Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Classification of Modes of Moral Reasoning in Judicial Decisions

What modes of moral reasoning do judges employ? We construct a linear SVM classifier for moral reasoning mode trained on applied ethics articles written by consequentialists and deontologists. The model can classify a paragraph of text in held out data with over 90 percent accuracy. We then apply this classifier to a corpus of circuit court opinions. We show that the use of consequentialist reasoning has increased over time. We report rankings of relative use of reasoning modes by legal topic, by judge, and by judge law school.
Measuring the Effectiveness of Using "Memrise" on High School Students' Perceptions of Learning EFL

Mobile applications of language learning have the capacity to revolutionize the way languages are learned. This study examined the students' perceptions of the effectiveness of Mobile Assisted Language Learning (MALL) based instruction as a complement to direct instruction for 36 high schoolstudents inIran. Specifically, student perceptionusing direct instructioncombined with"Memrise" Mobile-based language learning versus direct language instruction only.The findings of this research suggest that Memrise is an effective method of English language instruction. It is important to note that Memrise is not meant to replace direct language instruction, but its purpose is to serve as an effective supplement to state language instruction.
Capacity-Achieving Codes That Mitigate Intercell Interference and Charge Leakage in Flash Memories

We investigate constant-composition constrained codes for the mitigation of intercell interference for multilevel cell flash memories with a dynamic threshold scheme. The first explicit formula for the maximum size of a <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-ary <inline-formula> <tex-math notation="LaTeX">${\mathcal F}$ </tex-math></inline-formula>-avoiding code with a given composition and certain families of substrings <inline-formula> <tex-math notation="LaTeX">${\mathcal F}$ </tex-math></inline-formula> is presented. In addition, we provide methods to determine the asymptotic rate for <inline-formula> <tex-math notation="LaTeX">${\mathcal F}$ </tex-math></inline-formula>-avoiding codes with any composition ratio and to find the optimal composition ratio that maximizes the asymptotic rate. We also give the first efficient encoder/decoder for these <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-ary constant-composition codes achieving the channel capacity, for all <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> values.
Learner-Controlled Practice Difficulty and Task Exploration in an Active-Learning Gaming Environment

Background. With the use of computer-based simulations and games becoming increasingly common in education and organizational contexts for delivering training and instruction, learners are being granted unprecedented control over their learning. This increase in learner control leaves large differences in how learners challenge themselves and explore their learning environments. Learners can benefit from task difficulty, an aspect of challenge, when they are neither overwhelmed nor underwhelmed. The purpose of the present lab study was to test a model of how encouraging learners to challenge themselves with greater task difficulty when practicing a video game could be a “double-edged sword” in terms of their skill-based learning. Method. Using a first-person shooter computer game as the criterion task, 120 undergraduate males were randomly assigned to one of two groups. Half of the participants were encouraged to practice at a difficulty level that matched their skill level, while the other half were encouraged to practice at a difficulty that greatly exceeded their skill. Exploratory behavior was reflected in the variety of weapons used. Results. Findings supported a model of inconsistent mediation predicting that difficulty encouragement instructions would lead to higher selected practice difficulty, which in turn would have positive direct effects on skill transfer yet negative indirect effects through exploratory behavior. Discussion. The present study demonstrated that encouraging learners to practice at high levels of task difficulty is a “double-edged sword. ” Although high levels of task difficulty can help in the preparation for future difficulties, it can also undermine exploratory behavior which is an important aspect of the learning process. This research speaks to the potential of encouraging learners to practice under difficult conditions without undermining their learning.
Mobility-Aware Fog Computing in Dynamic Environments: Understandings and Implementation

Fog computing is an epitome to enable provisioning resources and services beyond the cloud, at the edge of the network, and nearer to end devices. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. However, the mobility of end users and/or fog nodes brings a major dilemma in the implementation of real-life scenarios. Therefore, we deliver the state-of-the-art research about mobility in fog computing. By identifying the mobility problems, requirements, and features of different proposals, we discover the open problems from subsisting studies and summarize the advantages of mobility for readers. This will help the researchers and developers avoid the current misapprehensions and capture the real-life scenarios, such as business, government, and education institutions. In spite of the extensive state-of-the-art research work, we also present the diverse mobility factors to investigate the correlation between dynamic nodes with fog nodes in order to accomplish better successful tasks. We conceive that the investigations of mobility factors in the fog computing will furnish the novel perspective on not only dynamic connections but also network dynamics. To revolutionize the follow-up research, we distinguish and foreground futurity directions concerning real-life scenarios of people and vehicles in a dynamic fog environment.
Using Information Gain in Data Fusion and Ranking

