Reviewerly/exHarmony huggingface dataset:
Supergene mobilization and redistribution of platinum-group elements in the Merensky Reef, eastern Bushveld Complex, South Africa

ABSTRACT Near-surface supergene ores of the Merensky Reef in the Bushveld Complex, South Africa, contain economic grades of platinum-group elements, however, these are currently uneconomic due to low recovery rates. This is the first study that investigates the variation in platinum-group elements in pristine and supergene samples of the Merensky Reef from five drill cores from the eastern Bushveld. The samples from the Richmond and Twickenham farms show different degrees of weathering. The whole-rock platinum-group element distribution was studied by inductively coupled plasma-mass spectrometry and the platinum-group minerals were investigated by reflected-light microscopy, scanning electron microscopy, and electron microprobe analysis. In pristine (“fresh”) Merensky Reef samples, platinum-group elements occur mainly as discrete platinum-group minerals, such as platinum-group element-sulfides (cooperite–braggite) and laurite as well as subordinate platinum-group element-bismuthotellurides and platinum-group element-arsenides, and also in solid solution in sulfides (especially Pd in pentlandite). During weathering, Pd and S were removed, resulting in a platinum-group mineral mineralogy in the supergene Merensky Reef that mainly consists of relict platinum-group minerals, Pt-Fe alloys, and Pt-oxides/hydroxides. Additional proportions of platinum-group elements are hosted by Fe-hydroxides and secondary hydrosilicates (e.g., serpentine group minerals and chlorite). In supergene ores, only low recovery rates (ca. 40%) are achieved due to the polymodal and complex platinum-group element distribution. To achieve higher recovery rates for the platinum-group elements, hydrometallurgical or pyrometallurgical processing of the bulk ore would be required, which is not economically viable with existing technology.
On the Recognition Performance of BioHashing on state-of-the-art Face Recognition models

Face recognition has become a popular authentication tool in recent years. Modern state-of-the-art (SOTA) face recognition methods rely on deep neural networks, which extract discriminative features from face images. Although these methods have high recognition performance, the extracted features contain privacy-sensitive information. Hence, the users' privacy would be jeopardized if the features stored in the face recognition system were compromised. Accordingly, protecting the extracted face features (templates) is an essential task in face recognition systems. In this paper, we use BioHashing for face template protection and aim to establish the minimum BioHash length that would be required in order to maintain the recognition accuracy achieved by the corresponding unprotected system. We consider two hypotheses and experimentally show that the performance depends on the value of the BioHash length (as opposed to the ratio of the BioHash length to the dimension of the original features). To eliminate bias in our experiments, we use several SOTA face recognition models with different network structures, loss functions, and training datasets, and we evaluate these models on two different datasets (LFW and MOBIO). We provide an open-source implementation of all the experiments presented in this paper so that other researchers can verify our findings and build upon our work.
Future memory: a digital humanities approach for the preservation and presentation of the history of the Holocaust and Nazi crimes

We describe the concepts, methodology, technology, and practice of a user-centric and historical space-oriented approach towards Historical and Cultural Learning (HCL). The Future Memory project pursues the unifying hypothesis that physical space can play a critical role in the authentication and education of this singular historical event, or space as a frame for memory formation and a source of authentication. We illustrate these aspects in the context of concrete Future Memory projects realized over the last ten years in collaboration with several memorial sites, museums, and archives. Based on these experiences, we subsequently analyze the lessons learned and future challenges.
Constant Subjective Quality Database: The Research and Device of Generating Video Sequences of Constant Quality

Video databases with the collection of subjective assessments form an important basis for learning algorithms, testing, and creating an objective video quality assessment. In this paper, presented is a novel device to measure coded video quality was proposed using the finding of an acceptable minimum perception threshold, which allows one to generate video sequences of constant quality. Also, this work describes the method, which allows generating video sequences of constant quality, and a specialized installation that allows you to dynamically change the visual quality of typical media content during the examination process. This approach will allow obtaining well-labelled data in a larger volume compared to the currently used binary estimates. The novel databases with this approach will be used both to create effective adaptive codecs built according to the hybrid model, and new ones based on machine learning.
Authorship Attribution for a Resource Poor Language—Urdu

