Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognition of Affective States in Virtual Rehabilitation using Late Fusion with Semi-Naive Bayesian Classifier

Virtual rehabilitation platforms may tailor the rehabilitation tasks to the patients' needs if they could recognize the patient's affective state. Affective states recognition systems can enhance their performance if they receive data coming from different sensors of human behaviour. In this work, we propose a late Fusion using Semi-Naive Bayesian classifier (FSNB) as a multimodal affective states recognition system to infer four states: tiredness, anxiety, pain, and motivation, from observable metrics of fingers pressure, hand movements, and facial expressions of post-stroke patients. Data streams were recorded from 5 post-stroke patients while they attended virtual rehabilitation therapies along 10 sessions over 4 weeks, manifesting the aforementioned states spontaneously. Recognition rates of the FSNB classifier were over 90% (with a standard deviation of around ± 0.06) of AUC for the four states. These results represent contributions for enhancing the development of affective states recognition systems in virtual rehabilitation.
Multimodal Car Driver Stress Recognition

In this paper we address the problem of multimodal car driver stress recognition. To this aim, four different signals are considered: heart rate (HR), breathing rate (BR), palm EDA (P-EDA), and perinasal perspitation (PER-EDA). The raw signals are windowed and for each window 21 different features, including both time-domain and frequency-domain descriptors, are extracted. The recognition problem is formulated as a stress vs no-stress binary problem, and is addressed in two different experimental setups: five-fold cross validation and leave one subject out. In both setups the extracted features are classified, both individually and concatenated, with three different classifiers (k--NN, SVM, and ANN) using them both alone and stacking their predictions. Experiments run on a publicly available database of multimodal signals acquired in a controlled experiment on a driving simulator show that the best recognition results are obtained feeding the classifiers with the concatenation of the features of all the signals considered, reaching a micro average accuracy of 77.25% and 65.09% in the two experimental setups respectively.
Serial Recall Effects in Neural Language Modeling

Serial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: word length and frequency, primacy and recency, semantic confusion, repetition, and transposition effects. In this research, we investigate neural language models in the context of these serial recall effects. Our work provides a framework to better understand and analyze neural language models and opens a new window to develop accurate language models.
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization

Non-negative matrix factorization is a powerful tool for learning useful representations in the data and has been widely applied in many problems such as data mining and signal processing. Orthogonal NMF, which can further improve the locality of decomposition, has drawn considerable interest in clustering problems. However, imposing simultaneous non-negative and orthogonal structure can be difficult, and so existing algorithms can only solve it approximately. To address this challenge, we propose an innovative procedure called Greedy Orthogonal Pivoting Algorithm (GOPA). The GOPA method fully exploits the sparsity of non-negative orthogonal solutions to break the global problem into a series of local optimizations, in which an adaptive subset of coordinates are updated in a greedy, closed-form manner. The biggest advantage of GOPA is that it promotes exact orthogonality and provides solid empirical evidence that stronger orthogonality does contribute favorably to better clustering performance. On the other hand, we have designed randomized and batch-mode version of GOPA, which can further reduce the computational cost and improve accuracy, making it suitable for large data.
Using Design Fiction to Explore the Ethics of VR ‘In the Wild’

In this half-day workshop, we will explore the ethics of Virtual Reality (VR) through conversations framed around design fictions. Affordable head-mounted displays (HMDs) and accessible VR content are now within reach of large audiences, yet many of VR's most urgent challenges remain under-explored. In addition to the many known unknowns (e.g. how do we manage sensory conflicts and spatial limitations in VR? ), there are many more unknown unknowns (e.g. what kinds of psychological, social and cultural impact will VR provoke? ). By bringing together diverse scenarios from workshop participants, and bespoke design fictions created specifically to explore the ethics of VR, we will facilitate a rich discussion that will inform the development of three high-fidelity design fictions that will be used to explore the ethics of VR in future workshops, including one in Bristol, UK in November 2019, part of the Virtual Realities Immersive Documentary Encounters project.
Towards Understanding and Reasoning About Android Interoperations

