Reviewerly/exHarmony huggingface dataset:
ACDC: Online unsupervised cross-domain adaptation

We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces – a fully labeled source stream and an unlabeled target stream – are learned together. Unique characteristics and challenges such as covariate shift, asynchronous concept drifts, and contrasting data throughput arise. We propose ACDC, an adversarial unsupervised domain adaptation framework that handles multiple data streams with a complete self-evolving neural network structure that reacts to these defiances. ACDC encapsulates three modules into a single model: A denoising autoencoder that extracts features, an adversarial module that performs domain conversion, and an estimator that learns the source stream and predicts the target stream. ACDC is a flexible and expandable framework with little hyper-parameter tunability. Our experimental results under the prequential test-then-train protocol indicate an improvement in target accuracy over the baseline methods, achieving more than a 10% increase in some cases. • A novel online unsupervised cross-domain transfer. • A fully autonomous structure that can grow and prune nodes on all training phases. • The usage of a domain-adversarial bias–variance trade-off to adapt the discriminator. • Domain-adversarial learning on online unsupervised cross-domain configuration. • Source-code is made publicly available for further study.
Code Semantic Detection

Code semantics play a crucial part in code classification, code summarization, code search, and code optimization. This tool has been developed to classify programs based on the high-level logic of the programs. The developed solution uses the idea of supervised learning classification for a set of a labeled dataset of code snippets. The code is vectorized using two novel techniques based on dynamic and static analysis. The former uses a new technique to generate a code vector by approximating the given function using a neural network. The weights of the neural network being the heart of the neural network are used to generate the final vector, which represents the code/function snippet. The latter, i.e. Static Analysis uses the structural information of code to generate the representation. Our two new techniques perform better than the state-of-the-art systems code2vec and neural code comprehension. The obtained accuracies for dynamic and static analysis-based techniques are 98% and 80% respectively. The dynamic model decides the semantics only on the basis of input and output of the programs, hence it can be easily extended to support multiple languages. The dynamic model has been implemented for C++ programs and gives an accuracy of 99% highlighting the easily extendible and language-agnostic feature.
AR-some Chemistry Models:Interactable 3D Molecules through Augmented Reality

Augmented Reality (AR) presents many opportunities to design systems that can aid students in learning complex chemistry concepts. Chemistry is a 3D concept that student soften have trouble visualizing using 2D media. AR-some Chemistry Models is an AR application to visualize complex chemical molecules. Our app can generate 3D interactive molecules from chemical names, hand-drawn and printed line-angle structures of complex molecules. Allowing for handwritten input gives a student instant feedback on their self-generated understanding of a molecule and can improve knowledge retention. Students have the opportunity to visualize molecules from lecture notes, or even homework problems. Our application allows the student to interact with this molecule using simple touch screen commands to zoom in, zoom out, rotate and move the molecule in AR to understand the spatial relationship among the components. Our application also highlights and displays relevant textual information about a molecule’s functional groups when they are tapped to further improve student learning. We present an affordable and accessible study tool to help students with their learning of chemistry molecules.
Simulation of Multiple-Quantum NMR Dynamics of Spin Dimer on Quantum Computer

Dynamics of spin dimers in multiple quantum NMR experiment is studied on the 5-qubit superconducting quantum processor of IBM Quantum Experience for the both pure ground and thermodynamic equilibrium (mixed) initial states. The work can be considered as a first step towards an application of quantum computers to solving problems of magnetic resonance. This article is dedicated to Prof. Klaus Möbius and Prof. Kev Salikhov on the occasion of their 85th birthdays.
Technical Analysis of Twitter Data in Preparation of Prediction using Multilayer Perceptron Algorithm

