Reviewerly/exHarmony huggingface dataset:
DiCENet: Dimension-wise Convolutions for Efficient Networks

We introduce a novel and generic convolutional unit, DiCE unit, that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor. The DiCE unit is simple and can be seamlessly integrated with any architecture to improve its efficiency and performance. Compared to depth-wise separable convolutions, the DiCE unit shows significant improvements across different architectures. When DiCE units are stacked to build the DiCENet model, we observe significant improvements over state-of-the-art models across various computer vision tasks including image classification, object detection, and semantic segmentation. On the ImageNet dataset, the DiCENet delivers 2-4 percent higher accuracy than state-of-the-art manually designed models (e.g., MobileNetv2 and ShuffleNetv2). Also, DiCENet generalizes better to tasks (e.g., object detection) that are often used in resource-constrained devices in comparison to state-of-the-art separable convolution-based efficient networks, including neural search-based methods (e.g., MobileNetv3 and MixNet).
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems

Automatic speech recognition (ASR) systems can be fooled via targeted adversarial examples, which induce the ASR to produce arbitrary transcriptions in response to altered audio signals. However, state-of-the-art adversarial examples typically have to be fed into the ASR system directly, and are not successful when played in a room. Previously published over-the-air adversarial examples fall into one of three categories: they are either handcrafted examples, they are so conspicuous that human listeners can easily recognize the target transcription once they are alerted to its content, or they require precise information about the room where the attack takes place, and are hence not transferable to other rooms.
Gaussian mixture model with feature selection: An embedded approach

Gaussian Mixture Model (GMM) is a popular clustering algorithm due to its neat statistical properties, which enable the “soft” clustering and the determination of the number of clusters. Expectation-Maximization (EM) is usually applied to estimate the GMM parameters. While promising, the inclusion of features that are not contributing to clustering may confuse the model and increase computational cost. Recognizing the issue, in this paper, we propose a new algorithm, termed Expectation Selection Maximization (ESM), by adding a feature selection step (S). Specifically, we introduce a relevancy index (RI), a metric indicating the probability of assigning a data point to a specific clustering group. The RI index reveals the contribution of the feature to the clustering process thus can assist the feature selection. We conduct theoretical analysis to justify the use of RI for feature selection. Also, to demonstrate the efficacy of the proposed ESM, two synthetic datasets, four benchmark datasets, and an Alzheimer’s Disease dataset are studied.
Comparative Performance Analysis of Lightweight Cryptography Algorithms for IoT Sensor Nodes

The Internet of Things (IoT) has become an integral part of future solutions, ranging from industrial to everyday human life applications. Adding a new level of intelligence to objects and automating decisions make this new technology appealing to everyone. However, applications that involve data are more vulnerable to various types of attacks. As a result, researchers are constantly exploring secure connections between IoT edge nodes. On one hand, suitable IoT nodes should be cheap and require low power, which means lower computational performance. On the other hand, a secure connection layer is power hungry and requires powerful hardware resources. Lightweight cryptography (LWC) algorithms are a promising solution to reduce computation complexity while maintaining a desired level of security. In the presented work, we attempt to address the issue of adding security to the IoT network layer by comparing the performance of 32 LWC algorithms with currently well-known algorithms on multiple IoT platforms (Raspberry Pi 3, Raspberry Pi Zero W, and iMX233). These 32 authenticated encryption with associated data algorithms have been selected from the second round of the LWC standardization process conducted by the National Institute of Standards and Technology. Power consumption, random access memory usage, and execution time are measured for these algorithms using the targeted embedded platforms that are used as IoT sensor nodes. The results of this study will assist researchers in choosing a suitable platform and optimal LWC algorithm for IoT applications.
Fundamental clustering algorithms suite

The article presents immediate access to over fifty fundamental clustering algorithms. Additionally, access to clustering benchmark datasets published priorly as “Fundamental Clustering Problems Suite” (FCPS) is provided. The software library is named “FCPS”, available in R on CRAN and accessible within Python. The input and output of clustering algorithms are standardized to enable users a swift execution of cluster analysis. By combining mirrored-density plots (MD plots) with statistical testing, FCPS provides a tool to investigate the cluster-tendency quickly before the cluster analysis itself. Common clustering challenges can be generated with an arbitrary sample size. Additionally, FCPS sums up 26 indicators intending to estimate the number of clusters and provides an appropriate implementation of the clustering accuracy for more than two clusters.
Quantum Semantic Learning by Reverse Annealing of an Adiabatic Quantum Computer

