Miracle-dz/UniFAR-FTUs huggingface dataset:
PShifter: feedback-based dynamic power shifting within HPC jobs for performance

The US Department of Energy (DOE) has set a power target of 20-30MW on the first exascale machines. To achieve one exaFLOPS under this power constraint, it is necessary to manage power intelligently while maximizing performance. Most production-level parallel applications suffer from computational load imbalance across distributed processes due to non-uniform work decomposition. Other factors like manufacturing variation and thermal variation in the machine room may amplify this imbalance. As a result of this imbalance, some processes of a job reach the blocking calls, collectives or barriers earlier and wait for others to reach the same point. This waiting results in a wastage of energy and CPU cycles which degrades application efficiency and performance. We address this problem for power-limited jobs via Power Shifter (PShifter), a dual-level, feedback-based mechanism that intelligently and automatically detects such imbalance and reduces it by dynamically re-distributing a job's power budget across processors to improve the overall performance of the job compared to a naïve uniform power distribution across nodes. In contrast to prior work, PShifter ensures that a given power budget is not violated. At the bottom level of PShifter, local agents monitor and control the performance of processors by actuating different power levels. They reduce power from the processors that incur substantial wait times. At the top level, the cluster agent that has the global view of the system, monitors the job's power consumption and provides feedback on the unused power, which is then distributed across the processors of the same job. Our evaluation on an Intel cluster shows that PShifter achieves performance improvement of up to 21% and energy savings of up to 23% compared to uniform power allocation, outperforms static approaches by up to 40% and 22% for codes with and without phase changes, respectively, and outperforms dynamic schemes by up to 19%. To the best of our knowledge, PShifter is the first approach to transparently and automatically apply power capping non-uniformly across processors of a job in a dynamic manner adapting to phase changes.
LAB Bacteriocins Controlling the Food Isolated (Drug-Resistant) Staphylococci

Staphylococci are a group of microorganisms that can be often found in processed food and they might pose a risk for human health. In this study we have determined the content of staphylococci in 7 different fresh goat-milk cheeses. These bacteria were present in all of them, ranging from 103 to 106 CFU/g based on growth on selective media. Thus, a set of 97 colonies was randomly picked for phenotypic and genotypic identification. They could be clustered by RAPD-PCR in 10 genotypes, which were assigned by 16S rDNA sequencing to four Staphylococcus species: Staphylococcus aureus, Staphylococcus chromogenes, S. simulans, and S. xylosus. Representative strains of these species (n = 25) were tested for antibiotic sensitivity, and 11 of them were resistant to at least one of the antibiotics tested, including erythromycin, amoxicillin-clavulanic acid and oxacillin. We also tested two bacteriocins produced by lactic acid bacteria (LAB), namely the circular bacteriocin AS-48 and the lantibiotic nisin. These peptides have different mechanism of action at the membrane level. Nevertheless, both were able to inhibit staphylococci growth at low concentrations ranging between 0.16–0.73 μM for AS-48 and 0.02–0.23 μM for nisin, including the strains that displayed antibiotic resistance. The combined effect of these bacteriocins were tested and the fractional inhibitory concentration index (FICI) was calculated. Remarkably, upon combination, they were active at the low micromolar range with a significant reduction of the minimal inhibitory concentration. Our data confirms synergistic effect, either total or partial, between AS-48 and nisin for the control of staphylococci and including antibiotic resistant strains. Collectively, these results indicate that the combined use of AS-48 and nisin could help controlling (pathogenic) staphylococci in food processing and preventing antibiotic-resistant strains reaching the consumer in the final products.
Compact lensless digital holography

Microscopy is of high interest for biology since it allows imaging features that are too small to be seen with naked eyes. However, cells are mostly transparent to visible and infrared light which makes it difficult to see with a traditional microscope. To do so, phase imaging was invented by F. Zernike, who received the Nobel prize in 1953 for this invention. It provides intensity contrast to visualize transparent samples such as those found in biology without any staining. Among the different implementations of phase imaging, digital holographic microscopy (DHM) is a well-known phase method which provides a quantitative value of the phase generated by the sample under test. Implementations of DHMs without the help of any lens element (so called lensless) offer the added advantage to provide large field of views (several mm2 compared to several hundred μm2) and more compact setups than traditional DHMs which have high quality microscope objectives. The lateral resolution is limited by the pixel size of the camera. However, several techniques have been developed to obtain subpixel resolution with lensless setups, hence giving a resolution smaller than the pixel size. In this thesis, two lensless transmission DHMs are presented using a side illumination technique in order to further reduce the device size and keep a visual access to the sample. The first one operates in an in-line configuration: the most compact but using time-consuming image post-processing. The second one is also lensless and uses an off-axis configuration allowing quasi-real time imaging but less compact. Their practical use is described and images of transparent (phase only) samples are shown. Super-resolution (subpixel) using several subpixel shifted holograms was then investigated for the in-line configuration and demonstrated on absorptive samples. Since those microscopes are compact and made out of off-the-shelf low priced elements, they could be broadly spread in schools for educational purposes. They could also be implemented in cells incubators, allowing visualization inside processes at low cost and multiplication of the number of measurements made at the same time.
Assessment of Perceptual-Cognitive Abilities among Athletes in Virtual Environments: Exploring Interaction Concepts for Soccer Players

