Miracle-dz/UniFAR-FTUs huggingface dataset:
An Airborne Conflict Resolution Approach Using a Genetic Algorithm

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.
WBAN CLIENT VERIFICATION USING REMOTE ANONYMOUS AUTHENTICATION SCHEMES WITHOUT CERTIFICATES

In this paper, communication between Client and Application provider should be authentic; so two protocols are used for authentication which is trivial and coherent performed through Network manager (NM). The authentication success between WBAN client and Application provider (AP) is used to activate users of WBAN in distant to access services such as healthcare, medical applications, military application etc, unknowingly. To design this authentication protocols a novel certificate less scheme with signature (CLS) is developed. Its computed, coherent, verified and secured. Service providers or Application providers do not have any advantage to reveal the private information of users. Private Key generator (PKG) served by Network manager cannot imitate legal users. Theoretic analysis, experimental simulations and comparative studies are conducted and evaluated. Computation cost, implementation & security gives better tradeoffs between two authenticated protocols. Comparison of running time gives better performance and results.
Soil Carbon and Nutrient Dynamics Following Cessation of Anthropogenic Disturbances in Degraded Subtropical Forests

Soil carbon (C) and nutrient availability is fundamental to terrestrial biodiversity and functionality. In recent decades, the restoration of degraded forests has become a major concern worldwide, and recent studies have demonstrated that soil C and nitrogen (N) increase over time following restoration. However, our understanding of the responses of soil phosphorus (P), calcium (Ca), potassium (K) and magnesium (Mg) and elemental stoichiometric ratios to restoration remains elusive. We employed a chronosequence to examine the responses of C and macronutrients in three soil layers (0–10 cm, 10–20 cm and 20–30 cm) to the time (0 to 31 years) since the cessation of anthropogenic disturbances in the degraded subtropical forests of Eastern China. We found that stand basal area, soil water content, organic matter content and C concentration and stock increased, while soil pH and bulk density decreased over time, with the most pronounced effects within the 0–10 cm layer. Total and available N and K as well as available P and total Mg increased, while total P in the 20–30 cm layer and Ca in all soil layers decreased over time. The mass concentration ratios of soil C to total N and available N, P and K decreased, whereas those for C to total P and Ca as well as total N to total P and available N to available P increased over time. Our results suggest that soil C accumulation and biologically driven nutrients increase, while nutrients that are driven primarily through geochemical mechanisms decrease with live biomass accumulation following restoration. Copyright © 2017 John Wiley & Sons, Ltd.
A Scalable, Flexible Augmentation of the Student Education Process Anonymous MAIS Submission

We present a novel intelligent tutoring sys-tem, which builds upon well-established hypotheses in educational psychology and incorporates them inside of a scalable software architecture. Speciﬁcally, we build upon the beneﬁts of knowledge vocalization (Ausubel, 1961), parallel learning (Topping, 1996), and immediate feedback (Samuels and Wu, 2003) in the context of student learning. Driven by free, online resources, our work scales easily in terms of class size while still operating at the granularity of individual students. Additionally, we allow teachers to retain full con-trol of the outputs, and provide student statistics to help them better steer their classroom discussions. Our experiments show promising results, and cement our hypothesis that the sys-tem is ﬂexible enough to serve a wide variety of purposes in a classroom setting.
Quarterly seasonality and the relative performance of momentum strategies

Momentum strategies for US equities exhibit quarterly return seasonality, where average long-short strategy returns are 2-3% higher in the third month of the calendar quarter than in the first month. We document quarterly seasonality as a feature of momentum strategies based on prior returns, earnings, industries, and investment styles in US and foreign markets. We observe that seasonality is largest for strategies that form portfolios using prior returns and more recent performance, driven by the incentives of institutional investors to purchase winning stocks at the end of the quarter. Quarterly seasonality also increases the monthly standard deviation and can explain why some momentum strategies produce superior results in asset pricing tests. Rowan University, Rohrer College of Business. Email: folkinshteyn@rowan.edu, moorejs@rowan.edu. We are grateful to Ron Kaniel, Robert Novy-Marx, Bryce Schonberger, Mihail Velikov, and Hao Zou for helpful comments and suggestions.
Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seen in vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels were returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury. Author Summary In this study, we study the impact of neuronal degeneration – a process that commonly occurs after traumatic injury and neurodegenerative disease – on the neuronal dynamics in a cortical network. We create computational models of neural networks and include spike timing plasticity to alter the synaptic strength among connections as networks remodel after simulated injury. We find that spike-timing dependent plasticity helps recover the neural dynamics of an injured microcircuit, but it frequently cannot recover the original oscillation dynamics in an uninjured network. In addition, we find that selectively injuring excitatory neurons with the highest firing rate reduced the neuronal oscillations in a circuit much more than either random deletion or the removing neurons with the lowest firing rate. In all, these data suggest (a) plasticity reduces the consequences of neurodegeneration and (b) losing the most active neurons in the network has the most adverse effect on neural oscillations.
Event-based Hyperspace Analogue to Language for Query Expansion Conference Item Event-based Hyperspace Analogue to Language for Query Expansion

