Miracle-dz/UniFAR-FTUs huggingface dataset:
A Learning-Based Thermal-Sensitive Power Optimization Approach for Optical NoCs

Optical networks-on-chip (NoCs) based on silicon photonics have been proposed as emerging on-chip communication architectures for chip multiprocessors with large core counts. However, due to the thermal sensitivity of optical devices used in optical NoCs, on-chip temperature variations cause significant thermal-induced optical power loss, which would counteract the power advantages of optical NoCs. To tackle this problem, in this work, we propose a learning-based thermal-sensitive power optimization approach for mesh- or torus-based optical NoCs in the presence of temperature variations. The key techniques proposed include an initial device-setting and thermal-tuning mechanism that is a device-level optimization technique, and a learning-based thermal-sensitive adaptive routing algorithm that is a network-level optimization technique. Simulation results of an 8x8 mesh-based optical NoC show that the proposed initial device-setting and thermal-tuning mechanism confines the worst-case thermal-induced optical energy consumption to be on the order of tens of pJ/bit, by avoiding significant thermal-induced optical power loss caused by temperature-dependent wavelength shifts. Besides, it shows that the learning-based thermal-sensitive adaptive routing algorithm is able to find an optimal path with the minimum estimated thermal-induced optical power consumption for each communication pair. The proposed routing has a greater space for optimization, especially for applications with more long-distance traffic.
Automatically assessing vulnerabilities discovered by compositional analysis

Testing is the most widely employed method to find vulnerabilities in real-world software programs. Compositional analysis, based on symbolic execution, is an automated testing method to find vulnerabilities in medium- to large-scale programs consisting of many interacting components. However, existing compositional analysis frameworks do not assess the severity of reported vulnerabilities. In this paper, we present a framework to analyze vulnerabilities discovered by an existing compositional analysis tool and assign CVSS3 (Common Vulnerability Scoring System v3.0) scores to them, based on various heuristics such as interaction with related components, ease of reachability, complexity of design and likelihood of accepting unsanitized input. By analyzing vulnerabilities reported with CVSS3 scores in the past, we train simple machine learning models. By presenting our interactive framework to developers of popular open-source software and other security experts, we gather feedback on our trained models and further improve the features to increase the accuracy of our predictions. By providing qualitative (based on community feedback) and quantitative (based on prediction accuracy) evidence from 21 open-source programs, we show that our severity prediction framework can effectively assist developers with assessing vulnerabilities.
ApproxIoT: Approximate Analytics for Edge Computing

IoT-enabled devices continue to generate a massive amount of data. Transforming this continuously arriving raw data into timely insights is critical for many modern online services. For such settings, the traditional form of data analytics over the entire dataset would be prohibitively limiting and expensive for supporting real-time stream analytics. In this work, we make a case for approximate computing for data analytics in IoT settings. Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing– based on the chosen sample size – can make a systematic tradeoff between the output accuracy and computation efficiency. This motivated the design of APPROXIOT– a data analytics system for approximate computing in IoT. To realize this idea, we designed an online hierarchical stratified reservoir sampling algorithm that uses edge computing resources to produce approximate output with rigorous error bounds. To showcase the effectiveness of our algorithm, we implemented APPROXIOT based on Apache Kafka and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. Our results show that APPROXIOT achieves a speedup 1:3×–9:9× with varying sampling fraction of 80% to 10% compared to simple random sampling.
How Do Design Decisions Affect the Distribution of Software Metrics?

Background. Source code analysis techniques usually rely on metric-based assessment. However, most of these techniques have low accuracy. One possible reason is because metric thresholds are extracted from classes driven by distinct design decisions. Previous studies have already shown that classes implemented according to some coarse-grained design decisions, such as programming languages, have different distribution of metric values. Therefore, these design decisions should be taken into account when using benchmarks for metric-based source code analysis. Goal. Our goal is to investigate whether other fine-grained design decisions also influence over distribution of software metrics. Method. We conduct an empirical study to evaluate the distributions of four metrics applied over fifteen real-world systems based on three different domains. Initially, we evaluated the influence of the class design role on the distributions of measures. For this purpose, we have defined an automatic approach to identify the design role played by each class. Then, we looked for other fine-grained design decisions that could have influenced the measures. Results. Our findings show that distribution of metrics are sensitive to the following design decisions: (i) design role of the class (ii) used libraries, (iii) coding style, (iv) exception handling, and (v) logging and debugging code mechanisms. Conclusion. The distribution of software metrics are sensitive to fine-grained design decisions and we should consider taking them into account when building benchmarks for metric-based source code analysis.
LogLens: A Real-Time Log Analysis System

