Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Metaphor Detection in Context

We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.
Robot Joint Module with a Reactive-type Joint Torque Sensor

Robot safety is very important when human worker and robots are working side by side in the shard space. In the early stage, force control and collision detection of the robot were performed by attaching a six-axis force/torque sensor to the robot end. However, this method was costly due to the expensive sensor and could not detect a collision occurring in the robot body. An alternative is to use a joint module equipped with a joint torque sensor (JTS) that can measure the exact torque transmitted to the link. However, the current JTS is affected by the vibration of the harmonic drive, so there is a limitation in accurate torque measurement. Furthermore, there is a problem that the stiffness of JTS is low enough to degrade the performance of the robot. The proposed joint module minimizes the error due to the torque ripple by adopting the reactive type JTS for which the location of JTS is separated from the harmonic drive and by having a ring structure which results in high stiffness and compact structure. Various experiments verified that the proposed joint module and JTS show very good performance.
Maximum Correlationwith Migration Control Based on Modified Knapsack(MC_MC) Approach for VM Selection for Green Cloud Computing

In today's time of fast-growing technology, the need for large computing is provided by cloud infrastructure. Therefore, how to reduce energy consumption in the data centers is a maj or current research area. Our research area is Dynamic VM consolidation where monitoring of server load and optimizing the use of datacenters is done by intelligent decision. In our work, we proposed Maximum Correlation with Migration Control (MC_MC) approach for Virtual Machine selection to achieve the less number of VM migration for achieving best VM consolidation. The proposed (MC_MC) Maximum Correlation with minimum VM selection by modified Knapsack approach handles the heavily loaded servers by migration of the most pertinent VM to the appropriate destination server. The implementation of our approach is simulated in CloudSim. Our results depict that both energy consumption and thenumber of VM migration is decreased as well as performancesof cloud datacenters is improved.
Structure of TRAF Family: Current Understanding of Receptor Recognition

Tumor necrosis factor receptor–associated factor (TRAF) proteins are key signaling molecules that function in various cellular signaling events including immune response, cell death and survival, development, and thrombosis. Their roles in cellular signaling are mediated mostly by direct interactions with various receptors via the TRAF domain. To determine how specific TRAF domains can interact with various receptors with a limited binding interface and how similar binding interfaces of TRAF family members can recognize their specific binding partners, extensive structural studies on TRAF family proteins have been conducted for several decades. In this review, we discuss the current understanding of the structural and molecular diversity of the TRAF domain and TRAF-binding motifs in many receptors according to available structural information.
ErgoTac: A Tactile Feedback Interface for Improving Human Ergonomics in Workplaces

In this letter, we present a novel wearable feedback interface with the aim to improve human ergonomics in the execution of heavy or repetitive industrial tasks. The proposed interface incorporates a reduced-complexity and real-time model for the estimation of human joints’ load variations, based on the estimated translational displacement of the center of pressure in the presence of external forces and loads. Wireless vibrotactile displays are then developed to inform humans about excessive overloading of body joints by applying appropriate tactile stimuli, and to guide them towards more ergonomic and suitable physical loading conditions. We validated our framework by a user study during which the subjects while performing a heavy object lifting task, reconfigured the body pose to minimize the overloading efforts. This was achieved by following the vibrotactile feedback guidance, provided by wearable tactile devices. Experimental results include a statistical analysis of ten subjects that demonstrate the potential of the proposed method in assisting human workers. The integration of such wearable devices in working environments can contribute to the improvement of human ergonomics, due to the monitoring and reduction of physical overloadings.
Design of a Portable Programming Abstraction for Data Transformations

Novel data intensive applications and the diversification of data processing platforms have changed data management significantly over the last decade. In this changed environment, the expressiveness of the traditional relational algebra is often insufficient and data management systems have started to provide more powerful special purpose programming languages. However, these languages create a tight coupling between applications and specific systems that can hinder further development on both sides of the equation. The goal of this article is to start a discussion on the future of platform independent programming models for data processing that re-establish the separation of application logic and implementation details that used to be a cornerstone of data management systems. As a guide for that discussion, we introduce several recent related works on that topic and also outline our own contribution, the Analytical Calculus.
Improving ID3 Algorithm by Combining Values from Equally Important Attributes

