Miracle-dz/UniFAR-FTUs huggingface dataset:
Trustworthy Vehicular CommunicationEmploying Multidimensional Diversificationfor Moving-target Defense

Enabling trustworthy Vehicle to Vehicle (V2V) communication given the wireless medium and the highly dynamic nature of the vehicular environment is a hard challenge. Eavesdropping and signal jamming in such highly dynamic environment is a real problem. This paper proposes a nature inspired multidimensional Moving-Target Defense (MTD) that employs real time spatiotemporal diversity to obfuscate wireless signals against attacker reach. In space: the mechanism manipulates the wireless transmission pattern and configuration to confuse eavesdroppers. In Time: we manipulate the transmission payload, by intentionally injecting some fake data into the real transmission. Further, the mechanism changes the data transmission granularity over time from fine to coarse grained data chunks. As a case study, we assumed the direct transmission model across dynamic multi-paths relayed communication via vehicles traveling on a multi-lane road. The system is evaluated based Journal of Cyber Security and Mobility, Vol. 8 2, 133–164. River Publishers doi: 10.13052/jcsm2245-1439.821 This is an Open Access publication. c © 2018 the Author(s). All rights reserved. 134 Esraa M. Ghourab et al. on a complete analysis of the system model and comprehensive simulated scenarios. Results showed the effectiveness of the presented approach with an increased confusion factor, a massive reduction in the intercept probability and clear increase in the channel secrecy.
Exploring Constraints and policy guidelines of Eco-friendly Jute Micro-Enterprises Development in Bangladesh

The study investigates the obstacles face by eco-friendly jute micro-enterprises in Bangladesh. The principal source for collecting data was the face to face survey during February to June 2015. Moreover, a few qualitative interviews were also conducted. The data was collected from 150 jute micro-enterprises located in Dhaka, Norsingdi, Tangail and Rangpur. A principal component analysis approach was used to rank the highest and lowest obstacles factor for doing business in jute micro-enterprises of Bangladesh. Among major obstacles, Human Resource related constraints was ranked highest constrains which items are included such as lack of skilled workers, high labor cost and no appropriate training facilities. In addition, the second highest ranked obstacles were labeled as Financial and resource Constraints. Second component consists of six items such as lack of loan facilities, lack of availability of capital; lack of raw materials, lack of quality fabrics, high price of raw materials, and long process of loan approval. The other obstacles respectively determined by the principal component analysis included “third constraints is labeled as physical resource constraints (utility shortages , lack of supporting factories), fourth is labeled as marketing and policy inputs constraints (lack of foreign customers, lack of incentives for jute diversified products, lack of market information, modern equipments, research and development facilities. Besides from the qualitative study it is found that corruption and long process in getting benefits from government offices incentives and other benefits, Lack of raw materials bank (RMB) in the districts level, High fees in national and local trade fair (Bengali: Mela), lack of training program on diversified designs, lack of knowledge on eco-compliance which also causes of slow growth. Research concludes that the findings of the study will have practical implications for policies makers to overcome major obstacles for the environmental friendly jute microenterprises development in Bangladesh.
Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.
The spec is out there, Extracting contracts from code

A contract is a formal specification of the properties of a method. It has many uses in testing, verification and documentation. Despite all these benefits, developers almost never write contracts for their code due to the large amount of time required to write correct and expressive contracts. The project described in this thesis sets out to evaluate the possibility of extracting contracts from code using syntactic analysis, which is a not yet evaluated technique. Syntactic analysis is done by looking at the operations performed by code, rather than the result of those operations. Contracts were extracted by first parsing target source code into an abstract syntax tree, which was then analyzed to find defined code patterns which signaled the presence of some implicit contract. The information found within these patterns was then extracted and written as an explicit contract. In experiments involving 4 projects over 35 000 methods, our syntactic analysis tool extracted close to 60 000 behaviors, of which one-third were regarded as successful. The results are promising, even though performance of the syntactic analysis was somewhat inconsistent, it could produce contracts on a comparable level to those written by a human.
Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing

The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.
On Human Information Processing in Information Retrieval ( Position Paper )

Experimental psychology, cognitive science or, more recently, cognitive neuroscience, is the main framework to place human information processing under extensive empirical scrutiny. The last decade has seen a surge of interest in the application of psychological measurements for evaluating increasingly complex human-technology interactions. While most welcome from the psychological perspective, we propose that the use of these methodologies should not rely only on the application of sophisticated measurement tools, but also on the application of contemporary knowledge on psychological phenomena and dynamics of human information processing. In addition, we argue that the latest developments in multimodal signals and data mining techniques offer a unique opportunity to extend psychological methodologies to large scale testing grounds. Thus, the application of psychological knowledge to information retrieval research will not only be beneficial for the latter, but for the former as well, inasmuch as information retrieval provides a real field of application for its hypotheses about human information processing.
HUMAN RESOURCE INFORMATION SYSTEMS ( HRIS ) : REPLACING OR ENHANCING HRM

