Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Training Of Neural Trans-Dimensional Random field Language Models with Dynamic Noise-Contrastive Estimation

A new whole-sentence language model - neural trans-dimensional random field language model (neural TRF LM), where sentences are modeled as a collection of random fields, and the potential function is defined by a neural network, has been introduced and successfully trained by noise-contrastive estimation (NCE). In this paper, we extend NCE and propose dynamic noise-contrastive estimation (DNCE) to solve the two problems observed in NCE training. First, a dynamic noise distribution is introduced and trained simultaneously to converge to the data distribution. This helps to significantly cut down the noise sample number and reduce the training cost over NCE. Second, DNCE discriminates between sentences generated from the noise distribution and sentences generated from the interpolation of the data distribution and the noise distribution. This alleviates the overfitting problem caused by the sparseness of the training set. With DNCE, we can successfully and efficiently train neural TRF LMs on large corpus (about 0.8 billion words) with large vocabulary (about 568 K words). Neural TRF LMs perform as good as LSTM LMs with less parameters and being 5x~114x faster in rescoring sentences. Interpolating neural TRF LMs with LSTM LMs and n-gram LMs can further reduce the error rates.
Error analysis and topology modifications of a self-organizing incremental neural network

This paper proposes a mathematical analysis and topological modifications of an online learning method, self-organizing incremental neural network. First, based on investigating its network learning process, the asymptotic mean squared error is derived and the differences in topological structure of its several versions are explained theoretically. Second, considering its shortcomings in determining appropriate threshold regions and inferring insignificant edges, we 1) adopt Gaussian process with an adaptive RBF kernel to generate the probability over the function space of the mapping from the underlying local distribution to neighbouring connection relationships and update the threshold region of each node, and 2) propose a novel model of generalized hyper-geometric ensembles with adaptive dyadic propensities and a modified non-parametric support vector clustering approach. The experimental results on both synthetic data and real-world data sets show that our method generates more even networks with fewer representative nodes and achieves remarkable improvements in terms of accuracy and robustness for complicatedly distributed big data with noise.
Conditions Associated with Essential Tremor in Veterans: A Potential Role for Chronic Stress

Background Increased depression, hearing loss, dementia, alcoholism, and mortality in essential tremor patients remain unexplained. We investigated whether conditions associated with tremor are linked to chronic stress. Methods The FY2013 Veterans Affairs database was queried for 38 selected dual diagnosis combinations in 5,854,223 veterans aged 21–95 years. Results Post-traumatic stress disorder, anxiety, and depression were the most common psychiatric diagnoses in tremor patients, with the odds ratio exceeding 2 in all 15-year cohorts. Depending on age, patients with essential tremor were more likely than those without to have obsessive–compulsive disorder, bipolar illness, schizophrenia, use tobacco and abuse alcohol, have hypertension, obesity, hyperlipidemia, diabetes, vitamin D deficiency, coronary and cerebrovascular diseases, congestive heart failure, stroke, asthma, hypothyroidism, irritable bowel syndrome, renal insufficiency, alcoholic liver disease, hearing loss, glaucoma, macular degeneration, migraine, epilepsy, idiopathic polyneuropathy, history of head trauma, and ‘Alzheimer’s dementia. In contrast, lung and colorectal cancer, amyotrophic lateral sclerosis, psychostimulant abuse, and rheumatoid arthritis were not more common. Discussion Post-traumatic stress disorder, anxiety, and depression, strongly associated with essential tremor, are known risk factors for poor health habits, tobacco use and alcohol abuse; collectively these are risk factors for vascular disease, with further negative health consequences for multiple organ systems. As essential tremor is associated with all these conditions, we propose that chronic stress is not only responsible for the conditions associated with tremor but in some cases itself directly and indirectly induces essential tremor, so that tremor and poor health share a common cause.
Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations

ABSTRACT Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but suffer from approximating a potentially indefinite Hessian with a positive-definite matrix. Hessian-free methods leverage the ability to perform Hessian-vector multiplication without needing the entire Hessian matrix, but each iteration's complexity is significantly greater than quasi-Newton methods. In this paper we propose an alternative approach for solving ML problems based on a quasi-Newton trust-region framework for solving large-scale optimization problems that allow for indefinite Hessian approximations. Numerical experiments on a standard testing data set show that with a fixed computational time budget, the proposed methods achieve better results than the traditional limited-memory BFGS and the Hessian-free methods.
A framework for dynamic inter-device task dispatch with eventual consistency