Cognitive skills and their assessment gain increasing importance in soccer. In the past, athletes' perceptual-cognitive capabilities were only assessed using 2D media with its limitations. We used a virtual reality 360° video environment, where 15 high-skilled and 15 low-skilled soccer players (age 24 ± 3 years) experienced nine real-life soccer scenes from varying perspectives. The experience was frozen at crucial time points where they had to decide for one of three soccer actions. The recognition-action time and risk-level for decision were determined. Furthermore, six different interaction concepts were evaluated with respect to user experience, presence and immersion to find the most adequate and appealing one for assessment and training in soccer. Results show that high-skillers had a significantly lower overall recognition-action time. Risk-values for decisions did not differ significantly between skill levels.
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning

Our goal is for AI systems to correctly identify and act according to their human user's objectives. Cooperative Inverse Reinforcement Learning (CIRL) formalizes this value alignment problem as a two-player game between a human and robot, in which only the human knows the parameters of the reward function: the robot needs to learn them as the interaction unfolds. Previous work showed that CIRL can be solved as a POMDP, but with an action space size exponential in the size of the reward parameter space. In this work, we exploit a specific property of CIRL---the human is a full information agent---to derive an optimality-preserving modification to the standard Bellman update; this reduces the complexity of the problem by an exponential factor and allows us to relax CIRL's assumption of human rationality. We apply this update to a variety of POMDP solvers and find that it enables us to scale CIRL to non-trivial problems, with larger reward parameter spaces, and larger action spaces for both robot and human. In solutions to these larger problems, the human exhibits pedagogic (teaching) behavior, while the robot interprets it as such and attains higher value for the human.
Access Control Enforcement within MQTT-based Internet of Things Ecosystems

Confidentiality and privacy of data managed by IoT ecosystems is becoming a primary concern. This paper targets the design of a general access control enforcement mechanism for MQTT-based IoT ecosystems. The proposed approach is presented with ABAC, but other access control models can be similarly supported. The solution is based on an enforcement monitor that has been designed to operate as a proxy between MQTT clients and an MQTT server. The monitor enforces access control constraints by intercepting and possibly manipulating the flow of exchanged MQTT control packets. Early experimental evaluations have overall shown low enforcement overhead.
The Establishment of Intelligent Detection Method and Monitoring System for Underwater Target Based on Imaging Sonar

For the practical requirement of underwater safety protection, the conception of target precautionary area is put forward combined with the technical characteristic of imaging sonar and the analysis of small underwater target imaging feature. And a detection method for underwater moving target based on image processing is build up, so that the intelligent detection and recognition of the underwater specific target is realized. Meanwhile, the intelligent detection and monitoring system of underwater target based on imaging sonar is designed and developed with the use of multi-level component-based architecture according to the practical application requirements. The system has obtained remarkable economic benefit in practical use and has good prospects for application.
Binary Coding by Matrix Classifier for Efficient Subspace Retrieval

Fast retrieval in large-scale database with high-dimensional subspaces is an important task in many applications, such as image retrieval, video retrieval and visual recognition. This can be facilitated by approximate nearest subspace (ANS) retrieval which requires effective subspace representation. Most of the existing methods for this problem represent subspace by point in the Euclidean space or the Grassmannian space before applying the approximate nearest neighbor (ANN) search. However, the efficiency of these methods can not be guaranteed because the subspace representation step can be very time consuming when coping with high dimensional data. Moreover, the transforming process for subspace to point will cause subspace structural information loss which influence the retrieval accuracy. In this paper, we present a new approach for hashing-based ANS retrieval. The proposed method learns the binary codes for given subspace set following a similarity preserving criterion. It simultaneously leverages the learned binary codes to train matrix classifiers as hash functions. This method can directly binarize a subspace without transforming it into a vector. Therefore, it can efficiently solve the large-scale and high-dimensional multimedia data retrieval problem. Experiments on face recognition and video retrieval show that our method outperforms several state-of-the-art methods in both efficiency and accuracy.
INTERRUPTIONS AND INDIVIDUAL PERFORMANCE : IS THERE A SILVER LINING ? 1

