Reviewerly/exHarmony huggingface dataset:
Causal Discovery in Physical Systems from Videos

Causal discovery is at the core of human cognition. It enables us to reason about the environment and make counterfactual predictions about unseen scenarios that can vastly differ from our previous experiences. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure. In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of the dynamical system. Our model consists of (a) a perception module that extracts a semantically meaningful and temporally consistent keypoint representation from images, (b) an inference module for determining the graph distribution induced by the detected keypoints, and (c) a dynamics module that can predict the future by conditioning on the inferred graph. We assume access to different configurations and environmental conditions, i.e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions. We evaluate our method in a planar multi-body interaction environment and scenarios involving fabrics of different shapes like shirts and pants. Experiments demonstrate that our model can correctly identify the interactions from a short sequence of images and make long-term future predictions. The causal structure assumed by the model also allows it to make counterfactual predictions and extrapolate to systems of unseen interaction graphs or graphs of various sizes.
Chatbot Integration in Few Patterns

Chatbots are software agents that are able to interact with humans in natural language. Their intuitive interaction paradigm is expected to significantly reshape the software landscape of tomorrow, while already today chatbots are invading a multitude of scenarios and contexts. This article takes a developer's perspective, identifies a set of architectural patterns that capture different chatbot integration scenarios, and reviews state-of-the-art development aids.
Blockchain platforms in supply chains

Purpose The purpose of this study is to analyze blockchain (Bc) platforms in order to expand knowledge about the functionalities applied to supply chains (SCs). Therefore, this research seeks to address what these functionalities are and whether they are aligned with the theory. Design/methodology/approach The research utilizes a literature review on Bc in SC (Bc-SC) and an exploratory study focusing on the developers of Bc platforms. Findings The research maps and synthesizes concepts and applications of the platforms for SC. The platforms involved with the development of solutions with Bc technology are relatively representative (138 found by this research, and 47 offer solutions in SC), as well as the diversity of solutions and application (101 applications), covering different activities and areas of the SC. Research limitations/implications Despite extensive research and data analysis, there may be other platforms and solutions not analyzed in this paper. Practical implications The narrative and the different examples provided contribute to managers' understanding of the different possibilities for Bc-SC. Studying the services offered by the various platforms also enables managers to better understand what options exist and could potentially be utilized. Originality/value This is one of the first papers in a leading international OSCM (operations and supply chain management) journal to analyze the Bc platforms in SC and thereby has its relevance by synthesizing the knowledge that is dispersed in software developers (platforms).
Device-Independent Certification of Genuinely Entangled Subspaces

Self-testing is a procedure for characterizing quantum resources with the minimal level of trust. Up to now it has been used as a device-independent certification tool for particular quantum measurements, channels, and pure entangled states. In this work we introduce the concept of self-testing more general entanglement structures. More precisely, we present the first self-tests of an entangled subspace - the five-qubit code and the toric code. We show that all quantum states maximally violating a suitably chosen Bell inequality must belong to the corresponding code subspace, which remarkably includes also mixed states.
On Regularization Based Twin Support Vector Regression with Huber Loss

Twin support vector regression (TSVR) is generally employed with ε -insensitive loss function which is not well capable to handle the noises and outliers. According to the definition, Huber loss function performs as quadratic for small errors and linear for others and shows better performance in comparison to Gaussian loss hence it restrains easily for a different type of noises and outliers. Recently, TSVR with Huber loss (HN-TSVR) has been suggested to handle the noise and outliers. Like TSVR, it is also having the singularity problem which degrades the performance of the model. In this paper, regularized version of HN-TSVR is proposed as regularization based twin support vector regression (RHN-TSVR) to avoid the singularity problem of HN-TSVR by applying the structured risk minimization principle that leads to our model convex and well-posed. This proposed RHN-TSVR model is well capable to handle the noise as well as outliers and avoids the singularity issue. To show the validity and applicability of proposed RHN-TSVR, various experiments perform on several artificial generated datasets having uniform, Gaussian and Laplacian noise as well as on benchmark different real-world datasets and compare with support vector regression, TSVR, ε -asymmetric Huber SVR, ε -support vector quantile regression and HN-TSVR. Here, all benchmark real-world datasets are embedded with a different significant level of noise 0%, 5% and 10% on different reported algorithms with the proposed approach. The proposed algorithm RHN-TSVR is showing better prediction ability on artificial datasets as well as real-world datasets with a different significant level of noise compared to other reported models.
Digitalization of Modern Education: Problems and Solutions

