Using text mining for personalization and recommendation for an enriched hybrid learning experience

Students are nowadays given many options to consume educational content in digital formats as alternatives to printed material. Previous research suggests that while digital content has advantages, printed media still provides other benefits that cannot be matched by digital. Therefore, technology should leverage the benefits of both. In this paper, we present the Meaningful Education and Training Information System, a multifaceted hybrid textbook learning platform. The goal of the system is to provide an easy digital‐to‐print‐to‐digital content creation and reading service. The Meaningful Education and Training Information System incorporates technologies for layout, personalization, cocreation, and assessments. These facilitate common teacher/student tasks and help provide a richer, more effective learning experience. Our system has been demonstrated in multiple international education events, partner engagements, and pilots with local universities and high schools.     

Perception,navigation, and manipulation in the team KAUST approach to the MBZIRC ground robotics challenge

The ground robotics challenge in the Mohammed Bin Zayed International Robotics Challenge required a ground vehicle equipped with a robotic arm to autonomously locate a panel, select a proper size wrench among several options mounted on the panel, and use the wrench to rotate a valve. Autonomy was the critical factor in this challenge, which required the teams to devise algorithms that can operate successfully in a semistructured environment without human supervision. This paper presents the approaches taken by team KAUST to meet this challenge, ranging from in‐house hardware designs to algorithm integration and customization. We separated the whole objective into three interconnected tasks: Navigation, perception, and manipulation. For the navigation task, we developed a basic robotic exploration scheme to find the panel front side where the wrenches were present. For the perception task, we integrated common object detection algorithms with neural networks to identify the proper size wrench precisely. For successful manipulation, we designed and built a custom gripper, which was inspired by the common grasping behavior of a human hand under tight clearance conditions. The modular structure of the proposed approach allowed the team to progress in several subtasks simultaneously. However, the interconnection between the subtasks necessitated a reliable integration framework between these modules for effective implementation. We tuned our algorithms in extensive experimental studies and eventually obtained 10 consecutive successful navigation runs, 96% true wrench detection rate, and high success rate in wrench grasping. Furthermore, successful complete tests proved the reliability and repeatability of our system.     

A method for the optimum selection of datacenters in geographically distributed clouds

The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user’s requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user’s request and SLAs. Various attributes are considered in the SLA; in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user’s request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective NSGA-II algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as NSGAII_Cluster, is implemented with the Random, Greedy and MOPSO algorithms; the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.     

CCFinder: using Spark to find clustering coefficient in big graphs

Networks with billions of vertices introduce new challenges to perform graph analysis in a reasonable time. Clustering coefficient is an important analytical measure of networks such as social networks and biological networks. To compute clustering coefficient in big graphs, existing distributed algorithms suffer from low efficiency such that they may fail due to demanding lots of memory, or even, if they complete successfully, their execution time is not acceptable for real-world applications. We present a distributed MapReduce-based algorithm, called CCFinder, to efficiently compute clustering coefficient in very big graphs. CCFinder is executed on Apache Spark, a scalable data processing platform. It efficiently detects existing triangles through using our proposed data structure, called FONL, which is cached in the distributed memory provided by Spark and reused multiple times. As data items in the FONL are fine-grained and contain the minimum required information, CCFinder requires less storage space and has better parallelism in comparison with its competitors. To find clustering coefficient, our solution to triangle counting is extended to have degree information of the vertices in the appropriate places. We performed several experiments on a Spark cluster with 60 processors. The results show that CCFinder achieves acceptable scalability and outperforms six existing competitor methods. Four competitors are those methods proposed based on graph processing systems, i.e., GraphX, NScale, NScaleSpark, and Pregel frameworks, and two others are the Cohen’s method and NodeIterator++, introduced based on MapReduce.     

A novel nanosensor based on Pt:Co nanoalloy ionic liquid carbon paste electrode for voltammetric determination of vitamin B9 in food samples

In this work we describe a novel nanoalloy (Pt:Co) room temperature ionic liquid (RTIL) modified carbon paste electrode as a high sensitive sensor for voltammetric determination of vitamin B₉ in food samples. The sensor exhibits an enhanced effectiveness for the electro-oxidation of vitamin B₉ in aqueous solution. The oxidation peak potential for this matter at a surface of the ionic liquid (n-hexyl-3-methylimidazolium hexafluoro phosphate) Pt:Co carbon paste electrode (Pt:Co/IL/CPE) appeared at 685 mV that was about 110 mV lower than the oxidation peak potential at the surface of the traditional carbon paste electrode (CPE) under similar condition. The mechanism of the electro-oxidation process on the surface of the modified electrode was analyzed. Square wave voltammetry (SWV) was applied as a very sensitive electrochemical method for the determination of sub-micro-molar amounts of vitamin B₉. The linear response range and detection limit were found to be 1.0 × 10⁻⁷ to 5.0 × 10⁻⁴ M and 4.0 × 10⁻⁸ M, respectively. The prepared modified electrode shows several advantages such as simple preparation method, high stability, high sensitivity, and excellent catalytic activity, long-term stability and remarkable voltammetric reproducibility for eletrooxidation of vitamin B₉. The proposed sensor was successfully applied for the determination of vitamin B₉ in food samples.     