ID3 is a well-known algorithm which is used in the classification task of the decision tree learning. Although a lot of research provides improvements on the traditional ID3 algorithm with various strategies, no attempt was made on ID3 to address the problem when there are more than one attribute that can be placed at a particular node, i.e. those attributes are equally important. This paper proposes a new variation of ID3 to combine equally important attributes into a single node of the decision tree classification. The Connect-4 dataset from UCI is used in our experiment since the dataset contains many attributes and instances which can easily encounter the equally important attribute problem. The experimental results show that our proposed method significantly reduces the average depth of the decision tree generated by ID3 algorithm while the average accuracy rate is still preserved.
A systematic review of community Leg Clubs for patients with chronic leg ulcers

Abstract Aim Appraise the evidence on the outcomes of Leg Clubs on ulcer healing, psychosocial outcomes, patient safety, cost and experiences of Leg Club members. Background The Leg Club is a community-based social model of care in 30 UK locations and nine overseas for treating patients with chronic leg wounds. However, its cumulative effectiveness has not been reviewed to-date. Methods Systematic review of primary research relating to the impact and quality of care of Leg Clubs treating patients with leg ulcers. Six electronic databases were systematically searched using the MeSH term ‘leg ulcer’, including other representative terms, in combination with ‘Leg Club’. The quality of individual studies was assessed using appraisal tools. The confidence in the quantitative evidence was evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE); and the Confidence in the Evidence from Reviews of Qualitative Research (CERQual) assessed the quality of qualitative findings. Findings A total of 17 relevant publications were identified. Out of the 17 articles, four publications represent findings from randomised controlled trial (RCT). Thus, evidence from 14 independent studies involving at least 532 participants were included in the synthesis of this review. The quality of the evidence varied across the different outcomes and were mostly low or of very low quality. Findings from one underpowered RCT from Australia reporting on clinical, patient-reported outcomes and economic outcomes were evaluated as moderate quality. Studies indicate that the Leg Club model has a positive impact on ulcer healing and recurrence, mood, sleep, quality of life and pain. Moreover, only three studies assessed wound infections and reported no infections had occurred during treatment at the Leg Clubs. Economic evaluations find Leg Clubs to be probably more cost-effective than usual care. Both patients and nurses projected positive views about the Leg Clubs, with particular emphasis on improved social interactions and delivery of patient-centred care.
Model-Based Vestibular Afferent Stimulation: Evaluating Selective Electrode Locations and Stimulation Waveform Shapes

A dysfunctional vestibular system can be a severe detriment to the quality of life of a patient. Recent studies have shown the feasibility for a vestibular implant to restore rotational sensation via electrical stimulation of vestibular ampullary nerves. However, the optimal stimulation site for selective elicitation of the desired nerve is still unknown. We realized a finite element model on the basis of μCT scans of a human inner ear and incorporated naturally distributed, artificial neural trajectories. A well-validated neuron model of myelinated fibers was incorporated to predict nerve responses to electrical stimulation. Several virtual electrodes were placed in locations of interest inside the bony labyrinth (intra-labyrinthine) and inside the temporal bone, near the target nerves (extra-labyrinthine), to determine preferred stimulation sites and electrode insertion depths. We investigated various monopolar and bipolar electrode configurations as well as different pulse waveform shapes for their ability to selectively stimulate the target nerve and for their energy consumption. The selectivity was evaluated with an objective measure of the fiber recruitment. Considerable differences of required energy and achievable selectivity between the configurations were observed. Bipolar, intra-labyrinthine electrodes provided the best selectivities but also consumed the highest amount of energy. Bipolar, extra-labyrinthine configurations did not offer any advantages compared to the monopolar approach. No selective stimulation could be performed with the monopolar, intra-labyrinthine approach. The monopolar, extra-labyrinthine electrodes required the least energy for satisfactory selectivities, making it the most promising approach for functional vestibular implants. Different pulse waveform shapes did not affect the achieved selectivity considerably but shorter pulse durations showed consistently a more selective activation of the target nerves. A cathodic, centered triangular waveform shape was identified as the most energy-efficient of the tested shapes. Based on these simulations we are able to recommend the monopolar, extra-labyrinthine stimulation approach with cathodic, centered triangular pulses as good trade-off between selectivity and energy consumption. Future implant designs could benefit from the findings presented here.
Distributed Extremum Seeking with Stochastic Perturbations