Abstract Restricted Boltzmann machines (RBMs) constitute a class of neural networks for unsupervised learning with applications ranging from pattern classification to quantum state reconstruction. Despite the potential representative power, the diffusion of RBMs is quite limited since their training process proves to be hard. The advent of commercial adiabatic quantum computers (AQCs) raised the expectation that the implementations of RBMs on such quantum devices can increase the training speed with respect to conventional hardware. Here, the feasibility of a complete RBM on AQCs is demonstrated, thanks to an embedding that associates the nodes of the neural networks to virtual qubits. A semantic quantum search is implemented thanks to a reverse annealing schedule. Such an approach exploits more information from the training data, mimicking the behavior of the classical Gibbs sampling algorithm. The semantic training is shown to quickly raise the sampling probability of a subset of the set of the configurations. Even without a proper optimization of the annealing schedule, the RBM semantically trained achieves good scores on reconstruction tasks. The development of such techniques paves the way toward the establishment of a quantum advantage of adiabatic quantum computers, especially given the foreseen improvement of such hardware.
Interaction–Transformation Evolutionary Algorithm for Symbolic Regression

Interaction-Transformation (IT) is a new representation for Symbolic Regression that reduces the space of solutions to a set of expressions that follow a specific structure. The potential of this representation was illustrated in prior work with the algorithm called SymTree. This algorithm starts with a simple linear model and incrementally introduces new transformed features until a stop criterion is met. While the results obtained by this algorithm were competitive with the literature, it had the drawback of not scaling well with the problem dimension. This article introduces a mutation-only Evolutionary Algorithm, called ITEA, capable of evolving a population of IT expressions. One advantage of this algorithm is that it enables the user to specify the maximum number of terms in an expression. In order to verify the competitiveness of this approach, ITEA is compared to linear, nonlinear, and Symbolic Regression models from the literature. The results indicate that ITEA is capable of finding equal or better approximations than other Symbolic Regression models while being competitive to state-of-the-art nonlinear models. Additionally, since this representation follows a specific structure, it is possible to extract the importance of each original feature of a data set as an analytical function, enabling us to automate the explanation of any prediction. In conclusion, ITEA is competitive when comparing to regression models with the additional benefit of automating the extraction of additional information of the generated models.
Oblivious Pseudorandom Functions from Isogenies

An oblivious PRF, or OPRF, is a protocol between a client and a server, where the server has a key k for a secure pseudorandom function F, and the client has an input x for the function. At the end of the protocol the client learns F(k, x), and nothing else, and the server learns nothing. An OPRF is verifiable if the client is convinced that the server has evaluated the PRF correctly with respect to a prior commitment to k. OPRFs and verifiable OPRFs have numerous applications, such as private-set-intersection protocols, password-based key-exchange protocols, and defense against denial-of-service attacks. Existing OPRF constructions use RSA-, Diffie-Hellman-, and lattice-type assumptions. The first two are not post-quantum secure. In this paper we construct OPRFs and verifiable OPRFs from isogenies. Our main construction uses isogenies of supersingular elliptic curves over $$\mathbb {F}_{p^{2}}$$ and tries to adapt the Diffie-Hellman OPRF to that setting. However, a recent attack on supersingular-isogeny systems due to Galbraith et al. [ASIACRYPT 2016] makes this approach difficult to secure. To overcome this attack, and to validate the server’s response, we develop two new zero-knowledge protocols that convince each party that its peer has sent valid messages. With these protocols in place, we obtain an OPRF in the SIDH setting and prove its security in the UC framework. Our second construction is an adaptation of the Naor-Reingold PRF to commutative group actions. Combining it with recent constructions of oblivious transfer from isogenies, we obtain an OPRF in the CSIDH setting.
Deep learning, machine learning and internet of things in geophysical engineering applications: An overview

The earthquakes in Eastern Mediterranean are mostly tectonic. The earthquakes that are 60 km deep in the ground are called Shallow earthquakes. The earthquakes in the eastern Mediterranean are generally shallow-orientated, and their depth often varies between 0 and 30 km. When there is a sudden plate movement within the earth's crust, increasing friction between two plates can result in earthquakes, which are harmful to human lives, buildings and the economy. With the technological developments in the world over the years, more information has been accessible and reliable. The Internet of things (IoT) and crowdsourcing have proven to be effective in predicting and preparing for the future natural hazards when combined with Machine Learning or Deep Learning. Machine learning does not require human interaction, as the machines automatically gets information and distributes in real-time to prevent major or severe damages, which are low-cost, has lower power consumption with reliable information. Short Message Service and Global Positioning System are still functional, even though powerlines and mobile beacons, were cut during recent massive earthquakes in some countries of Asia. The Seismic Alert System (SAS) is another design to prevent and reduce earthquake damages. The SAS was designed in 1996, to be effective with real time data from seismograms. The Earth cloud uses geophones which uses MEMS (micro electro-mechanical system). Geophones have an accelerometer and seismometers to detect the earth movement. Therefore, usage of sensors and the Internet of Things (IoT) to monitor earthquakes and send early warning signals to prevent destruction of buildings and loss of life play a significant role.
Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI

Multi-parametric prostate MRI (mpMRI) is a powerful tool to diagnose prostate cancer, though difficult to interpret even for experienced radiologists.A common radiological procedure is to compare a magnetic resonance image with similarly diagnosed cases.To assist the radiological image interpretation process, computerized Content-Based Image Retrieval systems (CBIRs) can therefore be employed to improve the reporting workflow and increase its accuracy.In this article, we propose a new, supervised siamese deep learning architecture able to handle multi-modal and multi-view MR images with similar PIRADS score.An experimental comparison with well-established deep learning-based CBIRs (namely standard siamese networks and autoencoders) showed significantly improved performance with respect to both diagnostic (ROC-AUC), and information retrieval metrics (Precision-Recall, Discounted Cumulative Gain and Mean Average Precision).Finally, the new proposed multi-view siamese network is general in design, facilitating a broad use in diagnostic medical imaging retrieval.
Implementation of Sobel operator based image edge detection on FPGA

Image edge detection plays a major role in image processing and computer vision. In an image most important parameter is the edges which are mainly used in image analysis and processing. In Sobel edge detection algorithm hardware implementation is chosen because it can work with less decline in noise at high level. Sobel operator is a derivative mask and it is used for edge detection. Here two different types of edges of an image can be detected. The edge detected image consists of thick boundaries and broken edges. Where the edge detected image cannot obtained completely and the time and space complexity are also high. In order to rectify these drawbacks, the proposed system has the addition of extra masks and also changes the coefficients of each masks in image filter. The proposed system is done by both MATLAB AND SIMULINK for appropriate results. Then the Simulink’s subsystem generates the HDL code to implement in the Xilinx ISE 14.6 software and in FPGA hardware.
New Genuinely Multipartite Entanglement

Abstract The quantum entanglement as one of the important resources has been verified by using different local models. There is no efficient method to verify single multipartite entanglement that is not generated by multisource quantum networks with local operations and shared randomness. The goal in this work is to solve this problem. First, a new local model is proposed for describing all the states that can be generated by using distributed entangled states and shared randomness without classical communication. This model is stronger than the biseparable model and implies new genuinely multipartite entanglement. With the present local model it is proved that all the permutationally symmetric entangled pure states are new genuinely multipartite entanglement. It is further proved that the new feature holds for all the multipartite entangled pure states in the biseparable model with the dimensions of local systems being not larger than three. The new multipartite entanglement is also robust against general noises. Finally, a simple Bell inequality is provided to verify new genuinely multipartite entangled pure qubit states in the present model. The results show new insight into featuring the genuinely multipartite entanglement in the distributive scenarios.
Mad Max: Affine Spline Insights Into Deep Learning

We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs) that provide a powerful portal through which we view and analyze their inner workings. For instance, conditioned on the spline partition region containing the input signal, the output of an MASO DN can be written as a simple affine transformation of the input. This implies that a DN constructs a set of signal-dependent, class-specific templates against which the signal is compared via a simple inner product; we explore the links to the classical theory of optimal classification via matched filters and the effects of data memorization. Going further, we propose a simple penalty term that can be added to the cost function of any DN learning algorithm to force the templates to be orthogonal with each other; this leads to significantly improved classification performance and reduced overfitting with no change to the DN architecture. The spline partition of the input signal space that is implicitly induced by an MASO directly links DNs to the theory of vector quantization (VQ) and K-means clustering, which opens up new geometric avenues to study how DNs organize signals in a hierarchical fashion. To validate the utility of the VQ interpretation, we develop and validate a new distance metric for signals and images that quantify the difference between their VQ encodings.
Implementation of an encrypted-compressed image wireless transmission scheme based on chaotic fractional-order systems

