Reviewerly/exHarmony huggingface dataset:
DMMAM: Deep Multi-source Multi-task Attention Model for Intensive Care Unit Diagnosis

Disease diagnosis can provide crucial information for clinical decisions that influence the outcome in acute serious illness, and this is particularly in the intensive care unit (ICU). However, the central role of diagnosis in clinical practice is challenged by evidence that does not always benefit patients and that factors other than disease are important in determining patient outcome. To streamline the diagnostic process in daily routine and avoid misdiagnoses, in this paper, we proposed a deep multi-source multi-task attention model (DMMAM) for ICU disease diagnosis. DMMAM exploits multi-sources information from various types of complications, clinical measurements, and the medical treatments to support the diagnosis. We evaluate the proposed model with 50 diseases of 9 classifications on an extensive collection of real-world ICU Electronic Health Records (EHR) dataset with 151729 ICU admissions from 46520 patients. Experiments results demonstrate the effectiveness and the robustness of our model.
The Development of Digital Competences and Emotional Skills Through the Use of Audio-Visual Technologies

School is more and more engaged in giving an importance to all that concerns the emotional aspects of an educational process with the goal to promote a total emotional development of the students. The aim of this paper is to explore the use of audio-visual technologies and products as instruments to help the socio-emotional education and to develop the digital competence in the educational field. The audio-visual communication has specific peculiarities that make it fit to reproduce the real world, so favouring the involvement of the public. In particular, the authors discuss the educational use of the movies in order to participate and commit in the digital culture, improving emotional awareness, controlling emotions, helping students to utilize their emotions in a positive way, developing their empathetic capacities, and controlling in a more functional way their own interpersonal relationships. The acquisition of all these capacities in the developmental years should have direct repercussions on the educational processes of the students.
Computing exact score vectors for linear Gaussian state space models

A recursive formula for computing the exact value of score vectors is proposed for a general form of the linear Gaussian state space model, which is more desirable than approximate values in some statistical analyses. Unlike most extant methods, our formula calculates all components of the score vector simultaneously. This approach significantly simplifies its programing, in particular, with some matrix-oriented programing languages, such as MATLAB. We also consider a way of handling initial conditions that depend on unknown parameters. This issue has not yet been explicitly addressed in the existing literature in the context of exact score computing for a general case, such as the one that we consider in this paper. It is also shown that our formula is especially useful for calculating score tests with an outer product of gradient asymptotic covariance matrix estimator.
MReC: A Multilayer Photonic Reservoir Computing Architecture

Photonic reservoir computing is a promising paradigm for large-scale classification and prediction problems. However, its single-layer nature is a bottleneck for higher performance and accuracy. This paper proposes MReC, a novel multi-layer photonic reservoir computing architecture made-up of photonic components such as Mach-Zehnder interferometer and optical fiber spool. The proposed design involves a deep pipelined architecture and has been synthesized using commercial photonic CAD tools. System-level simulation of large-scale tasks like spoken digit recognition, channel equalization, and time-series prediction on MReC demonstrates an accuracy improvement up to 50% compared to the state-of-the-art photonic architecture in the literature. Further results indicate a power reduction up to 34% and at least 132x speedup when compared with the best reported results in the literature at a cost of 12% area overhead.
Estimation of Pozzolanic Activity of Scoria Rocks Using ASTER Remote Sensing

The method of gathering data using remote sensing enables efficient selection of different target regions for the procurement of various scoria rocks (SR), which are under investigation. Thermal Emission and Reflection Radiometer (ASTER) is one of the five-spectroradiometer sensors mounted onto the Japan and the National Aeronautics and Space Administration’s (NASA) TERRA spacecraft. Informative radiance data reflected from different regions, containing SR, collected by ASTER could be converted into reflectance. Pozzolanic activity index (PAI) of ground SR samples was measured in their standard mortar mixtures. PAIs of the procured samples could be well correlated to their reflectance values at two effective ASTER Bands (B4 and B5). It is concluded that SR samples with lower reflectance are characterized with high PAIs that varied from about 91%–100% at 28 and 90 days. The reflectance of different regions in the same area of reported PAIs could be obtained using established mathematical correlation techniques and verified from reported data. Results have shown that there is a reasonable agreement between the estimated and reported ranges of PAI values.
Identifying Fixations in Gaze Data via Inner Density and Optimization