Authorship attribution refers to examining the writing style of authors to determine the likelihood of the original author of a document from a given set of potential authors. Due to the wide range of authorship attribution applications, a plethora of studies have been conducted for various Western, as well as Asian, languages. However, authorship attribution research in the Urdu language has just begun, although Urdu is widely acknowledged as a prominent South Asian language. Furthermore, the existing studies on authorship attribution in Urdu have addressed a considerably easier problem of having less than 20 candidate authors, which is far from the real-world settings. Therefore, the findings from these studies may not be applicable to the real-world settings. To that end, we have made three key contributions: First, we have developed a large authorship attribution corpus for Urdu, which is a low-resource language. The corpus is composed of over 2.6 million tokens and 21,938 news articles by 94 authors, which makes it a closer substitute to the real-world settings. Second, we have analyzed hundreds of stylometry features used in the literature to identify 194 features that are applicable to the Urdu language and developed a taxonomy of these features. Finally, we have performed 66 experiments using two heterogeneous datasets to evaluate the effectiveness of four traditional and three deep learning techniques. The experimental results show the following: (a) Our developed corpus is many folds larger than the existing corpora, and it is more challenging than its counterparts for the authorship attribution task, and (b) Convolutional Neutral Networks is the most effective technique, as it achieved a nearly perfect F1 score of 0.989 for an existing corpus and 0.910 for our newly developed corpus.
Classification of Organic and Solid Waste Using Deep Convolutional Neural Networks

The total amount of waste is increasing all around the world day-by-day especially in urban areas. The increasing amount of unprocessed waste is very dangerous to mankind as it creates severe pollution in the environment. Most of this wastage is recyclable. For recycling, the waste needs to be separated at first, as different types of waste require different recycling techniques. But unfortunately, categorizing waste manually is very costly and time-consuming. So, in this work, a method is proposed to automatically classify waste into four categories. For this, a dataset named OrgalidWaste is prepared by collecting images from four other waste datasets. The prepared dataset contains around 5600 images with four classes including one organic waste class and three solid waste classes (glass, metal, and plastic). On this dataset, several CNN architectures including 3-layer CNN, VGG16, VGG19, Inception-V3, and ResNet50 have been implemented for training. Among them, VGG16 outperforms other models with 88.42% accuracy. It is believed that this work will be greatly beneficial in the waste management sector.
Towards a Trusted and Unified Consortium-Blockchain-Based Data Sharing Infrastructure for Open Learning—TolFob Architecture and Implementation

Open learning is now facing a complex higher education ecosystem that involves a variety of heterogeneous information systems and comprises decentralized stakeholders, such as universities, professors, students, and software vendors. Authentic, non-repudiable, and fast available data sharing among open learning information systems and stakeholders is a key issue that remains unresolved. To solve this problem, this paper proposes a consortium blockchain extended architecture featuring integration and cross-chain functions to provide a unified and trusted data-sharing infrastructure for open learning. The overall architecture consists of three elements: a blockchain-integrated open learning scenario schema; a blockchain-integrated open learning application model; and a pragmatic blockchain integration framework. The proposed blockchain integration framework is implemented based on Hyperledger Fabric 1.4. A trusted open-learning behavior and achievement management application is developed as a proof-of-concept which integrates two educational institutions’ four productional learning systems into a blockchain network and has stably run over six months. A suite of experiments is designed and executed to verify our blockchain system’s viability and scalability. The test result shows the implementation of the blockchain system is competent for the production environment and outperforms related works investigated. However, it does have limitations and optimization potential, which will be studied in the future.
A spatially adaptive hybrid total variation model for image restoration under Gaussian plus impulse noise

In this paper, a spatially adaptive hybrid total variation model is proposed to recover blurred images corrupted by mixed Gaussian-impulse noise. The model consists of a combined L1/L2 data fidelity term and two regularization terms including total variation and high-order total variation. The spatially adaptive parameters with multiple windows are utilized by the model to adequately smooth homogeneous areas while preserving small features. A strategy for adaptively selecting the locally varying parameters together with a solver of the constituted optimisation problem are presented. Experimental results demonstrate the excellent performance of the new approach compared with current state-of-the-art methods with respect to digital indicators and visual quality.
Incorporation of geological constraints into geochemical anomaly identification using BME-GWR: A case study from Inner Mongolia of China