Administrators of most user-facing systems depend on periodic log data to get an idea of the health and status of production applications. Logs report information, which is crucial to diagnose the root cause of complex problems. In this paper, we present a real-time log analysis system called LogLens that automates the process of anomaly detection from logs with no (or minimal) target system knowledge and user specification. In LogLens, we employ unsupervised machine learning based techniques to discover patterns in application logs, and then leverage these patterns along with the real-time log parsing for designing advanced log analytics applications. Compared to the existing systems which are primarily limited to log indexing and search capabilities, LogLens presents an extensible system for supporting both stateless and stateful log analysis applications. Currently, LogLens is running at the core of a commercial log analysis solution handling millions of logs generated from the large-scale industrial environments and reported up to 12096x man-hours reduction in troubleshooting operational problems compared to the manual approach.
Deploying Machine Learning Models for Public Policy: A Framework

Machine learning research typically focuses on optimization and testing on a few criteria, but deployment in a public policy setting requires more. Technical and non-technical deployment issues get relatively little attention. However, for machine learning models to have real-world benefit and impact, effective deployment is crucial. In this case study, we describe our implementation of a machine learning early intervention system (EIS) for police officers in the Charlotte-Mecklenburg (North Carolina) and Metropolitan Nashville (Tennessee) Police Departments. The EIS identifies officers at high risk of having an adverse incident, such as an unjustified use of force or sustained complaint. We deployed the same code base at both departments, which have different underlying data sources and data structures. Deployment required us to solve several new problems, covering technical implementation, governance of the system, the cost to use the system, and trust in the system. In this paper we describe how we addressed and solved several of these challenges and provide guidance and a framework of important issues to consider for future deployments.
A Preliminary Structure of Software Security Assurance Model

Software security is an important aspect that needs to be considered during the entire software development life cycle (SDLC). Integrating software security at each phase of SDLC has become an urgent need. To address software security, various approaches, techniques, methods, practices, and models have been proposed and developed. However, recent research shows that many software development methodologies do not explicitly include methods for incorporating software security during the development of software as it evolves from requirements engineering to its final disposal. The primary objective of this research is to study the state-of-the-art of security in the context of SDLC by following systematic mapping study (SMS). In the second phase, we will identify, through systematic literature review (SLR) and empirical study in the industry, the software security contributions, security challenges and their practices for global software development (GSD) vendors. The ultimate aim is to develop a Software Security Assurance Model (SSAM) to assist GSD vendor organisations in measuring their readiness towards the development of secure software.
A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss

The supervised deep networks have shown great potential in improving the classification performance. However, training these supervised deep networks is very challenging for hyperspectral image given the fact that usually only a small amount of labeled samples are available. In order to overcome this problem and enhance the discriminative ability of the network, in this paper, we propose a deep network architecture for a super-resolution (SR)-aided hyperspectral image classification with classwise loss (SRCL). First, a three-layer SR convolutional neural network (SRCNN) is employed to reconstruct a high-resolution image from a low-resolution image. Second, an unsupervised triplet-pipeline CNN (TCNN) with an improved classwise loss is built to encourage intraclass similarity and interclass dissimilarity. Finally, SRCNN, TCNN, and a classification module are integrated to define the SRCL, which can be fine-tuned in an end-to-end manner with a small amount of training data. Experimental results on real hyperspectral images demonstrate that the proposed SRCL approach outperforms other state-of-the-art classification methods, especially for the task in which only a small amount of training data are available.
Sustainable Urban Growth using Geoinformatics and CA based Modelling

