Miracle-dz/UniFAR-FTUs huggingface dataset:
Governance of sustainable development : a case study of the bamboo shoot production industry in Lin'An, China

Since 1978 China has transformed itself from a planned economy to a market economy, which has been accompanied by the shift from state-centric government to the co-existence of hierarchical and multi-nuclei forms of governance. This research argues that there is a gap in the analysis of rural sustainability governance in China in which insufficient attention is given to: (1) how different governance forms, objectives, and goals affect policy implementation to achieve sustainable development; and (2) how the local state re-organises its functions to achieve socio-economic and ecological sustainability during the reform period. To evaluate the effectiveness of the local state to govern for the sustainable development of the bamboo shoot industry, this research integrates insights from ecological modernisation, political ecology, and eco-Marxism. These perspectives allow a more fruitful understanding of the factors that enable and constrain steering approaches, policy design, and implementation in the bamboo shoot industry. This dissertation explores resource allocation and management in the bamboo shoot industry in Lin’an, China. It critically examines how governance operates at the county level, which is a key level of governance for the delivery and practice of sustainable development programmes and policies. Data was collected through fieldwork in two towns, one in the upland and the other in the lowland, which enabled an exploration of networks linked to supply chains and geographical differences including both physical and social settings. Under market reform, the Central State decentralises economic rights and autonomy to the local state in Lin’an County, which has increased the synergy, interdependency, and partnership between the local state and non-state actors in the bamboo shoot production industry. This emerges as a multi-nuclei form of governance structure to manage bamboo shoot resources, maintain the local state’s administrative authority and intervention, and to increase the indirect rule of the local state through new knowledge and technology production
Fingerprint-based mutual authentication RFID protocol

Low-cost radio frequency identification (RFID) communication system is exposed to various security and privacy threats. Most of the previous studies focused on prevention techniques to protect RFID communication system from eavesdropping, impersonation and data leakage problems. This paper proposes the use of both prevention and detection techniques to improve and solve the security issues. A mutual authentication protocol with integration of tag's unique power response is proposed to enhance the security level in RFID communication. The received power of tag is used as a unique electronic fingerprint to detect cloning tags. A lightweight cryptographic algorithm that conforms to the EPCglobal Class-1 Generation-2 standard is used to prevent replay attack, denial of service, and data leakage issues.
Mommy Wars: Stay-at-Home and Career Moms Face Off on Their Choices, Their Lives, Their Families

ore and more these days, reading women’s writing fills me with a vague, creeping, slightly nauseating feeling. Lying in bed the other night, cradling some seltzer water, my stomach gurgling, the word for my malaise suddenly came to me: “afflufemza,” wherein the problems of affluence are recast as the struggles of feminism, and you find yourself in a dreamlike state of reading firstperson essays about it, over and over again. We’ve always had rich mothers, of course; it’s just that the boundaries between the privileged and the unused to be clearer. Back in the eighties, for instance, I was among the many couch, or at least futon, potatoes who used to love Dynasty—the Mothra-versus-Godzilla grapplings of the Carringtons and the Colbys, of Joan Collins’s deliciously nasty Alexis and Linda Evans’s nurturing, oddly affectless Krystle. Alexis was the Execu-Bitch; Krystle, the Saintly Wife. It was the eternal female ur-struggle, ever campy, ever watchable, ever conveniently framed for us—out there in the distance—by that swoopily hammy Bill Conti score, those soaring trumpets, those glittering Denver skyscrapers.
SelectCast: a scalable and self-repairing multicast overlay routing facility

In this paper we describe SelectCast, a self-repairing multicast overlay routing facility for supporting publish/subscribe applications. Select Cast is a peer-to-peer protocol, and lever-ages Astrolabe, a secure distributed information management system. SelectCast uses replication to recover quickly from transient failures, as well as Astrolabe's aggregation facilities to recover from long-term failures or adapt to changes in load or QoS requirements. In order to evaluate the scalability and performance of SelectCast, and compare these with other multicast facilities, we built a multicast testing facility on NetBed. This paper reports latency and load results for SelectCast, compared to both native IP multicast and Yoid.
There is more to life stories than memories

Current theories focus on the role of specific memories in organising the life story. However, temporally extended structures of autobiographical memory, like lifetime periods and mini-narratives (here termed chapters), may also play a central role in the organisation of the life story. Here, 30 elderly participants were asked to tell their life story in a free format. The life stories were divided into components and coded as chapters, specific memories, categoric memories, facts, chapters about other people, and autobiographical reasoning categories, i.e., reflections, evaluations, life lessons, and inferences about personality. The results show that chapters were much more common than specific memories in the life stories, indicating that chapters may play a role in the structuring of life stories. The number of chapters and specific memories in the life stories were unrelated, suggesting that the recounting of chapters versus specific memories does not reflect a preferred recall style.
Identity in Dialogue: Using Semantic Role Labelling to Construct a Moral Framework