Eye tracking is an increasingly common technology with a variety of practical uses. Eye-tracking data, or gaze data, can be categorized into two main events: fixations represent focused eye movement, indicative of awareness and attention, whereas saccades are higher-velocity movements that occur between fixation events. Common methods to identify fixations in gaze data can lack sensitivity to peripheral points and may misrepresent positional and durational properties of fixations. To address these shortcomings, we introduce the notion of inner density for fixation identification, which concerns both the duration of the fixation and the proximity of its constituent gaze points. Moreover, we demonstrate how to identify fixations in a sequence of gaze data by optimizing for inner density. After decomposing the clustering of a temporal gaze data sequence into successive regions (chunks), we use nonlinear and linear 0–1 optimization formulations to identify the densest fixations within a given data chunk. Our approach is parametrized by a unique constant that adjusts the degree of desired density, allowing decision makers to have fine-tuned control over density during the process. Computational experiments on real data sets demonstrate the efficiency of our approach and its effectiveness in identifying fixations with greater density than existing methods, thereby enabling the refinement of key gaze metrics such as fixation duration and fixation center.
Semantic Modeling of Cascading Risks in Interoperable Socio-technical Systems

We present a method to create semantic representations of cascading risks of interoperable socio-technical systems. This is based on a structured domain ontology representing socio-technical systems, their interdependencies, environmental and anthropic hazards, and the related threats. The ontology is accompanied by a software application, i.e., the CREAtivity Machine that generates cascades of risks by means of semantic and computational creativity techniques. The presented running prototype refers to risk assessment of critical infrastructures; however, the same method can be applied to risks concerning other system types like businesses, ecosystems, and financial networks.
An Internet Voting Proposal Towards Improving Usability and Coercion Resistance

This paper proposes a coercion-resistant internet voting protocol using a re-voting approach. It is not assumed for voters to own cryptographic keys prior to the election and the voting experience remains simple by only requiring voters to keep their authentication credentials. Furthermore, we reduce complexity in the filtering stage by leveraging the so-called Millionaires Protocol.
Deep successor feature learning for text generation

In this paper we present an approach to training neural network to generate sequences using successor feature learning from reinforcement learning. The model can be thought as two components, an MLE-based token generator and an estimator that predicts the future value of whole sentence. As we know, reinforcement learning has been applied to dealing with the exposure bias problem of generating sequences. Compared with other RL algorithm, successor feature(SF) can learn robust value function provided observations and reward by decomposing the value function into two components - a reward predictor and a successor map. The encoder-decoder framework with SF enables the decoder to generate outputs that receive more future reward, which means that the model pays attention on not only the current word but also the rest words. We demonstrate that the approach improves performance on two translation tasks.
The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface

Recently, many applications are developed in numerous domains with various environments.Since some environments require hands-free applications, new technology is needed for the input interfaces other than the mouse and keyboard.Therefore, to meet the needs, many researchers have begun to investigate the gaze and voice for the input technology.In particular, there are many approaches to render virtual keyboards with the gaze.However, since the virtual keyboards hide the screen space, this technique can only be applied in limited environments.In this paper, we propose a classification technique for gaze-written numbers as the hands-free interface.Since the gaze-writing is less accurate compared to the virtual keyboard typing, we apply the convolutional neural network (CNN) deep learning algorithm to recognize the gaze-writing and improve the classification accuracy.Besides, we create new gaze-writing datasets for training, gaze MNIST (gMNIST), by modifying the MNIST data with features of the gaze movement patterns.For the evaluation, we compare our approach with the basic CNN structures using the original MNIST dataset.Our study will allow us to have more options for the input interfaces and expand our choices in hands-free environments.
Evolving indoor navigational strategies using gated recurrent units in NEAT

Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run on smaller robotic platforms such as Micro-Aerial Vehicles. Bug algorithms are an alternative that use relatively little processing power, and avoid high memory consumption by not building an explicit map of the environment. In this work we explore the performance of Neuroevolution - specifically NEAT - at evolving control policies for simulated differential drive robots carrying out generalised maze navigation. We compare this performance with respect to one particular bug algorithm known as I-Bug. We extend NEAT to include Gated Recurrent Units (GRUs) to help deal with long term dependencies. We show that both NEAT and our NEAT-GRU can repeatably generate controllers that outperform I-Bug on a test set of 209 indoor maze like environments. We show that NEAT-GRU is superior to NEAT in this task. Moreover, we show that out of the 2 systems, only NEAT-GRU can continuously evolve successful controllers for a much harder task in which no bearing information about the target is provided to the agent.
Adaptive neuro-fuzzy classifier for evaluating the technology effectiveness based on the modified Wang and Mendel fuzzy neural production MIMO-network