Electronic governance (e-governance) can be used as a vehicle for better decision making, resources monitoring, budget allocation, planning and development of cities as per the aspirations of people. Urban growth simulation and modelling in association with information technologies like Geographical Information Systems (GIS) can be used to ascertain possible impacts of urbanization on climate, ecology and environment. Such techniques can assist in effective governance of cities in making optimum decisions of urban development by developing different alternatives urbanization scenarios corresponding to different policies to achieve sustainable and environment friendly smart cities. Present study is aimed to demonstrate the application of geo-spatial techniques and urban growth modelling in determining different urban growth scenarios corresponding to different land use policies and sustainable developmental goals. A cellular automata (CA) based SLEUTH urban growth model and GIS has been used to simulate the urban growth of Pushkar fringe. Pushkar has experienced scattered and rapid urban development in recent past owing to increased tourism and industrial activities causing significant ecological and environmental problems. The SLEUTH model was conceptualized and calibrated using historical urban growth obtained from classification of multi-spectral satellite data. Further, calibrated model has been used to simulate urban growth of the Puskar fringe for year 2040 corresponding to six land use policy scenarios formulated to achieve sustainable developmental objectives and smart Pushkar town. (The study demonstrated the ability of GIS and CA based urban growth modelling to improvise the existing urban growth practices and in developing growth scenarios corresponding to different sustainable and smart city goals.
Prioritizing Alerts from Multiple Static Analysis Tools, Using Classification Models

Static analysis (SA) tools examine code for flaws without executing the code, and produce warnings ("alerts") about possible flaws. A human auditor then evaluates the validity of the purported code flaws. The effort required to manually audit all alerts and repair all confirmed code flaws is often too much for a project's budget and schedule. An alert triaging tool enables strategically prioritizing alerts for examination, and could use classifier confidence. We developed and tested classification models that predict if static analysis alerts are true or false positives, using a novel combination of multiple static analysis tools, features from the alerts, alert fusion, code base metrics, and archived audit determinations. We developed classifiers using a partition of the data, then evaluated the performance of the classifier using standard measurements, including specificity, sensitivity, and accuracy. Test results and overall data analysis show accurate classifiers were developed, and specifically using multiple SA tools increased classifier accuracy, but labeled data for many types of flaws were inadequately represented (if at all) in the archive data, resulting in poor predictive accuracy for many of those flaws.
Text Analysis on User Generated Content: The Rap Lyrics Challenge

This research proposes a hybrid approach that uses User Generated Contents as a corpus in order to create a multi-level resource, by combining linguistic, statistics and semantic data. The tool we create overcomes the lack of domain resources and provides a distant reading of one of the most significant cultural and linguistic phenomenon in recent years. We take advantage of techniques from Web Mining, Computational Linguistics and Information Design with the aim to make a large amount of extremely sparse data more accessible and understandable.
Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations

Spiking Neural Networks (SNNs) play an important role in neuroscience as they help neuroscientists understand how the nervous system works. To model the nervous system, SNNs incorporate the concept of time into neurons and inter-neuron interactions called spikes; a neuron's internal state changes with respect to time and input spikes, and a neuron fires an output spike when its internal state satisfies certain conditions. As the neurons forming the nervous system behave differently, SNN simulation frameworks must be able to simulate the diverse behaviors of the neurons. To support any neuron models, some frameworks rely on general purpose processors at the cost of inefficiency in simulation speed and energy consumption. The other frameworks employ specialized accelerators to overcome the inefficiency; however, the accelerators support only a limited set of neuron models due to their model-driven designs, making accelerator-based frameworks unable to simulate target SNNs. In this paper, we present Flexon, a flexible digital neuron which exploits the biologically common features shared by diverse neuron models, to enable efficient SNN simulations. To design Flexon, we first collect SNNs from prior work in neuroscience research and analyze the neuron models the SNNs employ. From the analysis, we observe that the neuron models share a set of biologically common features, and that the features can be combined to simulate a significantly larger set of neuron behaviors than the existing model-driven designs. Furthermore, we find that the features share a small set of computational primitives which can be exploited to further reduce the chip area. The resulting digital neurons, Flexon and spatially folded Flexon, are flexible, highly efficient, and can be easily integrated with existing hardware. Our prototyping results using TSMC 45 nm standard cell library show that a 12-neuron Flexon array improves energy efficiency by 6,186x and 422x over CPU and GPU, respectively, in a small footprint of 9.26 mm2. The results also show that a 72-neuron spatially folded Flexon array incurs a smaller footprint of 7.62 mm2 and achieves geomean speedups of 122.45x and 9.83x over CPU and GPU, respectively.
Impact of Longitudinal Variation of the Coupling Coefficient Due to Bending and Twisting on Inter-Core Crosstalk in Weakly-Coupled MCFs

