Reviewerly/exHarmony huggingface dataset:
Neural Network Identifiability for a Family of Sigmoidal Nonlinearities

Abstract This paper addresses the following question of neural network identifiability: Does the input–output map realized by a feed-forward neural network with respect to a given nonlinearity uniquely specify the network architecture, weights, and biases? The existing literature on the subject (Sussman in Neural Netw 5(4):589–593, 1992; Albertini et al. in Artificial neural networks for speech and vision, 1993; Fefferman in Rev Mat Iberoam 10(3):507–555, 1994) suggests that the answer should be yes, up to certain symmetries induced by the nonlinearity, and provided that the networks under consideration satisfy certain “genericity conditions.” The results in Sussman (1992) and Albertini et al. (1993) apply to networks with a single hidden layer and in Fefferman (1994) the networks need to be fully connected. In an effort to answer the identifiability question in greater generality, we derive necessary genericity conditions for the identifiability of neural networks of arbitrary depth and connectivity with an arbitrary nonlinearity. Moreover, we construct a family of nonlinearities for which these genericity conditions are minimal , i.e., both necessary and sufficient . This family is large enough to approximate many commonly encountered nonlinearities to within arbitrary precision in the uniform norm.
Towards the automatic data annotation for human activity recognition based on wearables and BLE beacons

In machine learning, the data annotation process is an essential, but error-prone and time-consuming manual activity, which associates metadata to the samples of a dataset. In the context of Human Activity Recognition (HAR) this generally reflects in a human watching the video recordings of the activities carried out by the target user to assign a label to each video frame. The label can refer, for example, to the nature of the performed activity, or to the time series collected through sensors worn by the user or present in the environment. This paper deals with the automation of the data annotation process in the HAR context by presenting a methodology that (i) maps Bluetooth Low Energy (BLE) beacons distributed in the environment to the locations where a human typically performs activities like eating, cooking, working, and resting, and (ii) associates the data collected by sensors embedded in the smartwatch worn by the user (i.e., acceleration, angular velocity, and magnetometer) to the nearest BLE beacon. In this way, data gathered through the smartwatch are automatically annotated with the human activity associated to the nearest beacon.
LSTM RNN: detecting exploit kits using redirection chain sequences

Abstract While consumers use the web to perform routine activities, they are under the constant threat of attack from malicious websites. Even when visiting ‘trusted’ sites, there is always a risk that site is compromised, and, hosting a malicious script. In this scenario, the injected script would typically force the victim’s browser to undergo a series of redirects before reaching an attacker-controlled domain, which, delivers the actual malware. Although these malicious redirection chains aim to frustrate detection and analysis efforts, they could be used to help identify web-based attacks. Building upon previous work, this paper presents the first known application of a Long Short-Term Memory (LSTM) network to detect Exploit Kit (EK) traffic, utilising the structure of HTTP redirects. Samples are processed as sequences, where each timestep represents a redirect and contains a unique combination of 48 features. The experiment is conducted using a ground-truth dataset of 1279 EK and 5910 benign redirection chains. Hyper-parameters are tuned via K-fold cross-validation (5f-CV), with the optimal configuration achieving an F1 score of 0.9878 against the unseen test set. Furthermore, we compare the results of isolated feature categories to assess their importance.
Multiple slit interference: a hyperbola based analysis

Abstract The textbook description of multiple slit interference employs a standard path difference formula that was originally calculated for the purpose of analyzing Young’s double slit experiment. It was arrived at on the basis of a pair of assumptions that help simplify the geometry of the slit-barrier-screen arrangement and the ensuing formalism needed to estimate the positions of interference fringes on the detection screen. According to the conventional approach, convergent rays of light that emanate from different slits can be treated as effectively parallel in the far field limit. Such a parallel ray approximation-based analysis was shown by Thomas (2019; 2020) to be redundant, paradoxical and limited in accuracy. In this paper, the task of reformulation that began previously with the elementary two-slit experiment is now extended to encompass the N -slit scenario, diffraction at a single-slit and the formation of circular fringes. In order to derive one of the key results (the generalized hyperbola theorem for N -point sources), a novel computational technique called analytical induction is introduced. The procedure involves a synthesis of concepts from Cartesian geometry and discrete mathematics, namely coordinate transformation by Euclidean translation and a method of proof called the principle of mathematical induction, respectively. The final goal attained are two distribution functions that succinctly and precisely describe the variation of intensity of interference fringes on the distant screen, when it is oriented parallel and perpendicular relative to the line joining the sources. A comparison is drawn between the predictions of the conventional and the new analyses by means of numerical-graphical simulation with MATLAB. The theoretical methods and results presented herein may have a significant bearing in areas of applied optics like interferometry and diffraction crystallography. On the pedagogic front, it is suggested that the current geometrical program being visually more intuitive, be incorporated into the standard curriculum of an advanced undergraduate/graduate level course in physical optics, to complement the conventional approach.
Oblique predictive clustering trees