The Internet of Things (IoT) allows various things like mobile devices and electronic appliances to communicate over network. Inter-device apps can share data between devices and dispatch specific tasks to other devices to utilize their resources. The prevalence of JavaScript web apps that can run anywhere providing any browsers opens the gate to unanticipated interactions between devices. However, the current techniques require developers construct tasks to dispatch statically with strong consistency, and they do not provide any disciplined way to develop inter-device apps. In this paper, we propose IDTD (Inter-Device Task Dispatch), a framework that allows developers to construct and dispatch tasks into multiple devices dynamically with eventual consistency in a systematic manner. We provide a high-level architecture of IDTD, prove the soundness and eventual consistency of the framework, and present its practical usability.
Quantifying the Gain of Multi-Connectivity in Wireless LAN

Multi-Connectivity (MC) using multi-link diversity is a promising approach to enable Ultra-Reliable Low-Latency Communications. While recent analytical results about MC already indicate improved reliability compared to a single link, we are interested in the actual gain of implementing MC into an established wireless standard. Therefore, we evaluate inter-frequency MC over independent fading channels for the physical layer of Wireless LAN (WLAN). We compare three combining schemes, namely Selection Combining (SC), Maximal Ratio Combining (MRC), and the recently emerging Joint Decoding (JD), by their respective packet error rates in Monte-Carlo simulations. Our results show that MC outperforms a single link for all of the three combining schemes, even if the total transmit power is not increased but shared among the available links. JD performs best and is found to be considerably less dependent on SNR estimation than MRC and SC. We show that packet synchronization in WLAN causes an error floor in frequency-selective channels which can be effectively mitigated by MC.
Design of a New Stream Cipher: PARS

In this paper, a new stream cipher is designed as a clock-controlled one, but with a new mechanism of altering steps based on system theory in such a way that the structures used in it are resistant to conventional attacks. Our proposed algorithm (PALS) uses the main key with the length of 256 bits and a 32-bit message key. The most important criteria considered in designing the PALS are resistance to known attacks, maximum period, high linear complexity, and good statistical properties. As a result, the output keystream is very similar to the perfectly random sequences and resistant to conventional attacks such as correlation attacks, algebraic attack, divide & conquer attack, time-memory tradeoff attack and AIDA/cube attacks. The base structure of the PALS is a clock-controlled combination generator with memory and we obtained all the features according to design criteria with this structure. PALS can be used in many applications, especially in financial cryptography due to its proper security features
Self-Adaptation Approaches for Energy Efficiency: A Systematic Literature Review

The increasing energy demands of software systems have set an essential software quality requirement: energy efficiency. At the same time, the many contextual changes faced by software systems during execution can hamper their functionality and overall quality. To address both problems, self-adaptation approaches can empower software systems, at both design-time and runtime, to adapt to dynamic conditions. In this way, software systems can be more resilient to failure, hence more trustful to satisfy the demands of modern digital society. In this paper, we perform a systematic literature review to study the state-of-the-art on existing self-adaptation approaches for energy efficiency. We analyze the identified approaches from three different perspectives, namely publication trends, application domains, and types of software systems. Our findings can help solution providers to make guided decisions to enable self-adaptability in designing and engineering software systems.
On the future of research VMs: a hardware/software perspective

In the recent years, we have witnessed an explosion of the usages of Virtual Machines (VMs) which are currently found in desktops, smartphones, and cloud deployments. These recent developments create new research opportunities in the VM domain extending from performance to energy efficiency, and scalability studies. Research into these directions necessitates research frameworks for VMs that provide full coverage of the execution domains and hardware platforms. Unfortunately, the state of the art on Research VMs does not live up to such expectations and lacks behind industrial-strength software, making it hard for the research community to provide valuable insights. This paper presents our work in attempting to tackle those shortcomings by introducing Beehive, our vision towards a modular and seamlessly extensible ecosystem for research on virtual machines. Beehive unifies a number of existing state-of-the-art tools and components with novel ones providing a complete platform for hardware/software co-design of Virtual Machines.
Dynamic Security Analysis of Power Systems by a Sampling-Based Algorithm

