Reviewerly/exHarmony huggingface dataset:
Joint Analysis of Port and Protocol via Endpoint Measurement: An Empirical Study

As network services continuously evolving, accurately classifying traffic is important for network operators to optimize QoS and customize policy. Network service uses non-standard ports and protocol obfuscation causing damage to the accurate port-based and payload-based traffic classification. However, Deep Packet Inspection (DPI) technique, which combines the payload-based method and port-based method, is still adopted by practitioners from the academic and industrial community. In this paper, we investigate the DPI classification result on a large network to estimate the impact of two factors. We qualify the popularity of non-standard port among different protocols. By endpoint filtering, we discover a large proportion of non-standard ports are opened temporally. We show there still is strong association between P2P protocols and camouflaged protocol. In particular, using both host and label association between endpoints, we find camouflaged protocols exhibit an abnormal port span that is different with the original protocol and are similar to the port span of P2P protocols.
Learning to Generate Reformulation Actions for Scalable Conversational Query Understanding

The ability of conversational query understanding (CQU) is indispensable to multi-turn QA. However, existing methods are data-driven and expensive to extend to new conversation domains, or under specific frameworks and hard to apply to other underlying QA technologies. We propose a novel contextual query reformulation (CQR) module based on reformulation actions for general CQU. The actions are domain-independent and scalable, since they capture syntactic regularities of conversations. For action generation, we propose a multi-task learning framework enhanced by coreference resolution, and introduce grammar constraints into the decoding process. Then CQR synthesizes standalone queries based on the actions, which naturally adapts to original downstream technologies. Experiments on different CQU datasets suggest that action-based methods substantially outperform direct reformulation, and the proposed model performs the best among the methods.
ROS Based Software Framework for Kid-Size Humanoid Marathon Robot

This paper presents a ROS based software framework for kid-size humanoid marathon robot. Marathon track identification was solved by using colour filtering and contour selection approach. Two-stages ConvNet object detection algorithm with colour-based region proposal was proposed to recognize a marker with train accuracy 99.92% and validation accuracy 99.87%. The ConvNet based object detector is run on a mini-computer with Intel Core i3 processor and reached 41.13 FPS processing speed. In order to perform behaviour control, the robot implemented a PID controller for head movement and a proportional controller for line follower behaviour. Finite State Machine is used to control high-level behaviour for performing navigation in the marathon track. The proposed software framework was applied to robot that won the Iran FIRA RoboWorldCup Open 2019 and Taiwan Humanoid 2019 competition.
Computational Science and Engineering in 2020

Presents the introductory editorial for this issue of the publication.
Implementation of New Approach to Secure IoT Networks with Encryption and Decryption Techniques

The diversity of the internet can be fascinating with our everyday devices getting automated. However, such technology of connecting everything to the internet can actually be daunting. This data could easily be attacked by malicious users and hence hangs the usage of such technologies in a precarious condition. To prevent such attacks, we have used AES algorithm to secure data bits and also designed a two-stage encryption and decryption algorithm. This algorithm was successfully tested by transmitting data bits through a channel between two wireless sensor nodes for five test cases. The model is tested in such a way that it can be universally applicable to all IoT networks.
Opportunities in Conflict on the Trail

People spend time on trails for a great many reasons. Often their reasons overlap—sometimes in positive ways but occasionally in conflict. Although there have been studies of individuals and unique groups that utilize the trail, there is a need first to first understand trail users. These users span different groups that use the trail, and the communities that inhabit the region surrounding the trail. It is importanat to the understand the group–community interaction especially in the presence of technology. In this chapter, we methodically consider these groups and communities, and identify relationships and tensions that emerge from their interactions with each other. We argue that exploring tensions provide a space to identify design opportunities to mitigate conflicts and improve the sense of community on the trail.
Bayesian Network Parameter Learning Method Based on AHP/D-S Evidence Theory

Aiming at the problem of prior knowledge acquisition in the process of Bayesian network construction, AHP/D-S evidence theory is introduced into Bayesian network parameter learning. An algorithm that uses AHP/D-S evidence theory to integrate expert prior knowledge, integrates monotonic constraints and near-equal constraints for parameter learning is proposed, and simulation cases are studied. Given corrective expert prior knowledge, the new parameter-learning algorithm overcomes the shortcomings of miscalculation and miscalculation of certain small probability parameters under the condition of small sample set by MLE, and was obviously better than MLE and MAP without prior information. This paper provides a new method for acquiring prior knowledge in the Bayesian network parameter learning process.
Using AI Technology to Support Speaking Skill Development for the Teaching of Chinese as a Foreign Language

