Miracle-dz/UniFAR-FTUs huggingface dataset:
Extractive Document Summarization Using a Supervised Learning Approach

In this paper, we present a model for extractive multi-document text summarization using a supervised learning approach. The model uses a convolutional neural networks (CNN) which is capable of learning sentence features on its own for sentence ranking. This approach has been used in order to avoid the overhead of extracting features from sentences manually. Integer linear programming (ILP) approach has been adopted for selecting sentences to generate the summary based on sentence ranks. This ILP model minimizes the redundancy in the generated summary. We have evaluated our proposed approach on the DUC 2007 dataset and its performance is found to be competitive or better in comparison with state-of-the-art systems.
A Study of Initiative Decision-Making in Distributed Human-Robot Teams

Dull, dirty, and dangerous emergency response tasks (e.g., Fukushima nuclear disaster) typically call for human-robot teams such that the robots are teleoperated by one or more operators at a remote location. However, this distance creates a disconnect between human and robot that hinders effective teamwork. Mixed-initiative interaction has the potential to bridge the disconnect by enabling the human and robot to opportunistically interleave their complementary capabilities. However, allowing robots to intervene and take over from the human could be ill-fated if the intervention is illtimed. Hence, in this article, we examine the question of when should a robot intervene and seize control from a human operator, through a human subject study where the human-robot team is tasked to search for disaster victims in an unknown environment. Keywords—mixed-initiative interaction; human-robot team; computational model
Adaptive Voltage/Frequency Scaling and Core Allocation for Balanced Energy and Performance on Multicore CPUs

—Energy efﬁciency is a known major concern for computing system designers. Signiﬁcant effort is devoted to power optimization of modern systems, especially in large-scale installations such as data centers, in which both high performance and energy efﬁciency are important. Power optimization can be achieved through different approaches, several of which focus on adaptive voltage regulation. In this paper, we present a comprehensive exploration of how two server-grade systems behave in different frequency and core allocation conﬁgurations beyond nominal voltage operation. Our analysis, which is built on top of two state-of-the-art ARMv8 microprocessor chips (Applied Micro’s X-Gene 2 and X-Gene 3) aims (1) to identify the best performance per watt operation points when the servers are operating in various voltage/frequency combinations, (2) to reveal how and why the different core allocation options on the available cores of the microprocessor affect the energy consumption, and (3) to enhance the default Linux scheduler to take task allocation decisions for balanced performance and energy efﬁciency. Our ﬁndings, on actual servers’ hardware, have been integrated into a lightweight online monitoring daemon which decides the optimal combination of voltage, core allocation, and clock frequency to achieve higher energy efﬁciency. Our approach reduces on average the energy by 25.2% on X-Gene 2, and 22.3% on X-Gene 3, with a minimal performance penalty of 3.2% on X-Gene 2 and 2.5% on X-Gene 3, compared to the default system conﬁguration.
Point-Cloud Mapping and Merging Using Mobile Laser Scanner

This paper presents a method for building a 3D point-cloud map in a residential environment using a mobile laser scanner. Several submaps are generated in different small areas using normal distributions transform (NDT) scan matching based simultaneous localization and mapping (SLAM) and full graph-based SLAM. When a loop closure (revisit) of the vehicle is detected, two constraints are used in the graph-based SLAM to improve submap accuracy: the poses of the vehicle obtained by NDT scan matching and oneto-one matching of pole-like landmarks, such as utility poles and traffic sign poles. The merging of the submaps is also performed using the full graph-based SLAM. The experimental results for three-submap generation and merging in a residential environment validate the effectiveness of the proposed method. Keywords-mobile laser scanner; map generation; map merging; NDT scan matching; full graph-based SLAM
Single Pixel Time-of-Flight Sensors for Object Detection and Self-Detection in Three-Sectional Single-Arm Robot Manipulators