How can one identify moral values? Does dialogue betray some notion of an individual's moral orientation? This poster proposes a computational method to extract normative evaluations from natural language and construct a moral framework for fictional characters or for a human author. Operating within the StorMinG framework [1], this project is largely influenced by Charles Taylor and intends to be a first empirical approach to apply the concept of moral frameworks [2] to the field of NLP. Taylor describes moral frameworks as an essential and inescapable element of an individual's identity. Because they constitute one's identity, frameworks play an important role in decision making, i.e., in the valuation of consequences and goal planning. Our model of moral frameworks relies on strong evaluations: qualitative and normative distinctions an individual makes about the moral value of properties of the world. The goal of this project is to construct a representation of an individual's moral values for applications in personality simulation for interactive storytelling, and as an empirical tool for the digital humanities. The proposed method uses a PropBank[3]­trained Semantic Role Labelling (SRL) tool to identify predicates and label the thematic relations bound to it. A mixed approach is then used to identify arguments that select strongly for moral evaluations. The identification criteria include judgement­oriented verb classes identified using VerbNet [4], normative inducing adverbials, and moral nouns. Sentiment analysis is performed on the selected arguments to determine the positive or negative relationship of the individual to the object of moral valuation. The data is stored as a relational network connecting individuals to moral objects with edges weighted by sentiment. By selecting n binary pairs of moral objects, we transform the data into a Cartesian coordinate system in n­dimensional space. The inferred moral orientation of individuals is plotted in this manner and compared with assessments obtained by a human expert panel to measure the success of the method.
International Journal of Video& Image Processing and Netwo Rk Security Intelligent Adaptive Intrusion Detection Systems Using Neural Networks (comparitive Study )

Intrusion Detection S ystems (IDS s) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS 's responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. Classification algorithms are used to discriminate between normal and different types of attacks. In this paper, a comparative study between the performances of recent nine artificial neural networks (ANNs) based classifiers is evaluated, based on a selected set of features. The results showed that; the Multilayer perceptrons (MLPS) based classifier provides the best results; about 99.63% true positive attacks are detected. I. INTRODUCTION Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of intrusions defined as attempts to compromise the confidentiality, integrity, availability, or to bypass the security mechanisms of a computer or a network. Given the level and nature of modern network security threats, the question for security professionals should not be whether to use intrusion detection, but which intrusion detection features and capabilities to use. IDSs have gained acceptance as a necessary addition to every organization's security infrastructure despite the documented contributions intrusion detection technologies make to system security, in many organizations one must still justify the acquisition of IDSs. We may use IDSs to prevent problem behaviors by increasing the perceived risk of discovery of those who would attack or abuse the system. There are two general categories of attacks which intrusion detection technologies attempt to identify-anomaly detection and misuse detection. Anomaly detection identifies activities that vary from established patterns for users, or groups of users. Anomaly detection typically involves the creation of knowledge bases that contain the profiles of the monitored activities. The second general approach to intrusion detection is misuse detection. This technique involves the comparison of a
Working Paper 188 - Remittances and their Macroeconomic Impact: Evidence from Africa

This paper examines macroeconomic trends, drivers and impact of remittances in Africa. First, it documents the increasing share of remittances relative to other foreign capital flows to Africa, distribution of remittance inflows across countries, and some key properties. This is followed by some analysis of the macroeconomic drivers of remittances in recipient countries, such as the level of income, inflation and nominal exchange rate depreciation. Specifically, remittances are positively impacted by higher income, but deterred by an unstable macroeconomic environment, pointing to the investment motive in remitting to Africa. The paper also examines the role of remittances in funding Africa’s external balances. Finally, drawing on the case of Egypt, the paper shows the positive impact that rising remittances can have on public debt sustainability.
A Stall-Aware Warp Scheduling for Dynamically Optimizing Thread-level Parallelism in GPGPUs