Delineation of geochemical anomalies is a fundamental task in mineral and environmental investigation. Mineralization area of interest is mainly controlled by underlying geological environment. These geological constraints facilitate to identify mineralization anomalies and remove pseudo-anomalies. Regional geochemical data is one of most commonly used in deriving mineralization information, which is also considered to be the primary data for direct geochemical anomaly identification. The selected study area, Jining in central Inner Mongolia of China, is mostly covered with Quaternary sediments, whereas is located in the important molybdenum polymetallic mineralized belt. The overburden shields the geochemical signatures from deep, resulting in missing some important information. The NE-trending faults, intersection of faults, Jining formation and mid-acid granites is mainly ore-forming factors in this study area, which are used as the geological constraints for identifying molybdenum polymetallic mineralization. Geographical weighted regression (GWR) generates the mean and variance of probability soft data based on these geological constraints. Bayesian maximum entropy (BME) integrates the constructed soft data with geochemical data for obtaining practical mineralization anomalies. These results shown that the combination of BME and GWR can identify local anomalies and weak anomalies compared with the results obtained from geochemical data alone. Specially, some anomalies of different periods of deposits have been highlighted in the results of BME-GWR model. The discovered deposits correspond to the identified mineralization anomalies well. The BME-GWR model provides a potentially powerful tool for geochemical anomaly identification under consideration of geological constraints.
SP-ant: An ant colony optimization based operator scheduler for high performance distributed stream processing on heterogeneous clusters

A key feature of distributed stream processing (DSP) systems is the scheduling of operators on clustered computers. In scheduling, the assignment plan of operators to nodes of the cluster, requirements of operators, and the computational power of each worker node must be considered with the goal of finding a tradeoff between the communication latency of operators and the utilization of worker nodes to minimize the overall system response time. To reach this goal is quite challenging, especially in heterogeneous clusters, because there are no accurate estimations about the loads of worker nodes at run time. To address this challenge, we propose a novel stream processing scheduling using ant colony algorithm (SP-Ant). SP-Ant finds the best operator assignment plan considering the inter-node communication latencies of operators by initially collocating highly communicative operators on the same worker nodes using the bin-packing algorithm and iteratively (re-)scheduling only the less communicative operators using the exploration and exploitation phases of the evolutionary ant colony optimization (ACO) algorithm in order to reduce its convergence time. SP-Ant is implemented on the standard Apache Storm. Using several standard benchmark topologies of Storm, it is shown that SP-Ant outperforms the R-Storm and Storm default schedulers by at least 50% in reducing the response time.
A Survey: Handling Irregularities in Neural Network Acceleration with FPGAs

In the last decade, Artificial Intelligence (AI) through Deep Neural Networks (DNNs) has penetrated virtually every aspect of science, technology, and business. Many types of DNNs have been and continue to be developed, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs). The overall problem for all of these Neural Networks (NNs) is that their target applications generally pose stringent constraints on latency and throughput, while also having strict accuracy requirements. There have been many previous efforts in creating hardware to accelerate NNs. The problem designers face is that optimal NN models typically have significant irregularities, making them hardware-unfriendly. In this paper, we first define the problems in NN acceleration by characterizing common irregularities in NN processing into 4 types; then we summarize the existing works that handle the four types of irregularities efficiently using hardware, especially FPGAs; finally, we provide a new vision of next-generation FPGA-based NN acceleration: that the emerging heterogeneity in the next-generation FPGAs is the key to achieving higher performance.
Leveraging contextual embeddings and self-attention neural networks with bi-attention for sentiment analysis

Abstract People express their opinions and views in different and often ambiguous ways, hence the meaning of their words is often not explicitly stated and frequently depends on the context. Therefore, it is difficult for machines to process and understand the information conveyed in human languages. This work addresses the problem of sentiment analysis (SA). We propose a simple yet comprehensive method which uses contextual embeddings and a self-attention mechanism to detect and classify sentiment. We perform experiments on reviews from different domains, as well as on languages from three different language families, including morphologically rich Polish and German. We show that our approach is on a par with state-of-the-art models or even outperforms them in several cases. Our work also demonstrates the superiority of models leveraging contextual embeddings. In sum, in this paper we make a step towards building a universal, multilingual sentiment classifier.
Real-time steganalysis for streaming media based on multi-channel convolutional sliding windows

In recent years, covert communication technologies based on Voice over Internet Protocol (VoIP) have received more and more attention, which meanwhile poses a significant threat to the security of cyberspace. In this paper, we are chiefly concerned with improving the accuracy and efficiency of detection of covert communications, and we propose a real-time VoIP steganalysis model to tackle these issues. Multi-channel convolutional sliding windows (CSW) are developed to analyze the correlations between a given frame and its neighboring frames in a VoIP signal. Within each sliding window, we employ two feature extraction channels to extract correlation features from the input signal. Each channel is constructed of multiple convolutional layers having a large number of convolution kernels. The extracted features are then fed to a forward fully connected network for feature fusion. By analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not. We designed several experiments to test the proposed model's detection performance under various conditions, including different embedding rates, different speech lengths, etc. Experimental results show that the proposed model can efficiently and accurately detect steganographic voice streams, especially in the case of low embedding rates. In addition, further experiments demonstrate that the proposed model can attain nearly real-time detection of VoIP speech signals and achieve state-of-the-art performance.
Deep Learning Based Hand Gesture Recognition for Emergency Situation: A Study on Indian Sign Language