The paper describes practical results gained during synthesis of the classification model for estimating technology effectiveness when solving multi-criteria problems of expert data analysis (Foresight) aimed to identify technological breakthroughs and strategic perspectives of scientific, technological and innovative development.
ATLASCAR: A Sample of the Quests and Concerns for Autonomous Cars

The ATLASCAR project started in 2010 as an engineering and scientific project at the University of Aveiro, Portugal, with the goal to develop a system to study and improve autonomous driving and driving assistance capabilities. The focus was made both at the engineering level to develop a real scale instrumented and automatic vehicle prototype, and at the research level for advanced perception and system command using a rich set of sensors and the associate computational and software architecture. Besides the expected challenges, new fronts appeared and a set of concerns has been studied which lightened up challenges that will certainly continue to drive other authors and technological players concerned with autonomous cars and the automotive industry.
A Hybrid Energy-Aware Video Bitrate Adaptation Algorithm for Mobile Networks

The number of mobile devices that use video streaming applications has been steadily rising year after year. Platforms responsible for providing multimedia service face great challenges in delivering high-quality content for mobile users. One main problem in sharing video flows is the high energy consumption in mobile devices, which reduces their lifetime. A video adaptation approach with Quality of Experience (QoE) support is a key issue to increase the user experience while watching videos as well as reduce the energy consumption in mobile devices. In this paper, we propose a hybrid energy-aware video bitrate adaptation algorithm to deliver videos with high QoE and energy-efficiency for mobile users. Simulation results show the efficiency of the proposed algorithm compared to existing adaptation video bitrate algorithms, reducing the number and duration of stalls, as well as saving energy.
Teachers' Expected and Perceived Gains of Participation in Classroom Based Design Activities

This paper explores teachers' expected and perceived gains from classroom participation in design projects. The results indicate that teachers hope the experience will be fun for the children, and that it will increase both children's and their own knowledge about technology. Although they consider learning goals important, these do not necessarily have to be communicated to the children, since the teachers experience that the children are learning several skills anyway. However, early involvement in the definition of learning goals could make participation more beneficial. The teachers also see several gains from partication for themselves, especially related to using a design approach in the classroom. We discuss the implications of these finding and suggest a way to increase the user gains for both children and teachers by considering the opportunity to use classroom participation as a way to support teachers' competence development, thereby fulfilling the promise of mutual learning as advocated in Participatory Design.
SyncTriage: Using synchronisation artefacts to optimise acquisition order

While the number and variety of devices can be problematic in a digital investigation, it is also a problem for consumers. As a result, software developers have implemented synchronisation features to assist customers handle the multitude of devices that they now use. This paper describes how these synchronisation features can be exploited as part of a digital investigation to use the results from the examination of one device to infer content of other devices. This extracted information is potentially useful in determining the devices that should have the most resources expended during an investigation to obtain the most actionable evidence in the quickest and most efficient manner.
MergedTrie: Efficient textual indexing

The accessing and processing of textual information (i.e. the storing and querying of a set of strings) is especially important for many current applications (e.g. information retrieval and social networks), especially when working in the fields of Big Data or IoT, which require the handling of very large string dictionaries. Typical data structures for textual indexing are Hash Tables and some variants of Tries such as the Double Trie (DT). In this paper, we propose an extension of the DT that we have called MergedTrie. It improves the DT compression by merging both Tries into a single and by segmenting the indexed term into two fixed length parts in order to balance the new Trie. Thus, a higher overlapping of both prefixes and suffixes is obtained. Moreover, we propose a new implementation of Tries that achieves better compression rates than the Double-Array representation usually chosen for implementing Tries. Our proposal also overcomes the limitation of static implementations that does not allow insertions and updates in their compact representations. Finally, our MergedTrie implementation experimentally improves the efficiency of the Hash Tables, the DTs, the Double-Array, the Crit-bit, the Directed Acyclic Word Graphs (DAWG), and the Acyclic Deterministic Finite Automata (ADFA) data structures, requiring less space than the original text to be indexed.
Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