This paper considers seeking an unknown source in the two-dimensional (2D) space with a multi-agent system. Each agent has no knowledge about its own position and the distribution of the source signal. To resolve these challenges, we design distributed extrmum seeking (ES) controllers with stochastic perturbations for the multi-agent system, where each agent carries a single sensor to measure the value of the signal strength at its current position and cooperates with its neighbors. We prove the local exponential convergence of the multi-agent system, both almost surely and in probability, to a small neighborhood of the source by stochastic averaging theory. In addition, the distributed ES controllers are applied into nonholonomic unicycles by introducing a coordinate transformation. Finally, simulations illustrate the effectiveness of the distributed ES controllers.
PtSe2/graphene hetero-multilayer: gate-tunable Schottky barrier height and contact type

Graphene-based two-dimensional hybrid materials are attracting significant attention because they can preserve novel characteristics of Dirac cone. Here, based on first-principles methods, we focus on the electronic characteristics of PtSe2/graphene hetero-multilayer. The negative binding energies indicate that the hybrid materials can be fabricated easily in practice. Also, the n-type Schottky contact is formed and its barrier height is robust to the number of graphene layer. Moreover, the gate-voltage can effectively induce the Schottky barrier transformation from n-type to p-type and contact type transformation from Schottky to Ohmic in the PtSe2/graphene hetero-multilayer. Thus, the work demonstrates that the graphene stacking configuration and gate-voltage will tune the electronic characteristics of PtSe2/graphene-based nanodevices.
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users’ preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april.
Energy-Aware and Cost Effective VM Allocation Strategy Based on Best Fit MultiValued Binpaking Algorithm

Cloud computing is growing by providing features like rapid elasticity, measured services, on demand self-service, broad network access and pay-as-you-go concepts. Cloud providers are building large data centers to achieve the regularly growing demand of cloud users. Virtualization technology is the core concept behind cloud computing which provides computing services in the form of virtual machines. A large number of cloud services are requested by a large number of cloud users dynamically in the cloud computing. So, an efficient policy must be used for providing all resources to requesting users to fulfill their needs. Therefore, one of the central challenges in cloud computing is optimal resource allocation. Cloud providers use various virtual machine placement (VMP) policies to allocate the virtual machine to the available minimum number of physical servers. In this paper, Best Fit Multi valued Bin Packing approach is proposed for optimum resources allocation in which different virtual machines are allocated to minimum number of hosts by considering three parameters: RAM, CPU processing (MIPS) and network bandwidth. This approach also uses these parameters of the available physical servers to maximize the resource utilization for reducing energy consumption. The simulations of cloud environment for this approach are achieved by using CloudSim simulator. Our results show that maximum utilization of resources is achieved by choosing least number of hosts.
Determining the Appropriate Number of Observers in Eye-Tracking Based Video Saliency Computation

This paper proposes a new method for suggesting the appropriate number of observers needed in eye-tracking experiments for a given video. We first use the fixation consistency computed as the Similarity between different observers' fixation maps with the ground truth as a feature vector to classify sample video clips into four types. Then these videos are labeled and used to train a support vector machine (SVM) for identifying the class of a given video clip.
Five-Level Inverter-Fed Space Vector Based Direct Torque Control of Open-End Winding Induction Motor Drive

This work proposes a space vector based direct torque control scheme implemented on an open-end winding type induction motor fed by a five-level inverter. The modulation algorithm uses a 60-degree coordinate system to avoid fractional arithmetic as present in the standard Cartesian coordinate system, which reduces the arithmetic calculation complexity. Making use of the natural fractal structure of a hexagonal space vector diagram of the multilevel inverter, this scheme uses a fractal-based algorithm for sector identification and switching vector generation. In this scheme, three-phase signals to the space vector modulator are generated based on a torque-flux error by means of transformation logic. Two three-level inverters are fed from both open ends of an induction motor for realizing a five-level inverter. The scheme includes implementation of different levels of inverter-fed direct torque control drive. The control scheme is implemented on a laboratory prototype 2hp induction motor through a TMS320F240 DSP processor based dSPACE DS-1104 controller. The inverter gating signals are generated by using a Virtex-II FPGA board. The hardware results of different direct torque control configurations are presented for validating the algorithm and all the measurements are done under closed-loop operation.
Human Emotional Care Purposed Automatic Remote Portrait Drawing Generation and Display System Using Wearable Heart Rate Sensor and Smartphone Camera with Depth Perception

