Reviewerly/exHarmony huggingface dataset:
Robust Learning From Demonstrations With Mixed Qualities Using Leveraged Gaussian Processes

In this paper, we focus on the problem of learning from demonstration (LfD) where demonstrations with different proficiencies are provided without labeling. To this end, we model multiple policies with different qualities as correlated Gaussian processes and present a leverage optimization method that estimates the leverage of each policy where the difference between two leverages defines the correlation between the corresponding policies. To recover a single policy function of an expert, we present a sparsity constraint on the leverage parameters. We first show that the proposed leverage optimization method can recover the correlations between sensory fields where the fields are realized from correlated Gaussian processes and sensor measurements are collected from the fields. Furthermore, we applied the proposed method to autonomous driving experiments, where demonstrations are collected from three different driving modes. While the driving policies are not realized from correlated processes, the proposed method assigns reasonable leverages to the driving demonstrations. The estimated driving policy of an expert, which incorporates the optimized leverages, outperforms previous LfD methods in terms of both safety and driving quality.
An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. In this article, we present a new Bayesian hierarchical framework that is able to model the relationship between system inputs (sensor measurements) and outputs (response variables) without imposing strong distributional/parametric assumptions while using only a subset of training samples and sensor attributes. Then, an optimal cost-sensitive anomaly detection framework is proposed to determine whether a sample is an anomalous one taking into consideration the trade-off between misclassification errors and detection rates. The model can be used for both supervised and unsupervised settings depending on the availability of data regarding the behavior of the system under anomaly conditions. The unsupervised model is particularly useful when it is prohibitive to identify in advance the anomalies that a system may present and where no data are available regarding the behavior of the system under anomaly conditions. A Bayesian hierarchical setting is used to structure the proposed framework and help with accommodating uncertainty, imposing interpretability, and controlling the sparsity and complexity of the proposed anomaly detection framework. A Markov chain Monte Carlo algorithm is also developed for model training using past data. The numerical experiments conducted using a simulated data set and a wind turbine data set demonstrate the successful application of the proposed work for system response modeling and anomaly detection.
Deep Reinforcement Learning for Query-Conditioned Video Summarization

Query-conditioned video summarization requires to (1) find a diverse set of video shots/frames that are representative for the whole video, and that (2) the selected shots/frames are related to a given query. Thus it can be tailored to different user interests leading to a better personalized summary and differs from the generic video summarization which only focuses on video content. Our work targets this query-conditioned video summarization task, by first proposing a Mapping Network (MapNet) in order to express how related a shot is to a given query. MapNet helps establish the relation between the two different modalities (videos and query), which allows mapping of visual information to query space. After that, a deep reinforcement learning-based summarization network (SummNet) is developed to provide personalized summaries by integrating relatedness, representativeness and diversity rewards. These rewards jointly guide the agent to select the most representative and diversity video shots that are most related to the user query. Experimental results on a query-conditioned video summarization benchmark demonstrate the effectiveness of our proposed method, indicating the usefulness of the proposed mapping mechanism as well as the reinforcement learning approach.
Semi-Supervised Learning with Auto-Weighting Feature and Adaptive Graph

Traditional graph-based Semi-Supervised Learning (SSL) methods usually contain two separate steps. First, constructing an affinity matrix. Second, inferring the unknown labels. While such a two-step method has been successful, it cannot take full advantage of the correlation between affinity matrix and label information. In order to address the above problem, we propose a novel graph-based SSL method. It can learn the affinity matrix and infer the unknown labels simultaneously. Moreover, feature selection with auto-weighting is introduced to extract the effective and robust features. Further, the proposed method learns the data similarity matrix by assigning the adaptive neighbors for each data point based on the local distance. We solve the unified problem via an alternative minimization algorithm. Extensive experimental results on synthetic data and benchmark data show that the proposed method consistently outperforms the state-of-the-art approaches.
The Witwatersrand Basin and Its Gold Deposits