General-Purpose Graphic Processing Units (GPGPU) have been widely used in high performance computing as application accelerators due to their massive parallelism and high throughput. A GPGPU generally contains two layers of schedulers, a cooperative-thread-array (CTA) scheduler and a warp scheduler, which administer the thread level parallelism (TLP). Previous research shows the maximized TLP does not always deliver the optimal performance. Unfortunately, existing warp scheduling schemes do not optimize TLP at runtime, which is impossible to fit various access patterns for diverse applications. Dynamic TLP optimization in the warp scheduler remains a challenge to exploit the GPGPU highly-parallel compute power. In this paper, we comprehensively investigate the TLP performance impact in the warp scheduler. Based on our analysis of the pipeline efficiency, we propose a Stall-Aware Warp Scheduling (SAWS), which optimizes the TLP according to the pipeline stalls. SAWS adds two modules to the original scheduler to dynamically adjust TLP at runtime. A trigger-based method is employed for a fast tuning response. We simulated SAWS and conducted extensive experiments on GPGPU-Sim using 21 paradigmatic benchmarks. Our numerical results show that SAWS effectively improves the pipeline efficiency by reducing the structural hazards without causing extra data hazards. SAWS achieves an average speedup of 14.7% with a geometric mean, even higher than existing Two-Level scheduling scheme with the optimal fetch group sizes over a wide range of benchmarks. More importantly, compared with the dynamic TLP optimization in the CTA scheduling, SAWS still has 9.3% performance improvement among the benchmarks, which shows that it is a competitive choice by moving dynamic TLP optimization from the CTA to warp scheduler.
A ROS framework for audio-based activity recognition

Research on robot perception mostly focuses on visual information analytics. Audio-based perception is mostly based on speech-related information. However, non-verbal information of the audio channel can be equally important in the perception procedure, or at least play a complementary role. This paper presents a framework for audio signal analysis that utilizes the ROS architectural principles. Details on the design and implementation issues of this workflow are described, while classification results are also presented in the context of two use-cases motivated by the task of medical monitoring. The proposed audio analysis framework is provided as an open-source library at github (https://github.com/tyiannak/AUROS).
Structural Vulnerability Analysis in Complex Networks Based on Core Theory

Recent studies show that the failures of just a few nodes or edges may disable the operation of the whole network, or even lead to the corruption of the entire system. For a given network and a ratio k, we aim to find out the set with the least number of vulnerable nodes whose removals reduce the connectivity of network structures (suppose to be m) to k*m. We introduce core theory to address this problem. Coritivity is an important measurement to assess the connectivity of network. It is capable of showing not only the difficulty to break down network but also how fragmental the network becomes. We demonstrate that the problem of finding core vertices is an NP-Hard problem. In this case, we propose an approximation algorithm to calculate coritivity of network. The most vulnerable nodes are selected by greedy methods from the set of core nodes found by the previous algorithm. We empirically test our approach on both synthetic and real-world datasets, the results confirm its efficacy compared to random method and three centrality-based algorithms.
Research of Information Extraction Algorithm based on Hidden Markov Model

Based on the research of Web Information Extraction Algorithm of Hidden Markov Model, this paper focus on the application of HMM in text information extraction, and improved methods of information extraction with constructing granularity refined DOM tree combined with regular expression to extract detailed information points. At the same time, we smooth the probability of unknown observations. Test result show that, the improved HMM has better extraction performance.
Utility Accrual Real-Time Scheduling Under the Unimodal Arbitrary Arrival Model with Energy Bounds

In this paper, we consider timeliness and energy optimization in battery-powered dynamic embedded real-time systems, which must remain functional during an operation/mission with a bounded energy budget. We consider application activities that are subject to time/utility function time constraints, statistical assurance requirements on timeliness behavior, and an energy budget which cannot be exceeded at runtime. To account for the inevitable variability in activity arrivals in dynamic systems, we describe arrival behaviors using the unimodal arbitrary arrival model (UAM) [15]. For such a model, we present a dynamic voltage scaling (DVS)-based CPU scheduling algorithm called the energy-bounded utility accrual algorithm (EBUA). Since the scheduling problem is intractable, EBUA allocates CPU cycles, scales clock frequency, and heuristically computes schedules using statistical estimates of cycle demands in polynomial time. We analytically establish EBUA's properties, including satisfaction of energy bounds, statistical assurances on individual activity timeliness behavior, optimal timeliness during underloads, and bounded time for mutually exclusively accessing shared non-CPU resources. Our simulation experiments validate our analytical results and illustrate the algorithm's effectiveness and superiority over past algorithms.
Update Legal Documents Using Hierarchical Ranking Models and Word Clustering

Our research addresses the task of updating legal documents when new information emerges. In this paper, we employ a hierarchical ranking model to the task of updating legal documents. Word clustering features are incorporated to the ranking models to exploit semantic relations between words. Experimental results on legal data built from the United States Code show that the hierarchical ranking model with word clustering outperforms baseline methods using Vector Space Model, and word cluster-based features are effective features for the task.
Changes in monetary policy effectiveness: evidence from large macroeconomic models

This article evaluates changes in the aggregate effectiveness of monetary policy and changes in monetary policy transmission mechanisms by examining how traditional large-scale macroeconometric models have evolved in the last ten to fifteen years. The article analyzes shifts in model structure and sheds some light on the changing relationship between policy and the real economy by reporting simulations that use different historical versions of the models.
A robust voice activity detection technique based on combined framework of lacunarity and empirical mode decomposition

