Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Method to Predict the Accuracy of QoS by Using User Acceptance Rate

In web services the effective QoS-based approach to service recommendation is becoming more and more important. The Performance in existing is not satisfactory even when the service recommendation is studied in recent literature 1) previous recommender systems are all black boxes providing limited information on the performance of the service candidates 2) previous approaches fail to consider the QoS variance according to users’ locations. In this project, for large scale web service recommendation a collaborative filtering is designed. This approach employs the characteristic of QoS and achieves considerable improvement on the recommendation accuracy compared with previous approach. To help service users better understand the rationale of the recommendation and remove some of the mystery, a recommendation visualization technique to show how a recommendation is grouped with other choices. Comprehensive experiments are conducted using more than 1.5 million QoS records of real-world web service invocations. The experimental results show the efficiency and effectiveness of our approach.
Development of In Vitro Methodologies to Investigate Binding by Sodium Hyaluronate in Eye Drops to Corneal Surfaces

Purpose: To develop in vitro methods to assess binding by sodium hyaluronate in eye drops to corneal surfaces. Methods: Two different, complementary corneal binding set-ups were developed. In a dynamic in vitro model, confluent corneal epithelial cells (HCE-T) were assembled in chamber slides and a declining channel. A static model was constructed with ex vivo porcine corneas clamped in Franz cells. To test the predictive capacity of models, four different eye drops containing sodium hyaluronate were spiked with tritium-labeled sodium hyaluronate to standardize quantification. In both settings, eye drops were applied for 5 min and physiological conditions were mimicked by flushing with artificial tear fluid. Spreading experiments on HCE-T next to synthetic membranes were used for further characterization. Results: Binding was more pronounced in dynamic HCE-T model. Three of the four eye drops demonstrated sigmoidal elution of sodium hyaluronate, suggesting pronounced binding. One solution eluted distinctly faster, likewise the buffer control. The static method produced a similar ranking but at lower levels. When eye drops in which phosphate buffer was replaced by citrate buffer (i.e., to prevent calcification) were used, binding was not influenced. All eye drops spread immediately when placed on HCE-T and at the same order of magnitude on glass and polyethylene terephthalate surfaces. Conclusion: Dynamic and static models performed on different corneal sources were used to determine sodium hyaluronate binding kinetics in solutions under physiological conditions. These methodologies resulted in a ranking of the capacity of sodium hyaluronate to bind in vitro to corneal surfaces.
High-precision measurement of satellite range and velocity using the EISCAT radar

Abstract. This paper is a continuation of an earlier work by Nygren et al. (2012), where the velocity of a hard target was determined from a set of echo pulses reflected by the target flying through the radar beam. Here the method is extended to include the determination of range at a high accuracy. The method is as follows. First, the flight time of the pulse from the transmitter to the target is determined at an accuracy essentially better than the accuracy given by the sampling interval. This method makes use of the fact that the receiver filtering creates slopes at the phase flips of the phase modulated echo pulse. A precise flight time is found by investigating the echo amplitude within this slope. A value of velocity is calculated from each echo pulse as explained in the earlier paper. Next, the ranges together with velocities from a single beam pass are combined to a measurement vector for a linear inversion problem. The solution of the inversion problem gives the time-dependent range and velocity from the time interval of satellite flight through the radar beam. The method is demonstrated using the EISCAT (European Incoherent Scatter) UHF radar and radio pulses reflected by a satellite. The achieved standard deviations of range are about 5–50 cm and those of velocity are about 3–25 mm s−1.
Thermal and epithermal neutron fluence rates in the irradiation facilities of the TRIGA IPR-R1 nuclear reactor