The Mesoarchaean Witwatersrand Basin represents the largest known gold anomaly and has produced more gold than any other ore province in the world. Deposition of the predominantly siliciclastic basin fill began shortly after 2985 Ma in a shallow marine environment, maybe along a passive margin to an old continent to the north(west), and under generally cool climatic conditions (West Rand Group). Tectonic inversion following the extrusion of 2914 Ma andesitic lava led to a shift towards continental sedimentation. Following a basin-wide hiatus at around 2900 Ma, the largely arenitic Central Rand Group was deposited into a retroarc foreland basin that regressed as it was filled between <2902 and >2780 Ma in response to crustal accretion along the western and northern margins of the basin. This stage was accompanied by the largest known concentration of gold in Earth's history, initially by microbial fixation on emerging colonies of probably cyanobacteria in near-coastal environments, subsequently by mechanical reworking of the gold-rich microbial mats to form conglomerate-hosted placer deposits. The source of the huge amount of gold in the Witwatersrand is thought to be the entire greenstone-dominated Archaean cratonic surface, which was subjected to intensive chemical weathering permitting large-scale leaching of gold by contemporaneous surface waters. Syn-depositional tectonism that extended from the hinterland into the foreland basin led to repeated further physical reworking of gold-rich sediments to form more placer deposits higher up in the stratigraphy, even above the Witwatersrand Supergroup. The Witwatersrand Basin fill was subjected to a series of alteration events, ranging from burial and regional low-grade metamorphism to heating in the course of the emplacement of the 2054 Ma Bushveld Igneous Complex and catastrophic shattering during the 2023 Ma Vredefort impact, which enabled renewed fluid flow long after primary rock porosity had been obliterated. This caused some short-range mobilisation of ore components, including gold, but without changing the overall sedimentological and stratigraphic control on ore grade.
Dejavu

Videoconferencing over the Internet routinely suffers from poor quality as videoconferencing systems, in order to guarantee interactive delays which is critical to user experience, are commonly designed to stream at conservative qualities in the face of variable bandwidths. In this paper, we present Dejavu, a system that enables existing videoconferencing systems to alleviate this problem. The key insight that powers Dejavu is that recurring videoconferencing sessions, e.g., in the same conference room or by the same person, have a lot of visual similarities that can be encoded based on the sender's historical videoconferencing sessions, and shared with the receiver in advance. Accordingly, Dejavu first learns an offline mapping between low-quality and high-quality versions of frames in the sender's past videoconferencing sessions, and then applies this mapping in real time at the receiver to convert the low-quality frames into high-quality frames. As a result, a videoconferencing system equipped with Dejavu can continue to stream at conservative qualities to guarantee interactive delays like today, but can now additionally enhance the video quality at the receiver. Our evaluation shows that Dejavu can provide a 1.3 dB increase in PSNR for the same bandwidth consumption, or equivalently save up to 30% in bandwidth to deliver the same PSNR.
Mineral Mapping and Vein Detection in Hyperspectral Drill-Core Scans: Application to Porphyry-Type Mineralization

The rapid mapping and characterization of specific porphyry vein types in geological samples represent a challenge for the mineral exploration and mining industry. In this paper, a methodology to integrate mineralogical and structural data extracted from hyperspectral drill-core scans is proposed. The workflow allows for the identification of vein types based on minerals having significant absorption features in the short-wave infrared. The method not only targets alteration halos of known compositions but also allows for the identification of any vein-like structure. The results consist of vein distribution maps, quantified vein abundances, and their azimuths. Three drill-cores from the Bolcana porphyry system hosting veins of variable density, composition, orientation, and thickness are analysed for this purpose. The results are validated using high-resolution scanning electron microscopy-based mineral mapping techniques. We demonstrate that the use of hyperspectral scanning allows for faster, non-invasive and more efficient drill-core mapping, providing a useful tool for complementing core-logging performed by on-site geologists.
TarGAN: Generating target data with class labels for unsupervised domain adaptation

The recent successes of deep neural networks heavily rely on massive amounts of data annotations. For a certain task that is short of labeled data, the cost of large-scale data annotation is usually prohibitively expensive. Through transferring the already learnt knowledge from a different but related source domain that is sufficiently labeled, domain adaptation aims to relieve such a burden for the target domain. The recent studies reveal that learning domain-invariant features has gained great successes in unsupervised domain adaptation. However, domain invariance does not necessarily imply discriminative representations in the target domain due to the lack of labeled target data. To address this issue, we propose a new unsupervised domain adaptation method named TarGAN, which can successfully disentangle the class and the style codes of the target generator and generate Target samples with given class labels based on the recent advances of Generative Adversarial Networks. This is achieved by tying the high-level layers of the source generator and the target generator, and concurrently enforcing high mutual information between the class code and the generated images. Accompanied with given class labels, these generated target images can significantly improve the classifier’s discriminative ability on the target domain. Experiments on several standard benchmarks demonstrate that our proposed TarGAN can outperform the existing state-of-the-art domain adaptation methods.
Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.
Multimodal image registration using Laplacian commutators

