Thermomechanical analysis of Tg and T > Tg transitions in polystyrene

Phenomenological softening points were measured on a series of 13 anionic, nearly monodisperse, atactic polystyrenes using a DuPont 943 thermomechanical analyzer (TMA) in a penetration mode. Although TMA cannot identify the nature of the “transition” observed as such, the results obtained support the evidence for the T_g, T_ll, and T′_ll transition in polystyrene discussed in recent literature. T_g and T_ll were found to vary with molecular weight in a systematic manner, while T′_ll could only be observed at very high molecular weight. The technique appears to be quite useful in offering rapid and reproducible information on the various transitions in the liquid state of polystyrene.     

Molecular modeling of antibody-antigen complexes between the Brucella abortus O-chain polysaccharide and a specific monoclonal antibody

A molecular model of the binding site of an anti-carbohydrateantibody (YsT9.1) has been developed using computer-assistedmodeling techniques and molecular dynamics calculations. Sequencehomologies among YsT9.1 and the Fv regions of McPC603, J539and human Bence-Jones protein REI, all of which have solvedcrystal structures, provided the basis for the modeling. Thegroove-type combining site model had a topography which wascomplementary to low energy confonners of the polysaccharide,a Brucella O-antigen, and the site could be almost completelyfilled by a pentasaccharide epitope in either of two dockingmodes. Putative interactions between this epitope and the antibodyare consistent with the known structural requirements for bindingand lead to the design of oligosaccharide inhibitors that probethe veracity of the modeled docked complex. Ultimately boththe Fv model and the docked complex will be compared with independentcrystal structures of YsT9.1 Fab with and without pentasaccharideinhibitor, currently at the stage of refinement.     

Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features

Fraudulent online sellers often collude with reviewers to garner fake reviews for their products. This act undermines the trust of buyers in product reviews, and potentially reduces the effectiveness of online markets. Being able to accurately detect fake reviews is, therefore, critical. In this study, we investigate several preprocessing and textual-based featuring methods along with machine learning classifiers, including single and ensemble models, to build a fake review detection system. Given the nature of product review data, where the number of fake reviews is far less than that of genuine reviews, we look into the results of each class in detail in addition to the overall results. We recognise from our preliminary analysis that, owing to imbalanced data, there is a high imbalance between the accuracies for different classes (e.g., 1.3% for the fake review class and 99.7% for the genuine review class), despite the overall accuracy looking promising (around 89.7%). We propose two dynamic random sampling techniques that are possible for textual-based featuring methods to solve this class imbalance problem. Our results indicate that both sampling techniques can improve the accuracy of the fake review class—for balanced datasets, the accuracies can be improved to a maximum of 84.5% and 75.6% for random under and over-sampling, respectively. However, the accuracies for genuine reviews decrease to 75% and 58.8% for random under and over-sampling, respectively. We also discover that, for smaller datasets, the Adaptive Boosting ensemble model outperforms other single classifiers; whereas, for larger datasets, the performance improvement from ensemble models is insignificant compared to the best results obtained by single classifiers.

     

Ethereum smart contract security research: survey and future research opportunities

Blockchain has recently emerged as a research trend, with potential applications in a broad range of industries and context. One particular successful Blockchain technology is smart contract, which is widely used in commercial settings (e.g., high value financial transactions). This, however, has security implications due to the potential to financially benefit froma security incident (e.g., identification and exploitation of a vulnerability in the smart contract or its implementation). Among, Ethereum is the most active and arresting. Hence, in this paper, we systematically review existing research efforts on Ethereum smart contract security, published between 2015 and 2019. Specifically, we focus on how smart contracts can be maliciously exploited and targeted, such as security issues of contract program model, vulnerabilities in the program and safety consideration introduced by program execution environment. We also identify potential research opportunities and future research agenda.     

A two-stage hardware scheduler combining greedy and optimal scheduling

Greedy scheduling heuristics provide a low complexity and scalable albeit particularly sub-optimal strategy for hardware-based crossbar schedulers. In contrast, the maximum matching algorithm for Bipartite graphs can be used to provide optimal scheduling for crossbar-based interconnection networks with a significant complexity and scalability cost. In this paper, we show how maximum matching can be reformulated in terms of Boolean operations rather than the more traditional formulations. By leveraging the inherent parallelism available in custom hardware design, we reformulate maximum matching in terms of Boolean operations rather than matrix computations and introduce three maximum matching implementations in hardware. Specifically, we examine a Pure Logic Scheduler with three dimensions of parallelism, a Matrix Scheduler with two dimensions of parallelism and a Vector Scheduler with one dimension of parallelism. These designs reduce the algorithmic complexity for an N×N$N\times N$ network from O(N³)$O\left(N^3\right)$ to O(1)$O\left(1\right)$, O(K)$O\left(K\right)$, and O(KN)$O\left(KN\right)$, respectively, where K$K$ is the number of optimization steps. While an optimal scheduling algorithm requires K=2N−1$K=2N-1$ steps, by starting with our hardware-based greedy strategy to generate an initial schedule, our simulation results show that the maximum matching scheduler can achieve 99% of the optimal schedule when K=9$K=9$. We examine hardware and time complexity of these architectures for crossbar sizes of up to N=1024$N=1024$. Using FPGA synthesis results, we show that a greedy schedule for crossbars, ranging from 8×8 to 256×256, can be optimized in less than 20 ns per optimization step. For crossbars reaching 1024×1024 the scheduling can be completed in approximately 10 μs with current technology and could reach under 90 ns with future technologies.     

A compositional Petri net translation of general π-calculus terms

We propose a finite structural translation of possibly recursive π-calculus terms into Petri nets. This is achieved by using high-level nets together with an equivalence on markings in order to model entering into recursive calls, which do not need to be guarded. We view a computing system as consisting of a main program (π-calculus term) together with procedure declarations (recursive definitions of π-calculus identifiers). The control structure of these components is represented using disjoint high-level Petri nets, one for the main program and one for each of the procedure declarations. The program is executed once, while each procedure can be invoked several times (even concurrently), each such invocation being uniquely identified by structured tokens which correspond to the sequence of recursive calls along the execution path leading to that invocation.     

IT employment prospects: beyond the dotcom bubble

In the 1990s, enrollments grew rapidly in information systems (IS) and computer science. Then, beginning in 2000 and 2001, enrollments declined precipitously. This paper looks at the enrollment bubble and the dotcom bubble that drove IT enrollments. Although the enrollment bubble occurred worldwide, this paper focuses primarily on U.S. data, which is widely available, and secondarily on Western Europe data. The paper notes that the dotcom bubble was an investment disaster but that U.S. IT employment fell surprisingly little and soon surpassed the bubble's peak IT employment. In addition, U.S. IT unemployment rose to almost the level of total unemployment in 2003, then fell to traditional low levels by 2005. Job prospects in the U.S. and most other countries are good for the short term, and the U.S. Bureau of Labor Statistics employment projections for 2006–2016 indicate that job prospects in the U.S. will continue to be good for most IT jobs. However, offshoring is a persistent concern for students in Western Europe and the United States. The data on offshoring are of poor quality, but several studies indicate that IT job losses from offshoring are small and may be counterbalanced by gains in IT inshoring jobs. At the same time, offshoring and productivity gains appear to be making low-level jobs such as programming and user support less attractive. This means that IS and computer science programs will have to focus on producing higher-level job skills among graduates. In addition, students may have to stop considering the undergraduate degree to be a terminal degree in IS and computer science.