Avoiding collisions between a robot arm and any obstacle in its path is essential to human-robot collaboration. Multiple systems are available that can detect obstacles in the robot’s way prior and subsequent to a collision. The systems work well in different areas surrounding the robot. One area that is difficult to handle is the area that is hidden by the robot arm. This paper focuses on pick and place maneuvers, especially on obstacle detection in between the robot arm and the table that robot is located on. It introduces the use of single pixel time-offlight sensors to detect obstacles directly from the robot arm. The proposed approach reduces the complexity of the problem by locking axes of the robot that are not needed for the pick and place movement. The comparison of simulated results and laboratory measurements show concordance.
Walking the Plank: An Experimental Paradigm to Investigate Safety Voice

The investigation of people raising or withholding safety concerns, termed safety voice, has relied on report-based methodologies, with few experiments. Generalisable findings have been limited because: the behavioural nature of safety voice is rarely operationalised; the reliance on memory and recall has well-established biases; and determining causality requires experimentation. Across three studies, we introduce, evaluate and make available the first experimental paradigm for studying safety voice: the “Walking the plank” paradigm. This paradigm presents participants with an apparent hazard (walking across a weak wooden plank) to elicit safety voice behaviours, and it addresses the methodological shortfalls of report-based methodologies. Study 1 (n = 129) demonstrated that the paradigm can elicit observable safety voice behaviours in a safe, controlled and randomised laboratory environment. Study 2 (n = 69) indicated it is possible to elicit safety silence for a single hazard when safety concerns are assessed and alternative ways to address the hazard are absent. Study 3 (n = 75) revealed that manipulating risk perceptions results in changes to safety voice behaviours. We propose a distinction between two independent dimensions (concerned-unconcerned and voice-silence) which yields a 2 × 2 safety voice typology. Demonstrating the need for experimental investigations of safety voice, the results found a consistent mismatch between self-reported and observed safety voice. The discussion examines insights on conceptualising and operationalising safety voice behaviours in relationship to safety concerns, and suggests new areas for research: replicating empirical studies, understanding the behavioural nature of safety voice, clarifying the personal relevance of physical harm, and integrating safety voice with other harm-prevention behaviours. Our article adds to the conceptual strength of the safety voice literature and provides a methodology and typology for experimentally examining people raising safety concerns.
Frequency Reconfigurable Antenna with Air Substrate for S-band

This paper proposes a frequency reconfigurable microstrip patch antenna with air as substrate. The frequency reconfigurability has been achieved by means of the ON and OFF states of a combination of three PIN diodes that make the antenna operate in eight different states. Each state exhibits a different set of characteristics. The proposed antenna operates in S band. The antenna is fed by a coaxial feed that would provide an additional support to it along with the dielectric screws which will help the patch to suspend in air above the ground plane. The antenna finds its applications in Bluetooth, WiFi, WiMAX, WLAN, RFID, LTE, microwave ovens, medical diathermy machines, keyless vehicle locks etc. As compared to other reconfigurable antenna designs the proposed antenna is an economical alternative because of its air substrate. It also provides a range of resonant frequencies with stable radiation pattern, higher directivity and higher front to back lobe ratio.
mTORC1 Signaling Is Palmitoylation-Dependent in Hippocampal Neurons and Non-neuronal Cells and Involves Dynamic Palmitoylation of LAMTOR1 and mTOR

