Reviewerly/exHarmony huggingface dataset:
Scrutinizing XAI using linear ground-truth data with suppressor variables

Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of procedures has been developed to shed light on their inner workings and the ways in which their predictions come about, defining the field of 'explainable AI' (XAI). Saliency methods rank input features according to some measure of 'importance'. Such methods are difficult to validate since a formal definition of feature importance is, thus far, lacking. It has been demonstrated that some saliency methods can highlight features that have no statistical association with the prediction target (suppressor variables). To avoid misinterpretations due to such behavior, we propose the actual presence of such an association as a necessary condition and objective preliminary definition for feature importance. We carefully crafted a ground-truth dataset in which all statistical dependencies are well-defined and linear, serving as a benchmark to study the problem of suppressor variables. We evaluate common explanation methods including LRP, DTD, PatternNet, PatternAttribution, LIME, Anchors, SHAP, and permutation-based methods with respect to our objective definition. We show that most of these methods are unable to distinguish important features from suppressors in this setting.The online version contains supplementary material available at 10.1007/s10994-022-06167-y.
EMG Based Controller for a Wheelchair with Robotic Manipulator

There is an increasing demand for the wheelchairs which could make decisions by acquiring information from the surrounding, for the wheelchair users all around the globe. Such type of wheelchairs is called intelligent wheelchairs and are using different types of high level controlling algorithms to control itself. Using EMG based controller for high level decision making has become a reliable and efficient method due to the high signal to noise ratio of EMG signals and the ability of them to bring out the users' intentions successfully. Usually, those EMG based controllers are incorporated with another modality to achieve the desired tasks through the intelligent wheelchairs. An EMG based high level controller which can be stand-alone without the aid of other modalities is rarely found in the literature. Even the available such type of controllers is mostly designed for the users with full/partial upper limb functions. However, such types of controllers cannot be used by the wheelchair users with trans-humeral amputation/ trans- radial amputation due to the absence of the required muscles. This paper proposes an EMG based high level controlling algorithm for the navigation input generation and to detect the intention for using a robot manipulator for reach to grasp task, for an intelligent wheelchair with a robotic manipulator. The proposed controller has been designed for wheelchair users with trans- humeral amputation/ trans- radial amputation. But it can be used by any type of wheelchair user with neck functions and full/ partial functions of both upper limbs up to the humerus. Experiments have been done with the healthy human subjects to validate the efficacy of the proposed high level controller.
Intrinsic quantum correlations for Gaussian localized Dirac cat states in phase space

Following the information-based approach to Dirac spinors under a constant magnetic field, the phase-space representation of symmetric and antisymmetric localized Dirac cat states is obtained. The intrinsic entanglement profile implied by the Dirac Hamiltonian is then investigated so as to shed a light on quantum states as carriers of qubits correlated by phase-space variables. Corresponding to the superposition of Gaussian states, cat states exhibit nontrivial elementary information dynamics which include the interplay between intrinsic entanglement and quantum superposition as reported by the corresponding Dirac archetypes. Despite the involved time evolution as nonstationary states, the Wigner function constrains the elementary information quantifiers according to a robust framework which can be consistently used for quantifying the time-dependent $\text{SU}(2)\ensuremath{\bigotimes}\text{SU}(2)$ (spin projection and intrinsic parity) correlation profile of phase-space localized Dirac spinor states. Our results show that the Dirac Wigner functions for cat states---described in terms of generalized Laguerre polynomials---exhibit an almost maximized timely persistent mutual information profile which is engendered by either classical- or quantumlike spin-parity correlations, depending on the magnetic field intensity.
Leveraging Noise and Aggressive Quantization of In-Memory Computing for Robust DNN Hardware Against Adversarial Input and Weight Attacks

In-memory computing (IMC) substantially improves the energy efficiency of deep neural network (DNNs) hardware by activating many rows together and performing analog computing. The noisy analog IMC induces some amount of accuracy drop in hardware acceleration, which is generally considered as a negative effect. However, in this work, we discover that such hardware intrinsic noise can, on the contrary, play a positive role in enhancing adversarial robustness. To achieve that, we propose a new DNN training scheme that integrates measured IMC hardware noise and aggressive partial sum quantization at the IMC crossbar. We show that this effectively improves the robustness of IMC DNN hardware against both adversarial input and weight attacks. Against black-box adversarial input attacks and bit-flip weight attacks, DNN robustness has improved by up to 10.5% (CFAR-10 accuracy) and 33.6% (number of bit-flips), respectively, compared to conventional DNNs.
Complex Terrain Navigation via Model Error Prediction

Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target. In deformable, complex terrain, using geometric-based approaches can fail to find a path due to mischaracterizing deformable objects as rigid and impassable. Instead, we learn to predict an estimate of traversability of terrain regions and to prefer regions that are easier to navigate (e.g., short grass over small shrubs). Rather than predicting collisions, we instead regress on realized error compared to a canonical system model. We train with an on-policy approach, resulting in successful navigation policies using as little as 50 minutes of training data split across simulation and real world. Our learning-based navigation system is a sample efficient short-term planner that we demonstrate on a Clearpath Husky navigating through a variety of terrain including grassland and forest.
3-Party Distributed ORAM from Oblivious Set Membership

Distributed Oblivious RAM (DORAM) protocols allow a group of participants to obliviously access a secret-shared array at a secret-shared index, and DORAM is the key tool for secure multiparty computation (MPC) in the RAM model. In this work we present an efficient DORAM protocol with $$O((\kappa + D) \log N)$$ communication per access, where N is the size of the memory, $$\kappa $$ is a security parameter and D is the block size. Our DORAM protocol is secure in the 3-party honest-majority setting, and is built from two novel, efficient components. The first is a novel data structure for answering set membership queries. This data structure has asymptotically optimal (with tiny constants) memory usage, lookup cost and negligible failure probability. We show how this data structure can also be efficiently instantiated under MPC. The second is a Distributed Oblivious Hash Table protocol (in the 3-party honesty-majority setting) with asymptotically optimal memory usage and $$O(\kappa + D)$$ communication per access. To our knowledge, this is the first Distributed Oblivious Hash Table with this efficiency that does not use homomorphic encryption. Finally, we use this to build the aforementioned DORAM protocol. Our protocol performs polylogarithmic computation and does not require homomorphic encryption. Under natural parameter choices, it is the most communication-efficient DORAM with these properties.
Detecting Network State in the Presence of Varying Levels of Congestion

We consider the problem of estimating the state of computer networks which are delivering video in the presence of other interfering services. Existing methods for measuring jitter, a Quality of Delivery (QoD) measure for video, are based on statically configured IIR filters. They do not attempt to estimate the congestion in the network that caused the jitter to change. As a result, steps taken to improve QoD are frequently taken blindly. We pose the problem of estimating jitter as the problem of estimating a target source in the presence of interfering sources. To evaluate the approach we capture QoD measurements for a target video client from a six router networking test-bed where video is delivered over a substrate which is shared with varying levels of interfering sources which cause congestion. We demonstrate the performance of the new jitter estimator as part of a background congestion level detector. Numerical results based on real data show that considerable gains in congestion state classification are achieved for all congestion levels.
Self-Attention Agreement Among Capsules

At the state of the art, Capsule Networks (CapsNets) have shown to be a promising alternative to Convolutional Neural Networks (CNNs) in many computer vision tasks, due to their ability to encode object viewpoint variations. Network capsules provide maps of votes that focus on entities presence in the image and their pose. Each map is the point of view of a given capsule. To compute such votes, CapsNets rely on the routing-by-agreement mechanism. This computationally costly iterative algorithm selects the most appropriate parent capsule to have nodes in a parse tree for all the active capsules but this behaviour is not ensured by the routing, hence it possibly causes vanishing weights during training. We hypothesise that an attention-like mechanism will help capsules to select the predominant regions among the maps to focus on, hence introducing a more reliable way of learning the agreement between the capsules in a single pass. We propose the Attention Agreement Capsule Networks (AA-Caps) architecture that builds upon CapsNet by introducing a self-attention layer to suppress irrelevant capsule votes thus keeping only the ones that are useful for capsules agreements on a specific entity. The generated capsule attention map is then assigned to classification layer responsible of emitting the predicted image class. The proposed AA-Caps model has been evaluated on five benchmark datasets to validate its ability in dealing with the diverse and complex data that CapsNet often fails with. The achieved results demonstrate that AA-Caps outperforms existing methods without the need of more complex architectures or model ensembles.
Mitigating Crosstalk in Quantum Computers through Commutativity-Based Instruction Reordering