Studying software logging using topic models

Software developers insert logging statements in their source code to record important runtime information; such logged information is valuable for understanding system usage in production and debugging system failures. However, providing proper logging statements remains a manual and challenging task. Missing an important logging statement may increase the difficulty of debugging a system failure, while too much logging can increase system overhead and mask the truly important information. Intuitively, the actual functionality of a software component is one of the major drivers behind logging decisions. For instance, a method maintaining network communications is more likely to be logged than getters and setters. In this paper, we used automatically-computed topics of a code snippet to approximate the functionality of a code snippet. We studied the relationship between the topics of a code snippet and the likelihood of a code snippet being logged (i.e., to contain a logging statement). Our driving intuition is that certain topics in the source code are more likely to be logged than others. To validate our intuition, we conducted a case study on six open source systems, and we found that i) there exists a small number of “log-intensive” topics that are more likely to be logged than other topics; ii) each pair of the studied systems share 12% to 62% common topics, and the likelihood of logging such common topics has a statistically significant correlation of 0.35 to 0.62 among all the studied systems; and iii) our topic-based metrics help explain the likelihood of a code snippet being logged, providing an improvement of 3% to 13% on AUC and 6% to 16% on balanced accuracy over a set of baseline metrics that capture the structural information of a code snippet. Our findings highlight that topics contain valuable information that can help guide and drive developers’ logging decisions.     

Optimal power flow of HVDC system using teaching–learning-based optimization algorithm

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

     

Regulation of Amylose Content by Single Mutations at an Active Site in the Wx-B1 Gene in a Tetraploid Wheat Mutant

The granule-bound starch synthase I (GBSSI) encoded by the waxy gene is responsible for amylose synthesis in the endosperm of wheat grains. In the present study, a novel Wx-B1 null mutant line, M3-415, was identified from an ethyl methanesulfonate-mutagenized population of Chinese tetraploid wheat landrace Jianyangailanmai (LM47). The gene sequence indicated that the mutated Wx-B1 encoded a complete protein; this protein was incompatible with the protein profile obtained using sodium dodecyl sulfate–polyacrylamide gel electrophoresis, which showed the lack of Wx-B1 protein in the mutant line. The prediction of the protein structure showed an amino acid substitution (G470D) at the edge of the ADPG binding pocket, which might affect the binding of Wx-B1 to starch granules. Site-directed mutagenesis was further performed to artificially change the amino acid at the sequence position 469 from alanine (A) to threonine (T) (A469T) downstream of the mutated site in M3-415. Our results indicated that a single amino acid mutation in Wx-B1 reduces its activity by impairing its starch-binding capacity. The present study is the first to report the novel mechanism underlying Wx-1 deletion in wheat; moreover, it provided new insights into the inactivation of the waxy gene and revealed that fine regulation of wheat amylose content is possible by modifying the GBSSI activity.     

Polyolefin elastomer grafted unsaturated hindered phenol esters: synthesis and antioxidant behavior

Monomeric antioxidants are synthesized from esterification of 3,5-di-tert-butyl-4-hydroxybenzoic acid and 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionic acid with unsaturated fatty alcohols. The antioxidant activity is evaluated both in blending and radical grafting processes. The effect of chain length and phenolic group is investigated on efficiency of antioxidants. It is demonstrated that the esters of 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionic acid exhibit significantly longer induction time. The results of radical grafting reaction shows synthesized antioxidants can be successfully grafted onto polymer chains and the phenolic moiety is functional after extraction process, while pure and commercially stabilized samples are degraded instantaneously. Also, different initiator systems are utilized to enhance the extent of grafting. Among MEK, DCP, and DHBP peroxides, DHBP can be more effective in increasing the antioxidant grafted onto polymer. In addition, possibility of rising in graft content is investigated in presence of redox initiator. Using this approach, polymer-bound antioxidant with prolonged thermal stability can be achieved.