In this paper, we report early results on the deployment of the growing neural gas algorithm in online incremental learning of traversability assessment with a multi-legged walking robot. The addressed problem is to incrementally build a model of the robot experience with traversing the terrain that can be immediately utilized in the traversability cost assessment of seen but not yet visited areas. The main motivation of the studied deployment is to improve the performance of the autonomous mission by avoiding hard to traverse areas and support planning cost-efficient paths based on the continuously collected measurements characterizing the operational environment. We propose to employ the growing neural gas algorithm to incrementally build a model of the terrain characterization from exteroceptive features that are associated with the proprioceptive based estimation of the traversal cost. Based on the reported results, the proposed deployment provides competitive results to the existing approach based on the Incremental Gaussian Mixture Network.
Congestion‐aware HTTP adaptive streaming system over SDN‐enabled Wi‐Fi network

Summary This paper proposes a novel congestion‐aware HTTP adaptive streaming (HAS) system to improve video quality of clients over software‐defined network (SDN)–enabled Wi‐Fi network. The proposed system employs SDN technology to efficiently control the network resources and video quality over the competing Wi‐Fi network. In the proposed system, an SDN‐enabled access point (AP) can monitor the streaming status of clients, such as the buffered playback time and information of requested segments. An SDN controller collects not only the streaming status of clients but also the Wi‐Fi network conditions from the AP using OpenFlow. Additionally, when a congestion is going to occur, it determines the segment bitrate of each client to provide seamless and high‐quality streaming services. The proposed system is fully implemented using open‐source programs, such as ONOS and hostapd, and is examined in a real Wi‐Fi network. The experimental results verify that the proposed system can provide seamless and high‐quality HAS services for the clients compared with existing systems.
TF-Miner: Topic-Specific Facet Mining by Label Propagation

Mining facets of topics is an essential task nowadays. Facet heterogeneity and long tail characteristic of information make facet mining tasks difficult. In this paper we propose a weakly supervised approach, called Topic-specific Facet (TF)-Miner, to mine TFs automatically by a Label Propagation algorithm (LPA). The process of propagation helps us mine complete facet sets. Experiments on several real-world datasets show that TF-Miner achieves better performance than the facet mining approaches which rely on the texts only.
Applying Wavelet Transforms for Web Server Load Forecasting

The study focuses on increasing the effectiveness of web server load forecasting systems, which are utilized for technical state diagnostics and ensuring data security of distributed computer systems and networks. The analysis of applied research papers has shown that a promising way of web server load forecasting systems development is improving their mathematical background by using modern frequency-time signal analysis methods based on the wavelet transformation theory. It has been established that the challenges of using the wavelet transformation theory are primarily related to the choice of basis wavelet type, parameters of which shall be adapted to the application conditions in a particular forecasting system. A new basis wavelet type selection method, which is most effective for web server load parameters forecasting, has been proposed. This method is based on a series of conditions and criteria to achieve significant effectiveness for the given forecasting task by choosing a basis wavelet type. Also, simulation modelling based on the web server request statistics collected by the authors in a Ukrainian university has shown that the method allows selecting the basis wavelet type, which ensures the approximation error level similar to that of the modern web server load forecasting systems. The possibility to avoid long-term simulation modelling typically used for basis wavelet type selection is a significant advantage of the proposed method for wavelet model development. Prospects for further research are related to the refinement of the effectiveness criteria calculation process and improvement of the proposed method by developing a basis wavelet parameters calculation procedure.
Leon: A Distributed RDF Engine for Multi-query Processing

As similar queries keep springing up in real query logs, few RDF systems address this problem. In this paper, we propose Leon, a distributed RDF system, which can also deal with multi-query problem. First, we apply a characteristic-set-based partitioning scheme. This scheme (i) supports the fully parallel processing of join within characteristic sets; (ii) minimizes data communication by applying direct transmission of intermediate results instead of broadcasting. Then, Leon revisits the classical problem of multi-query optimization in the context of RDF/SPARQL. In light of the NP-hardness of the multi-query optimization for SPARQL, we propose a heuristic algorithm that partitions the input batch of queries into groups, and discover the common sub-query of multiple SPARQL queries. Our MQO algorithm incorporates with a subtle cost model to generate execution plans. Our experiments with synthetic and real datasets verify that: (i) Leon's startup overhead is low; (ii) Leon consistently outperforms centralized RDF engines by 1–2 orders of magnitude, and it is competitive with state-of-the-art distributed RDF engines; (iii) Our MQO approach consistently demonstrates 10 $$\times $$ speedup over the baseline method.