Entropy and information gain have been traditionally used to measure association between inputs and outputs. In this paper, Information gain is used to measure and decide the level of dependency or relevance between attributes. A data fusion technique based on information gain measures in a virtual data integration environment is introduced. After the detection and clustering of duplicates, the fused records are ranked and provided to the user in the final answer set with a preference score associated with each answer. Key-Words: Data integration, data fusion, information gain, duplicates detectors, conflict resolution, ranking answers
Active Planning of AUVs for 3D Reconstruction of Underwater Object using Imaging Sonar

In this paper, we propose an underwater object scanning scheme using AUV and imaging sonar. The 3D point cloud generation method using imaging sonar has vulnerabilities such as the undesired slope of the front surface, the sparsity of data, the absence of side and back data. To solve these problems, we present a multi-view scanning method instead of a single scan. To efficiently perform multiple scans without excessive time-consuming, the unit vector of the next path was selected by maximizing the area of beam reflection and orthogonality with previous paths. To verify the validity of the proposed method, simulation experiments were conducted for several cases such as various shapes of the object and the angle variation between AUV and object.
Generic input template for cloud simulators: A case study of CloudSim

Cloud computing and its service models, such as Platform as a Service (PaaS), have changed the way that computing resources are allocated to Information and Communications Technology enterprises and users. Although multiple cloud providers support dynamic service provisioning, it is necessary to facilitate the management of the cloud infrastructure and applications in order to allow the continuous refinement of cloud models. Therefore, issues are raised regarding the cloud orchestration, including the flexible portability and interoperability of cloud applications among multiple cloud providers. Having said that, there is a need for a standardized design and management of the cloud use cases (during the creation of scenarios, application's deployment, and patching) to ensure efficient applications' migration between different providers. This paper proposes an artifact, GITS, a generic input template for CloudSim and other cloud simulators. GITS can be provided by PaaS offering to manage the creation, monitoring, administration, and patching of infrastructure and applications in the cloud. GITS defines the cloud schema that can be used with conforming cloud models and independent cloud providers; thus, portability and interoperability can be enabled in PaaS cloud models. GITS focuses on the architecture‐based modeling for cloud infrastructure and application not only in terms of computational resources but also in terms of high availability properties associated with infrastructure and applications. The main objective of the GITS template is to provide the cloud user with a modular, simple, readable, and reusable model that still supports the essential components and provide them with the ability to control the applications' execution, deployment, and other management needs in addition to the allocation environment. This paper describes GITS usage, specifically as an input template for CloudSim.
Atomic Layer Deposition Inducing Integration of Co, N Codoped Carbon Sphere on 3D Foam with Hierarchically Porous Structures for Flexible Hydrogen Producing Device

Integration of metal–organic frameworks on deformation tolerant substrates exhibits a promising prospect in flexible electrode applications. A straightforward synthesis utilizing atomic layer deposition pretreating to induce the growth of a zeolitic imidazolate framework‐67 (ZIF‐67) layer on carbon foam (CF), which maintains high ZIF‐67 loading with a hierarchically porous structure and large surface area of 453 m2 g−1 is presented. With a subsequent pyrolysis process, three‐dimensional composite structures are obtained with Co, N codoped carbon spheres attached firmly on the CF framework, and CF bridges the individual carbon spheres to construct a conductive pathway. The composites are used as a flexible electrode for hydrogen production both in acid and alkaline electrolytes. The advances in the composite structure, such as the hierarchically porous structure, large surface area, and high loading of active material, lead to excellent electrochemical performance in terms of low overpotential of 142 mV and low Tafel slope of 73 mV dec−1 in 0.5 m H2SO4. Most importantly, the composite structure with outstanding flexible property shows good catalytic performance under remarkable deformation, and after 100 repeated compression–recovery cycles, the performance degrades slightly. This work provides a new design of flexible electrode, which is promising for the hydrogen production industry.
A scanning 3D sensor and its object recognition for autonomous robots