Dynamic security analysis is an important problem of power systems on ensuring safe operation and stable power supply even when certain faults occur. No matter if such faults are caused by vulnerabilities of system components, physical attacks, or cyber-attacks that are more related to cyber-security, they eventually affect the physical stability of a power system. Examples of the loss of physical stability include the Northeast Blackout of 2003 in North America and the 2015 system-wide blackout in Ukraine. The nonlinear hybrid nature, that is, nonlinear continuous dynamics integrated with discrete switching, and the high degree of freedom property of power system dynamics make it challenging to conduct the dynamic security analysis. In this article, we use the hybrid automaton model to describe the dynamics of a power system and mainly deal with the index-1 differential-algebraic equation models regarding the continuous dynamics in different discrete states. The analysis problem is formulated as a reachability problem of the associated hybrid model. A sampling-based algorithm is then proposed by integrating modeling and randomized simulation of the hybrid dynamics to search for a feasible execution connecting an initial state of the post-fault system and a target set in the desired operation mode. The proposed method enables the use of existing power system simulators for the synthesis of discrete switching and control strategies through randomized simulation. The effectiveness and performance of the proposed approach are demonstrated with an application to the dynamic security analysis of the New England 39-bus benchmark power system exhibiting hybrid dynamics. In addition to evaluating the dynamic security, the proposed method searches for a feasible strategy to ensure the dynamic security of the system in the face of disruptions.
Groundwater Depth and Soil Properties Are Associated with Variation in Vegetation of a Desert Riparian Ecosystem in an Arid Area of China

Groundwater is a major driving force for plant community distribution in arid areas worldwide. Although it is well known that groundwater has a significant impact on soil and vegetation, there is little information on how groundwater depth affects soil and vegetation in an arid inland basin desert riparian ecosystem. Therefore, quantitative analysis of the relationships among groundwater depth, soil properties and plant community distribution is necessary. A desert riparian ecosystem in the lower reaches of the Heihe River in an arid area of Northwest China was used to determine quantitative relationships among groundwater depth, soil and vegetation. Groundwater depth significantly increased with increased distance from the river. Soil and vegetation characteristics showed a significant trend with increasing groundwater depth. With increasing groundwater depth, soil water content, soil total nitrogen, soil total carbon, soil available phosphorus and soil available potassium decreased, while the soil bulk density and soil carbon:nitrogen (C:N) ratio increased. Soil pH and soil electrical conductivity followed quadratic function relationships with groundwater depth. Species richness, aboveground biomass, community coverage, community height, foliage projective cover and leaf area index all significantly decreased with increased groundwater depth. Groundwater depth and soil were associated with vegetation variance, explaining 85.8% of the vegetation variance. Groundwater depth was more important in explaining vegetation variance than soil properties (soil bulk density) and soil pH. Our observations indicate that changes in groundwater depth would have a significant influence on desert riparian forest vegetation, and that maintaining appropriate groundwater depth is necessary to preserve the riparian ecosystem.
Metabolic Risk Factors of Alzheimer's Disease, Dementia with Lewy Bodies, and Normal Elderly: A Population-Based Study

Background Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) share many risk factors. Evidence suggests that metabolic risk factors are important to AD; however, their association with DLB is unclear. The risk of cardiovascular diseases (CVD) associated with AD and DLB is also uncertain. Thus, this nationwide, population-based study was designed to evaluate the metabolic and CVD risks in AD and DLB. Materials and Methods Data were obtained from the Taiwan National Health Insurance Research Database. AD patients, DLB patients, and normal control (NC) individuals from 1996 to 2013 were enrolled for risk assessment. Results In total, 7544 NC individuals, 1324 AD patients, and 562 DLB patients were enrolled. Participants with one or more metabolic risk factors had significantly higher odds of AD or DLB. No significant differences in metabolic risk factors were observed between DLB and AD patients. AD patients had a lower risk of CVD (aHR = 0.67, 95% CI = 0.59–0.76, p value < 0.001) and coronary artery disease (CAD) (aHR = 0.59, 95% CI = 0.51–0.69, p value < 0.001) than NC. DLB patients had a higher risk of ischemic stroke (aHR = 2.27, 95% CI = 1.68–3.06, p value < 0.001) than NC. Conclusion Metabolic risk factors are important in AD and DLB. Patients with AD might have a lower risk of CAD and ischemic strokes. Patients with DLB might have a higher risk of ischemic stroke.
Identification of fruit fly in intelligent traps using techniques of digital image processing and machine learning