The mechanistic target of rapamycin (mTOR) Complex 1 (mTORC1) controls growth and proliferation of non-neuronal cells, while during neuronal development mTORC1 responds to glutamate and neurotrophins to promote neuronal migration and dendritic arborization. Recent studies reveal that mTORC1 signaling complexes are assembled on lysosomal membranes, but how mTORC1 membrane targeting is regulated is not fully clear. Our examination of palmitoyl-proteomic databases and additional bioinformatic analyses revealed that several mTORC1 proteins are predicted to undergo covalent modification with the lipid palmitate. This process, palmitoylation, can dynamically target proteins to specific membranes but its roles in mTORC1 signaling are not well described. Strikingly, we found that acute pharmacological inhibition of palmitoylation prevents amino acid-dependent mTORC1 activation in HEK293T cells and brain-derived neurotrophic factor (BDNF)-dependent mTORC1 activation in hippocampal neurons. We sought to define the molecular basis for this finding and found that the mTORC1 proteins LAMTOR1 and mTOR itself are directly palmitoylated, while several other mTORC1 proteins are not palmitoylated, despite strong bioinformatic prediction. Interestingly, palmitoylation of LAMTOR1, whose anchoring on lysosomal membranes is important for mTORC1 signaling, was rapidly increased prior to mTORC1 activation. In contrast, mTOR palmitoylation was decreased by stimuli that activate mTORC1. These findings reveal that specific key components of the mTOR pathway are dynamically palmitoylated, suggesting that palmitoylation is not merely permissive for mTOR activation but is instead actively involved in mTORC1-dependent signaling.
Early Visibility Resolution for Removing Ineffectual Computations in the Graphics Pipeline

GPUs' main workload is real-time image rendering. These applications take a description of a (animated) scene and produce the corresponding image(s). An image is rendered by computing the colors of all its pixels. It is normal that multiple objects overlap at each pixel. Consequently, a significant amount of processing is devoted to objects that will not be visible in the final image, in spite of the widespread use of the Early Depth Test in modern GPUs, which attempts to discard computations related to occluded objects. Since animations are created by a sequence of similar images, visibility usually does not change much across consecutive frames. Based on this observation, we present Early Visibility Resolution (EVR), a mechanism that leverages the visibility information obtained in a frame to predict the visibility in the following one. Our proposal speculatively determines visibility much earlier in the pipeline than the Early Depth Test. We leverage this early visibility estimation to remove ineffectual computations at two different granularities: pixel-level and tile-level. Results show that such optimizations lead to 39% performance improvement and 43% energy savings for a set of commercial Android graphics applications running on stateof-the-art mobile GPUs.
The Binocular Balance at High Spatial Frequencies as Revealed by the Binocular Orientation Combination Task

How to precisely quantify the binocular eye balance (i.e., the contribution that each eye makes to the binocular percept) across a range of spatial frequencies using a binocular combination task, is an important issue in both clinical and basic research. In this study, we aimed to compare the precision of a binocular orientation combination paradigm with that of the standard binocular phase combination paradigm in measuring the binocular eye balance at low to high spatial frequencies. Nine normal adults (average age: 24.6 ± 2.0 years old) participated. Subjects viewed an LED screen dichoptically with polarized glasses in a dark room. The method of constant stimuli was used to quantitatively assess the point of subjective equality (PSE), i.e., the interocular contrast ratio when two eyes are balanced in binocular combination, for stimulus spatial frequencies from 0.5 to 8 cycles/degree. Precision was quantified by the variance [i.e., standard error (SE), obtained from 100 bootstrap estimates] associated to the PSE. Using stimuli whose interocular phase difference at the edge of the gratings was matched at 45°, we found that the orientation paradigm provides more precision than the standard binocular phase combination paradigm, especially at high frequencies (Experiment 1). Such differences remained when using stimuli that had three times larger interocular phase difference (Experiment 2) or displayed at four times higher stimuli resolution (Experiment 3). Our results indicate that a binocular combination tasked based on orientation rather than phase, provides a more precise estimate of binocular eye balance in human adults at high spatial frequencies, thus allowing a binocular balance to be assessed within the spatial region where amblyopes are most defective (i.e., high spatial frequencies).
Bingo Spatial Data Prefetcher

