JIGSAW: Efficient and Scalable Path Constraints Fuzzing

Coverage-guided testing has shown to be an effective way to find bugs. If we model coverage-guided testing as a search problem (i.e., finding inputs that can cover more branches), then its efficiency mainly depends on two factors: (1) the accuracy of the searching algorithm and (2) the number of inputs that can be evaluated per unit time. Therefore, improving the search throughput has shown to be an effective way to improve the performance of coverage-guided testing.In this work, we present a novel design to improve the search throughput: by evaluating newly generated inputs with JIT-compiled path constraints. This approach allows us to significantly improve the single thread throughput as well as scaling to multiple cores. We also developed several optimization techniques to eliminate major bottlenecks during this process. Evaluation of our prototype JIGSAW shows that our approach can achieve three orders of magnitude higher search throughput than existing fuzzers and can scale to multiple cores. We also find that with such high throughput, a simple gradient-guided search heuristic can solve path constraints collected from a large set of real-world programs faster than SMT solvers with much more sophisticated search heuristics. Evaluation of end-to-end coverage-guided testing also shows that our JIGSAW-powered hybrid fuzzer can outperform state-of-the-art testing tools

Using Pattern Recognition for Investment Decision Support in Taiwan Stock Market

In Taiwan stock market, it has been accumulated large amounts of time series stock data and successful investment strategies. The stock price, which is impacted by various factors, is the result of buyer-seller investment strategies. Since the stock price reflects numerous factors, its pattern can be described as the strategies of investors. In this paper, pattern recognition concept is adapted to match the current stock price trend with the repeatedly appearing past price data. Accordingly, a new method is introduced in this research that extracting features quickly from stock time series chart to find out the most critical feature points. The matching can be processed via the corresponding information of the feature points. In other words, the goal is to seek for the historical repeatedly appearing patterns, namely the similar trend, offering the investors to make investment strategies

College Students’ Perspectives of E-Learning System Use in High Education

The purpose of this study was primarily to create, on a digital learning platform, a digital learning environment with freedom from such constraints for a group of seven college students. The researchers selected seven students from the Applied English program that were taking part in an internship combination program at San Diego State University. The study makes use of in-depth interviews in carrying out a qualitative investigation to determine whether or not utilizing a digital learning system provides students with a superior mode of educational interaction. Thirty-minute interviews inquiring into the students' feelings about handling all coursework over the digital learning platform were held once every two months, for a total of three interviews per student during the semester. At the same time, the researchers made use of questions and uploaded homework from the students to record each student's progress, which formed the second class of data for analysis. Results indicate that compared with the traditional ways of learning, through the digital learning server system, teacher-student interactions increased, with the Real-time Discussion Area emerging as the students' favorite function. Moreover, the instructor was able to provide timely feedback, which was encouraging to these students living so far from home for the first time. In sum, the digital learning environment effectively assisted the teacher in establishing a ubiquitous learning environment

catena-Poly[[[bis­(3-hy­droxy­adamantane-1-carboxyl­ato-κO 1)(3-hy­droxy­adamantane-1-carb­oxy­lic acid-κO 1)zinc(II)]-μ2-4,4′-bipyridine] monohydrate]

In the title coordination polymer, {[Zn(C11H15O3)2(C10H8N2)(C11H16O3)]·H2O}n, the ZnII ion is five coordinated by two N atoms from two 4,4′-bipyridine (4,4′-bpy) mol­ecules and three O atoms from two 3-hy­droxy­adamantane-1-carboxyl­ate anions (L) and one 3-hy­droxy­adamantane-1-carb­oxy­lic acid (HL) mol­ecule. The resulting coordination polyhedron is a near regular ZnN2O3 trigonal bipyramid, with the N atoms in axial sites. The 4,4′-bpy mol­ecules [dihedral angle between the aromatic rings = 17.2 (2)°] act as bridges, connecting the metal ions into an infinite polymeric chain propagating in [01]. O—H⋯O hydrogen bonds help to consolidate the packing