The integrated pest management (IPM) is an approach that seeks to keep pests always below the level at which they cause damage to crops. A technique widely employed in monitoring the population of pests is based on the use of traps and consists in regular field visits to make visual observations of this traps by a human operator. This method is intensive, unhealthy and costly for the operator. Based on this context, this work aims to take the first steps into the development of an intelligent trap, with its scope focused on developing a classification system of the insects Ceratitis capitata and Anastrepha fraterculus, using digital image processing and machine learning techniques. In this work, was built a dataset of collected images consisting of 99 images in which 4338 regions were extracted. Each one of these regions has been transformed into a feature vector based on the bag-of-words model. For the classification, the algorithms support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT) and Gaussian Naive Bayes (GNB) were used. SVM performed better than other approaches, achieving an accuracy of 86.38%. Due to its satisfactory accuracy, it is possible to state that it is feasible to classify different insects in intelligent traps.
A quality control method for fraud detection on utility customers without an active contract

Fraud detection in energy consumption has proven to be a difficult problem for current techniques. In general, the approaches used in this area are restricted to compute a fraud score for each client based on its behaviour. The problem gets much more complicated on customers with no contract, since the company does not have enough information from them to compute an accurate profile. On this paper, we introduce a semi-autonomous method that combines different machine learning algorithms and human knowledge to alleviate the lack of information to build a framework that detects fraud nimbly.
User Behavior Analysis and Video Popularity Prediction on a Large-Scale VoD System

Understanding streaming user behavior is crucial to the design of large-scale Video-on-Demand (VoD) systems. In this article, we begin with the measurement of individual viewing behavior from two aspects: the temporal characteristics and user interest. We observe that active users spend more hours on each active day, and their daily request time distribution is more scattered than that of the less active users, while the inter-view time distribution differs negligibly between two groups. The common interest in popular videos and the latest uploaded videos is observed in both groups. We then investigate the predictability of video popularity as a collective user behavior through early views. In the light of the limitations of classical approaches, the Autoregressive-Moving-Average (ARMA) model is employed to forecast the popularity dynamics of individual videos at fine-grained time scales, thus achieving much higher prediction accuracy. When applied to video caching, the ARMA-assisted Least Frequently Used (LFU) algorithm can outperform the Least Recently Used (LRU) by 11--16%, the well-tuned LFU by 6--13%, and the LFU is only 2--4% inferior to the offline LFU in terms of hit ratio.
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition

We present a bootstrapping framework to simultaneously improve multi-person tracking and activity recognition at individual, interaction and social group activity levels. The inference consists of identifying trajectories of all pedestrian actors, individual activities, pairwise interactions, and collective activities, given the observed pedestrian detections. Our method uses a graphical model to represent and solve the joint tracking and recognition problems via multi-stages: (1) activity-aware tracking, (2) joint interaction recognition and occlusion recovery, and (3) collective activity recognition. We solve the where and when problem with visual tracking, as well as the who and what problem with recognition. High-order correlations among the visible and occluded individuals, pairwise interactions, groups, and activities are then solved using a hypergraph formulation within the Bayesian framework. Experiments on several benchmarks show the advantages of our approach over state-of-art methods.
Human uncertainty and ranking error: fallacies in metric-based evaluation of recommender systems

One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or explicit knowledge, either by observing user interactions, or by asking users directly. But these sources of information are always subject to the volatility of human decisions, making the so gathered data uncertain to a particular extent. In this contribution, we elaborate on the impact of this human uncertainty when it comes to comparative assessments of different recommender systems. In particular, we reveal two problems: (1) biasing effects on various metrics of model-based prediction and (2) the propagation of uncertainty and its thus induced error probabilities for algorithm rankings. For this purpose, we introduce a probabilistic view and prove the existence of those problems mathematically, as well as provide possible solution strategies.
Competence, Collaboration, and Time Management: Barriers and Recommendations for Crowdworkers

Software crowdsourcing development requires a continuous influx of crowdworkers for their continuity. Crowdworkers should be encouraged to play an important role in the online communities by being active members, but they face difficulties when attempting to participate. For this reason, in this paper, we investigated the difficulties that crowdworkers face in crowdsourcing software development platforms. To achieve this, we conducted a study relying on multiple data sources and research methods including literature review, peer review, field study, and procedures of grounded theory. We observed that crowdworkers face many barriers – related to competence, collaboration, and time management – when making their contributions in software crowdsourcing development, which can result in dropouts. The main contributions of this paper are: a) empirical identification of barriers faced by crowdsourcing software development crowdworkers, and b) recommendations on how to minimize the barriers.