In this paper, an encryption, compression and transmission scheme is proposed. The scheme is based on fractional-order chaotic systems combined with Discrete Wavelet Transform (DWT) and Quadrature Phase Shift Keying (QPSK) modulation. The cipher performs rounds of digital operations between the vector states of the fractional-order system and the image to be encrypted. The implementation of the transmission process is made between a pair of software defined radio modules through a QPSK modulation scheme. The experimental results and security analysis show adequate performances for several applications in the presence of multiple attacks and environmental noise.
One-size does not fit all: Towards an adaptive model to develop preservice teachers’ digital competencies

Despite the existing knowledge base, it remains unclear how to best prepare preservice teachers with different profiles. While it is generally agreed that multiple strategies are required to develop preservice teachers' digital competency, the question remains how these strategies may come together to support preservice teachers' different needs in training. Therefore, this study examines 1) the key relationships among strategies to develop preservice teachers' digital competencies and 2) how the self-organization of these strategies changes in relation to pre-service teachers' attitudes towards digital technology. To explore these questions, we analyzed preservice teacher questionnaire data (N = 931) from 20 teacher training institutions in Belgium. Data is analyzed through the grouping of preservice teachers based on high and low attitudes towards digital technologies. Association rules analysis was then run on each group to identify patterns among six strategies to teach digital competency. Results were visualised as directed graphs. Findings show different patterns of associations among strategies for the two groups. Results from preservice teachers with more positive attitudes showed a very specific focus on Collaboration, while those with lower attitudes had an emphasis on Feedback. This finding suggests that important differences may exist in how preservice teachers experience the development of digital competency in their training, which can inform future design of teacher training programs.
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph

Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also a vital source for distilling latent users' information needs. Most present works leverage unobserved samples for extracting negative signals. However, such an optimisation strategy can lead to biased results toward already popular items by frequently handling new items as negative instances. In this study, we tackle the cold-start problems for new users/items by appropriately leveraging unobserved samples. We propose a knowledge graph (KG)-aware recommender based on graph neural networks, which augments labelled samples through pseudo-labelling. Our approach aggressively employs unobserved samples as positive instances and brings new items into the spotlight. To avoid exhaustive label assignments to all possible pairs of users and items, we exploit a KG for selecting probably positive items for each user. We also utilise an improved negative sampling strategy and thereby suppress the exacerbation of popularity biases. Through experiments, we demonstrate that our approach achieves improvements over the state-of-the-art KG-aware recommenders in a variety of scenarios; in particular, our methodology successfully improves recommendation performance for cold-start users/items.
Minimally overlapping words for sequence similarity search

Analysis of genetic sequences is usually based on finding similar parts of sequences, e.g. DNA reads and/or genomes. For big data, this is typically done via 'seeds': simple similarities (e.g. exact matches) that can be found quickly. For huge data, sparse seeding is useful, where we only consider seeds at a subset of positions in a sequence.Here, we study a simple sparse-seeding method: using seeds at positions of certain 'words' (e.g. ac, at, gc or gt). Sensitivity is maximized by using words with minimal overlaps. That is because, in a random sequence, minimally overlapping words are anti-clumped. We provide evidence that this is often superior to acclaimed 'minimizer' sparse-seeding methods. Our approach can be unified with design of inexact (spaced and subset) seeds, further boosting sensitivity. Thus, we present a promising approach to sequence similarity search, with open questions on how to optimize it.Software to design and test minimally overlapping words is freely available at https://gitlab.com/mcfrith/noverlap.Supplementary data are available at Bioinformatics online.
FastForest: Increasing random forest processing speed while maintaining accuracy

Random Forest remains one of Data Mining’s most enduring ensemble algorithms, achieving well-documented levels of accuracy and processing speed, as well as regularly appearing in new research. However, with data mining now reaching the domain of hardware-constrained devices such as smartphones and Internet of Things (IoT) devices, there is continued need for further research into algorithm efficiency to deliver greater processing speed without sacrificing accuracy. Our proposed FastForest algorithm achieves this result through a combination of three optimising components - Subsample Aggregating (‘Subbagging’), Logarithmic Split-Point Sampling and Dynamic Restricted Subspacing. Empirical testing shows FastForest delivers an average 24% increase in model-training speed compared with Random Forest whilst maintaining (and frequently exceeding) classification accuracy over tests involving 45 datasets on both PC and smartphone platforms. Further tests show FastForest achieves favourable results against a number of ensemble classifiers including implementations of Bagging and Random Subspace. With growing interest in machine-learning on mobile devices, FastForest provides an efficient ensemble classifier that can achieve faster results on hardware-constrained devices, such as smartphones.