The relevance of the research topic is due to modern global digital transformations and the transition to the digital economy and digital society. Social digitalization is caused by qualitative technological changes. All over the world, digitalization in education is closely related to a new way of life in a digital environment and the emergence of generations who were born and study in the specific environment. The study was based on a survey assessing the quality of digitalization in education, which involved 475 students and 118 teachers of the Russian State Vocational Pedagogical University (Yekateringburg). Based on the analysis of modern research on digital education, the most important components of digitalization have been identified and assessed in the survey. The survey was based on a Likert scale; the importance of each significant aspect of digitalization was assessed from 0 to 10. Unanimously, the research participants rated the level of penetration of digital technologies into Russian higher education as low: 3.15 (students) and 3.43 (teachers). The contribution of digitalization to academic performance was rated above average (7.25 and 5.21, respectively). There were some differences in the assessment of the role of the teacher in the digital environment (4.65 and 7.14), the convenience of the digital environment (7.11 and 2.53) and its positive impact on student motivation (8.09 and 3.61, respectively). The increase in the cognitive capabilities of students due to digitalization was also differently assessed by the students and teachers (6.88 and 3.11, respectively). Thus, digitalization in Russian universities generally reflects the expectation of its future development and a positive student attitude to the available opportunities. The novelty of the research is an attempt to objectively assess the internal process of digitalization and determine further research directions. In practice, the study can reform the education system, accelerate and improve the process of higher education digitalization.
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancements into which could influence several human-machine interaction applications. To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing representations conditioned on the input question. Our proposed Transformer variant uses a shuffling scheme on their multi-head outputs, demonstrating better regularization. To encode fine-grained visual information, we present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing spatio-semantic graph representations for every frame, and an inter-frame aggregation module capturing temporal cues. Our entire pipeline is trained end-to-end. We present experiments on the benchmark AVSD dataset, both on answer generation and selection tasks. Our results demonstrate state-of-the-art performances on all evaluation metrics.
Code Smells Detection and Visualization: A Systematic Literature Review

Code smells tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Researchers have argued that due to the subjectiveness of the code smells detection process, proposing an effective use of automatic support for this end is a non trivial task. This systematic literature review (SLR) has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Over eighty primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. We found that the most commonly used approaches to code smells detection are search-based (30.1%), metric-based (24.1%), and symptom-based approaches (19.3%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning (ML) techniques are used in 35% of the studies, with genetic programming, decision tree, support vector machines and association rules being the most used algorithms. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches several methods are used, such as: city metaphors, 3D visualization techniques, interactive ambient visualization, polymetric views, or graph models. This paper presents an up-to-date review on the state-of-the-art techniques and tools used for code smells detection and visualization. We confirm that the detection of code smells is a non trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of code smells; increasing the diversity of detected code smells and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of code smells detection and visualization techniques validation experiments.
A Time-Varying Adaptive Inertia Weight based Modified PSO Algorithm for UAV Path Planning

Path planning is an integral part of the execution of an autonomous Unmanned Ariel Vehicle (UAV). Finding the optimum path is an NP-hard problem and metaheuristics algorithms have been showing promising results in finding the optimum path. Particle Swarm optimization (PSO) is one of the commonly used metaheuristic optimization algorithms for path planning. However, PSO suffers from limitations such as falls for local minima. Particle diversity plays an important role in generating better results in path planning while avoiding local minima. Besides, parameters such as inertia weight are added to increase the diversity of particles. In this paper, we have provided an analysis of various inertia weight proposed for PSO to improve the particle diversity. Then we proposed a time-varying adaptive inertia weight parameter for our previously proposed version of PSO called nPSO for UAV path planning and compared the performance to other inertia weight parameter strategies.
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models

Chinese pre-trained language models usually process text as a sequence of characters, while ignoring more coarse granularity, e.g., words.In this work, we propose a novel pre-training paradigm for Chinese -Lattice-BERT, which explicitly incorporates word representations along with characters, thus can model a sentence in a multi-granularity manner.Specifically, we construct a lattice graph from the characters and words in a sentence and feed all these text units into transformers.We design a lattice position attention mechanism to exploit the lattice structures in self-attention layers.We further propose a masked segment prediction task to push the model to learn from rich but redundant information inherent in lattices, while avoiding learning unexpected tricks.Experiments on 11 Chinese natural language understanding tasks show that our model can bring an average increase of 1.5% under the 12-layer setting, which achieves new state-of-the-art among base-size models on the CLUE benchmarks.Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multigranularity representations.
Potentials of Airborne Hyperspectral AVIRIS-NG Data in the Exploration of Base Metal Deposit—A Study in the Parts of Bhilwara, Rajasthan