The TRIGA IPR-R1 nuclear reactor at the Centro de Desenvolvimento da Tecnologia Nuclear CDTN/CNEN, Belo Horizonte, Brazil, has three irradiation facilities: a rotary specimen rack outside the reactor core with 40 irradiation channels which provide large scale isotope irradiation; the central tube placed at the center of the reactor core which permits sample irradiation with maximum neutron flux, as well as neutron beam extraction; a pneumatic transfer tube which makes possible the analysis of short half-life radioisotopes. The aim of this work is to evaluate the thermal and epithermal neutron fluence rates in the three irradiation facilities of the IPR-R1. Al-0.1%Au reference materials were irradiated bare and Cd-covered, at 100 kW thermal power, and gamma spectrometry was applied with an HPGe detector. For the rotary specimen rack, the values obtained for thermal and epithermal neutron fluence rates are (8.2 ± 0.2) × 1011n.cm-2.s-1 and (3.5 ± 0.1) × 1010n.cm-2.s-1, respectively. For the pneumatic transfer tube and the central tube, epithermal neutron fluence rate values are respectively (3.3 ± 0.2) ×109n.cm-2.s-1 and (2.6 ± 0.1) x 1011n.cm-2.s-1. For these facilities, thermal neutron fluence rate values are (2.5 ± 0.2) × 1011n.cm-2.s-1 and (2.8 ± 0.1) × 1012n.cm-2.s-1, respectively. Knowledge of these parameters will be fundamental in the planning of sample irradiation for analysis or radioisotope production in a more accurate way.
Accountability for Data Sharing in the Cloud

: Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A major feature of the cloud services is that users’ data are usually processed remotely in unknown machines that users do not own or operate. While enjoying the convenience brought by this new emerging technology, users’ fears of losing control of their own data (particularly, financial and health data) can become a significant barrier to the wide adoption of cloud services. To address this problem, in this paper, we propose a novel highly decentralized information accountability framework to keep track of the actual usage of the users’ data in the cloud. In particular, we propose an object-centered approach that enables enclosing our logging mechanism together with users’ data and policies. We leverage the JAR programmable capabilities to both create a dynamic and traveling object, and to ensure that any access to users’ data will trigger authentication and automated logging local to the JARs.
Hybrid Method for Maximum Power Point Tracking in Photovoltaic Cell using Perturb Observe and Least Mean Square

At a geographical location of Indian sub-continent, solar energy has been recommended and implemented as an alternative to traditional methods of electricity generation. It allows direct conversion of sun photonic radiations to electrical energy. This is conceivable due to Photo-Voltaic (PV) systems, boost converters and maximum power tracking control system. An effective photovoltaic system should operate at its best i.e. should extract maximum power from the system with least fluctuations in voltages/currents. The tracking becomes complicated due to solar irradiance level &temperature, varieties in the maximum power point and the non-linear conduct of PV systems. Maximum Power Point Tracking (MPPT) can be accomplished in the PV cells at particular operating point but it needs good control at the output boost converter side by using an effective control technique. There are many techniques available i.e. Perturb-Observe (PO) etc. but they do not consider the predictive or future power in making decisions for IGBT on/off operation; hence have slow initial start points and fluctuations as well. Here, a hybrid technique is proposed that make use of perturbobserve at output and Least Mean Square (LMS) for filtering to get the predictive power values which results in maximum point tracking and has lesser oscillations.
Modeling the Internet of Things- Towards the Information Systems Research View

Being a complex topic Internet of Things (IoT) involves multiple disciplines and approaches. In this paper an initial overview of the modeling techniques within the IoT by the Information Systems researchers is provided. These descriptive results offer a basis for discussion about the research topics that are important in IoT for the Information Systems Research community as well as the first overview of the keywords that the authors use to describe their work in IoT- related context. Publications from the IoT context, including some of the topic areas in smart environment from the AIS electronic library were analyzed towards their application of models within their result presentation. The focus of this preliminary analysis is the description of the application and dissemination of modeling in IoT research. Additionally, the results offer insights into the purpose of the model usage by the topic areas under analysis. The findings indicate that the publications that put themselves directly into the IoT context by mentioning it in the paper title, abstract or keywords frequently provide a general overview on the area and mostly do not involve formal modeling, while a more specific specialization requires the usage of formal modeling tools.
Financial Reporting Quality, Free Cash Flow, and Investment Efficiency

This paper based on the perspective of firm’s agency conflicts to examine the relationship between financial reporting quality and investment efficiency and to analyze the interaction effect between financial reporting and free cash flow on investment efficiency. We use 3,726 samples of Chinese listed firms during the period 2008–2012 to test the empirical models and find that financial reporting quality is negatively associated with both underinvestment and overinvestment. Further, we find that financial reporting quality is more strongly associated with overinvestment for firms with large free cash flow, which suggests that financial reporting quality can reduce information asymmetry arising from agency conflicts between the managers and investors. This paper extends the field of application of financial reporting quality and investment efficiency in the emerging capital markets in the world. Moreover, this is the first study that analyzes the interaction effect between financial reporting quality and free cash flow on investment efficiency.
Machine Learning Applications in Detecting Rip Currents from Images

