Miracle-dz/UniFAR-FTUs huggingface dataset:
FlexMonitor: A Flexible Monitoring Framework in SDN

Efficient network monitoring is an important basis work for network management. Generally, many management applications require accurate and timely statistics about network states at different aggregation levels at low cost, such as malicious traffic detection, traffic engineering, etc. Moreover, the network environment to be monitored is constantly changing and expanding, including not only the data center for cloud computing but also the Internet of Things (IoT) for smart urban sensing, which requires the intensive study of more fine-grained network monitoring. As is well known, the development of efficient network monitoring approaches greatly relies on a flexible monitoring framework. Software defined network (SDN) can provide dramatic advantages for network management by separating the control plane and data plane. Therefore, it is a good choice to design a flexible monitoring framework based on the advantages of SDN. However, most research works only take advantage of the centralized control feature in SDN, which leads to limited improvement in the flexibility of the monitoring framework. This paper proposes a flexible monitoring framework named FlexMonitor, which can realize greater flexibility based on not only the centralized control feature, but also the high programmability in the controller and the limited programmability in the openflow switches in SDN. There are two key parts in FlexMonitor, namely the monitoring strategy deployment part and the monitoring data collection part, which can enrich the deployment methods of monitoring strategies and increase the kinds of monitoring data sources, respectively. Based on the NetMagic platform, this monitoring framework was implemented and evaluated through realizing a distributed denial of service (DDoS) detection approach. The experimental results show that the proposed DDoS detection approach has a better detection performance compared with other related approaches as well as indirectly show that FlexMonitor can flexibly support a variety of efficient monitoring approaches.
News Across Languages - Cross-Lingual Document Similarity and Event Tracking

In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.
The Complexity of Some Classes of Pyramid Graphs Created from a Gear Graph

The methods of measuring the complexity (spanning trees) in a finite graph, a problem related to various areas of mathematics and physics, have been inspected by many mathematicians and physicists. In this work, we defined some classes of pyramid graphs created by a gear graph then we developed the Kirchhoff's matrix tree theorem method to produce explicit formulas for the complexity of these graphs, using linear algebra, matrix analysis techniques, and employing knowledge of Chebyshev polynomials. Finally, we gave some numerical results for the number of spanning trees of the studied graphs.
Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks

It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz.
Automated Essay Scoring: A Siamese Bidirectional LSTM Neural Network Architecture

Essay scoring is a critical task in education. Implementing automated essay scoring (AES) helps reduce manual workload and speed up learning feedback. Recently, neural network models have been applied to the task of AES and demonstrates tremendous potential. However, the existing work only considered the essay itself without considering the rating criteria behind the essay. One of the reasons is that the various kinds of rating criteria are very hard to represent. In this paper, we represent rating criteria by some sample essays that were provided by domain experts and defined a new input pair consisting of an essay and a sample essay. Corresponding to this new input pair, we proposed a symmetrical neural network AES model that can accept the input pair. The model termed Siamese Bidirectional Long Short-Term Memory Architecture (SBLSTMA) can capture not only the semantic features in the essay but also the rating criteria information behind the essays. We use the SBLSTMA model for the task of AES and take the Automated Student Assessment Prize (ASAP) dataset as evaluation. Experimental results show that our approach is better than the previous neural network methods.
Developing Secure IoT Services: A Security-Oriented Review of IoT Platforms

Undoubtedly, the adoption of the Internet of Things (IoT) paradigm has impacted on our every-day life, surrounding us with smart objects. Thus, the potentialities of this new market attracted the industry, so that many enterprises developed their own IoT platforms aiming at helping IoT services’ developers. In the multitude of possible platforms, selecting the most suitable to implement a specific service is not straightforward, especially from a security perspective. This paper analyzes some of the most prominent proposals in the IoT platforms market-place, performing an in-depth security comparison using five common criteria. These criteria are detailed in sub-criteria, so that they can be used as a baseline for the development of a secure IoT service. Leveraging the knowledge gathered from our in-depth study, both researchers and developers may select the IoT platform which best fits their needs. Additionally, an IoT service for monitoring commercial flights is implemented in two previously analyzed IoT platforms, giving an adequate detail level to represent a solid guideline for future IoT developers.
Reusing the Optimized Code for JavaScript Ahead-of-Time Compilation

As web pages and web apps increasingly include heavy JavaScript code, JavaScript performance has been a critical issue. Modern JavaScript engines achieve a remarkable performance by employing tiered-execution architecture based on interpreter, baseline just-in-time compiler (JITC), and optimizing JITC. Unfortunately, they suffer from a substantial compilation overhead, which can take more than 50% of the whole running time. A simple idea to reduce the compilation overhead is ahead-of-time compilation (AOTC), which reuses the code generated in the previous run. In fact, existing studies that reuse the bytecode generated by the interpreter or the machine code generated by the baseline JITC have shown tangible performance benefits [12, 31, 41]. However, there has been no study to reuse the machine code generated by the optimizing JITC, which heavily uses profile-based optimizations, thus not easily reusable. We propose a novel AOTC that can reuse the optimized machine code for high-performance JavaScript engines. Unlike previous AOTCs, we need to resolve a few challenging issues related to reusing profile-based optimized code and relocating dynamic addresses. Our AOTC improves the performance of a commercial JavaScript engine by 6.36 times (max) and 1.99 times (average) for Octane benchmarks, by reducing the compilation overhead and by running the optimized code from the first invocation of functions. It also improves the loading time of six web apps by 1.28 times, on average.
A Fuzzy Logic Based Intelligent System for Measuring Customer Loyalty and Decision Making