Predictive clustering trees (PCTs) are a well-established generalization of standard decision trees, which can be used to solve a variety of predictive modeling tasks, including structured output prediction. Combining them into ensembles of PCTs yields state-of-the-art performance. However, they scale poorly to problems with high-dimensional output spaces and cannot exploit sparsity in data. Both of these issues are typically highlighted in (hierarchical) multi-label classification tasks, where the output can consist of hundreds of labels (high dimensionality), among which only a few are relevant for each example (sparsity). Sparsity is also often encountered in the input space (molecular fingerprints, bag-of-words representations, etc.). In this paper, we propose oblique predictive clustering trees capable of addressing these limitations. We design and implement two methods for learning oblique splits that contain linear combinations of features in the tests, hence a split corresponds to an arbitrary hyperplane in the input space. The resulting oblique trees are efficient for high-dimensional data and are capable of exploiting sparse data. We experimentally evaluate the proposed methods on 60 benchmark datasets for 6 predictive modeling tasks. The results of the experiments show that oblique predictive clustering trees achieve performance on par with state-of-the-art methods and are orders of magnitude faster than standard predictive clustering trees. We also show that meaningful feature importance scores can be extracted from the models learned with the proposed methods.
Deep kernel processes

We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distributions. Remarkably, we find that deep Gaussian processes (DGPs), Bayesian neural networks (BNNs), infinite BNNs, and infinite BNNs with bottlenecks can all be written as deep kernel processes. For DGPs the equivalence arises because the Gram matrix formed by the inner product of features is Wishart distributed, and as we show, standard isotropic kernels can be written entirely in terms of this Gram matrix (we do not need knowledge of the underlying features). We define a tractable deep kernel process, the deep inverse Wishart process and give a doubly-stochastic inducing-point variational inference scheme that operates on the Gram matrices, not on the features (as in DGPs). We show that the deep inverse Wishart process gives superior performance to DGPs and infinite BNN on standard fully-connected baselines.
Parallel polynomial multiplication optimized scheme for CRYSTALS-KYBER Post-Quantum Cryptosystem based on FPGA

With the development of quantum information theory, classical cryptographical schemes will be threatened. CRYSTALS-KYBER (KYBER) is a kind of lattice-based post quantum cryptographical algorithms which can resist the quantum attack to a large extent. The polynomial multiplication is the most computationally intensive operation in the KYBER algorithm. In this paper, we proposed a Number Theoretic Transformation (NTT) implementation strategy to speed up the polynomial multiplication in FPGA platform. We optimized the structure by adopting a parallel approach, this can further reduce the NTT iteration time and achieve better performance.
Vote-Based: Ensemble Approach

Vote-based is one of the ensembles learning methods in which the individual classifier is situated on numerous weighted categories of the training datasets. In designing a method, training, validation and test sets are applied in terms of an ensemble approach to developing an efficient and robust binary classification model. Similarly, ensemble learning is the most prominent and broad research area of Machine Learning (ML) and image recognition, which assists in enhancing the capability of performance. In most cases, the ensemble learning algorithm yields better performance than ML algorithms. Unlike existing methods, the proposed technique aggregates an ensemble classifier, known as vote-based, to employ and integrate the advantage of ML classifiers, which are Artificial Neural Network (ANN), Naive Bayes (NB) and Logistic Model Tree (LMT). This paper proposes an ensemble framework that aims to evaluate datasets from the UCI ML repository by adopting performance analysis. Furthermore, the experimental outcomes indicate that the proposed method provides more accurate results according to the base learner approaches in terms of accuracy rates, an area under the curve (AUC), precision, recall, and F-measure values.
Learning Syntax from Naturally-Occurring Bracketings

Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries.Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsupervised constituency parsing.But they are noisy and incomplete; to address this challenge, we develop a partial-brackets-aware structured ramp loss in learning.Experiments demonstrate that our distantly-supervised models trained on naturally-occurring bracketing data are more accurate in inducing syntactic structures than competing unsupervised systems.On the English WSJ corpus, our models achieve an unlabeled F1 score of 68.9 for constituency parsing. 1
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation

Documentation of endangered languages (ELs) has become increasingly urgent as thousands of languages are on the verge of disappearing by the end of the 21st century.One challenging aspect of documentation is to develop machine learning tools to automate the processing of EL audio via automatic speech recognition (ASR), machine translation (MT), or speech translation (ST).This paper presents an open-access speech translation corpus of Highland Puebla Nahuatl (glottocode high1278), an EL spoken in central Mexico.It then addresses machine learning contributions to endangered language documentation and argues for the importance of speech translation as a key element in the documentation process.In our experiments, we observed that state-of-the-art end-to-end ST models could outperform a cascaded ST (ASR > MT) pipeline when translating endangered language documentation materials.
Machine Unlearning for Random Forests

Responding to user data deletion requests, removing noisy examples, or deleting corrupted training data are just a few reasons for wanting to delete instances from a machine learning (ML) model. However, efficiently removing this data from an ML model is generally difficult. In this paper, we introduce data removal-enabled (DaRE) forests, a variant of random forests that enables the removal of training data with minimal retraining. Model updates for each DaRE tree in the forest are exact, meaning that removing instances from a DaRE model yields exactly the same model as retraining from scratch on updated data. DaRE trees use randomness and caching to make data deletion efficient. The upper levels of DaRE trees use random nodes, which choose split attributes and thresholds uniformly at random. These nodes rarely require updates because they only minimally depend on the data. At the lower levels, splits are chosen to greedily optimize a split criterion such as Gini index or mutual information. DaRE trees cache statistics at each node and training data at each leaf, so that only the necessary subtrees are updated as data is removed. For numerical attributes, greedy nodes optimize over a random subset of thresholds, so that they can maintain statistics while approximating the optimal threshold. By adjusting the number of thresholds considered for greedy nodes, and the number of random nodes, DaRE trees can trade off between more accurate predictions and more efficient updates. In experiments on 13 real-world datasets and one synthetic dataset, we find DaRE forests delete data orders of magnitude faster than retraining from scratch while sacrificing little to no predictive power.
Significant Association Rule Mining with High Associability

Traditional support-confidence framework based association rule mining approaches often generate huge number of rules including the insignificant ones. These insignificant association rules are useless in knowledge discovery. One of the main reasons behind the generation of insignificant association rules is the use of confidence as the lonely interestingness measure in defining rule significance. Confidence only expresses the strength of the probability between the antecedent and consequent itemsets andignores their associability. Associability refers to closeness between the antecedent and consequent itemsets of an association rule. Assessment of associability against dissociation is an effective technique in this respect. Traditional approaches follow rule pruning with fixed maximum dissociation threshold. In reality, association rules formed with large antecedent or consequent possess high dissociation by nature. The fact leads to lose of valuable association rules. To solve the problems, this paper introduces the concept of flexible dissociation and thereafter a novel framework of significant association rule mining with high associability. Rule pruning with flexible dissociation and confidence ensures high associability of the rules. Experimental studies on real world datasets also show the effectiveness of the proposed approach.
Nonlinear mathematical model and numerical algorithm for monitoring and predicting the concentration of harmful substances in the atmosphere

The article developed a nonlinear mathematical model and an effective numerical algorithm for monitoring and forecasting the process of spreading industrial emissions in the atmosphere, taking into account the orography of the area and the speed of movement of fine substances. Multidimensional partial differential equations describe the model with corresponding initial, internal, and boundary conditions of the third kind to consider mass transfer across the interface. In the work, to prove the adequacy of the developed mathematical apparatus, the calculated data are compared with field data taken from a specific object, in which the computational algorithm provides sufficient accuracy of the solution, based on which it can be concluded that the developed mathematical model and the numerical algorithm have a certain advantage over others numerical methods.
On the Generation of Rank 3 Simple Matroids with an Application to Terao's Freeness Conjecture