Hybrid applications (apps) have become one of the most attractive options for mobile app developers thanks to its support for portability and device-specific features. Android hybrid apps, for example, support portability via JavaScript, device-specific features via Android Java, and seamless interactions between them. However, their interoperation semantics is often under-documented and unintuitive, which makes hybrid apps vulnerable to errors. While recent research has addressed such vulnerabilities, none of them are based on any formal grounds. In this paper, we present the first formal specification of Android interoperability to establish a firm ground for understanding and reasoning about the interoperations. We identify its semantics via extensive testing and thorough inspection of Android source code. We extend an existing multi-language semantics to formally express the key features of hybrid mechanisms, dynamic and indistinguishable interoperability. Based on the extensions, we incrementally define a formal interoperation semantics and disclose its numerous unintuitive and inconsistent behaviors. Moreover, on top of the formal semantics, we devise a lightweight type system that can detect bugs due to the unintuitive inter-language communication. We show that it detects more bugs more efficiently than HybriDroid, the state-of-the-art analyzer of Android hybrid apps, in real-world Android hybrid apps.
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples

Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases.
A Deep Reinforcement Learning Perspective on Internet Congestion Control

We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task of modulat-ing trafﬁc sources’ data-transmission rates to efﬁciently utilize network capacity, and is the subject of extensive attention in light of the advent of Internet services such as live video, virtual reality, Internet-of-Things, and more. We show that casting congestion control as RL enables training deep network policies that capture intricate patterns in data trafﬁc and network conditions, and leverage this to outperform the state-of-the-art. We also highlight signiﬁcant challenges facing real-world adoption of RL-based congestion control, including fairness, safety, and generalization, which are not trivial to address within conventional RL formalism. To facilitate further research and reproducibility of our results, we present a test suite for RL-guided congestion control based on the OpenAI Gym interface.
Vector of Locally Aggregated Embeddings for Text Representation

We present Vector of Locally Aggregated Embeddings (VLAE) for effective and, ultimately, lossless representation of textual content. Our model encodes each input text by effectively identifying and integrating the representations of its semantically-relevant parts. The proposed model generates high quality representation of textual content and improves the classification performance of current state-of-the-art deep averaging networks across several text classification tasks.
Crowdtesting : When is The Party Over?

Trade-offs such as "how much testing is enough" are critical yet challenging project decisions in software engineering. Most existing approaches adopt risk-driven or value-based analysis to prioritize test cases and minimize test runs. However, none of these is applicable to the emerging crowd testing paradigm where task requesters typically have no control over online crowdworkers's dynamic behavior and uncertain performance. In current practice, deciding when to close a crowdtesting task is largely done by guesswork due to lack of decision support. This paper intends to fill this gap by introducing automated decision support for monitoring and determining appropriate time to close the crowdtesting tasks. First, this paper investigates the necessity and feasibility of close prediction of crowdtesting tasks based on industrial dataset. Then,it designs 8 methods for close prediction, based on various models including the bug trend, bug arrival model, capture-recapture model.Finally, the evaluation is conducted on 218 crowdtesting tasks from one of the largest crowdtesting platforms in China, and the results show that a median of 91% bugs can be detected with 49% saved cost.
Power k-Means Clustering

Clustering is a fundamental task in unsupervised machine learning. Lloyd’s 1957 algorithm for kmeans clustering remains one of the most widely used due to its speed and simplicity, but the greedy approach is sensitive to initialization and often falls short at a poor solution. This paper explores an alternative to Lloyd’s algorithm that retains its simplicity and mitigates its tendency to get trapped by local minima. Called power k-means, our method embeds the k-means problem in a continuous class of similar, better behaved problems with fewer local minima. Power k-means anneals its way toward the solution of ordinary k-means by way of majorization-minimization (MM), sharing the appealing descent property and low complexity of Lloyd’s algorithm. Further, our method complements widely used seeding strategies, reaping marked improvements when used together as demonstrated on a suite of simulated and real data examples.
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders

In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theory-driven phonological features are encoded in the latent bit patterns, finding that some (e.g. [+-approximant]), are well represented by the network in both languages, while others (e.g. [+-spread glottis]) are less so. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition.
AutoTap: Synthesizing and Repairing Trigger-Action Programs Using LTL Properties

End-user programming, particularly trigger-action programming (TAP), is a popular method of letting users express their intent for how smart devices and cloud services interact. Unfortunately, sometimes it can be challenging for users to correctly express their desires through TAP. This paper presents AutoTap, a system that lets novice users easily specify desired properties for devices and services. AutoTap translates these properties to linear temporal logic (LTL) and both automatically synthesizes property-satisfying TAP rules from scratch and repairs existing TAP rules. We designed AutoTap based on a user study about properties users wish to express. Through a second user study, we show that novice users made significantly fewer mistakes when expressing desired behaviors using AutoTap than using TAP rules. Our experiments show that AutoTap is a simple and effective option for expressive end-user programming.
Trainable Decoding of Sets of Sequences for Neural Sequence Models

Many sequence prediction tasks admit multiple correct outputs and so, it is often useful to decode a set of outputs that maximize some task-speciﬁc set-level metric. However, retooling standard sequence prediction procedures tailored towards predicting single best outputs tends to produce sets containing very similar sequences; failing to capture the variation in the output space. To address this, we propose ∇ BS, a trainable decoding procedure that outputs a set of sequences, highly valued according to the metric. Our method tightly integrates the training and decoding phases and further allows for the optimization of the task-speciﬁc metric addressing the shortcomings of standard sequence prediction. Further, we discuss the trade-offs of commonly used set-level metrics and motivate a new set-level metric that naturally evaluates the notion of “capturing the variation in the output space”. Finally, we show results on the image captioning task and ﬁnd that our model outperforms standard techniques and natural ablations.
Fairness without Harm: Decoupled Classifiers with Preference Guarantees

In domains such as medicine, it can be acceptable for machine learning models to include sensitive attributes such as gender and ethnicity. In this work, we argue that when there is this kind of treatment disparity then it should be in the best interest of each group. Drawing on ethical principles such as beneﬁcence (“do the best”) and non-maleﬁcence (“do no harm”), we show how to use sensitive attributes to train decoupled clas-siﬁers that satisfy preference guarantees . These guarantees ensure the majority of individuals in each group prefer their assigned classiﬁer to (i) a pooled model that ignores group membership ( rationality ), and (ii) the model assigned to any other group ( envy-freeness ). We introduce a recursive procedure that adaptively selects group attributes for decoupling, and present formal conditions to ensure preference guarantees in terms of generalization error. We validate the effectiveness of the procedure on real-world datasets, showing that it improves accuracy without violating preference guarantees on test data.
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN

Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.
Disentangled Graph Convolutional Networks

The formation of a real-world graph typically arises from the highly complex interaction of many latent factors. The existing deep learning methods for graph-structured data neglect the entanglement of the latent factors, rendering the learned representations non-robust and hardly ex-plainable. However, learning representations that disentangle the latent factors poses great challenges and remains largely unexplored in the literature of graph neural networks. In this paper, we introduce the disentangled graph convolutional network (DisenGCN) to learn disentangled node representations. In particular, we propose a novel neighborhood routing mechanism, which is capable of dynamically identifying the latent factor that may have caused the edge between a node and one of its neighbors, and accordingly assigning the neighbor to a channel that extracts and convolutes features speciﬁc to that factor. We theoretically prove the convergence properties of the routing mechanism. Empirical results show that our proposed model can achieve signiﬁcant performance gains, especially when the data demonstrate the existence of many entangled factors.
Towards Understanding Knowledge Distillation