—Applications extensively use data objects with a regular and ﬁxed layout, which leads to the recurrence of access patterns over memory regions. Spatial data prefetching techniques exploit this phenomenon to prefetch future memory references and hide the long latency of DRAM accesses. While state-of-the-art spatial data prefetchers are effective at reducing the number of data misses, we observe that there is still signiﬁcant room for improvement. To select an access pattern for prefetching, existing spatial prefetchers associate observed access patterns to either a short event with a high probability of recurrence or a long event with a low probability of recurrence. Consequently, the prefetchers either offer low accuracy or lose signiﬁcant prediction opportunities. We identify that associating the observed spatial patterns to just a single event signiﬁcantly limits the effectiveness of spatial data prefetchers. In this paper, we make a case for associating the observed spatial patterns to both short and long events to achieve high accuracy while not losing prediction opportunities. We propose Bingo spatial data prefetcher in which short and long events are used to select the best access pattern for prefetching. We propose a storage-efﬁcient design for Bingo in such a way that just one history table is needed to maintain the association between the access patterns and the long and short events. Through a detailed evaluation of a set of big-data applications, we show that Bingo improves system performance by 60% over a baseline with no data prefetcher and 11% over the best-performing prior spatial data prefetcher.
Maximum Power Point Tracking based Solar Charge Regulator

The purpose of this paper is to describe the maximum power point tracking (MPPT) design and algorithm for power generation in the satellite. First, a brief introduction of the commonly used design approach is discussed and later followed by a detailed description of a MPPT technique. MPPT provides efficient transfer of energy between the panel and the load. The system uses a DC-DC converter to interface a PV array with a resistive load. These converters can be controlled by a Texas instruments based MSP430G2553 controller IC through the PWM method at a fixed frequency and varying duty cycle. The duty cycle of the PWM signal is determined on the basis of different atmospheric condition using some algorithm. The duty cycle of the PWM signal is adjusted such that the load resistance as seen by the photo voltaic (PV) panel matches with the output resistance of the PV panel, so that energy transfer between PV panel and load is maximized. This technique will help in reducing the overall cost of the small satellite, mainly where the solar panel is body mounted.
Two-Layer Beamforming for Virtual Cell in Ultra Dense Network

Virtual cell can be viewed as an effective way to solve the challenges in the ultra-dense network (UDN), however it suffers from severe and complex interference with the increase of cell density. In this paper, we exploit the similarity of interference channel among users, and assume a two-layer beamforming scheme to eliminate both intra-cell and inter-cell interference. To ensure the feasibility of the two-layer beamforming in UDN, we adopt a strategy to jointly optimize the virtual cell selection and user scheduling based on the interference channel similarity. Different from the user group schemes exploiting the channel similarity of users, we extend this similarity concept to the case of user grouping by interference channel. Analysis and simulation results conducted in virtual cell scenario confirm the improvements of the proposed scheme in complexity and performance aspects.
Exercise-Induced Changes in Pulmonary Artery Stiffness in Pulmonary Hypertension

Background: Pulmonary hypertension causes pulmonary artery (PA) stiffening, which overloads the right ventricle (RV). Since symptoms of pulmonary hypertension (PH) are exacerbated by exercise, exercise-induced PA stiffening is relevant to cardiopulmonary status. Here, we sought to demonstrate the feasibility of using magnetic resonance imaging (MRI) for non-invasive assessment of exercise-induced changes in PA stiffness in patients with PH. Methods: MRI was performed on 7 PH patients and 8 age-matched control subjects at rest and during exercise stress. Main pulmonary artery (MPA) relative area change (RAC) and pulse wave velocity (PWV) were measured from 2D-PC images. Invasive right heart catheterization (RHC) was performed on 5 of the PH patients in conjunction with exercise stress to measure MPA pressures and stiffness index (β). Results: Heart rate and cardiac index (CI) were significantly increased with exercise in both groups. In controls, RAC decreased from 0.27 ± 0.05 at rest to 0.22 ± 0.06 with exercise (P < 0.05); a modest increase in PWV was not significant (P = 0.06). In PH patients, RAC decreased from 0.15 ± 0.02 to 0.11 ± 0.01 (P < 0.05) and PWV and β increased from 3.9 ± 0.54 m/s and 1.86 ± 0.12 at rest to 5.75 ± 0.70 m/s and 3.25 ± 0.26 with exercise (P < 0.05 for both), respectively. These results confirm increased MPA stiffness with exercise stress in both groups and the non-invasive metrics of MPA stiffness correlated well with β. Finally, as assessed by PWV but not RAC, PA stiffness of PH patients increased more than that of controls for comparable levels of moderate exercise. Conclusion: These results demonstrate the feasibility of using MRI for non-invasive assessment of exercise-induced changes in MPA stiffness in a small, heterogeneous group of PH patients in a research context. Similar measurements in a larger cohort are required to investigate differences between PWV and RAC for estimation of MPA stiffness.
Ontologies to Model User Profiles in Personalized Job Recommendation