Sign Language is used to convey feelings and thoughts, as well as to reinforce information given in everyday discussions. The goal of Sign Language recognition is to recognize and comprehend important human body gestures. Deep learning is a subset of machine learning that has lately gained traction in the recognition of sign languages. The current research focuses on how deep learning may be used to solve the challenge of identifying hand gestures in a collection of videos for emergency situations. To feed the model, a number of frames were taken from the videos. A pre-trained VGG-16 and a recurrent neural network with a large short-term memory make up the model (RNN-LSTM). The model achieved an accuracy of 98% on an Indian Sign Language Dataset of Hand Gestures for Emergency Situations. Deaf people can use sign language as a kind of emergency communication to help them deal with these circumstances. In this study, sign recognition could be utilised to address circumstances like pain, calling for help, or having to see a doctor.
3D Md-Unet: A novel model of multi-dataset collaboration for medical image segmentation

• The multi-dataset collaborative network can process different organs or lesions for medical image segmentation at the same time. • The proposed adapter (SSA) can extract specific and common features from multiple classes within a dataset and various datasets. • The proposed adaptive weight update strategy can balance multi-dataset better, which is based on classes instead of voxels. • The dual-branched (DB) structure is more effective than the single one for multi-dataset collaboration. Image segmentation is widely used in the medical field. Convolutional neural network has become more diverse and effective in recent years. However, at present, most networks are designed for a single dataset (i.e., a single organ or target). The designed network is only suitable for a single dataset, and its accuracy is very different (especially small-size image datasets). In response to this problem, a collaborative network can be designed to simultaneously extract the specific and common features of a multi-dataset (i.e., multiple organs or targets). The network can be used for multi-dataset segmentation and help to balance the segmentation performance of different datasets, especially to improve the accuracy of small-size image datasets. By exploring the adapters modified by the convolution kernels, the adaptive weight update strategy and the network branched structure, the paper proposes a multi-dataset collaborative image segmentation network, called Md-Unet, which integrates a shared-specific adapter (SSA), an asymmetric similarity loss function with the proposed adaptive weight update strategy, and a dual-branch. Experimental results showed that compared with the baseline 3D U 2 Net, the accuracy of the module using the SSA was improved by 3.7%, using several loss functions with the proposed adaptive weight update strategy was improved by 0.64%–30.63%, and using dual-branch integrated architecture was improved by 17.47%. Moreover, Md-Unet had a significant improvement on small-size image datasets compared with single-dataset models.
An Adaptive, Affordable, Open-Source Robotic Hand for Deaf and Deaf-Blind Communication Using Tactile American Sign Language

Currently, ~ 1.5 million American deaf-blind individuals depend on the availability of interpreting services to communicate in their primary conversational language, tactile American Sign Language (ASL). In an effort to give the deaf-blind community access to a device that facilitates independent communication using tactile ASL, we developed TATUM (Tactile ASL Translational User Mechanism). TATUM employs 15 degrees of actuation in a hand-wrist system that is capable of signing the 26-letter ASL alphabet. Leveraging Interpres, an independent cloud-based service, all servo sequences that render desired fingerspelled letters and ASL words are stored in a web application programming interface (API). A validation study including both deaf and deaf-blind participants confirmed that the TATUM hand mimics a human hand both in size and feel. The current design of TATUM attained an average recognition rate of 94.7% in visual validation, indicative of the potential to support deaf and deaf-blind individuals in communicating via visual and tactile ASL.
CIDOC2VEC: Extracting Information from Atomized CIDOC-CRM Humanities Knowledge Graphs

The development of the field of digital humanities in recent years has led to the increased use of knowledge graphs within the community. Many digital humanities projects tend to model their data based on CIDOC-CRM ontology, which offers a wide array of classes appropriate for storing humanities and cultural heritage data. The CIDOC-CRM ontology model leads to a knowledge graph structure in which many entities are often linked to each other through chains of relations, which means that relevant information often lies many hops away from their entities. In this paper, we present a method based on graph walks and text processing to extract entity information and provide semantically relevant embeddings. In the process, we were able to generate similarity recommendations as well as explore their underlying data structure. This approach was then demonstrated on the Sphaera Dataset which was modeled according to the CIDOC-CRM data structure.