Social networking sites have been the partner of everyone today to express feelings and emotions, especially that physical communication has been a bit prohibited caused by the pandemic. The platforms like Twitter, Facebook, etc., has been the choice to express words relating to stress, anxieties, and depressions to which the World Health Organization states that once not paid with enough attention may lead to mental health issues. A depression today stands out to be the leading problem known as mental health disorders that if not to anticipate earlier may also lead to this so called self-harming. In this regard, this study wants to illustrate the technical analysis of twitter data in preparation of prediction using the Multilayer Perceptron (MLP) in Weka algorithms to help the data mining community to dig knowledge from the stored historical data. The technical analysis has been made through the process of extraction, validation, and preparation of the model ready for interpretation and evaluation of predicted model. The legality of data has been permitted by twitter developer's account that permits the study to extract 1000 tweets and eventually validated 931 tweets necessary for exploration. Optimization was made by setting the epoch to 200 and by testing the eight attributes to 70:30 split of test. The initial result of 79.9141% was optimized to 82.0789% accuracy rate and found reliable based on the result of kappa statistics. However, the study still suggests to explore on varied parameters to increase the reliability of data.
Deep Learning based Semantic Approach for Arabic Textual Documents Recommendation

Internet revolution has given readers access to vast and inexpensive resources with little effort. To deal with the proliferation of the exchanged data, textual documents recommendation has become a growing research topic of artificial intelligence and Natural Language Processing (NLP). It provided necessary guidance to choose the most suitable items. However, traditional rating based-methods had limits in terms of data sparsity. To address this lack, users’ reviews are used as a valuable source of knowledge. In this paper, deep learning based semantic approach is proposed for Arabic textual documents recommendation. This language received less attention compared to others because of its analysis complexity. Indeed, documents features from the reviews written for the item and user behaviors from the reviews written by the user are jointly modeled using deep models. The effectiveness of feedforward and recurrent neural architectures are studied to provide latent semantic representations. These features are further fed to a shared semantic space to capture complex user-item relations and predict finally ratings. Using an Arabic dataset, the experimental results demonstrated that the proposed approach was feasible to automate the process of recommending the most relevant Arabic textual documents.
Finding High-Value Training Data Subset Through Differentiable Convex Programming

Finding valuable training data points for deep neural networks has been a core research challenge with many applications. In recent years, various techniques for calculating the "value" of individual training datapoints have been proposed for explaining trained models. However, the value of a training datapoint also depends on other selected training datapoints - a notion which is not explicitly captured by existing methods. In this paper, we study the problem of selecting high-value subsets of training data. The key idea is to design a learnable framework for online subset selection, which can be learned using mini-batches of training data, thus making our method scalable. This results in a parameterised convex subset selection problem that is amenable to a differentiable convex programming paradigm, thus allowing us to learn the parameters of the selection model in an end-to-end training. Using this framework, we design an online alternating minimization based algorithm for jointly learning the parameters of the selection model and ML model. Extensive evaluation on a synthetic dataset, and three standard datasets, show that our algorithm finds consistently higher value subsets of training data, compared to the recent state of the art methods, sometimes \(\sim 20\%\) higher value than existing methods. The subsets are also useful in finding mislabelled training data. Our algorithm takes running time comparable to the existing valuation functions.
Outliers in Covid 19 data based on Rule representation - the analysis of LOF algorithm

The article concerns the detection of outliers in rule-based knowledge bases containing data on Covid 19 cases. The authors move from the automatic generation of a rule-based knowledge base from source data by clustering rules in the knowledge base to optimize inference processes and to detecting unusual rules allowing for the optimal structure of rule groups. The paper presents a two-phase procedure, wherein in the first phase, we look for the optimal structure of rule clusters when there are outlier rules in the knowledge base. In the second phase, we detect outliers in the rules using the LOF (Local Outlier Factor) algorithm. Then we eliminate the unusual rules from the database and check whether the selected cluster quality measures are responded positively to the elimination of outliers, which would indicate that the rules were rightly considered outliers. The performed experiments confirmed the effectiveness of the LOF algorithm and selected cluster quality measures in the context of detecting atypical rules. The detection of such rules can support knowledge engineers or domain experts in knowledge mining to improve the completeness of the knowledge base, which is usually the basis of the decision support system.
On the Impact of Affine Loop Transformations in Qubit Allocation

Most quantum compiler transformations and qubit allocation techniques to date are either peep-hole focused or rely on sliding windows that depend on a number of external parameters including the topology of the quantum processor. Thus, global optimization criteria are still lacking. In this article, we explore the synergies and impact of affine loop transformations in the context of qubit allocation and mapping. With this goal in mind, we designed and implemented AXL , a domain specific language and source-to-source compiler for quantum circuits that can be directly described with affine relations. We conduct an extensive evaluation spanning circuits from the recently introduced QUEKO benchmark suite, eight quantum circuits taken from the literature, three distinct coupling graphs, four affine transformations (including the Pluto dependence distance minimization and Feautrier’s minimum latency algorithms), four qubit allocators, and two back-end quantum compilers. Our results demonstrate that affine transformations using global optimization criteria can cooperate effectively in several scenarios with quantum qubit mapping algorithms to reduce the circuit depth, size and allocation time.
Improving question answering performance using knowledge distillation and active learning