Crosstalk is one of the major types of noise in quantum computers. To design high-fidelity quantum gates and large-scale quantum computers, effectively suppressing crosstalk is becoming increasingly important. Previous approaches to mitigate crosstalk rely on either hardware strategies, which are only applicable on limited platforms, or software techniques, which, however, cannot fully explore instruction parallelism. To address the above challenges, we propose a commutativity based compile-time instruction reordering approach. We first propose a complete set of generalized commutativity rules for main types of quantum gates, and then we design a two-level bidirectional reordering algorithm to reorder gates according to these rules for effective crosstalk mitigation. Our approach can not only capture both forward and backward instruction correlations but also reduce the effect of decoherence. We evaluate our approach with 117 quantum programs. Compared with the state-of-the-art, our approach can improve program fidelity by up to 120% (18% on average).
ChangeViz: Enhancing the GitHub Pull Request Interface with Method Call Information

Code review is a widely adopted software development practice aimed at finding defects, improving software quality, and transferring knowledge among developers.Performing an effective code review is a challenging task for developers.Two of the main challenges reviewers face are (1) understanding the content of a review change-set and (2) assessing the impact of a change on the codebase.Visualization techniques can be used to increase developers' understanding of a changeset to review and its context.However, only a few attempts have been made to apply visualization to code review.In this paper, we present a novel approach we devised to support developers in understanding GitHub pull requests.Our approach expands the GitHub interface with two lateral bars to let developers navigate to the definition/uses of the methods in the changeset under review.We evaluated our approach's interface through (1) interviews with eight developers and (2) a survey with 12 participants.Based on the results of this evaluation, we implemented our approach in a web-based tool, CHANGEVIZ.
A syntactic complexity analysis of translational English across genres

Abstract This study compares the syntactic complexity between translational and non-translational English across four genres (i.e. fiction, news, general prose, and academic prose) and examines the connections between various forms and degrees of syntactic complexity measures and explicitation. Fourteen syntactic complexity indices were examined based on a one-million-word translational English corpus (COTE) and a one-million-word non-translational English corpus (i.e. FLOB), respectively. This study shows that syntactic explicitation in translations varies with the formality of discourse. The most significant complexity difference between translational vis-à-vis non-translational English is found in fiction, which is regarded as the major contributor to translational English syntactic complexity. No significant difference in syntactic complexity was observed between the two types of academic English texts. Translational English news and general prose stand between fiction and academic texts. Translational fiction and news are characterised by more phrasal complexity features such as coordinate and complex nominal phrases, and a key indicator of translational English general prose complexity is subordination. The findings of this study will help students of translation to make informed decisions on the arrangement of sentence structures when given texts of different genres.
Effective Implementation of Convolutional Long Short-Term Memory (ConvLSTM) Network in Forecasting Solar Irradiance

Even though solar energy usage for power generation is currently gaining popularity in many nations of the world, its unpredictability is still posing a great challenge. This problem is mainly caused by the stochastic nature of radiative power responsible for solar energy use in electric power generation. In this paper, a Convolutional Long Short-Term Memory (ConvLSTM) network is introduced to predict solar irradiance on an hourly basis. Five-year historical weather information obtained from Johannesburg was utilized as the input dataset. Using 80% of the entire dataset, the model was trained up to 1,000 epochs until there was no significant improvement in its accuracy. The testing data were introduced to determine the effectiveness of the predictive model. To measure its accuracy, the Root Mean Square Error (RMSE) metric was used. The result obtained from this experiment was compared with the ones obtained using Support Vector Machine (SVM) and Extreme Gradient Boosting (XGB) models trained separately with the same amount of data. The performance indicators showed that the ConvLSTM model outperformed the XGB and SVM models with the nRMSE value of 1.62%. It is suggested that implementing ConvLSTM for solar power prediction in Johannesburg could ensure better control of the problems of cascading solar radiation on power grid connections.
Would You Imagine Yourself Negotiating With a Robot, Jennifer? Why Not?