(2010). Event-based hy-perspace analogue to language for query expansion. Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. Abstract Bag-of-words approaches to information retrieval (IR) are effective but assume independence between words. The Hy-perspace Analogue to Language (HAL) is a cognitively motivated and validated semantic space model that captures statistical dependencies between words by considering their co-occurrences in a surrounding window of text. HAL has been successfully applied to query expansion in IR, but has several limitations, including high processing cost and use of distribu-tional statistics that do not exploit syntax. In this paper, we pursue two methods for incorporating syntactic-semantic information from textual 'events' into HAL. We build the HAL space directly from events to investigate whether processing costs can be reduced through more careful definition of word co-occurrence, and improve the quality of the pseudo-relevance feedback by applying event information as a constraint during HAL construction. Both methods significantly improve performance results in comparison with original HAL, and interpolation of HAL and relevance model expansion outperforms either method alone.
e-Partnerships: Library information acquisition in the comfort of students’ digital homes

: This paper presents a case study of how a university lecturer and a faculty librarian built upon their shared concerns about first-year students’ lack of information literacy. It describes in some detail their underlying beliefs, which led to this experimental intervention design and the strategic e-partnership. Embedded library podcasts were used to push library information to students. The potential and actual impact of this e-partnership on personalised support for a diverse student body is explored and an argument is presented for e-partnerships among university staff in an effort to better assist first-year higher education students.
EmbRace: Accelerating Sparse Communication for Distributed Training of NLP Neural Networks

Distributed data-parallel training has been widely used for natural language processing (NLP) neural network models. However, the embedding tables in NLP models, holding a large portion of parameters and bringing dramatic sparsity in communication, make it a big challenge to efficiently scale the distributed training. Current distributed training frameworks mainly concentrate on dense models but neglect the sparsity of NLP models, resulting in significant communication overhead and relatively poor scalability. In this paper, we propose EmbRace, an efficient communication framework designed to accelerate sparse communication of distributed NLP model training. EmbRace introduces Sparsity-aware Hybrid Communication, which combines AlltoAll and AllReduce to optimize the communication overhead for sparse and dense data in NLP models. EmbRace further introduces a 2D Communication Scheduling approach to thoroughly overlap communication with computation by optimizing model computation procedure, relaxing the dependency of embeddings, and scheduling communication with a priority queue. We implement EmbRace based on PyTorch and Horovod, and conduct comprehensive evaluations with four representative NLP models on two high-performance GPU clusters. Experimental results show that EmbRace achieves up to 30.66× speedup on 16 GPUs clusters among four popular distributed training baselines
From predictive models to cognitive models: Separable behavioral processes underlying reward learning in the rat

Cognitive models are a fundamental tool in computational neuroscience, embodying in software precise hypotheses about the algorithms by which the brain gives rise to behavior. The development of such models is often a hypothesis-first process, drawing on inspiration from the literature and the creativity of the individual researcher to construct a model, and afterwards testing the model against experimental data. Here, we adopt a complementary approach, in which richly characterizing and summarizing the patterns present in a dataset reveals an appropriate cognitive model, without recourse to an ​ a priori hypothesis. We apply this approach to a large behavioral dataset from rats performing a dynamic reward learning task. The revealed model suggests that behavior in this task can be understood as a mixture of three components with different timescales: a quick-learning reward-seeking component, a slower-learning perseverative component, and a very slow “gambler’s fallacy” component.
DESIGNING CAPABILITY MATURITY MODEL FOR AGILE TRANSFORMATION EXCELLENCE

As organisations are challenged with volatility, uncertainty, complexity and ambiguity (VUCA), developing Agile capabilities becomes first priority for most organisations to innovate and disrupt entire markets. A great number of companies, however, face significant challenges to effectively manage organisational change while developing Agile capabilities. In this paper-in-progress the author adopts Design Science approach and develops an artefact Capability Maturity Model (CMM), which promises to solve abovementioned challenges. Although maturity models (MMs) are an established instrument to devise development paths, currently available Maturity Models often focus on the control of certain capabilities (doing things right) rather than on developing the necessary capabilities in a sequence appropriate for a given type of organization (doing the right things) (Winter, R., Aier, S., 2019). Therefore, the proposed artefact will focus on devising capability development sequences that correspond to organisational learning, rather than control levels. In particular, such management tool will help organisations (1) to assess the current maturity of their Corporate Agility capabilities, (2) to create a basis for defining their target state, (3) to enable fact-based communication with a large group of stakeholders concerning organisation’s agility transformation, and (4) to derive concrete measures (roadmap) for directed improvement of the capabilities.
Effects of noise suppression and envelope dynamic range compression on the intelligibility of 3 vocoded sentences for a tonal language 4 5