Interruption of work by e-mail and other communication technologies has become widespread and ubiquitous. However, our understanding of how such interruptions influence individual performance is limited. This paper distinguishes between two types of e-mail interruptions (incongruent and congruent) and draws upon action regulation theory and the computer-mediated communication literature to examine their direct and indirect effects on individual performance. Two empirical studies of sales professionals were conducted spanning different time frames: a survey study with 365 respondents and a diary study with 212 respondents. The results were consistent across the two studies, showing a negative indirect effect of exposure to incongruent interruptions (interruptions containing information that is not relevant to primary activities) through subjective workload, and a positive indirect effect of exposure to congruent interruptions (interruptions containing information that is relevant to primary activities) through mindfulness. The results differed across the two studies in terms of whether the effects were fully or partially mediated, and we discuss these differences using meta-inferences. Technology capabilities used during interruption episodes also had significant effects: rehearsing (fine-tuning responses to incoming messages) and reprocessing (reexamining received messages) were positively related to mindfulness, parallel communication (engaging in multiple e-mail conversations simultaneously) and leaving messages in the inbox were positively related to subjective workload, and deleting messages was negatively related to subjective workload. This study contributes to research by providing insights on the different paths that link e-mail interruptions to individual performance and by examining the effects of using capabilities of the interrupting technology (IT artifact) during interruption episodes. It also complements the experimental tradition that focuses on isolated interruptions. By shifting the level of analysis from specific interruption events to overall exposure to interruptions over time and from the laboratory to the workplace, our study provides realism and ecological validity. 1
Body ownership and agency altered by an electromyographically controlled robotic arm

Understanding how we consciously experience our bodies is a fundamental issue in cognitive neuroscience. Two fundamental components of this are the sense of body ownership (the experience of the body as one's own) and the sense of agency (the feeling of control over one's bodily actions). These constructs have been used to investigate the incorporation of prostheses. To date, however, no evidence has been provided showing whether representations of ownership and agency in amputees are altered when operating a robotic prosthesis. Here we investigated a robotic arm using myoelectric control, for which the user varied the joint position continuously, in a rubber hand illusion task. Fifteen able-bodied participants and three trans-radial amputees were instructed to contract their wrist flexors/extensors alternately, and to watch the robotic arm move. The sense of ownership in both groups was extended to the robotic arm when the wrists of the real and robotic arm were flexed/extended synchronously, with the effect being smaller when they moved in opposite directions. Both groups also experienced a sense of agency over the robotic arm. These results suggest that these experimental settings induced successful incorporation of the prosthesis, at least for the amputees who took part in the present study.
LSC2018 Panel - Challenges of Lifelog Search And Access

Lifelogging is becoming an increasingly important topic of research and this paper highlights the thoughts of the three panelists at the LSC - Lifelog Search Challenge at ICMR 2018 in Yokohama, Japan on June 11, 2018. The thoughts cover important topics such as the need for challenges in multimedia access, the need for a better user interface and the challenges in building datasets and organising benchmarking activities such as the LSC.
Association analysis of moodle e‐tests in blended learning educational environment

The paper suggests the implementation of association analysis for improving the process of e‐testing in blended learning environment. The research has been conducted using knowledge tests at the Computer Graphics Moodle Course. In the preprocessing phase, data matrices have been created and prepared for the process of discovering significant relationships and links between students' answers to the questions from preparatory tests and those for testing knowledge, the ways of doing, and achieved results. By implementing Apriori and Predictive Apriori algorithms, a great number of association rules has been discovered. Important and interesting rules have been singled out by implementing objective and subjective assessment measures. The examples of interesting rules, as well as discovered patterns in items' relationships, have also been presented. The contribution of the described case study is visible in providing important feedback which enables the teacher to get a better insight into the concepts of created tests and decide on how to make changes to improve testing.
Giving up Control - A Speculative Air Pollution Mask to Reflect on Autonomy and Technology Design

In many metropolitan cities air pollution regularly exceeds safe levels, with numerous consequences for health and well-being. Current technological solutions often aim to give users control over their air pollution exposure by measuring, processing and sharing data about pollutant levels. We created a speculative face mask that opens and closes autonomously, taking control away from the user. The goal of the speculative design was to highlight the urgency and effect of air pollution on individuals in a tangible and embodied way. Through this critical design object, we question existing solutionist approaches to air pollution and pose questions about autonomy and individual responsibility. In this paper, we share our development process and the conceptual idea behind the mask to inform and inspire other critical objects to address important societal issues at an individual level.
Deep Reinforcement Learning for Chinese Zero Pronoun Resolution