Real-time sensing of 3D geometric information is essential for autonomous vehicles and robots for detection of obstacles and environment. In most case of autonomously maneuvering robots, the relative motion of 3D sensor and the target object is involved so that accurate 3D geometry, as well as the relative velocity, need to be acquired for safe operation. In this paper, a small form-factor scanner-based 3D sensing system and operating architecture, so-called homodyne mixing method and its experimental verification are presented. Special attention is put on the accuracy improvement with small size realization under relative motion between sensor and objects in the application to autonomous working robots operating under various working environment. In the homodyne mixing method, as the working principle, phase delay induced by the time-of-flight of the amplitude-modulated light wave flying between camera and object is indirectly measured.1 The homodyne mixing method has less computational and hardware complexity than other 3D sensing methods and it is robust to external light and has advantages in miniaturization. However, the homodyne mixing method is sensitive to the relative movement between the sensor and targeting object because it uses continuously modulated light wave. In this paper, an improved light processing methodology is established to tackle this weakness in a moving situation. The presented light processing methodology is robust to the relative movement and has the advantage to control the measurement precision of 3D depth information through variable scanning FOV (Field of View). As the application of suggesting a 3D sensing device and system to recognition in the robot system, we propose a geometry recognition method that extracts typical geometric features of objects from point-cloud data obtained from the 3D sensor. The result shows that the recognition of the geometry of an object is quick and accurate more than previous recognition technology using only an RGB color image.2 By combining the sensor system and object geometry recognition method, we can provide the solution of the 3D object recognition system for autonomous robot operating in an undetermined environment. The experimental verification is presented for the evaluation of the 3D sensing system.
Energy optimization tool for mild hybrid vehicles with thermal constraints

The current global scenario is such where impact on the environment is becoming a rising concern. Global automotive manufacturers have focused more towards hybrid and electric vehicles as both more aware customers and governmental legislation have begun demanding higher emission standards. One of the many ways that Volvo Car Group approaches this trend is by mild hybridization which is by assisting the combustion engine by a small electric motor and a battery pack. A smart energy management strategy is needed in order to get the most out of the benefits that hybrid electric vehicles offer. The main objective of this strategy is to utilize the electrical energy on-board in such a manner that the overall efficiency of the hybrid powertrain becomes as high as possible. The current implementation is such that the decision for using the on-board battery is non-predictive. This results in a sub-optimal utilization of the hybrid powertrain. In this thesis, a predictive energy optimization tool is developed to maximize the utility of hybridization and the practical implementation of this tool is investigated. The optimization considers both the capacity as well as the thermal load constraints of the battery. The developed optimization tool uses information about the route ahead together with convex optimization to produce optimal reference trajectories of the battery states. These trajectories are used in a real-time controller to determine the battery use by controlling the adjoint states in the Equivalent Consumption Minimization Strategy equation. This optimization tool is validated and compared with the baseline controller in a simulation environment based on Simulink. When perfect information about the road ahead is known, the average reduction in fuel consumption is 0.99% relative the baseline controller. Several issues occurring in the real implementation are explored, such as the limited computational speed and the length of the route ahead that can be predicted. For this reason the information input to the optimization tool is segmented and the resulting performance is investigated. For a 30 second segmentation of the future route information, the average saving in fuel consumption is 0.13% relative to the baseline controller. It is shown that the main factor limiting the amount of savings in fuel consumption is the introduction of the thermal load constraints on the battery. (Less)
Dehydrogenase activity and zinc equivalent parameters as indicators for potential toxic elements remediation in polluted soil ecosystem