Over the last decade there has been a considerable increase in the number of organisations gathering, storing and analysing information regarding their human resources through the use of Human Resource Information Systems (HRIS) software or other types of software which include HRIS functionality (Ball, 2001; Barron, Chhabra, Hanscome, & Henson, 2004; Hussain, Wallace, & Cornelius, 2007; Ngai & Wat, 2006). The growing adoption of HRIS by organisations combined with the increasing sophistication of this software, presents the Human Resource function with new challenges. On one hand the role of HR can be enhanced through the combination of improved access to metrics and the automation of existing administrative functions thus enabling HR to make a greater contribution at a strategic level. However, the same capabilities also threaten the role of HR specialists as traditional HR work is both automated and distributed to line management. Through analysis of four Australian case study organisations we examine the impact of the HRIS on the HR function and find that the degree to which the system acts as an enabler of increased strategic focus for HR is contingent upon three factors: organisational attention, understanding of the technological responses to human resource management complexity, and the success of change management to support user acceptance * This research is funded by an Australian Research Council Linkage Grant (LPLP0882247) in collaboration with the Australian Senior Human Resources Roundtable (ASHRR).
C L ] 1 O ct 2 01 9 MULTILINGUAL END-TO-END SPEECH TRANSLATION

In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translated to the desired target languages with a universal sequence-to-sequence architecture. While multilingual models have shown to be useful for automatic speech recognition (ASR) and machine translation (MT), this is the first time they are applied to the end-to-end ST problem. We show the effectiveness of multilingual end-to-end ST in two scenarios: one-to-many and many-to-many translations with publicly available data. We experimentally confirm that multilingual end-to-end ST models significantly outperform bilingual ones in both scenarios. The generalization of multilingual training is also evaluated in a transfer learning scenario to a very low-resource language pair. All of our codes and the database are publicly available to encourage further research in this emergent multilingual ST topic.
Forthcoming at Manufacturing & Service Operations Management Loyalty Program Liabilities and Point Values

Problem definition. Loyalty programs (LP) introduce a new currency—the points—through which customers transact with firms. Such points represent a promise for future service, and their monetary value thus counts as a liability on the issuing firms’ balance sheets. Consequently, adjusting the value of points has a first order effect on profitability and performance, and emerges as a core operating decision. We study the problem of optimally setting the points’ value in view of their associated liabilities. Academic / Practical Relevance: Firms across numerous industries increasingly utilize LPs. The sheer magnitude of LPs coupled with recent changes in accounting rules have turned the associated liabilities into significant balance sheet items, amounting to billions of dollars. Managers (from CFOs to CMOs) struggle with the problem of adjusting the points’ value in view of these liabilities. Academic work is primarily aimed at understanding LPs as marketing tools, without studying the liability angle. Methodology. We develop a multi-period model and use dynamic programming techniques and comparative statics analysis. Results: We show that the optimal policies depend on a new financial metric, given by the sum of the firm’s realized cash flows and outstanding deferred revenue, which we refer to as the profit potential. The total value of loyalty points is set to hit a particular target, which increases with the profit potential. We find that loyalty programs can act as buffers against uncertainty, with the value of points increasing (decreasing) under strong (weak) operating performance, and increasing with uncertainty. Managerial Implications: Setting the point values and adjusting operating decisions in view of LP liabilities should be done by tracking the firm’s profit potential. Loyalty programs can act as hedging tools against uncertainty in future operating performance, which provides a new rationale for their existence, even in the absence of competition.
Research Based on Stock Predicting Model of Neural Networks Ensemble Learning

Financial time series is always one of the focus of financial market analysis and research. In recent years, with the rapid development of artificial intelligence, machine learning and financial market are more and more closely linked. Artificial neural network is usually used to analyze and predict financial time series. Based on deep learning, six layer long short-term memory neural networks were constructed. Eight long short-term memory neural networks were combined with Bagging method in ensemble learning and predicting model of neural networks ensemble learning was used in Chinese Stock Market. The experiment tested Shanghai Composite Index, Shenzhen Composite Index, Shanghai Stock Exchange 50 Index, Shanghai-Shenzhen 300 Index, Medium and Small Plate Index and Gem Index during the period from January 4, 2012 to December 29, 2017. For long short-term memory neural network ensemble learning model, its accuracy is 58.5%, precision is 58.33%, recall is 73.5%, F1 value is 64.5%, and AUC value is 57.67%, which are better than those of multilayer long short-term memory neural network model and reflect a good prediction outcome.
Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm

This paper presents the application of an iterative learning control scheme to improve the position tracking performance for an articulated soft robotic arm during aggressive maneuvers. Two antagonistically arranged, inflatable bellows actuate the robotic arm and provide high compliance while enabling fast actuation. Switching valves are used for pressure control of the soft actuators. A norm-optimal iterative learning control scheme based on a linear model of the system is presented and applied in parallel with a feedback controller. The learning scheme is experimentally evaluated on an aggressive trajectory involving set point shifts of 60 degrees within 0.2 seconds. The effectiveness of the learning approach is demonstrated by a reduction of the root-mean-square tracking error from 13 degrees to less than 2 degrees after applying the learning scheme for less than 30 iterations.
Leveraging General Knowledge Graphs in Crowd-powered Innovation

Despite the significant benefits of crowd ideation platforms, these introduced two main challenges: (1) many ideas generated are basic and repetitive (2) the high number of ideas generated makes it practically impossible for ideation experts to review ideas one by one in order to select novel and useful ones. A key feature to overcome these issues resides in understanding the ideas. General Knowledge Graphs describe themeaning of domain-independent terms in an computationally understandable way and therefore represent a promising solution in obtaining such meaning. In this paper, we describe our research in understanding ideas and preliminary findings in application for crowd-powered innovation. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. CHI’19, May 2019, Glasgow, United Kingdom © 2019 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn Leveraging General Knowledge Graphs in Crowd-powered Innovation CHI’19, May 2019, Glasgow, United Kingdom
Personalized Image Search Using Simple Case of One Word-Based Queries-An Overview

In today’s web search generates a large amount of data and search contents. Text based searching is most popular technique to search content based on available information. In social websites like Flicker, YouTube, Face book they provide features to users that create information, share information and allows the users to search information based on created tag, comment and annotation. This Create the user-generated metadata in very large amount which can be utilized for management and information or media retrieval. It’s found that use of tag, comment, and annotated data is convoluted in personalized search system. With Personalize search technique is improved by using user preferences and query-related search intention into user-specific topic spaces. In this paper the given model is tested for single word based query and showing satisfactory result. Keywords— Personalize search, Non-Personalize search, User preferences, Image annotation.
Empowering precision medicine through high performance computing clusters

medicine. Furthermore, Molecular Dynamics (MD) earned a great importance in aiding genome research. Sequencing studies of cancer have allowed to detect and characterize mutated genes that drive tumorigenesis. As a complementary approach, from a biophysical perspective, MD simulations, executed on HPC architectures, have permitted to investigate the role played by pathological mutations on the molecular mechanism of activation.
Integrating large-scale web data and curated corpus data in a search engine supporting German literacy education

Reading material that is of interest and at the right level for learners is an essential component of effective language education. The web has long been identiﬁed as a valuable source of reading material due to the abundance and variability of materials it offers and its broad range of attractive and current topics. Yet, the web as source of reading material can be problematic in low literacy contexts. We present ongoing work on a hybrid approach to text retrieval that combines the strengths of web search with retrieval from a high-quality, curated corpus resource. Our system, KANSAS Suche 2.0 , supports retrieval and reranking based on criteria relevant for language learning in three different search modes: unrestricted web search, ﬁltered web search, and corpus search. We demonstrate their complementary strengths and weaknesses with regard to coverage, readability, and suitability of the retrieved material for adult literacy and basic education. We show that their combination results in a very versatile and suitable text retrieval approach for education in the language arts.
Forecasting GDP Growth Using Disaggregated GDP Revisions

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include revisions to GDP and its components. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, models that include revisions to components of GDP improve forecast accuracy. The first revision to consumption is particularly relevant in that every model that includes the revision outperforms the baseline model. Measured by root mean squared forecasting error (RMSFE), improvements are quite sizable, with many models increasing forecasting performance by 5% or more, and with top-performing models forecasting 0.18 percentage points closer to the advance estimate of growth. We use Bayesian model averaging to underscore that our results are driven by the informational content of revisions. The posterior probability of models with the first revision to consumption is significantly higher than our baseline model, despite strong priors that the latter should be the preferred forecasting model.
SPONGE: Software-Defined Traffic Engineering to Absorb Influx of Network Traffic

Existing shortest path-based routing in wide area networks or equal cost multi-path routing in data center networks do not consider the load on the links while taking routing decisions. As a consequence, an influx of network traffic stemming from events such as distributed link flooding attacks and data shuffle during large scale analytics can congest network links despite the network having sufficient capacity on alternate paths to absorb the traffic. This can have several negative consequences such as service unavailability, delayed flow completion, packet losses, among others. In this regard, we propose SPONGE, a traffic engineering mechanism for handling sudden influx of network traffic. SPONGE models the network as a stochastic process, takes the switch queue occupancy and traffic rate as inputs, and leverages the multiple available paths in the network to route traffic in a way that minimizes the overall packet loss in the network. We demonstrate the practicality of SPONGE through an OpenFlow based implementation, where we periodically and pro-actively re-route network traffic to the routes computed by SPONGE. Mininet emulations using real network topologies show that SPONGE is capable of reducing packet drops by 20% on average even when the network is highly loaded because of an ongoing link flooding attack.