The teaching of Chinese as a foreign language can be supported by using AI technology. Traditionally, the non-native learners can only interact with the instructors and depend on them solely for speaking practices. However, with the advancement of AI technology, the learners can use AI technology for interactive speaking skill development. In this study, the learners were instructed to download an application at https://m.wandoujia.com/apps/6790950. The process on the preparation of this AI technology in supporting their speaking skill development can be accessed at https://sites.google.com/site/gohyi141/assignments/projectinteractivespeakingapp. By using this AI technology, the findings showed a tremendous affirmative responses pertaining to the use of this AI technology. Hence, AI technology should be encouraged in active utilization for the teaching of Chinese as a foreign language in particular and for all language speaking skill development in general.
Pangeo Benchmarking Analysis: Object Storage vs. POSIX File System

Pangeo is a community of scientists and software developers collaborating to enable Big Data Geoscience analysis interactively in the public cloud and on high-performance computing (HPC) systems. At the core of the Pangeo software stack is (1) Xarray, which adds labels to metadata such as dimensions, coordinates and attributes for raw array-oriented data, (2) Dask, which provides parallel computation and out-of-core memory capabilities, and (3) Jupyter Lab which offers the web-based interactive environment to the Pangeo platform. Geoscientists now have a strong candidate software stack to analyze large datasets, and they are very curious about performance differences between the Zarr and NetCDF4 data formats on both traditional file storage systems and object storage. We have written a benchmarking suite for the Pangeo stack that can measure scalability and performance information of both input/output (I/O) throughput and computation. We will describe how we performed these benchmarks, analyzed our results, and we will discuss the pros and cons of the Pangeo software stack in terms of I/O scalability on both cloud and HPC storage systems.
Design Space Exploration Scheme for Mapping Convolutional Neural Network on Zynq Zedboard

Convolutional Neural Network (CNN) is an important machine learning algorithm. Due to its broad applications and classification accuracy it has become hot topic in recent times. CNNs are both computationally expensive and have extensive memory accesses which has rendered it inefficient on general purpose computers. GPU implementations have improved the performance of algorithm but high energy consumption of GPUs doesn't allow its usage in robotics and mobile embedded platforms. In this paper we study the mapping of Convolutional Neural Networks on field programmable gate arrays (FPGAs). We implement a VGG-16 style network which are the most admired CNN architectures in community. We Used Xilinx Zynq Zedboard for analytical modeling and mapping of CNN. For a complete network implementation, we achieved a peak performance of 1.3 GMACCs at 120 MHz frequency. Our implementation achieves a speed up of 4 times compared to software implementation on General Purpose Computer.
Hate Crime Legislation and Jurisprudence

This chapter summarizes the problems with measuring the prevalence and severity hate crimes in the United States that are delineated in greater detail in later chapters. The chapter explains the various types of contemporary hate crime legislation with a focus on their similarities and differences and ramifications for accurately measuring the prevalence of hate crimes. The chapter examines federal and state variations in statutory provisions with particular emphasis on differences in statutorily protected bias groups and evidentiary criteria. This chapter focuses in some detail on evidentiary criteria including discriminatory selection, animus, “because of” and “by reason of” evidentiary standards. The chapter also summarizes landmark supreme court decisions that have set the legal parameters for the promulgation of federal and state hate crime statutes within the constitutional constraints of the First and Fourteenth Amendments. This chapter concludes with a discussion of the importance of accurate measurement as it relates to enacting evidence-based policies and practices that serve the most vulnerable and likely victims of hate crimes.
DeepCloud: An Investigation of Geostationary Satellite Imagery Frame Interpolation for Improved Temporal Resolution

Satellite imagery is a crucial product for today’s weather forecasts. Worldwide, billions of dollars are invested annually on state-of-the-art satellites to produce imagery with better resolution and more spectral channels. This paper proposes a deep-learning methodology to enhance the temporal resolution of multi-spectral weather products from already deployed geostationary spacecraft (GOES or Himawari). Our method can dramatically improve the smoothness of an animation made with a series of pictures from an area of interest (Hurricanes, Tropical Storms, or Polar Jet Streams or Cold Fronts) while maintaining a good level of detail. To do so, we applied the transfer learning technique to fine-tune an already published frame interpolation neural network with real satellite weather imagery. By doing so, our technique can synthesize an intermediate frame of two adjacent frames scanned at the standard image interval. We also investigated the effects of using multiple combinations of spectral channels (e.g. Visible, True-Color, and Infrared) to test and train this network. The proposed temporal improvement can be helpful for weather forecasters to recognize atmospheric trends faster and more precisely. Moreover, our method does not require space hardware modifications thus being a safe and cost-effective solution.
Measurements of a Finished Garment Using 3D Laser Image Processing