In this paper, an intelligent approach is presented to measure customers’ loyalty to a specific product and assist new customers regarding a product’s key features. Our approach uses an aggregated sentiment score of a set of reviews in a dataset and then uses a fuzzy logic model to measure customer’s loyalty to a product. Our approach uses a novel idea of measuring customer’s loyalty to a product and can assist a new customer to take a decision about a particular product considering its various features and reviews of previous customers. In this study, we use a large sized data set of online reviews of customers from Amazon.com to test the performance of the customer’s reviews. The proposed approach pre-processes the input text via tokenization, Lemmatization and removal of stop words and then applies fuzzy logic approach to take decisions. To find similarity and relevance to a topic, various libraries and API are used in this work such as SentiWordNet, Stanford Core NLP, etc. The approach utilized focuses on identifying polarity of the reviews that may be positive, negative and neutral. To find customer’s loyalty and help in decision making, the fuzzy logic approach is applied using a set of membership functions and rule-based system of fuzzy sets that classify data in various types of loyalty. The implementation of the approach provides high accuracy of 94% of correct loyalty to the e-commerce products that outperforms the previous approaches.
A COP Model for Graph-Constrained Coalition Formation (Extended Abstract)

We focus on Graph-Constrained Coalition Formation (GCCF), a widely studied subproblem of coalition formation where the set of valid coalitions is constrained by a graph. We propose COP-GCCF, a novel approach that models GCCF as a COP. We then solve such COP with a highly-parallel GPU implementation of Bucket Elimination, which is able to exploit the high constraint tightness of COP-GCCF. Results on realistic graphs, i.e., a crawl of the Twitter social graph, show that our approach outperforms state of the art algorithms (i.e., DyCE and IDP G ) by at least one order of magnitude, both in terms of runtime and memory.
Some Generating Functions for q-Polynomials

Demonstrating the striking symmetry between calculus and q-calculus, we obtain q-analogues of the Bateman, Pasternack, Sylvester, and Cesàro polynomials. Using these, we also obtain q-analogues for some of their generating functions. Our q-generating functions are given in terms of the basic hypergeometric series 4ϕ5, 5ϕ5, 4ϕ3, 3ϕ2, 2ϕ1, and q-Pochhammer symbols. Starting with our q-generating functions, we are also able to find some new classical generating functions for the Pasternack and Bateman polynomials.
Evaluating Stereotype and Non-Stereotype Recommender Systems

Stereotype-based user modeling was proposed by Elaine Rich in 1979 and has been applied to recommender systems on numerous instances since its conception. The key motivations for application of stereotyping in user modeling are resolution of the new user problem and space efficiency. Several claims have been made in the literature related to the effectiveness of stereotyping but only a few studies have validated them empirically. Furthermore, to the best of our knowledge, there has been no empirical study of itembased stereotype models for recommender systems. Our research empirically substantiates the efficacy of using stereotypes in item modeling and user modeling when compared with non-utilization of stereotypes. The empirical evaluation was performedwith a stateof-the-art machine learning algorithm (gradient boosted decision forests) applied to two datasets integrating MovieLens, IMDb and TMDB movie data.
Metric Selection for GPU Kernel Classification

Graphics Processing Units (GPUs) are vastly used for running massively parallel programs. GPU kernels exhibit different behavior at runtime and can usually be classified in a simple form as either “compute-bound” or “memory-bound. ” Recent GPUs are capable of concurrently running multiple kernels, which raises the question of how to most appropriately schedule kernels to achieve higher performance. In particular, co-scheduling of compute-bound and memory-bound kernels seems promising. However, its benefits as well as drawbacks must be determined along with which kernels should be selected for a concurrent execution. Classifying kernels can be performed online by instrumentation based on performance counters. This work conducts a thorough analysis of the metrics collected from various benchmarks from Rodinia and CUDA SDK. The goal is to find the minimum number of effective metrics that enables online classification of kernels with a low overhead. This study employs a wrapper-based feature selection method based on the Fisher feature selection criterion. The results of experiments show that to classify kernels with a high accuracy, only three and five metrics are sufficient on a Kepler and a Pascal GPU, respectively. The proposed method is then utilized for a runtime scheduler. The results show an average speedup of 1.18× and 1.1× compared with a serial and a random scheduler, respectively.
A modular bilateral haptic control framework for teleoperation of robots