A novel analytical discrete changes model (DCM) of inter-core crosstalk (ICXT) in weakly-coupled multi-core fibers (MCFs) that considers the inter-core coupling coefficient (ICCC) variation, induced by bending and twisting effects along the longitudinal direction of propagation, is proposed. An approximated closed-form expression of the ICCC is proposed for step-index MCFs and validated by comparison with the rigorous ICCC expression, solved numerically. The linearization of the ICCC, in a logarithmic scale, around the intrinsic ICCC, is proposed to simplify the derivation of the novel analytical DCM. The novel DCM is validated by comparison of the mean ICXT power estimates obtained analytically, which are very quick to estimate, with the ones obtained by solving the coupled-mode equations numerically, which are much more computationally demanding. It is shown that, in the range of tested MCF parameters, the proposed model allows for an improvement of the mean ICXT power estimates reaching 4.5 dB, compared to the estimates obtained without considering the longitudinal variation of the ICCC, depending on the fiber parameters. The improvement of the mean ICXT power estimates was assessed for MCFs between 100 m and 10 km long, core pitches between 30 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m and 50 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m, and normalized difference between the refractive index of the cores between <inline-formula><tex-math notation="LaTeX">$-0.028\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$0.028\%$</tex-math></inline-formula>, typical of weakly-coupled real homogeneous MCFs. The largest improvements of mean ICXT power estimates tend to occur for larger core pitches and for larger differences between the refractive index of the cores. Accurate mean ICXT power estimates obtained with the novel DCM for MCFs with typical fiber birefringence parameters are also reported.
Comparative Analysis of Various Similarity Measures for Finding Similarity of Two Documents

Similarity measurements are elemental concepts in text mining and information retrieval that helps us to quantify the similarity between documents, which is effective in the improvement of the performance of search engines and browsing techniques. Nowadays, varieties of similarity measures are available, but it is not clear that which similarity measure is more effective in finding the similarity of text documents. The aim of this paper is to provide a comparative analysis of various term based similarity measures such as Cosine similarity, Jaccard and Dice coefficient in order to evaluate the performance of this similarity measures in finding the similarity of two text documents.
Towards a Framework for Proximity-Based Hybrid Mobile Applications

Despite the increasing availability of IoT devices and technology, the current user interaction requires users to download dedicated ad-hoc mobile apps, each of these remotely monitoring and controlling a limited set of IoT devices. In the near future, with billions of deployed IoT devices, this approach will not scale since users cannot be expected to download different apps for every place they visit or every device they interact with. To overcome such limitation, this paper presents the vision, the risks, and opportunities, of the development and deployment of future hybrid proximity services, as a paradigm shift for an intuitive interaction between users and the surrounding IoT environment. Hybrid proximity services are user-facing mobile applications developed and configured for specific locations and automatically installed on user mobile devices when the user is in their proximity and without the need of explicit download. A proximity service enabled environment can directly provide the user with the right service for the right place, and automatically instructs the user device on how to interact with the surrounding IoT devices.
Structural Components for Amplification of Positive and Negative Strand VEEV Splitzicons

RNA is a promising nucleic acid technology for both vaccines and therapeutics, and replicon RNA has gained traction as a next-generation RNA modality. Replicon RNA self-amplifies using a replicase complex derived from alphaviral non-structural proteins and yields higher protein expression than a similar dose of messenger RNA. Here, we debut RNA splitzicons; a split replicon system wherein the non-structural proteins (NSPs) and the gene of interest are encoded on separate RNA molecules, but still exhibit the self-amplification properties of replicon RNA. We designed both positive and negative strand splitzicons encoding firefly luciferase as a reporter protein to determine which structural components, including the 5′ untranslated region (UTR), a 51-nucleotide conserved sequence element (CSE) from the first nonstructural protein, the subgenomic promoter (SGP) and corresponding untranslated region, and an internal ribosomal entry site (IRES) affect amplification. When paired with a NSP construct derived from the whole, wild type replicon, both the positive and negative strand splitzicons were amplified. The combination of the 51nt CSE, subgenomic promoter and untranslated region were imperative for the positive strand splitzicon, while the negative strand was amplified simply with inclusion of the subgenomic promoter. The splitzicons were amplified by NSPs in multiple cell types and show increasing protein expression with increasing doses of NSP. Furthermore, both the positive and negative strand splitzicons continued to amplify over the course of 72 h, up to >100,000-fold. This work demonstrates a system for screening the components required for amplification from the positive and negative strand intermediates of RNA replicons and presents a new approach to RNA replicon technology.