This paper presents a determine defect detection system, measurement error or gather problem for the garment industry. The measurement of the completed suit and the followed evaluation of sewing quality and the correct joining of clothes is an important part in the industrial garment industry. However, the main difficulty in using visual inspection to evaluate the finished garment quality is the time taken and labor wasted. In order to solve these problems, if we consider the structure of the dress, a three-dimensional measurement is required. The trouble with respect to vision-based system is the robustness of image processing algorithms. Safety and stability of finished goods are the key importance when dealing with an especially textile and automotive project. The stability of the finished products largely depends on their overall geometry, and the results of the analysis depend on the certainty that this geometry can be measured practically. A three-dimensional laser scan will be required to determine whether a finished industrial product is manufactured within nominal scales. it helps to find different points of based on 3D-point measurement, and to allows determining the lines of connections point, straightness, flatness, roundness, cylindrical shape, symmetry, co-axiality, parallelism, perpendicularity, inclination, uncertainty and position in the structure. This contribution gives a critical appraisal of the process used in the preliminary research phase: gathering the point clouds, 3D-modelling, structural analysis calculating from the normative design to the real design and the tolerance requirements that are obtained. The focus on the added value of the methodology is applicability of product specification, verification, cost-effectiveness, overall advantages and drawbacks.
University presses' e‐book dissemination strategies for academic library customers: An exploratory study

Abstract This work‐in‐progress paper examines university presses' e‐book dissemination strategies by focusing on how presses choose certain ways to distribute their e‐books to academic library customers, and the factors supporting their decisions. The study conducted five one‐on‐one interviews with employees from different university presses in the U.S. The preliminary findings reveal that although all participating university presses provided their e‐books to four main vendors, these presses adopted different strategies to distribute their e‐books to different vendors. Further, this study listed the factors affecting university presses' decision on their e‐book dissemination strategies, including increasing exposure of their e‐books, concerns about adding new vendors, and the vendors' imposition of DRM on e‐books. Then this study, by linking the findings to the framework of cultural commodity, argues that the university presses' dissemination strategies demonstrate their effort to reduce the fragility of their e‐books as a cultural commodity.
French subject island constraint?

Summary We present new experimental results (corpora and experiments) showing that extraction out of subject, compared with extraction out of object, obeys a pragmatic constraint and not a syntactic constraint. We show how such a constraint can be formalized in an HPSG grammar of French which views relative clauses, wh -questions and it -clefts as different constructions.
Software Patenting and Section 101’s Gatekeeping Function

Software-related inventions have had an uneasy relationship with the patent-eligible subject matter requirement of Section 101 of the Patent Act. In applying the requirement, the Supreme Court has historically characterized mathematical algorithms and formulas simpliciter as sufficiently analogous to laws of nature to warrant judicial exclusion as abstract ideas. The Court has also found “the mere recitation of a generic computer” in a patent claim as tantamount to “adding the words ‘apply it with a computer,’” a mere drafting effort that does not relieve “the pre-emption concern that undergirds our § 101 jurisprudence.” Lower courts, patent counsel, and commentators have struggled to apply these broad principles to specific software-related inventions, a difficulty largely rooted in the many forms and levels of abstraction in which mathematical algorithms can be situated, both in the computing context and in the terms of a patent claim. Consequently, widely varying approaches to claiming inventions that involve algorithms in their use have perennially complicated efforts to develop a coherent doctrine of unpatentable abstract ideas.
A PoL Protocol for Spatiotemporal Blockchain

Spatiotemporal data is the foundation of technologies such as the supply chain industry and autonomous driving. Recent years, the processing of spatiotemporal data has become a hot issue. The current processing method of spatiotemporal data is mainly a single point of centralized storage, which brings certain problems such as data islands. The blockchain is a peer-to-peer distributed ledger, which can ensure that the data is decentralized and not tampered. Using blockchain to process spatiotemporal data can solve important problems in the field of spatiotemporal data processing. A very important part of this process is that the proof-of-location (PoL) is created by consensus. This paper proposes a blockchain protocol for spatiotemporal data. The protocol includes the creation of PoL, consensus and block sequencing. We propose a PoL generation algorithm to generate PoL and create transactions. Further, a consensus algorithm for PoL is proposed to verify PoL and transaction and create blocks. We used a parallel DAG architecture to generate the topology of the blockchain for storing blocks. Finally, we conducted a threatening analysis of the proposed protocol. We simulated a supply chain case to evaluate the throughput, scalability, and fault tolerance of the protocol.
A Multi-Patch Aggregated Aesthetic Rating System Based on Eyefixation