Annotated rip current images are used in oceanographic climate studies and they are mostly annotated by hand. For thousands of images, this manual annotation becomes difficult. In recent years, object detection has become a successful approach for identifying regions of an image. There are several different algorithms currently used for detecting objects from images, however, there is no software tool to automate the detection of rip current from images. While developing the tool, this paper describes a comparison between the Viola-Jones object detection algorithm, deep learning or, convolution neural networks, and a meta-learner on a dataset of rip current images to find the most suitable algorithm for detecting rip currents. These algorithms are run on a benchmark of rip current images and compared based on the detection rate and the number of false positives contained in each benchmark image. In addition, this paper makes two more contributions. One is a new set of Haar features based upon the originals. The other contribution is the building up of a meta-classifier, which combines the outputs of several machine learning techniques to increase accuracy of the rip current classifier by reducing the false positives of the state-of-the-art approach. Note: This technical paper is submitted for journal review. The id of this technical paper is TR2018/3.
Compiling Programs and Proofs: FoCaLiZe Internals

Designing a tool to ease the development of high-level security or safety systems must consider to facilitate not only design and coding but also formal demonstrations of correctness and compliance to standards. This entails some requirements on the tool as these demonstrations ask to link together computational and logical aspects of the development. These requirements are briefly considered and a solution is proposed: functions, statements and proofs are handled in a unique language, offering inheritance and parametrized modules. The FoCaLiZe environment implements this language, which remains simple enough to be used in a usual engineering process. The code generation produces an executable functional code (in OCaml) and a checkable term of a logical Type Theory (verified by Coq), close enough to truly ease traceability. It ensures that OCaml and Coq produced codes are error-free and provides compact generated code. The main contribution of this paper is a detailed presentation of the compilation scheme, which is supported by an original treatment of the dependencies induced by the combination of computational and logical constructs. As the whole source code is translated to a logical term verified by Coq, we get a strong assurance in the correctness of the generated code, hence avoiding the need to prove correctness of the compiler itself.
Local Energy Markets: Paving the Path Toward Fully Transactive Energy Systems

Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone toward fully transactive energy systems. In this paper, we evaluate such a fully integrated transactive system by, first, modeling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), second, modeling a wholesale market and a local market, and, third, coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75% of the expected costs when local markets and flexibility are considered. This illustrates that how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.
Vision-Based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection

We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs.
MadMax: surviving out-of-gas conditions in Ethereum smart contracts

Ethereum is a distributed blockchain platform, serving as an ecosystem for smart contracts: full-fledged inter-communicating programs that capture the transaction logic of an account. Unlike programs in mainstream languages, a gas limit restricts the execution of an Ethereum smart contract: execution proceeds as long as gas is available. Thus, gas is a valuable resource that can be manipulated by an attacker to provoke unwanted behavior in a victim's smart contract (e.g., wasting or blocking funds of said victim). Gas-focused vulnerabilities exploit undesired behavior when a contract (directly or through other interacting contracts) runs out of gas. Such vulnerabilities are among the hardest for programmers to protect against, as out-of-gas behavior may be uncommon in non-attack scenarios and reasoning about it is far from trivial. In this paper, we classify and identify gas-focused vulnerabilities, and present MadMax: a static program analysis technique to automatically detect gas-focused vulnerabilities with very high confidence. Our approach combines a control-flow-analysis-based decompiler and declarative program-structure queries. The combined analysis captures high-level domain-specific concepts (such as "dynamic data structure storage" and "safely resumable loops") and achieves high precision and scalability. MadMax analyzes the entirety of smart contracts in the Ethereum blockchain in just 10 hours (with decompilation timeouts in 8% of the cases) and flags contracts with a (highly volatile) monetary value of over $2.8B as vulnerable. Manual inspection of a sample of flagged contracts shows that 81% of the sampled warnings do indeed lead to vulnerabilities, which we report on in our experiment.
Sensitive Determination of Cadmium at ng/mL Level Using Slotted Quartz Tube Atom Trapping and Metal Coating