Contemporary question answering (QA) systems, including Transformer-based architectures, suffer from increasing computational and model complexity which render them inefficient for real-world applications with limited resources. Furthermore, training or even fine-tuning such models requires a vast amount of labeled data which is often not available for the task at hand. In this manuscript, we conduct a comprehensive analysis of the mentioned challenges and introduce suitable countermeasures. We propose a novel knowledge distillation (KD) approach to reduce the parameter and model complexity of a pre-trained bidirectional encoder representations from transformer (BERT) system and utilize multiple active learning (AL) strategies for immense reduction in annotation efforts. We show the efficacy of our approach by comparing it with four state-of-the-art (SOTA) Transformers-based systems, namely KroneckerBERT, EfficientBERT, TinyBERT, and DistilBERT. Specifically, we outperform KroneckerBERT21 and EfficientBERTTINY by 4.5 and 0.4 percentage points in EM, despite having 75.0% and 86.2% fewer parameters, respectively. Additionally, our approach achieves comparable performance to 6-layer TinyBERT and DistilBERT while using only 2% of their total trainable parameters. Besides, by the integration of our AL approaches into the BERT framework, we show that SOTA results on the QA datasets can be achieved when we only use 40% of the training data. Overall, all results demonstrate the effectiveness of our approaches in achieving SOTA performance, while extremely reducing the number of parameters and labeling efforts. Finally, we make our code publicly available at https://github.com/mirbostani/QA-KD-AL.
Ranking Loss: A Ranking-Based Deep Neural Network for Colorectal Cancer Grading in Pathology Images

In digital pathology, cancer grading has been widely studied by utilizing hand-crafted features and advanced machine learning and deep learning methods. In most of such studies, cancer grading has been formulated as a multi-class categorical classification problem, likely overlooking the relationship among different cancer grades. Herein, we propose a ranking-based deep neural network for cancer grading in pathology images. Utilizing deep neural networks, pathology images are mapped into a latent space. Built based upon a triplet loss, a ranking loss is devised to maximize the inter-class distance among cancer grades in the latent space with respect to the aggressiveness of cancer, leading to the correct ordering or rank of pathology images. To evaluate the proposed method, a number of colorectal pathology images have been employed. The experimental results demonstrate that the proposed approach is capable of predicting cancer grades with high accuracy, outperforming the deep neural networks without the ranking loss.
With a Little Help From My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the dataset. Our method, Nearest-Neighbor Contrastive Learning of visual Representations (NNCLR), samples the nearest neighbors from the dataset in the latent space, and treats them as positives. This provides more semantic variations than pre-defined transformations. We find that using the nearest-neighbor as positive in contrastive losses improves performance significantly on ImageNet classification, from 71.7% to 75.6%, outperforming previous state-of-the-art methods. On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.8% to 56.5%. On transfer learning benchmarks our method outperforms state-of-the-art methods (including supervised learning with ImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate empirically that our method is less reliant on complex data augmentations. We see a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train using only random crops.
A Suspicion-Free Black-box Adversarial Attack for Deep Driving Maneuver Classification Models

The current autonomous vehicles are equipped with onboard deep neural network (DNN) models to process the data from different sensor and communication units. In the connected autonomous vehicle (CAV) scenario, each vehicle receives time-series driving signals (e.g., speed, brake status) from nearby vehicles through the wireless communication technologies. In the CAV scenario, several black-box adversarial attacks have been proposed, in which an attacker deliberately sends false driving signals to its nearby vehicle to fool its onboard DNN model and cause unwanted traffic incidents. However, the previously proposed black-box adversarial attack can be easily detected. To handle this problem, in this paper, we propose a Suspicion-free Boundary Black-box Adversarial (SBBA) attack, where the attacker utilizes the DNN model's output to design the adversarial perturbation. First, we formulate the attack design problem as a goal satisfying optimization problem with constraints so that the proposed attack will not be easily detectable by detection methods. Second, we solve the proposed optimization problem using the Bayesian optimization method. In our Bayesian optimization framework, we use the Gaussian process to model the posterior distribution of the DNN model, and we use the knowledge gradient function to choose the next sample point. We devise a gradient estimation technique for the knowledge gradient method to reduce the solution searching time. Finally, we conduct extensive experimental evaluations using two real driving datasets. The experimental results show that SBBA outperforms the previous adversarial attacks by 56% higher success rate under detection methods, 238% less time to launch the attacks, and 76% less perturbation (to avoid being detected), and 257% fewer queries (to the DNN model to verify the attack success).
Analysis of chaotic dynamical systems with autoencoders

