Reviewerly/exHarmony huggingface dataset:
AW-SOM, an Algorithm for High-Speed Learning in Hardware Self-Organizing Maps

Self-Organizing Maps (SOM) represent one of the most used clustering algorithms. A strong limitation in hardware SOM implementations is the decrease in processing speed when the number of neurons and features increase. For this reason, the literature provides different hardware solutions, often based on Field Programmable Gate Arrays (FPGAs). Unfortunately, even in these cases, the computing speed decreases as the number of neurons in the SOM increases. In this brief, authors present the All Winner-SOM (AW-SOM), a modified version of SOM algorithm. The proposed algorithm is suitable for hardware implementations and its main characteristic is a processing speed almost independent of the number of neurons reaching a 145 MHz clock within a range of 16 to 256 neurons. This feature allows to considerably increase the system performance in terms of computation rate.
Using Virtual Reality Tools for Teaching Foreign Languages

Among the wide application areas that Virtual Reality (VR) can have a major impact, one is Education. However, this potential is still unexplored, and one of these gaps has to do with language learning. Listening activities, which are often only supported by audio, are thought of to be demanding area when it comes to learning a second or foreign language and so therefore an interesting area for VR to take place. This pilot study therefore presents the perceptions of foreign language teachers regarding a novel medium for delivering listening activities to their students: Virtual Reality technology. The results show that foreign language teachers are of the opinion that this technology can help motivate students and potentiate the student’s learning curve regarding the listening of a foreign or second language.
Dictionary optimization and constraint neighbor embedding-based dictionary mapping for superdimension reconstruction of porous media

The three-dimensional (3D) structure of a digital core can be reconstructed from a single two-dimensional (2D) image via mathematical modeling. In classical mathematical modeling algorithms, such as multipoint geostatistics algorithms, the optimization of pattern sets (dictionaries) and the mapping problems are important issues. However, they have rarely been discussed thus far. Pattern set (dictionary)-related problems include the pattern set (dictionary) size problem and the one-to-many mapping problem in a pattern set (dictionary). The former directly affects the completeness of the dictionary, while the latter is manifested such that a single to-be-matched 2D patch has multiple matching patterns in the library and it is hence necessary to select these modes to establish an optimal mapping relationship. Whether the two above-mentioned problems can be properly resolved is directly related to the accuracy of the reconstruction results. Super-dimension reconstruction is a new 3D reconstruction method proposed by introducing the concepts of training dictionary, prior model, and mapping into the reconstruction of the digital core from the field of super-resolution reconstruction. In addition, mapping relationship extraction and dictionary building are also key issues in super-dimension reconstruction. Therefore, this paper discusses these common dictionary-related problems from the perspective of super-dimension dictionaries. We propose dictionary optimization using augmentation dictionaries and clustering based on the boundary features of the dictionary elements to improve the completeness and expand the expression ability of the dictionary. Furthermore, we propose constraint neighbor embedding-based dictionary mapping to establish a more reasonable dictionary mapping relationship for super-dimension reconstruction, and we solve the one-to-many mapping problem in the dictionary. Our experimental results show that the performance of the super-dimension dictionary can be improved by the above-mentioned algorithm. Thus, through the optimized dictionary structure and mapping relationship determined by the above-mentioned methods, the 2D patch to be reconstructed can match a more accurate 3D block in the dictionary. Consequently, the reconstruction precision is improved.
Biofeedback Augmented Software Engineering: Monitoring of Programmers' Mental Effort

This paper presents emergent experimental results showing that mental effort of programmers in code understanding tasks can be monitored through HRV (heart rate variability) using non-intrusive wearable devices. Results suggest that HRV is a good predictor for cognitive load when analyzing code and HRV results are consistent with the mental effort perceived by programmers using NASA-TLX. Furthermore, code complexity metrics do not correlate entirely with mental effort and do not seem a good indicator of the subjective perception of complexity felt by programmers. These first results are presented in the context of the project BASE-Biofeedback Augmented Software Engineering, which is briefly sketched, and proposes a radical neuroscience enabled approach to introduce biofeedback in software development.
Fine-Grained Continual Learning