With the improvement of intelligent systems and robotics, social robots are becoming part of our society. To accomplish complex tasks, robots and humans may need to collaborate, and when necessary, they need to negotiate with each other. While designing such socially interacting robots, it is crucial to consider human factors such as facial expression, emotions, and body language. Since gestures play a crucial role in interaction, this article studies the effect of gestures in human&#x2013;robot negotiation experiments. Additionally, it compares the performance of variants of the well-known negotiation tactics (i.e., time-based and behavior-based) in automated negotiation literature in the context of human&#x2013;robot negotiations. Our experimental results support the finding in automated negotiation. That is, the robot gained higher utility when it imitates its opponent&#x2019;s bidding strategy than employing a time-based negotiation strategy. When adopting a behavior-based technique, there is a statistically significant effect of gestures on the underlying negotiation process, and, therefore, on negotiation outcome.
Explainable AI and susceptibility to adversarial attacks: a case study in classification of breast ultrasound images

Ultrasound is a non-invasive imaging modality that can be conveniently used to classify suspicious breast nodules and potentially detect the onset of breast cancer. Recently, Convolutional Neural Networks (CNN) techniques have shown promising results in classifying ultrasound images of the breast into benign or malignant. However, CNN inference acts as a black-box model, and as such, its decision-making is not interpretable. Therefore, increasing effort has been dedicated to explaining this process, most notably through GRAD-CAM and other techniques that provide visual explanations into inner workings of CNNs. In addition to interpretation, these methods provide clinically important information, such as identifying the location for biopsy or treatment. In this work, we analyze how adversarial assaults that are practically undetectable may be devised to alter these importance maps dramatically. Furthermore, we will show that this change in the importance maps can come with or without altering the classification result, rendering them even harder to detect. As such, care must be taken when using these importance maps to shed light on the inner workings of deep learning. Finally, we utilize Multi-Task Learning (MTL) and propose a new network based on ResNet-50 to improve the classification accuracies. Our sensitivity and specificity is comparable to the state of the art results.
Acyclicity Programming for Sigma-Protocols

Cramer, Damgård, and Schoenmakers (CDS) built a proof system to demonstrate the possession of subsets of witnesses for a given collection of statements that belong to a prescribed access structure \(\mathcal {P}\) by composing so-called sigma-protocols for each atomic statement. Their verifier complexity is linear in the size of the monotone span program representation of \(\mathcal {P}\).
Interactive Slice Visualization for Exploring Machine Learning Models

Machine learning models fit complex algorithms to arbitrarily large datasets. These algorithms are well known to be high on performance and low on interpretability. We use interactive visualization of slices of predictor space to address the interpretability deficit; in effect opening up the black-box of machine learning algorithms, for the purpose of interrogating, explaining, validating and comparing model fits. Slices are specified directly through interaction, or using various touring algorithms designed to visit high-occupancy sections, or regions where the model fits have interesting properties. The methods presented here are implemented in the R package condvis2. Supplementary files for this article are available online.
Texture classification using Minkowski distance measure-based clustering for feature selection

Texture classification has been one of the problem-solving strategies. The feature selection technique is critical for increasing the efficiency of machine learning because it significantly improves the performance of texture classification by removing irrelevant features from the original collection. The majority of methods for selecting features are statistical in nature. Accuracy in database classification is primarily achieved by selecting attributes and simultaneously increasing the classification rate. The task’s primary objective is to select the most significant features from the feature database for execution. We use the cluster-based feature selection method to derive features for statistical and structural approaches. The proposed strategy should be implemented in three stages. To begin, the function can be extracted using the local binary pattern (LBP), the gray level co-occurrence matrix, or the Gabor filter. Following that, a modified K-means clustering method is used to select features using four standard distance measurements: Euclidean, Minkowski, Chebychev, and City Block. Finally, benchmark classifiers such as naive bayes (NB), support vector machine (SVM), and K-nearest neighbor (KNN) were used to effectively compute the classification accuracy of the research. The proposed approach achieves accuracy of 99.4328 for KNN, 98.6654 for NB, and 99.6712 for SVM with the highest classification rate.
Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR

We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language in question. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from the target language. We apply this decipherment to phone sequences generated by a universal phone recogniser trained on out-of-language speech corpora, which we follow with flat-start semi-supervised training to obtain an acoustic model for the new language. To the best of our knowledge, this is the first practical approach to zero-resource cross-lingual ASR which does not rely on any hand-crafted phonetic information. We carry out experiments on read speech from the GlobalPhone corpus, and show that it is possible to learn a decipherment model on just 20 minutes of data from the target language. When used to generate pseudo-labels for semi-supervised training, we obtain WERs that range from 25% to just 5% absolute worse than the equivalent fully supervised models trained on the same data.
Underwater Sonar Target Detection Based on YOLOv5

Sonar is widely used in seabed resources and military exploration. However, underwater sonar target detection methods are few and most of them are traditional algorithms. To solve this problem, an improved detection network based on YOLOV5 is designed in this paper. The model training and test results show that the model has a good ability to detect underwater sonar objects.