions using synthetic datasets in which the task-relevance of input features can be controlled directly. We find that when two features redundantly predict the labels, the model preferentially represents one, and its preference reflects what was most linearly decodable from the untrained model. Over training, task-relevant features are enhanced, and task-irrelevant features are partially suppressed. Interestingly, in some cases, an easier, weakly predictive feature can suppress a more strongly predictive, but more difficult one. Additionally, models trained to recognize both easy and hard features learn representations most similar to models that use only the easy feature. Further, easy features lead to more consistent representations across model runs than do hard features. Finally, models have greater representational similarity to an untrained model than to models trained on a different task. Our results highlight the complex processes that determine which features a model represents.
Modeling optical fiber space division multiplexed quantum key distribution systems

We report a model to evaluate the performance of multiple quantum key distribution (QKD) channel transmission using spatial division multiplexing (SDM) in multicore (MCF) and few-mode fibers (FMF).The model is then used to analyze the feasibility of QKD transmission in 7-core MCFs for two scenarios of practical interest.First for transmission of only QKD channels, the second for simultaneous transmission of QKD and classical channels.In the first case, standard homogeneous MCFs enable transmission distances per core compatible with transmission parameters (distance and net key rate) very close to those of single core singlemode fibers.For the second case, heterogeneous MCFs must be employed to make this option feasible.
Analysis and Classification of User Comments on YouTube Videos

We categorize the user comments posted on YouTube video sharing website based on their relevance to the video content given by the description associated with the video posted. Comments are analysed for polarity and are further segregated as positive or negative. A comparative analysis of classifier using the Bag of Words and Association List approaches is presented.
Password-Conditioned Anonymization and Deanonymization with Face Identity Transformers

Cameras are prevalent in our daily lives, and enable many useful systems built upon computer vision technologies such as smart cameras and home robots for service applications. However, there is also an increasing societal concern as the captured images/videos may contain privacy-sensitive information (e.g., face identity). We propose a novel face identity transformer which enables automated photo-realistic password-based anonymization and deanonymization of human faces appearing in visual data. Our face identity transformer is trained to (1) remove face identity information after anonymization, (2) recover the original face when given the correct password, and (3) return a wrong—but photo-realistic—face given a wrong password. With our carefully designed password scheme and multi-task learning objective, we achieve both anonymization and deanonymization using the same single network. Extensive experiments show that our method enables multimodal password conditioned anonymizations and deanonymizations, without sacrificing privacy compared to existing anonymization methods.
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability.

Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users. The Shapley framework for explainability has strength in its general applicability combined with its precise, rigorous foundation: it provides a common, model-agnostic language for AI explainability and uniquely satisfies a set of intuitive mathematical axioms. However, Shapley values are too restrictive in one significant regard: they ignore all causal structure in the data. We introduce a less restrictive framework, Asymmetric Shapley values (ASVs), which are rigorously founded on a set of axioms, applicable to any AI system, and flexible enough to incorporate any causal structure known to be respected by the data. We demonstrate that ASVs can (i) improve model explanations by incorporating causal information, (ii) provide an unambiguous test for unfair discrimination in model predictions, (iii) enable sequentially incremental explanations in time-series models, and (iv) support feature-selection studies without the need for model retraining.
Learning under Model Misspecification: Applications to Variational and Ensemble methods

Virtually any model we use in machine learning to make predictions does not perfectly represent reality. So, most of the learning happens under model misspecification. In this work, we present a novel analysis of the generalization performance of Bayesian model averaging under model misspecification and i.i.d. data using a new family of second-order PAC-Bayes bounds. This analysis shows, in simple and intuitive terms, that Bayesian model averaging provides suboptimal generalization performance when the model is misspecified. In consequence, we provide strong theoretical arguments showing that Bayesian methods are not optimal for learning predictive models, unless the model class is perfectly specified. Using novel second-order PAC-Bayes bounds, we derive a new family of Bayesian-like algorithms, which can be implemented as variational and ensemble methods. The output of these algorithms is a new posterior distribution, different from the Bayesian posterior, which induces a posterior predictive distribution with better generalization performance. Experiments with Bayesian neural networks illustrate these findings.
Dark Experience for General Continual Learning: a Strong, Simple Baseline

Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable. We work towards General Continual Learning (GCL), where task boundaries blur and the domain and class distributions shift either gradually or suddenly. We address it through mixing rehearsal with knowledge distillation and regularization; our simple baseline, Dark Experience Replay, matches the network's logits sampled throughout the optimization trajectory, thus promoting consistency with its past. By conducting an extensive analysis on both standard benchmarks and a novel GCL evaluation setting (MNIST-360), we show that such a seemingly simple baseline outperforms consolidated approaches and leverages limited resources. We further explore the generalization capabilities of our objective, showing its regularization being beneficial beyond mere performance.