SUMMARY This paper presents a novel approach to implement bilateral control loops between local haptic devices and remote industrial manipulators using a layer of simulation and virtual reality. The remote scene of manipulation has been visualized in an open-source software environment, where forward and inverse kinematics of the manipulators can be computed. Therefore, the explicit knowledge of mathematical models of the robots is not required for the implementation of the proposed bilateral control schemes. A haptic coupling has been designed between the human operator and the task in the remote environment. Virtually introduced force feedback has contributed to the performance of the proposed bilateral loop by facilitating the adaptation of unexperienced human operators. Teleoperation of one remote manipulator has been experimentally demonstrated with the proposed controllers. Structural modularity of the bilateral haptic control schemes makes them directly extendable for the teleoperation of multiple collaborative robots. Stability and transparency of the proposed bilateral haptic controllers have been theoretically and experimentally investigated.
The End of the x86 Dominance in Databases?

The explosion of “big-data” analytics imposes great challenges on traditional relational databases that are optimized for the CPU architecture. CPUs follow the von Neumann model, which is a control-flow architecture, and do not perform well on complex analytical queries backed by statistics and machine learning that involve massive data and parallelism. Therefore, emerging databases often offload the data-intensive part of the queries to hardware accelerators to achieve better performance and energy efficiency. Common hardware accelerators include GPUs and FPGAs. GPUs are highly-parallel and are efficient in performing sorting and sequential scans with predicates. They have been used in commercial products such as MapD (now Omnisci) [3] and Kinetica [2]. FPGAs are reconfigurable hardware and can be used to accelerate a variety of compute-intensive tasks such as data compression/decompression, predicate evaluation [6] and pattern matching [5]. The problem with today’s hardware accelerators, however, is that they are ad-hoc solutions for specific compute/data-intensive queries. Although FPGAs are programmable, reconfiguration including compilation, synthesis, and routing usually takes hours [6]. Therefore, applications must know the query patterns in advance so that they can prepare the hardware for the tasks. This problem might get solved by Intel’s new hardware design called the Configurable Spatial Accelerator (CSA) [1]. CSA is a dataflow architecture designed for the high-performance computing to assist or even replace the traditional superscalar out-of-order CPUs in supercomputers. Figure 1 shows the microarchitecture of CSA. CSA contains dense arrays of heterogeneous processing elements (PEs) that can handle integer and floating-point arithmetic, as well as communication routing and in-fabric storage. They are connected using the on-chip configurable network to form a desirable dataflow. PE executions are asynchronous. A PE is executed once the inputs are ready, and the outputs are immediately forwarded to the downstream PE. Intel claims that CSA can provide an order-of-magnitude improvement in performance and energy efficiency. But what is really amazing about this hardware are 1) it can directly map and execute the dataflow graphs generated by compilers (e.g., C++ compilers), and 2) it only takes several hundreds of nanoseconds to a few microseconds to reconfigure the PEs. Although this new hardware just got patented and is still under development (we have no idea when it will be available), it is not too early to think about how these new features will dramatically change the way reconfigurable hardware is used in a database. First, because of the short reconfiguration time, databases can determine
Image Feature Matching Based on Semantic Fusion Description and Spatial Consistency

Image feature description and matching is widely used in computer vision, such as camera pose estimation. Traditional feature descriptions lack the semantic and spatial information, and give rise to a large number of feature mismatches. In order to improve the accuracy of image feature matching, a feature description and matching method, based on local semantic information fusion and feature spatial consistency, is proposed in this paper. Once object detection is used on images, feature points are then extracted, and image patches with various sizes surrounding these points are clipped. These patches are sent into the Siamese convolution network to get their semantic vectors. Then, semantic fusion description of feature points is obtained by weighted sum of the semantic vectors, and their weights optimized by particle swarm optimization (PSO) algorithm. When matching these feature points using their descriptions, feature spatial consistency is calculated based on the spatial consistency of matched objects, and the orientation and distance constraint of adjacent points within matched objects. With the description and matching method, the feature points are matched accurately and effectively. Our experiment results showed the efficiency of our methods.
Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator

A stable electricity supply is the basis for ensuring the healthy and sustained development of a regional economy. Reasonable electricity prediction is the key to guaranteeing the stability and efficiency of electricity supply. To this end, we used a reformative grey prediction model to forecast electricity demand. In order to effectively improve the smoothness of a raw modelling sequence, we employed an existing smoothing algorithm that significantly compressed the amplitude of the random oscillation sequence. Then, an improved grey forecasting model with three parameters (IGFM_TP) was deduced. In the end, a new model was used to forecast the demand for electricity of one city in the western region of China, and comparisons of simulation values and errors with those of GFM_TP, GM(1,1), DGM(1,1) and SAIGM were conducted. The findings show that the mean absolute simulation percentage error of IGFM_TP was 7.8%, and those of the other four models were 12.1%, 12.3%, 11.1%, and 10.1%, respectively. Therefore, the simulation precision of the new model achieved an optimal effect. The proposed new grey model provides is an effective method for electricity demand prediction.