Robotic vision is a field where continual learning can play a significant role. An embodied agent operating in a complex environment subject to frequent and unpredictable changes is required to learn and adapt continuously. In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered. Ideally, continual learning should be triggered by the availability of short videos of single objects and performed online on onboard hardware. In this paper, we introduce a novel fine-grained continual learning protocol based on the CORe50 benchmark and propose two continual learning techniques that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches.
Equivalence of Equilibrium Propagation and Recurrent Backpropagation

Recurrent backpropagation and equilibrium propagation are supervised learning algorithms for fixed-point recurrent neural networks, which differ in their second phase. In the first phase, both algorithms converge to a fixed point that corresponds to the configuration where the prediction is made. In the second phase, equilibrium propagation relaxes to another nearby fixed point corresponding to smaller prediction error, whereas recurrent backpropagation uses a side network to compute error derivatives iteratively. In this work, we establish a close connection between these two algorithms. We show that at every moment in the second phase, the temporal derivatives of the neural activities in equilibrium propagation are equal to the error derivatives computed iteratively by recurrent backpropagation in the side network. This work shows that it is not required to have a side network for the computation of error derivatives and supports the hypothesis that in biological neural networks, temporal derivatives of neural activities may code for error signals.
From Bilingual to Multilingual Neural Machine Translation by Incremental Training

Multilingual Neural Machine Translation approaches are based on the use of task-specific models and the addition of one more language can only be done by retraining the whole system.In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation.This work in progress shows close results to the state-of-the-art in the WMT task.
A non-canonical hybrid metaheuristic approach to adaptive data stream classification

Data stream classification techniques have been playing an important role in big data analytics recently due to their diverse applications (e.g. fraud and intrusion detection, forecasting and healthcare monitoring systems) and the growing number of real-world data stream generators (e.g. IoT devices and sensors, websites and social network feeds). Streaming data is often prone to evolution over time. In this context, the main challenge for computational models is to adapt to changes, known as concept drifts, using data mining and optimisation techniques. We present a novel ensemble technique called RED-PSO that seamlessly adapts to different concept drifts in non-stationary data stream classification tasks. RED-PSO is based on a three-layer architecture to produce classification types of different size, each created by randomly selecting a certain percentage of features from a pool of features of the target data stream. An evolutionary algorithm, namely, Replicator Dynamics (RD), is used to seamlessly adapt to different concept drifts; it allows good performing types to grow and poor performing ones to shrink in size. In addition, the selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. PSO allows the types in each layer to go towards local (within the same type) and global (in all types) optimums with a specified velocity. A set of experiments are conducted to compare the performance of the proposed method to state-of-the-art algorithms using real-world and synthetic data streams in immediate and delayed prequential evaluation settings. The results show a favourable performance of our method in different environments.
Predicting the Visual Focus of Attention in Multi-Person Discussion Videos

Visual focus of attention in multi-person discussions is a crucial nonverbal indicator in tasks such as inter-personal relation inference, speech transcription, and deception detection. However, predicting the focus of attention remains a challenge because the focus changes rapidly, the discussions are highly dynamic, and the people's behaviors are inter-dependent. Here we propose ICAF (Iterative Collective Attention Focus), a collective classification model to jointly learn the visual focus of attention of all people. Every person is modeled using a separate classifier. ICAF models the people collectively---the predictions of all other people's classifiers are used as inputs to each person's classifier. This explicitly incorporates inter-dependencies between all people's behaviors. We evaluate ICAF on a novel dataset of 5 videos (35 people, 109 minutes, 7604 labels in all) of the popular Resistance game and a widely-studied meeting dataset with supervised prediction. See our demo at https://cs.dartmouth.edu/dsail/demos/icaf. ICAF outperforms the strongest baseline by 1%--5% accuracy in predicting the people's visual focus of attention. Further, we propose a lightly supervised technique to train models in the absence of training labels. We show that light-supervised ICAF performs similar to the supervised ICAF, thus showing its effectiveness and generality to previously unseen videos.
Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology

The success of graph convolutional neural networks (GCNNs) based semi-supervised node classification is credited to the attribute smoothing (propagating) over the topology. However, the attributes may be interfered by the utilization of the topology information. This distortion will induce a certain amount of misclassifications of the nodes, which can be correctly predicted with only the attributes. By analyzing the impact of the edges in attribute propagations, the simple edges, which connect two nodes with similar attributes, should be given priority during the training process compared to the complex ones according to curriculum learning. To reduce the distortions induced by the topology while exploit more potentials of the attribute information, Dual Self-Paced Graph Convolutional Network (DSP-GCN) is proposed in this paper. Specifically, the unlabelled nodes with confidently predicted labels are gradually added into the training set in the node-level self-paced learning, while edges are gradually, from the simple edges to the complex ones, added into the graph during the training process in the edge-level self-paced learning. These two learning strategies are designed to mutually reinforce each other by coupling the selections of the edges and unlabelled nodes. Experimental results of transductive semi-supervised node classification on many real networks indicate that the proposed DSP-GCN has successfully reduced the attribute distortions induced by the topology while it gives superior performances with only one graph convolutional layer.
Ontology-based venous thromboembolism risk assessment model developing from medical records

Padua linear model is widely used for the risk assessment of venous thromboembolism (VTE), a common but preventable complication for inpatients. However, genetic and environmental differences between Western and Chinese population limit the validity of Padua model in Chinese patients. Medical records which contain rich information about disease progression, are useful in mining new risk factors related to Chinese VTE patients. Furthermore, machine learning (ML) methods provide new opportunities to build precise risk prediction model by automatic selection of risk factors based on original medical records.Medical records of 3,106 inpatients including 224 VTE patients were collected and various types of ontologies were integrated to parse unstructured text. A workflow of ontology-based VTE risk prediction model, that combines natural language processing (NLP) and machine learning (ML) technologies, was proposed. Firstly ontology terms were extracted from medical records, then sorted according to their calculated weights. Next importance of each term in the unit of section was evaluated and finally a ML model was built based on a subset of terms. Four ML methods were tested, and the best model was decided by comparing area under the receiver operating characteristic curve (AUROC).Medical records were first split into different sections and subsequently, terms from each section were sorted by their weights calculated by multiple types of information. Greedy selection algorithm was used to obtain significant sections and terms. Top terms in each section were selected to construct patients' distributed representations by word embedding technique. Using top 300 terms of two important sections, namely the 'Progress Note' section and 'Admitting Diagnosis' section, the model showed relatively better predictive performance. Then ML model which utilizes a subset of terms from two sections, about 110 terms, achieved the best AUC score, of 0.973 ± 0.006, which was significantly better compared to the Padua's performance of 0.791 ± 0.022. Terms found by the model showed their potential to help clinicians explore new risk factors.In this study, a new VTE risk assessment model based on ontologies extraction from raw medical records is developed and its performance is verified on real clinical dataset. Results of selected terms can help clinicians to discover meaningful risk factors.
SAP HANA goes private

Over the last 20 years, the progress of information technology has allowed many companies to generate, integrate, store, and analyze data of unprecedented size and complexity. In many cases, this data is personal data and how it can be used is therefore subject to laws that depend on the specific countries and application domains. For example, the General Data Protection Regulation (GDPR) introduced in the European Union imposes strict rules on how personal data can be processed. Analyzing personal data can create tremendous value, but at the same time companies must ensure that they remain legally compliant. Unfortunately, existing systems offer only limited or no support at all for processing personal data in a privacy-aware manner. Approaches that have emerged from the academic and industrial research environments need to be integrated into large systems (like enterprise systems) in a manageable and scalable way. In many IT environments, it is also desirable and necessary to combine and to integrate personal data with other (non-personal) data in a seamless fashion. In this paper, we present the first steps that SAP has taken to provide its database management system SAP HANA with privacy-enhanced processing capabilities, referred to in the following as SAP HANA Data Anonymization. Various goals on both the conceptual and technical levels were followed with the aim of providing SAP customers today with an integrated processing environment for personal and non-personal data.
Dissecting Content and Context in Argumentative Relation Analysis

When assessing relations between argumentative units (e.g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.).We show that this dependency is much stronger than previously assumed.In fact, we show that by completely masking the EAU text spans and only feeding information from their context, a competitive system may function even better.We argue that an argument analysis system that relies more on discourse context than the argument's content is unsafe, since it can easily be tricked.To alleviate this issue, we separate argumentative units from their context such that the system is forced to model and rely on an EAU's content.We show that the resulting classification system is more robust, and argue that such models are better suited for predicting argumentative relations across documents.
Enhancing Explainability of Deep Reinforcement Learning Through Selective Layer-Wise Relevance Propagation