Due to individuals have their own aesthetics so evaluations of the same photograph may differ, it is essential to develop an assessment system that approximates the aesthetics of general public. This paper utilize the large-scale aesthetic Aesthetic Visual Analysis dataset (AVA) to help establish an aesthetic assessment system. In addition to a machine perspective, we further propose a human-like eye fixation method that enables machines to learn from human perspective when analyzing aesthetics from data, which can be used as a reference for future machine learning systems to learn abstract features from a human perspective. Our system selects the areas that attract the most attention of viewers as patches and uses the overall image as the overall image layout, which are then analyzed by the multi-patch aggregated aesthetic model. The performance improvements are validated by linear correlation coefficient and mean square error.
Ontology Applications in E-Learning Systems

In the development of ontologies within the context of e-learning, the system must follow strict set of principles that are government a body of knowledge and ethical values that would eventually enhance learners' experience in the gaining of knowledge across the various platforms. The technological advancements have enabled the inception of ontologies within the e-learning systems that has created high level of interoperability within our educational system. This analysis has explained a detailed approach to building of substantial ontology which can be used across different e-learning platforms. The steps such as definition of objectives, the capture, coding and integration of ontology, the evaluation and eventual documentation of the ontologies are quite essential in this course of action [1]. Through the inception of the ontologies, one is able to not only able to improve on the search for learning resources but also at a position to personalize content and enhance feedback generation [2]. With this research, one is able to grasp on some of the crucial elements ontology that can enhance e-learning systems.
Reinforcement Learning with Action-Specific Focuses in Video Games

It is intuitive that different actions prefer different information in human decisions. However, classical reinforcement learning models use the same information process procedure for all actions. In order to imitate human decision-making process closer, in this paper we investigate a new policy model, i.e., Action-Specific Focuses (ASF) framework, which enables different focuses when learning different actions. In the ASF framework, the whole action set is taken as part of the queries for the attention module, in which state-dependent action-specific features can be generated. Through extracting different action-specific features, our approach enables the agent to learn the action-focus map for each action separately. The ASF framework is also different from the previous usages of attention mechanisms in reinforcement learning that are mostly based on the state. Experiments on the Atari benchmark show that ASF is able to improve the performance in various types of games. Moreover, the visualizations of the attention weights suggest that ASF can learn meaningful focuses when taking different actions.
The role of mutual information in variational classifiers

Overfitting data is a well-known phenomenon related with the generation of a model that mimics too closely (or exactly) a particular instance of data, and may therefore fail to predict future observations reliably. In practice, this behaviour is controlled by various—sometimes based on heuristics—regularization techniques, which are motivated by upper bounds to the generalization error. In this work, we study the generalization error of classifiers relying on stochastic encodings which are trained on the cross-entropy loss, which is often used in deep learning for classification problems. We derive bounds to the generalization error showing that there exists a regime where the generalization error is bounded by the mutual information between input features and the corresponding representations in the latent space, which are randomly generated according to the encoding distribution. Our bounds provide an information-theoretic understanding of generalization in the so-called class of variational classifiers, which are regularized by a Kullback–Leibler (KL) divergence term. These results give theoretical grounds for the highly popular KL term in variational inference methods that was already recognized to act effectively as a regularization penalty. We further observe connections with well studied notions such as Variational Autoencoders, Information Dropout, Information Bottleneck and Boltzmann Machines. Finally, we perform numerical experiments on MNIST, CIFAR and other datasets and show that mutual information is indeed highly representative of the behaviour of the generalization error.
A model driven and clustering method for service identification directed by metrics

Abstract Service identification (SI) in the life cycle of service‐oriented architecture is a critical phase. Business models consisting of business process (BP) model and business entity (BE) model are the useful models that may be used for SI. To this end, SI is carried out by partitioning activities in BP based on the activities' use of the entities in BE. However, a proper partitioning activities to services, which is called a service design, is a challenge. This article aims to present a semiautomatized clustering method for partitioning the activities to services, which is directed by new proposed metrics cohesion, coupling, and granularity. With regard to the conflict of the metrics, a multiobjective evolutionary algorithm (MOEA) is used to clustering activities where the metrics are considered as objectives should be optimized. The MOEA produces a set of optimal solutions as proper identified services of a service design. Finally, we used three case studies to show the effectiveness of the proposed method and then evaluated the results.