The fusion and combination of images from multiple modalities is important in many applications. Typically, this process consists of the alignment of the images and the combination of the complementary information. In this work, we focused on the former part and propose a multimodal image distance measure based on the commutativity of graph Laplacians. The eigenvectors of the image graph Laplacian, and thus the graph Laplacian itself, capture the intrinsic structure of the image’s modality. Using Laplacian commutativity as a criterion of image structure preservation, we adapt the problem of finding the closest commuting operators to multimodal image registration. Hence, by using the relation between simultaneous diagonalization and commutativity of matrices, we compare multimodal image structures by means of the commutativity of their graph Laplacians. In this way, we avoid spectrum reordering schemes or additional manifold alignment steps which are necessary to ensure the comparability of eigenspaces across modalities. We show on synthetic and real datasets that this approach is applicable to dense rigid and non-rigid image registration. Results demonstrated that the proposed measure is able to deal with very challenging multimodal datasets and compares favorably to normalized mutual information, a de facto similarity measure for multimodal image registration.
ATLASLang MTS 1: Arabic Text Language into Arabic Sign Language Machine Translation System

Arabic Sign Language (ArSL) is a visual language used by deaf Arabs and some hard of hearing to translate their thinking. It is a language in its own right as well as spoken languages such as Arabic or English. It is produced by body, face, and gestures of the hands. Hundreds of thousands of deaf people around the world currently practice it. Each country has its own sign language that deaf people use so it is not universal, but deaf people from different countries communicate easily with each other after a short period of adaptation. This communication becomes difficult when a deaf want to communicate with a normal person with no interpreter available. In this way, building a translation system that can generate real-time statements via a signing avatar is helpful. The system to be developed is a machine translation system from Arabic text to the Arabic sign language, it will allow hearing persons to communicate more easily, using a text written in Arabic by normal person, with people suffering from hard hearing and knowing only sign language. The system must perform a morpho-syntactic analysis of the text in the input and convert it to video sequences phrases playing by a 3D human avatar who expresses the usual signs used by deaf people. In this paper, we present a first version of our machine translation system ATLASLang MTS 1 based on rule-based Interlingua and example-based approaches. It uses SAFAR Platform [11] and ALKHALIL morpho system [1] to extract the morphological properties of each word of the input sentence. Then it generates a video sequence representing the sentence in Arabic sign language based on well-established translation rules and the database of signs. During the translation, if a word of sentence is a proper noun or does not have a correspondence in the database, it will be finger spelled. The main constraint imposed by our machine translation system is the assumption that the sentence given in the input must be completely vowelized.
A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system

Nowadays, searching the relevant documents from a large dataset becomes a big challenge. Automatic query expansion is one of the techniques, which addresses this problem by refining the query. A new query expansion approach using cuckoo search and accelerated particle swarm optimization technique is proposed in this paper. The proposed approach mainly focused to find the most relevant expanded query rather than suitable expansion terms. In this paper, Fuzzy logic is also employed, which improves the performance of accelerated particle swarm optimization by controlling various parameters. We have compared the proposed approach with other existing and recently developed automatic query expansion approaches on various evaluating parameters such as average recall, average precision, Mean-Average Precision, F-measure and precision-recall graph. We have evaluated the performance of all approaches on three datasets CISI, CACM and TREC-3. The results obtained for all three datasets depict that the proposed approach gets better results in comparison to other automatic query expansion approaches.
How to Improve Performance in Bayesian Inference Tasks: A Comparison of Five Visualizations