Cadmium has no known biological function and is extremely toxic to human being. Upon exposure, cadmium accumulates in the body, particularly in kidneys and liver [1, 2]. These properties, along with its common usage make cadmium as one of the most common environmental hazards. Inhalation of fumes and ingestion of cadmium salts can also occur and these will result in acute poisoning; at least one death has been reported from selfpoisoning with cadmium chloride [3]. The cadmium may occur naturally or as a contaminant in sewage sludge, fertilizers, polluted groundwater and mining effluents and contaminated fish [2]. Enhancement of the analytical sensitivity for cadmium determination using a metal coated slotted quartz tube and Flame Atomic Absorption Spectrometry (FAAS) was achieved in this study. Slotted Quartz Tube (SQT) has been used as an atom trap (AT) where the analyte is accumulated in the inner wall prior to re-atomization [4]. In this study, SQT was coated with Zr in order to improve the sensitivity of Cd detection. Experimental parameters used in the system were optimized to obtain the best S/N. Sensitivity was improved 237, 136 and 2.8 fold for Zr-coated SQT-AT, SQT-AT and SQT, respectively, as compared to conventional FAAS. For Zr-Coated SQT-AT, characteristic concentration (C0) was found to be 0.093 ng/mL using 3.9 mL/min of sample flow rate and 4.0 min of collection period; the detection limit was found to be 0.24 ng/mL. EnviroMAT Waste Water LOW (EU-L-1) as a CRM was used to check accuracy and the results were found to be in good agreement with the certified ones.
The common sense of counseling psychology: introducing the Common-Sense Model of self-regulation

Abstract The goal of therapy is typically to improve clients’ self-management of their problems, not only during the course of therapy but also after therapy ends. Although it seems obvious that therapists are interested in improving clients’ self-management, the psychotherapy literature has little to say on the topic. This article introduces Leventhal’s Common-Sense Model of Self-Regulation, a theoretical model of the self-management of health, and applies the model to the therapeutic process. The Common-Sense Model proposes that people develop illness representations of health threats and these illness representations guide self-management. The model has primarily been used to understand how people self-manage physical health problems, we suggest it may also be useful to understand self-management of mental health problems. The Common-Sense Model’s strengths-based perspective is a natural fit for the work of counseling psychologists. The model has important practical implications for addressing how clients understand mental health problems over the course of treatment and self-manage these problems during and after treatment.
High resolution modelling of aerosol dispersion regimes during the CAPITOUL field experiment: from regional to local scale interactions

High resolution simulation of complex aerosol particle evolution and gaseous chemistry over an atmospheric urban area is of great interest for understanding air quality and processes. In this context, the CAPITOUL (Canopy and Aerosol Particle Interactions in the Toulouse Urban Layer) field experiment aims at a better understanding of the interactions between the urban dynamics and the aerosol plumes. During a two-day Intensive Observational Period, a numerical model experiment was set up to reproduce the spatial distribution of specific particle pollutants, from the regional scales and the interactions between different cities, to the local scales with specific turbulent structures. Observations show that local dynamics depends on the day-regime, and may lead to different mesoscale dynamical structures. This study focuses on reproducing these fine scale dynamical structures, and investigate the impact on the aerosol plume dispersion. The 500-m resolution simulation manages to reproduce convective rolls at local scale, which concentrate most of the aerosol particles and can locally affect the pollutant dispersion and air quality.
Field Challenges for Mobile Photo Recognition Food Logging

Food consumption heavily influences one's health and a poor diet can lead to chronic diseases. Food logging using mobile apps can provide a continuous way to longitudinally track dietary behaviors and nutritional intake. This can help users understand their eating habits and encourage healthier food choices. However, current apps still pose many usability challenges, including tedious manual text entry of food names. Fortunately, advances in computer vision and deep learning are enabling automatic food recognition for instant and convenient logging. We have developed a mobile app prototype and conducted formative investigations into the usability and usage of mobile photo recognition food logging in a series of studies: online requirements survey, usability lab study, and 1-week field trial in an Asian country. Our findings reveal patterns and challenges in usage, usability, food dataset coverage and accuracy, and localization issues. We further discuss opportunities for design and technology to address these challenges.