Knowledge distillation, i.e. one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifiers. It has even been observed that classifiers learn much faster and more reliably if trained with the outputs of another classifier as soft labels, instead of from ground truth data. So far, however, there is no satisfactory theoretical explanation of this phenomenon. In this work, we provide the first insights into the working mechanisms of distillation by studying the special case of linear and deep linear classifiers. Specifically, we prove a generalization bound that establishes fast convergence of the expected risk of a distillation-trained linear classifier. From the bound and its proof we extract three keyfactors that determine the success of distillation: data geometry – geometric properties of the datadistribution, in particular class separation, has an immediate influence on the convergence speed of the risk; optimization bias– gradient descentoptimization finds a very favorable minimum of the distillation objective; and strong monotonicity– the expected risk of the student classifier always decreases when the size of the training set grows.
Content-based Dwell Time Engagement Prediction Model for News Articles

The article dwell time (i.e., expected time that users spend on an article) is among the most important factors showing the article engagement. It is of great interest to predict the dwell time of an article before its release. This allows digital newspapers to make informed decisions and publish more engaging articles. In this paper, we propose a novel content-based approach based on a deep neural network architecture for predicting article dwell times. The proposed model extracts emotion, event and entity features from an article, learns interactions among them, and combines the interactions with the word-based features of the article to learn a model for predicting the dwell time. The experimental results on a real dataset from a major newspaper show that the proposed model outperforms other state-of-the-art baselines.
MQTT-RD: A MQTT based Resource Discovery for Machine to Machine Communication

The Internet of Things (IoT) is one of the key enablers for digital businesses and economic growth. By interconnecting objects and people through diverse heterogeneous networks, using Machine to Machine (M2M) communication, IoT enables the continuous monitoring of devices its surrounding environment, proving to have a huge potential in terms of new business opportunities. One of the biggest challenges nowadays in M2M communication, is the way devices are capable to look up for other devices and their services in local networks and internet. This paper proposes a distributed resource discovery architecture (MQTT-RD) based on the MQTT protocol. The proposed architecture enables decentralized discovery and management of devices in multiple networks, by introducing plug and play capabilities to devices, contributing for a mechanism for zero-configuration networking in IoT environments. This architecture was tested in an experimental environment, composed of multiple devices, in order to test resource discovery capabilities using an MQTT based protocol. The evaluated metrics were the overall message drop in the network, the delays in the delivery of messages and the processing time of each message.
Measuring Immediate Adaptation Performance for Neural Machine Translation

Incremental domain adaptation, in which a system learns from the correct output for each input immediately after making its prediction for that input, can dramatically improve system performance for interactive machine translation. Users of interactive systems are sensitive to the speed of adaptation and how often a system repeats mistakes, despite being corrected. Adaptation is most commonly assessed using corpus-level BLEU- or TER-derived metrics that do not explicitly take adaptation speed into account. We find that these metrics often do not capture immediate adaptation effects, such as zero-shot and one-shot learning of domain-specific lexical items. To this end, we propose new metrics that directly evaluate immediate adaptation performance for machine translation. We use these metrics to choose the most suitable adaptation method from a range of different adaptation techniques for neural machine translation systems.
Document-Level Event Factuality Identification via Adversarial Neural Network

Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP). Previous studies mainly suffer from the scarcity of suitable corpus and effective methods. To solve these two issues, we first construct a corpus annotated with both document- and sentence-level event factuality information on both English and Chinese texts. Then we present an LSTM neural network based on adversarial training with both intra- and inter-sequence attentions to identify document-level event factuality. Experimental results show that our neural network model can outperform various baselines on the constructed corpus.
When Software Development Meets the Shopfloor: The Case of Industrial Fablabs

This paper addresses the question of how software is being developed in industrial fabrication laboratories (fablabs) located in real production environments, by the example of a smart factory project conducted by a large company in Europe. Our findings suggest that, when developing software in industrial fablabs, teams tend to drift away from established software engineering methods and practices and to adapt them impromptu in order to cope with the seemingly arcane sociotechnical environment of the factory.