Recent neural network models for Chinese zero pronoun resolution gain great performance by capturing semantic information for zero pronouns and candidate antecedents, but tend to be short-sighted, operating solely by making local decisions. They typically predict coreference links between the zero pronoun and one single candidate antecedent at a time while ignoring their influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is crucial for classifying later zero pronoun-candidate antecedent pairs, a need which leads traditional models of zero pronoun resolution to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to deal with the task. With the help of the reinforcement learning agent, our system learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 show that our approach substantially outperforms the state-of-the-art methods under three experimental settings.
Boundary-Aware Superpixel Segmentation Based on Minimum Spanning Tree

SUMMARY In this paper, we propose a boundary-aware superpixel segmentation method, which could quickly and exactly extract superpixel with a non-iteration framework. The basic idea is to construct a minimum spanning tree (MST) based on structure edge to measure the local similarity among pixels, and then label each pixel as the index with shortest path seeds. Intuitively, we ﬁrst construct MST on the original pixels with boundary feature to calculate the similarity of adjacent pixels. Then the geodesic distance between pixels can be exactly obtained based on two-round tree recursions. We determinate pixel label as the shortest path seed index. Experimental results on BSD500 segmentation benchmark demonstrate the proposed method obtains best performance compared with seven state-of-the-art methods. Especially for the low density situation, our method can obtain the boundary-aware oversegmentation region.
Comparison between Ogawa-Kudoh and modified Petroff techniques for mycobacteria cultivation in the diagnosis of pulmonary tuberculosis

ABSTRACT Objective To compare the performance of the Ogawa-Kudoh method with the modified Petroff technique in diagnosis of pulmonary tuberculosis. Methods A total of 205 sputum samples from 166 patients with clinical suspicion or under pulmonary tuberculosis follow-up, seen at a public tertiary care hospital, from July 2014 to July 2016 were used. All samples were simultaneously processed using the Ogawa-Kudoh and modified Petroff decontamination methods, according to the recommendations of the Ministry of Health. In the statistical analysis, the McNemar test and the Kappa index were used, respectively, to compare proportions and verify agreement between data. Results The Ogawa-Kudoh and modified Petroff methods were efficient in mycobacteria detection, with no significant differences in results (p=0.549) and contamination rate of the cultures (p=0.065). The agreement between techniques was considered excellent (Kappa index of 0.877) and Ogawa-Kudoh, as compared to the modified Petroff technique, showed sensitivity of 90.4%, specificity of 96.6%, positive predictive value of 94.3% and negative predictive value of 94.2%. Conclusion The Ogawa-Kudoh technique proved to be sufficiently sensitive and specific for diagnosis of pulmonary tuberculosis, and, therefore, suitable for routine laboratory application. Since it is simple, low-cost and has less technical requirements for biosafety and professional training, Ogawa-Kudoh is an alternative for managers and healthcare professionals to promote the expansion of bacteriological diagnostic coverage of pulmonary tuberculosis.
Towards a Privacy-Aware Quantified Self Data Management Framework

Massive amounts of data are being collected, stored, and analyzed for various business and marketing purposes. While such data analysis is critical for many applications, it could also violate the privacy of individuals. This paper describes the issues involved in designing a privacy aware data management framework for collecting, storing, and analyzing the data. We also discuss behavioral aspects of data sharing as well as aspects of a formal framework based on rewriting rules that encompasses the privacy aware data management framework.
Fast Semi-dense Depth Map Estimation

We consider the problem of depth reconstruction from downsampled sparse depth values. We compare our approach with semi-dense depth map interpolation and direct RGB-to-Depth reconstruction solutions on several datasets, including Matterport 3D dataset containing RGB and depth images of 90 building-scale scenes. We demonstrate that the proposed model can produce approximate depth map for over two hundreds images per second.
High Quality and Efficient Direct Rendering of Massive Real-world Point Clouds

We present a novel real-time screen-space rendering algorithm for real-world 3D scanned datasets. Our method takes advantage of the pull phase of a pull-push pyramidal filling algorithm in order to feed a hidden point removal operator. The push phase is then used to fill the final framebuffer. We demonstrate on a real-world complex dataset that our method produces better visual results and is more efficient comparing to state of the art algorithms.