Modern deep reinforcement learning agents are capable of achieving super-human performance in tasks like playing Atari games, solely based on visual input. However, due to their use of neural networks the trained models are lacking transparency which makes their inner workings incomprehensible for humans. A promising approach to gain insights into the opaque reasoning process of neural networks is the layer-wise relevance propagation (LRP) concept. This visualization technique creates saliency maps that highlight the areas in the input which were relevant for the agents' decision-making process. Since such saliency maps cover every possible cause for a prediction, they are often accentuating very diverse parts of the input. This makes the results difficult to understand for people without a machine-learning background. In this work, we introduce an adjustment to the LRP concept that utilizes only the most relevant neurons of each convolutional layer and thus generates more selective saliency maps. We test our approach with a dueling Deep Q-Network (DQN) agent which we trained on three different Atari games of varying complexity. Since the dueling DQN approach considerably alters the neural network architecture of the original DQN algorithm, it requires its own LRP variant which will be presented in this paper.
Master Your Metrics with Calibration

Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall). Heavily dependent on the class prior, such metrics make it difficult to interpret the variation of a model’s performance over different subpopulations/subperiods in a dataset. In this paper, we propose a way to calibrate the metrics so that they can be made invariant to the prior. We conduct a large number of experiments on balanced and imbalanced data to assess the behavior of calibrated metrics and show that they improve interpretability and provide a better control over what is really measured. We describe specific real-world use-cases where calibration is beneficial such as, for instance, model monitoring in production, reporting, or fairness evaluation.
Virtual Reality Based Digital Chain for Maintenance Training

This paper presents ongoing research about the use of Virtual Reality (VR) to facilitate training for maintenance. VR systems are now widely accepted as training platforms for complex and highly demanding training tasks. In this paper, we focus on the application of VR technology for designing and evaluating the actual training process that relates to maintenance operations. Despite the availability of increasingly many and mature VR devices, it is still difficult to achieve the level of realism needed for the effective training of particular manipulation gestures that are vital for specific assembly and disassembly procedures. We propose a systematic approach to create a VR-based experimentation environment that facilitates the selection, calibration, and evaluation of different VR devices for the training of a specific maintenance operation. We present a case study to demonstrate the feasibility of our concept, as well as the huge potential VR has to even replace the traditional physical mock-up training. Via virtual hands, users can interact directly with virtual objects via various devices (mouse, keyboard and Leap Motion). Maintenance gestures can thereby be executed as naturally as possible and can be easily recorded to prepare maintenance learning service.
CuSP: A Customizable Streaming Edge Partitioner for Distributed Graph Analytics

Graph analytics systems must analyze graphs with billions of vertices and edges which require several terabytes of storage. Distributed-memory clusters are often used for analyzing such large graphs since the main memory of a single machine is usually restricted to a few hundreds of gigabytes. This requires partitioning the graph among the machines in the cluster. Existing graph analytics systems usually come with a built-in partitioner that incorporates a particular partitioning policy, but the best partitioning policy is dependent on the algorithm, input graph, and platform. Therefore, built-in partitioners are not sufficiently flexible. Stand-alone graph partitioners are available, but they too implement only a small number of partitioning policies. This paper presents CuSP, a fast streaming edge partitioning framework which permits users to specify the desired partitioning policy at a high level of abstraction and generates high-quality graph partitions fast. For example, it can partition wdc12, the largest publicly available web-crawl graph, with 4 billion vertices and 129 billion edges, in under 2 minutes for clusters with 128 machines. Our experiments show that it can produce quality partitions 6× faster on average than the state-of-the-art standalone partitioner in the literature while supporting a wider range of partitioning policies.
Understanding Patch-Based Learning of Video Data by Explaining Predictions

Deep neural networks have shown to learn highly predictive models of video data. Due to the large number of images in individual videos, a common strategy for training is to repeatedly extract short clips with random offsets from the video. We apply the deep Taylor/Layer-wise Relevance Propagation (LRP) technique to understand classification decisions of a deep network trained with this strategy, and identify a tendency of the classifier to look mainly at the frames close to the temporal boundaries of its input clip. This “border effect” reveals the model’s relation to the step size used to extract consecutive video frames for its input, which we can then tune in order to improve the classifier’s accuracy without retraining the model. To our knowledge, this is the first work to apply the deep Taylor/LRP technique on any neural network operating on video data.