We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input. This analysis results in the determination of the latent space dimension of each system and thus determines the minimal number of nodes necessary to capture the essential information contained in the chaotic time series. The constructed chaotic autoencoders generate similar maximal Lyapunov exponents as the original chaotic systems and thus encompass their essential dynamical information.
Improved Threshold Signatures, Proactive Secret Sharing, and Input Certification from LSS Isomorphisms

In this paper we present a series of applications steming from a formal treatment of linear secret-sharing isomorphisms, which are linear transformations between different secret-sharing schemes defined over vector spaces over a field \(\mathbb {F}\) and allow for efficient multiparty conversion from one secret-sharing scheme to the other. This concept generalizes the folklore idea that moving from a secret-sharing scheme over \(\mathbb {F}_{p}\) to a secret sharing "in the exponent" can be done non-interactively by multiplying the share unto a generator of e.g., an elliptic curve group. We generalize this idea and show that it can also be used to compute arbitrary bilinear maps and in particular pairings over elliptic curves.
Enhanced Local Features using Ridgelet Filters for Traffic Sign Detection and Recognition

Whenever it comes to road safety, traffic signs plays a vital role. Understanding and following different categories of traffic sign is also important. Several methods are available to tackle this problem of traffic sign detection and one of the method is the object detection algorithm. Generic object detectors such as R-CNN, Fast RCNN, Faster RCNN, YOLO, etc. can perform multiple detection in a frame. Generic object detectors fails in identifying small objects with in a frame due to the usage of pooling with the convolutional layer. Pooling layer, filters out the low level features, but this cannot happen when we deal with real time cases. Because in real scenarios the traffic sign contributes only a small portion of the image. TT100K dataset is the traffic sign dataset that can be used to train proposed model as it meets the needs of our problem. Here in this paper we try to overcome the issue of low level features by employing ridgelet filters to get the enhanced features.
Following the Correct Direction: Renovating Sparsified SGD Towards Global Optimization in Distributed Edge Learning

Distributed edge learning collaborates powerful edge devices to train a shared global model. Since the frequent communication between the server and workers is very expensive, it is desired to accelerate the learning process. The gradient sparsification is an efficient method that only uploads a small subset of gradient elements. However, most existing works neglect the distributed nature of local datasets, and consequently the local gradients uploaded by edge devices cannot follow the global correct optimization direction well, which results in the loss of accuracy. In this paper, we propose a new gradient sparsification with a renovating mechanism, called Global Renovating Stochastic Gradient Descent (GRSGD). GRSGD utilizes the previous-round global gradient to estimate the current global one and renovates the current zero-sparsified local gradients. It mitigates the communication overhead while making the convergence direction of training closer to the global optimization, accelerating the distributed edge learning process. We provide a theoretical convergence guarantee for our algorithm based on the non-convex assumption, which better fits most deep learning problems. With extensive experiments in PyTorch, we show that GRSGD effectively accelerates the learning process with a smaller communication cost and a faster convergence rate on most training tasks. For example, on ImageNet MnasNet, GRSGD cuts down the gradient size from 8.47MB to 2.13MB while achieving 9.6%+ higher accuracy.
RHH-LGP: Receding Horizon And Heuristics-Based Logic-Geometric Programming For Task And Motion Planning

Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. The efficiency of this planner is then further improved by a receding horizon formulation, resulting in RHH-LGP. We demonstrate the robustness and effectiveness of our approach on a diverse range of long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously. By combining geometry-based heuristics with iterative planning, our approach brings an order-of-magnitude reduction of planning time in all investigated problems.