Abstract As far as potential toxic elements (PTEs) have definite numerous adverse impacts on soil ecosystems; there is an urgent need for proper indicators to monitor their remediation process. Certainly, monitoring soil remediation process would enable assessing the efficiency of varied remediation techniques. Remediation of soil ecosystem pollution using protocols of chemical remediative amendments, i.e., (elemental sulfur, rock phosphate, bentonite and kaolinite) and biological remediative amendments, i.e., (Thiobacillus thiooxidans, Thiobacillus ferrooxidans, phosphate dissolving bacteria and Pseudomonas fluorescens) were evaluated under field conditions in a heavy metals contaminated soil ecosystem cultivated with the hyper accumulator turnip. Results showed a significant decrease in the hazards of PTEs in the studied soil ecosystem represented by decreasing Zinc equivalent (ZE) values from 854.5 ppm in the uncultivated control soil to 498.3 ppm in the cultivated control soil. Application of the treatment (T7), was the best treatment applied, this treatment decreased ZE value to 104.2 ppm. Significant negative correlations were detected between dehydrogenase activity (DHA) as a biological indicator for the whole microbial activity in the soil ecosystem and Zn Equivalent (ZE) as a chemical indicator parameter in both treated and untreated soils. The results of this work imply that the used treatments significantly returned the contaminated soil to a healthy soil.
Packed-Bed Column Adsorption of Metanil Yellow (MY) from Simulated Wastewater using Granular NaOH-Activated Carbon from Cassava (Manihot esculenta) Peels

The ability of NaOH-activated carbon derived from cassava peels waste to remove MY from aqueous solution was carried out in a fixed-bed column adsorber. The parameters studied included initial solution concentration, Co, carbon bed height and volume flow rate. The temperature of operation and initial solution pH were 29 °C and 5.32 respectively. The optimum adsorption capacity, qe 4.12 mg/g was obtained at Co 10mg/L, bed height 10cm and flow rate, 13.3ml/min. Experimental data were analyzed with Langmuir, Freundlich and Temkin isotherm, as well as the Thomas, YoonNelson and Clark kinetic models. All the models were good fits for experimental data based on correlation coefficient, R 2 values. The ΔGads and Freundlich n (1.587) show that the biosorption was spontaneous, good and physical. Results show that NaOH-activated carbon from cassava peels has low capacity in the removing MY from aqueous solution.
Extension of the effective solid-fluid Steele potential for Mie force fields

Molecular simulation of fluid systems in the presence of surfaces require computationally expensive calculations due to the large number of solid–fluid pair interactions involved. Representing the explicit solid as a continuous wall with an effective potential can significantly reduce the computational time and allows exploring larger temporal and spatial scales. The well-known (10-4-3) Steele potential is one such analytic expression that faithfully represents the effective solid–fluid interactions for homonuclear crystalline solids with hexagonal lattice symmetry. However, this and most of the effective potentials found in the literature have been developed for fluids and solids interacting exclusively through Lennard-Jones potentials. In this work, we extend the Steele model to obtain the effective wall–fluid potentials for Mie force fields. We perform molecular dynamics simulations of coarse-grained fluids modelled via the SAFT force field approach in the presence of explicit and implicit surfaces to compare structural and dynamic properties in both representations. Also, we study the adsorption of ethane into slit-like pores with explicit and implicit surfaces via grand canonical Monte Carlo simulations. We explore the validity and the improvement in the simulation performance as well as the limitations of the proposed expression. GRAPHICAL ABSTRACT
Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction

In e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research designed hybrid of Counter Propagation Neural Network and genetic algorithm (CPNN-GA) for the detection of anomaly in any online transactions
Managing the Cost of Usable Data Centers

The main topic of this paper is to identify problems and present an overview of Data Center environments. To identify problems and present the overviews of business data environments and the cost of usable data center for small-midsize business organization based type of requirements on the design is one of the most important concepts of managing cost. To maximized data center efficiency administrators implement Blade Server, Virtualization, SOA, and other recent technologies. The project process will focus on most leased data centers with provided space rather than specific applications that trend the way of design, and eliminating the significant impact of multiple physical storage devices. Data Centers are complex systems with a variety of technologies that require constantly evolving skills and knowledge that range from routing and switching to load balancing and security. This project will include research, collecting sources, discussing the issues associated with network attacks Data Centers, and reviewing the other key areas related to data center development will be cover the way server availability will describes how to design a highly available infrastructure, and describes how a load balancing device can monitor the availability of applications and servers. Managing the cost of usable Data Centers 6
Fast charging device for an electric vehicle

Subject herein is a battery charging apparatus and a method that includes the inverter output danreul designed to be connected to the power supply network and are designed to be connected to rectifier input terminals and the battery. The apparatus includes an average means according to the current to the maximum current supplied by the power supply network and to control so calculated according to the maximum voltage for larger coefficient than a ratio between battery voltage rectified by the input current value obtained from the input end.