In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments of the ore deposit.The results show that the AVIRIS-NG image products derived in this study can delineate surface signatures of mineralization in 1:10000 to 1:15000 scales to narrow down the targets for detailed exploration.This study alsoidentified the possible structural control over the knownsurface distribution of alteration and lithocap minerals of base metal mineralizationusing the ground-based residual magnetic anomaly map. This observationstrengthens the importance of the identified surface proxiesas an indicator of mineralization. X-ray fluorescence analysis of samples collectedfromselected locations within the study area confirms the Cu-Pb-Zn enrichment. The sulfide minerals were also identified in the microphotographs of polished sections of rock samples collected from the places where surface proxies of mineralization were observed in the field. This study justified the investigation to utilize surface signatures of mineralization identified using AVIRIS-NG dataand validated using field observations, geophysical, geochemical, and petrographical data.
Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments

In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments. This work combined deep models with shallow architecture, which resulted in novel hybrid classifiers. Four distinct hybrid models were utilized: deep neural network-hidden Markov model (DNN-HMM), deep neural network-Gaussian mixture model (DNN-GMM), Gaussian mixture model-deep neural network (GMM-DNN), and hidden Markov model-deep neural network (HMM-DNN). All models were based on novel implemented architecture. The comparative study used three distinct speech datasets: a private Arabic dataset and two public English databases, namely Speech Under Simulated and Actual Stress (SUSAS) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The test results of the aforementioned hybrid models demonstrated that the proposed HMM-DNN leveraged the verification performance in emotional and stressful environments. Results also showed that HMM-DNN outperformed all other hybrid models in terms of equal error rate (EER) and area under the curve (AUC) evaluation metrics. The average resulting verification system based on the three datasets yielded EERs of 7.19, 16.85, 11.51, and 11.90% based on HMM-DNN, DNN-HMM, DNN-GMM, and GMM-DNN, respectively. Furthermore, we found that the DNN-GMM model demonstrated the least computational complexity compared to all other hybrid models in both talking environments. Conversely, the HMM-DNN model required the greatest amount of training time. Findings also demonstrated that EER and AUC values depended on the database when comparing average emotional and stressful performances.
AI and robotics to help older adults: Revisiting projects in search of lessons learned

Abstract This article is a retrospective overview of work performed in the domain of Active Assisted Living over a span of almost 18 years. The authors have been creating and refining artificial intelligence (AI) and robotics solutions to support older adults in maintaining their independence and improving their quality of life. The goal of this article is to identify strong features and general lessons learned from those experiences and conceive guidelines and new research directions for future deployment, also relying on an analysis of similar research efforts. The work considers key points that have contributed to increase the success of the innovative solutions grounding them on known technology acceptance models. The analysis is presented with a threefold perspective: A Technological vision illustrates the characteristics of the support systems to operate in a real environment with continuity , robustness , and safety ; a Socio-Health perspective highlights the role of experts in the socio-assistance domain to provide contextualized and personalized help based on actual people’s needs; finally, a Human dimension takes into account the personal aspects that influence the interaction with technology in the long term experience. The article promotes the crucial role of AI and robotics in ensuring intelligent and situated assistive behaviours. Finally, considering that the produced solutions are socio-technical systems , the article suggests a transdisciplinary approach in which different relevant disciplines merge together to have a complete, coordinated, and more informed vision of the problem.
Optimal binary LCD codes

Linear complementary dual codes (or codes with complementary duals) are codes whose intersections with their dual codes are trivial. These codes were first introduced by Massey in 1964. Nowadays, LCD codes are extensively studied in the literature and widely applied in data storage, cryptography, etc. In this paper, we prove some properties of binary LCD codes using their shortened and punctured codes. We also present some inequalities for the largest minimum weight $d_{LCD}(n,k)$ of binary LCD [n,k] codes for given length n and dimension k. Furthermore, we give two tables with the values of $d_{LCD}(n,k)$ for $k\le 32$ and $n\le 40$, and two tables with classification results.
Bounding Box Tightness Prior for Weakly Supervised Image Segmentation

This paper presents a weakly supervised image segmentation method that adopts tight bounding box annotations. It proposes generalized multiple instance learning (MIL) and smooth maximum approximation to integrate the bounding box tightness prior into the deep neural network in an end-to-end manner. In generalized MIL, positive bags are defined by parallel crossing lines with a set of different angles, and negative bags are defined as individual pixels outside of any bounding boxes. Two variants of smooth maximum approximation, i.e., $\alpha$-softmax function and $\alpha$-quasimax function, are exploited to conquer the numeral instability introduced by maximum function of bag prediction. The proposed approach was evaluated on two pubic medical datasets using Dice coefficient. The results demonstrate that it outperforms the state-of-the-art methods. The codes are available at \url{https://github.com/wangjuan313/wsis-boundingbox}.
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.