In this paper we describe a parallel algorithm for generating all non-isomorphic rank $3$ simple matroids with a given multiplicity vector. We apply our implementation in the HPC version of GAP to generate all rank $3$ simple matroids with at most $14$ atoms and a splitting characteristic polynomial. We have stored the resulting matroids alongside with various useful invariants in a publicly available, ArangoDB-powered database. As a byproduct we show that the smallest divisionally free rank $3$ arrangement which is not inductively free has $14$ hyperplanes and exists in all characteristics distinct from $2$ and $5$. Another database query proves that Terao's freeness conjecture is true for rank $3$ arrangements with $14$ hyperplanes in any characteristic.
Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency

Machine learning models and recommender systems play a crucial role in web applications, providing personalized experiences to each customer. Recurring visits of the same customer raise a nontrivial question about the persistence of the experience. Given a changing user context, alongside online algorithms that update over time, the optimal treatment might differ from past model decisions. However, changing customer experience may create inconsistency and harm customer satisfaction and business process completion.
Batch Value-function Approximation with Only Realizability

We make progress in a long-standing problem of batch reinforcement learning (RL): learning $Q^\star$ from an exploratory and polynomial-sized dataset, using a realizable and otherwise arbitrary function class. In fact, all existing algorithms demand function-approximation assumptions stronger than realizability, and the mounting negative evidence has led to a conjecture that sample-efficient learning is impossible in this setting (Chen and Jiang, 2019). Our algorithm, BVFT, breaks the hardness conjecture (albeit under a stronger notion of exploratory data) via a tournament procedure that reduces the learning problem to pairwise comparison, and solves the latter with the help of a state-action partition constructed from the compared functions. We also discuss how BVFT can be applied to model selection among other extensions and open problems.
ReSPEcT: privacy respecting thermal-based specific person recognition

Video analytic techniques have been used to extract high level information from video streams. The technique leverages advances on machine learning to summarize complex image data into simple alert-signal to attract the attention of human operators. For example, in a station for the underground video analytic can help the operator to focus on an event from a specific camera rather than leaving this only to the human eye. A concern of such techniques is privacy as they expose people identity and enable profiling of personal habits and orientations. This work introduces ReSPEcT (Privacy Respecting theRmal basEd Specific Person rECogniTion), a privacy preserving video analytic system based on thermal video streams. ReSPEcT is able to identify a specific-human in thermal video streams from low-cost, low resolution cameras. The system leverages recent advances in machine learning (CNNs) and a plethora of pre-processing mechanisms, such as image automatic labeling, image segmentation, and image augmentation to reduce the stream background noise, improve resilience, strengthen human-body classification, and finally enable a specific human-target identification. ReSPEcT’s automatic labeling tool significantly reduces time thus automatically performing labeling using a model that can be retrained by an interactive web application. The experimental evaluation shows that overall ReSPEcT achieve 96.83% accuracy in identifying a specific person. Furthermore, is important to notice that while ReSPEcT can identify a specific human, the tool is not aware of the realidentity as it operates only on thermal images. ReSPEcT paves the way to use video analytic in a variety of privacyprotected scenarios, such as confidential meetings, sensitive spaces, or even public toilets.
Thyroid Disorder Diagnosis by Optimal Convolutional Neuron based CNN Architecture

The diagnosis of thyroid via appropriate interpretation of thyroid data is the vital classification issue. Only little contributions are made so far in the automatic diagnosis of thyroid disease. In order to solve Thyroid disorder this paper intends to propose a new thyroid diagnosis model, utilising two-phases includes Feature Extraction and Classification. In the first phase, two sorts of features are extracted that include image features like neighbourhood-based and gradient features, and Principal Component Analysis (PCA) is used to extract the data features as well. Subsequently, two sorts of classification processes are performed. Specifically, Convolutional Neural Network (CNN) is used for image classification by extracting deep features. Neural Network (NN) is used for classifying the disease by obtaining both the image and data features as the input. Finally, both the classified results (CNN and NN) are combined to increase the accuracy rate of diagnosis. Further, as the main aim of this work is to increase the accuracy rate, this paper aims to trigger the optimisation concept. The convolutional layer of CNN is optimally selected, and while classifying under NN the given features should be the optimal one. Hence, the required features are optimally selected. For these optimisations, a new modified algorithm is proposed in this work namely Worst Fitness-based Cuckoo Search (WF-CS) which is the modified form of Cuckoo Search Algorithm (CS). Finally, the performance of proposed WF-CS is compared over other conventional methods like Conventional CS, Genetic Algorithm (GA), FireFly (FF), Artificial Bee Colony (ABC), and Particle Swarm Optimisation (PSO) and proves the superiority of proposed work in detecting the presence of thyroid.