Vocoder simulation studies have suggested that the carrier signal type employed affects 31 the intelligibility of vocoded speech. The present work further assessed how carrier signal type 32 interacts with additional signal processing, namely, single-channel noise suppression and envelope 33 dynamic range compression, in determining the intelligibility of vocoder simulations. In 34 Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) 35 or two-talker babble (2TB) were processed by one of four single-channel noise-suppression 36 algorithms before undergoing tone(TV) or noise-vocoded (NV) processing. In Experiment 2, 37 dynamic ranges of multiband envelope waveforms were compressed by scaling of the 38 mean-removed envelope waveforms with a compression factor before undergoing TV or NV 39 processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) 40 listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed 41 vocoded speech depended on the masker type (SSN vs. 2TB). NV speech was more negatively 42 influenced by envelope dynamic range compression than was TV speech. These findings suggest 43 that an interactional effect exists between the carrier signal type employed in the vocoding process 44 and envelope distortion caused by signal processing. 45
Intelligent Tutoring Systems for Continuous , Embedded Assessment

This chapter describes how intelligent tutoring systems (ITS) can be used for assessment. It begins by describing intelligent tutoring systems and distinguishing them from an older, more widely available technology. It argues that this newer technology has some intrinsic benefits as a tool for assessment. It then describes a particular type of assessment widely used in the ITS community. The assessment technique is based on a mathematically sound inference method, Bayesian belief networks. The basic technique, an adaptation of Knowledge Tracing (Corbett & Anderson, 1995), is described along with several recent extensions.
Eeciently Ordering Query Plans for Data Integration

We describe Streamer, the query-reformulation component of a data integration system. Given a utility measure and a user query, Streamer uses abstraction-based reenement planning and exploits information on plan independence to produce, in decreasing order of utility, a set of plans that access data sources to obtain answers to the query. We then focus on plan coverage as an important utility measure. We show how to use statistic information about the domain and data sources to estimate plan coverage, and how to incorporate the plan-coverage framework into Streamer. In doing so, we provide the rst method for eeectively integrating the use of quantitative information into the query optimizer of a data-integration system. We present preliminary experimental results suggesting that Streamer runs an order of magnitude faster than brute-force plan-ordering methods, which are the only currently available methods to compute exact plan orderings. Finally, we propose methods to make Streamer scalable to large domains .
Tag-Based Cooperation in Peer-to-Peer Networks with Newscast 1

Recent work has proposed how socially inspired mechanisms, ba ed on “tags” and developed within social science simulations, might be applied in peer-to-peer overlay networks to maintain high cooperatio n between peers even when they act selfishly. The proposed mechanism involves a dy n mic re-wiring algorithm called “SLAC”. The algorithm assumes a random sampl ing service over the entire population of nodes but does not implement this it self. In this paper we re-implement SLAC on an open source peer-to-peer simulat ion testbed called “PEERSIM”. For the random sampling service we utilize an exi sting protocol called “NEWSCAST”. We present the results of some experimen ts we performed in which peers play the Prisoner’s Dilemma game with their ne ghbours. Our results demonstrate that SLAC augmented with NEWSCAST produc es high levels of cooperation. This increases our confidence that previous results from SLAC are generally applicable and valid and also that SLAC could have applications in real implemented systems. Finally we discuss the open issues tha t need to be addressed for SLAC to progress to a valuable deployable protocol.
On observing emergent properties and their compositions

of the original article: A set of hypotheses is formulated for a connectlonist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition.
Modeled sensitivity of Lake Michigan productivity and zooplankton to changing nutrient concentrations and quagga mussels

The recent decline in Lake Michigan productivity is often attributed to filter feeding by invasive quagga mussels, but some studies also implicate reductions in lakewide nutrient concentrations. We use a 3‐D coupled hydrodynamic‐biogeochemical model to evaluate the effect of changing nutrient concentrations and quagga mussel filtering on phytoplankton production and phytoplankton and zooplankton biomass. Sensitivity experiments are used to assess the net effect of each change separately and in unison. Quagga mussels are found to have the greatest impact during periods of isothermal mixing, while nutrients have the greatest impact during thermal stratification. Quagga mussels also act to enhance spatial heterogeneity, particularly between nearshore‐offshore regions. This effect produces a reversal in the gradient of nearshore‐offshore productivity: from relatively greater nearshore productivity in the prequagga lake to relatively lesser nearshore productivity after quaggas. The combined impact of both processes drives substantial reductions in phytoplankton and zooplankton biomass, as well as significant modifications to the seasonality of surface water pCO2, particularly in nearshore regions where mussel grazing continues year‐round. These results support growing concern that considerable losses of phytoplankton and zooplankton will yield concurrent losses at higher trophic levels. Comparisons to observed productivity suggest that both quagga mussel filtration and lower lakewide total phosphorus are necessary to accurately simulate recent changes in primary productivity in Lake Michigan.
Extract Object Boundaries in Noisy Images using Level Set

Finding object contours in noisy images is a challenging task because of the amorphous nature of the object and the lack of sharp boundaries. Classical edge-based segmentation methods have the drawback of not connecting edge segments to form a distinct and meaningful boundary. Many level set approaches, which can deal with changes of topology and the presence of corners, have been developed to extract object boundaries. Previous researchers have used image gradient, edge strength, area minimization and region intensity to define the speed function. However, no paper mentions the edge/gradient direction. Our approach will incorporate direction and magnitude in the speed function.