We propose a system that automatically generates portrait drawings for the purpose of human emotional care. Our system comprises two parts: a smartphone application and a server. The smartphone application enables the user to take photographs throughout the day while acquiring heart rates from the smartwatch worn by the user. The server collects the photographs and heart rates and displays portrait drawings automatically stylized from the photograph for the most exciting moment of the day. In the system, the user can recall the exciting and happy moment of the day through admiring the drawings and heal the emotion accordingly. To stylize photographs as portrait drawings, we employ nonphotorealistic rendering (NPR) methods, including a portrait etude stylization proposed in this paper. Finally, the effectiveness of our system is demonstrated through user studies.
Distributed Optimization of Classifier Committee Hyperparameters

In this paper, we propose an optimization workflow to predict classifiers accuracy based on the exploration of the space composed of different data features and the configurations of the classification algorithms. The overall process is described considering the text classification problem. We take three main features that affect text classification and therefore the accuracy of classifiers. The first feature considers the words that comprise the inputtext; here we use the N-gram concept with different N values. The second feature considers the adoption of textual pre-processing steps such as the stop-word filtering and stemming techniques. The third feature considers the classification algorithms hyperparameters. In this paper, we take the well-known classifiers K-Nearest Neighbors (KNN) and Naive Bayes (NB) where K (from KNN) and a-priori probabilities (from NB) are hyperparameters that influence accuracy. As a result, we explore the feature space (correlation among textual and classifier aspects) and we present an approximation model that is able to predict classifiers
Novelty and Diversity in Image Retrieval

This paper studies the formalization and the use of the concepts of novelty and diversity to diversify the result set of a multimedia query, avoiding the presence of uninformative results. First, we review and adapt several diversity measures proposed in the information retrieval literature. The problem of maximizing diversity being NP-complete, we propose a general greedy algorithm (dependent on a scoring function) for finding an approximate solution, and instantiate it using three scenarios: a probabilistic one, a fuzzy one, and a geometric one. Finally, we perform tests on two data sets, one in which retrieval is based on annotations and the other in which retrieval is purely visual.
Preparing for a paradigm shift in aging populations: listen to the oldest old

ABSTRACT Purpose: Current healthcare systems are not suitable for serving future societies in which the oldest old are commonplace. The objective of this study was to understand what the oldest old care most about in their daily lives. Methods: Semi-structured in-depth interviews and thematic analysis were used. Face-to-face interviews were conducted in 17 elderly residents (≥ 95 years) of Arakawa-ku, Tokyo, Japan from July to November 2017. Results: Three themes emerged from the interview responses: “unshakable beliefs and social ties,” “natural acceptance,” and “my day-to-day life with precious moments. ” The oldest old strongly believe in diligence and compassion and maintain strong relationships with people around them. Despite their small social networks, they are concerned about future society. They accept their selves and their lives, including their impending deaths. Despite their functional decline, they control their lives by making very small decisions. They live on a moment-to-moment basis, cherishing simple events. Conclusion: Maintaining autonomy through making small decisions and enjoying small pleasures are important to the oldest old. Understanding the needs of the oldest old is the first step towards developing optimal geriatric care for an aging population.
Towards a Taxonomy of Bad Smells Detection Approaches

Refactoring is a popular maintenance activity that improves the internal structure of a software system while maintaining its external behaviour. During the refactoring process, detecting bad smells plays a crucial role in establishing reliable and accurate results. So far, several approaches have been proposed in the literature to detect bad smells at different levels. In this paper, we focus on reviewing the state-of-the-art of object-oriented bad smells detection approaches. For the purpose of comparability, we propose a hierarchical taxonomy by following a development methodology. Our taxonomy encompasses three main dimensions describing the detection approach via the used method, analysis and assessment. The resulting taxonomy provides a deeper understanding of existing approaches. It highlights many key factors that concern the developers when making a choice of an existing detection approach or when proposing a new one.