Bayes' formula is a fundamental statistical method for inference judgments in uncertain situations used by both laymen and professionals. However, since people often fail in situations where Bayes' formula can be applied, how to improve their performance in Bayesian situations is a crucial question. We based our research on a widely accepted beneficial strategy in Bayesian situations, representing the statistical information in the form of natural frequencies. In addition to this numerical format, we used five visualizations: a 2 × 2-table, a unit square, an icon array, a tree diagram, and a double-tree diagram. In an experiment with 688 undergraduate students, we empirically investigated the effectiveness of three graphical properties of visualizations: area-proportionality, use of discrete and countable statistical entities, and graphical transparency of the nested-sets structure. We found no additional beneficial effect of area proportionality. In contrast, the representation of discrete objects seems to be beneficial. Furthermore, our results show a strong facilitating effect of making the nested-sets structure of a Bayesian situation graphically transparent. Our results contribute to answering the questions of how and why a visualization could facilitate judgment and decision making in situations of uncertainty.
Bowing Gestures Classification in Violin Performance: A Machine Learning Approach

Gestures in music are of paramount importance partly because they are directly linked to musicians' sound and expressiveness. At the same time, current motion capture technologies are capable of detecting body motion/gestures details very accurately. We present a machine learning approach to automatic violin bow gesture classification based on Hierarchical Hidden Markov Models (HHMM) and motion data. We recorded motion and audio data corresponding to seven representative bow techniques (Détaché, Martelé, Spiccato, Ricochet, Sautillé, Staccato, and Bariolage) performed by a professional violin player. We used the commercial Myo device for recording inertial motion information from the right forearm and synchronized it with audio recordings. Data was uploaded into an online public repository. After extracting features from both the motion and audio data, we trained an HHMM to identify the different bowing techniques automatically. Our model can determine the studied bowing techniques with over 94% accuracy. The results make feasible the application of this work in a practical learning scenario, where violin students can benefit from the real-time feedback provided by the system.
Annotation of Software Requirements Specification (SRS), Extractions of Nonfunctional Requirements, and Measurement of Their Tradeoff

Software requirement artifacts such as manuals request for proposals, and software requirements specification (SRS) are commonly focused on functional requirements. In most SRS files, nonfunctional requirements do not formally encoded or encoded as a whole, not for an individual design problem. Moreover, these nonfunctional requirements are intermingled with functional requirements. Therefore, these nonfunctional requirements need special attention to understand for successful project development. These nonfunctional requirements have an impact on each other and optimal tradeoff is required for balanced nonfunctional requirements set. NFRs have a negative and positive tradeoff with each other such as increase confidentiality, decrease the availability, and enhance authenticity. So, an optimum tradeoff among these design problem within a module is required to have better design decisions. Instead of considering all nonfunctional requirements, the NFRs that have mutual tradeoff is considered. In this paper, we devised a novel document annotation scheme for SRS and extracted nonfunctional requirements from these annotated artifacts. In the next step, we classified NFRs into two classes security triad and performance triad, and the cost is assumed constant for each NFR. From the design problem, the tradeoff ratio is calculated among NFRs associated with it. Then, the production possibility graph is plotted to estimate the optimum tradeoff ratio within the module. For estimation economic optimum from a set of NFR, iso-cost graphs by assuming the constant cost. Some hypothetical variations in cost are also examined using 3D iso-cost graph. The reason to measure these tradeoff is to make design decision more empirical and helpful for the selection of design patterns, especially secure design patterns.
Towards Structured Evaluation of Deep Neural Network Supervisors

Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years. However, before DNNs should be deployed to safety-critical applications, their robustness needs to be systematically analyzed. A common challenge for DNNs occurs when input is dissimilar to the training set, which might lead to high confidence predictions despite proper knowledge of the input. Several previous studies have proposed to complement DNNs with a supervisor that detects when inputs are outside the scope of the network. Most of these supervisors, however, are developed and tested for a selected scenario using a specific performance metric. In this work, we emphasize the need to assess and compare the performance of supervisors in a structured way. We present a framework constituted by four datasets organized in six test cases combined with seven evaluation metrics. The test cases provide varying complexity and include data from publicly available sources as well as a novel dataset consisting of images from simulated driving scenarios. The latter we plan to make publicly available. Our framework can be used to support DNN supervisor evaluation, which in turn could be used to motive development, validation, and deployment of DNNs in safety-critical applications.