In this paper, we have proposed a new technique for voice activity detection (VAD) using lacunarity index combined with empirical mode decomposition (EMD) technique. In the preprocessing stage of the proposed framework, the noisy speech signal is decomposed into several intrinsic mode functions (IMFs) based on EMD technique. After that more informative IMFs are selected using spectral flatness measurement (SFM) approach. In the decision stage, the speech signal frames are identified by a threshold limit. The threshold value is calculated using statistical parameter of the lacunarity index of the reconstructed speech signal. The proposed technique gives superior results over existing techniques and is more effective at moderate noise levels.
ARGUS: Supercomputing in 1/10 Cubic Meter

We propose ARGUS, a high density, low power supercomputer built from an IXIA network analyzer chassis and load modules. The prototype is a diskless MPP scalable to 128 processors in a single 9U chassis. The entire system has a footprint of 1/4 meter2 (2.5 ft2), a volume of 0.09 meter3 (3.3 ft3) and maximum power consumption of less than 2200 watts. We compare and contrast the characteristics of ARGUS against various machines including our 32-node Beowulf and LANL’s Green Destiny. Our results show that the computing density (GFLOP/ft3) of ARGUS is about 30 times higher than that of the Beowulf, about 3 times higher than that of Green Destiny while performance is comparable.
A Unification-based Approach to Mandarin Questions

This paper providers unification-based GPSG and LFG analyses of Mandarin questions. First, we briefly introduce four kinds of Mandarin question, namely, WH-questions, A-not-A questions, disjunctive questions, and particle questions. Their different interrogative messages are adequately encoded with different feature-value pairs. Then, the compatibility of this interrogative information in a simple sentence is investigated. Both the GPSG and LFG approaches provide a straight-forward account for their mutual exclusiveness. Finally, the scope of percolation of Mandarin interrogative information is examined. It is suggested that the matrix verb of a complex sentence is responsible for the scope of interrogative information in its complement sentence. According to our observations, Mandarin verbs should be divided into at least three classes. We provide preliminary analyses of the topic. The GPSG analysis relies on the Foot Feature Principle (FFP) and the LFG analysis relies on functional uncertainty. It is shown that the transmitting of Mandarin interrogative information can be adequately accounted for in both GPSG and LFG.
Probing microtubule polymerisation state at single kinetochores during metaphase chromosome motion

ABSTRACT Kinetochores regulate the dynamics of attached microtubule bundles (kinetochore-fibres, K-fibres) to generate the forces necessary for chromosome movements in mitosis. Current models suggest that poleward-moving kinetochores are attached to depolymerising K-fibres and anti-poleward-moving kinetochores to polymerising K-fibres. How the dynamics of individual microtubules within the K-fibre relate to poleward and anti-poleward movements is poorly understood. To investigate this, we developed a live-cell imaging assay combined with computational image analysis that allows eGFP-tagged EB3 (also known as MAPRE3) to be quantified at thousands of individual metaphase kinetochores as they undergo poleward and anti-poleward motion. Surprisingly, we found that K-fibres are incoherent, containing both polymerising and depolymerising microtubules – with a small polymerisation bias for anti-poleward-moving kinetochores. K-fibres also display bursts of EB3 intensity, predominantly on anti-poleward-moving kinetochores, equivalent to more coherent polymerisation, and this was associated with more regular oscillations. The frequency of bursts and the polymerisation bias decreased upon loss of kinesin-13, whereas loss of kinesin-8 elevated polymerisation bias. Thus, kinetochores actively set the balance of microtubule polymerisation dynamics in the K-fibre while remaining largely robust to fluctuations in microtubule polymerisation.
Modeling BPEL-Based Collaborations with Heterogeneous IoT Devices

A number of different IoT devices are already available and newer types would appear on the market. A technical difficulty in IoT computing is to manage the heterogeneity of IoT devices in terms of their network protocol supported, interface language, data exchange scheme, and type of mobility provided. Business Process Execution Language (BPEL) has been effectively utilized to collaborate multiple services by defining a business workflow. Due to the effectiveness of BPEL, a natural attempt to effectively develop IoT applications with complex workflows would be the adoption of BPEL and integrating IoT devices in BPEL-based collaborations. However, there exists two technical challenges, (1) Mismatch between BPEL collaboration models and IoT services and (2) heterogeneity of IoT devices. In this paper, we propose an effective method to remedy the technical challenges by presenting Adapters between BPEL documents and IoT services. With our method, heterogeneous IoT services can be seamlessly collaborated with the BPEL-based collaborations.