Personalized recommendation aims to provide results that are likely to be of interest to a particular user. Personalized recommendation is useful in the domain of job search in order to provide individuals more personalized recommendations of job listings based on their preferences. User profiles a re thus constructed based on the individual users preferences. On the other hand, user profiles a re helpful in improving the recommendations. In general, user profiles are structured based on the individual’s preferences. User profiles can be represented in various ways, one such way is ontology which is the systematic categorization and representation of relationships between various entities within a domain. Ontologies has been widely used in the domain of e-commerce and medicine. In this paper, we use ontologies in the domain of personalized job recommendation, to model user profiles. The major objective of this paper is to provide an ontology based user profile for the domain of job recommendation. In particular, we identified suitable classes, attributes and relations that are specific to job recommendation system. In addition, we presented OWL representation of the proposed ontological model such that it can be reused by domain experts.
Deep Reinforcement Learning Based Coded Caching Scheme in Fog Radio Access Networks

Fog radio access networks (F-RANs) have been presented as promising architectures for the future wireless system to provide high spectral and energy efficiency. With the help of the new designed fog access points (F-APs), F-RANs can take the full advantage of local caching capabilities, which relieves the load of fronthaul and reduces transmission delay. However, the cache resource optimization is a challenging task due to the uncertainty and dynamics of user file requests. Considering the high utilization of cache space and file diversity by coded caching, a deep reinforcement learning (DRL) based algorithm is developed for coded caching enabled F-RANs. The core idea of the proposal is that the network controller intelligently allocates the limited cache spaces of F-APs to different coded files based on the historical requests of the user. While the successful transmission probability of user requests is maximized during the learning process. Through numerical simulations, the convergence of the DRL based caching scheme is demonstrated, and the superiority of the proposal is verified by comparing with other baselines.
A fault-tolerant control strategy for non-linear system: An application to the two tank canonical noninteracting level control system

Two tank canonical system is highly used in process industries because of its mechanical structure which contributes fast release for liquid, slurries, viscous, and solid mixture. Two tank canonic system is highly nonlinear system due to its shape and varying cross-sectional area, hence the level control of this system is extremely difficult due to nonlinear behavior of the system. As a matter of facts, when abnormal activity occur such as system component, actuator and sensor faults occur in the system and hence system performances degrade drastically and a hazardous situation occurs. Fault-tolerant control is an advanced control strategy, which maintains control performance and system stability even though fault occurred in the system. The paper attributes to design a passive FTC strategy for a Two-Tank Canonical Non-Interacting Level Control System (TTCNILCS) with the constraint of a system, actuator faults, and process disturbances. FTC will increasing system performance in the presence of the faults. The simulation results demonstrate the proposed strategy has definite fault tolerant ability against the system and actuator faults also it has disturbance rejection capability. To verify the efficacy of the proposed passive FTC strategy Mean Square Error (MSE) indices is used.
A New Incentive Based Algorithm for Avoiding Free Riding in Peer to Peer Network

A peer-to-peer (P2P) network undergoes various type of cheating attacks. In recent literature, to combat this attack, various trust value-based mechanisms are employed. These mechanisms decrease the trust value of malicious nodes and counter many types of attacks. But this does not solve the problem of non-participation or free riding fully. Here, we propose an incentive based simple yet effective scheme for avoiding free riding taking trust value of nodes into account. The proposed scheme helps reducing non-participation in P2P networks. The results show that the scheme is efficient for a P2P network of few tens of nodes. With slight modifications, this mechanism can be extended for large sized networks.