Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Loss differentiation Algorithm for differentiation of non-congestion losses from congestion losses for improving TCP Performance over wireless networks

Presented in this paper is the simulation based study of the TCP performance issues over wireless network. The paper includes description of various LDA algorithms with its strengths and weaknesses. The author has implemented LDA scheme named spike scheme used for discriminating between wireless and congestion losses at TCP The entire discussion open up the grey area where an effort can be put up to further improve TCP performance particularly in presence of wireless losses.
The Acceptance of Energy Monitoring Technologies - the Case of Local Prosumers

With transformations happening in the electricity sector, we need to ensure consumers have access to updated and correct information to accompany such changes. Consumers need to understand technologies available to them but also, learn how to use them to optimize their personal investment in such types of equipment. In this paper, we explore how a group of local prosumers has adopted energy monitoring technologies, their dayto-day strategies, and expectations when handling such systems. We studied 11 prosumers and the technologies they have used for three years, evaluated their satisfaction with the feedback provided and analyzed how a more modern visualization of their energy practices was introduced and adopted into their daily lives. We conducted interviews and questionnaires to evaluate their engagement with these tools. This initial work suggests this particular group of users have already a high level of knowledge about their systems, and as a result have integrated these into their routines. However, more support would be needed from other local actors to help them reach more benefits and as such, more satisfaction as consumers. We conclude by reflecting on barriers that need to be addressed to increase user satisfaction with these systems.
Optimizing Automated Term Extraction for Terminological Saturation Measurement

Assessing the completeness of a document collection, within a domain of interest, is a complicated task that requires substantial effort. Even if an automated technique is used, for example, terminology saturation measurement based on automated term extraction, run times grow quite quickly with the size of the input text. In this paper, we address this issue and propose an optimized approach based on partitioning the collection of documents in disjoint constituents and computing the required term candidate ranks (using the c-value method) independently with subsequent merge of the partial bags of extracted terms. It is proven in the paper that such an approach is formally correct – the total c-values can be represented as the sums of the partial c-values. The approach is also validated experimentally and yields encouraging results in terms of the decrease of the necessary run time and straightforward parallelization without any loss in quality.
A Low Carbon Kubernetes Scheduler

A major source of global greenhouse gas emissions is the burning of fossil fuels for the generation of electricity. The portion of electricity generated from fossil fuel varies across regions, and within a region with demand for electricity and the availability of renewable energy sources. Cloud providers operate data centres in locations around the planet. And certain kind of server computation can tolerate migrating between data centres. In this paper we describe the design and implementation of a low carbon scheduling policy for the open-source Kubernetes container orchestrator. We apply this scheduler in a form of demand side management by migrating consumption of electric energy to countries with the lowest carbon intensity of electricity. The primary contributions of this text are (i) the scheduler’s design, which provides a generic model for optimising workload placement in regions with the lowest carbon intensity (ii) an evaluation of its performance in a case study with a major public cloud provider (iii) an implementation of a demand side management solution that consumes electricity where, instead of when, grid carbon intensity is lowest.
Finding a Strong Key Point Correspondences in Large-Scale Images and Depth Maps

Depth map fusion is an essential task in multi-view stereo vision and structure from motion methods, but there are some issues of the process of depth maps matching of large-scale outdoor scenes, which are acquired by unmanned aerial vehicles. First, they can have low depth resolution due to long distance from a scene to a camera, in the second, as a consequence of above problem and some camera defects, they can have some noise. In our work we propose an approach of matching the depth maps based on a local shape and a color similarity correspondences. The proposed idea is to ﬁnd similar regions by calculating local key points in small patches of two images by ORB feature detector. Then we ﬁnd corresponding regions assuming spatial proximity of movement vectors of key points, this is done by the search of the main vector direction and length in feature space. We test our approach with the help of video sequences acquired through a consumer multicopter.
Automatic interface of cross-modal nonverbal interactions in multiparty conversation

A novel probabilistic framework is proposed for analyzing cross-modal nonverbal interactions in multiparty face-to-face conversations. The goal is to determine “who responds to whom, when, and how” from multimodal cues including gaze, head gestures, and utterances. We formulate this problem as the probabilistic inference of the causal relationship among participants’ behaviors involving head gestures and utterances. To solve this problem, this paper proposes a hierarchical probabilistic model; the structures of interactions are probabilistically determined from high-level conversation regimes (such as monologue or dialogue) and gaze directions. Based on the model, the interaction structures, gaze, and conversation regimes, are simultaneously inferred from observed head motion and utterances, using a Markov chain Monte Carlo method. The head gestures, including nodding, shaking and tilt, are recognized with a novel Wavelet-based technique from magnetic sensor signals. The utterances are detected using data captured by lapel microphones. Experiments on four-person conversations confirm the effectiveness of the framework in discovering interactions such as question-and-answer and addressing behavior followed by back-channel responses.
Document Segmentation Labeling Techniques for Court Filings

Arguments, motions, and decisions in courts of the United States of America are recorded in PDF documents filed in each court’s docket. Utilization of these documents as data requires accurate and efficient information extraction methods. We take a supervised machine learning approach to a portion of this task, predicting metadata labels in court filings. On a dataset of about 2500 annotated scanned PDF images with 21 labels, we found that traditional classifiers such as MaxEnt achieved an average F1-score of 0.44 (micro-averaged across labels), with the highest label (Body) at 0.88. However, a 1-dimensional sequences in the text, Mallet’s CRF implementation, achieved an average F1-score of 0.6 across all labels, with some labels as high as 0.91. These results demonstrate the value of using sequence models over traditional classifiers in labeling the types of information in court filings. In: Proceedings of the Third Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2019), June 21, 2019. Montreal, QC, Canada. © 2019 Copyright held by the owner/author(s). Copying permitted for private and academic purposes. Published at http://ceur-ws.org. Approved for public release; distribution unlimited. Public Release Case Number 9-1137. © 2019 The MITRE Corporation. All Rights Reserved.
Understanding and expressing scalable concurrency

The Holy Grail of parallel programming is to provide good speedup while hiding or avoiding the pitfalls of concurrency. But some level in the tower of abstraction must face facts: parallel processors execute code concurrently, and the interplay between concurrent code, synchronization, and the memory subsystem is a major determiner of performance. Effective parallel programming must ultimately be supported by scalable concurrent algorithms —algorithms that tolerate (or even embrace) concurrency for the sake of scaling with available parallelism. This dissertation makes several contributions to the understanding and expression of such algorithms: • It shows how to understand scalable algorithms in terms of local protocols governing each part of their hidden state. These protocols are visual artifacts that can be used to informally explain an algorithm at a whiteboard. But they also play a formal role in a new logic for verifying concurrent algorithms, enabling correctness proofs that are local in space, time, and thread execution. Correctness is stated in terms of refinement: clients of an algorithm can reason as if they were using the much simpler specification code it refines. • It shows how to express synchronization in a declarative but scalable way, based on a new library providing "join patterns". By declarative, we mean that the programmer needs only to write down the constraints of a synchronization problem, and the library will automatically derive a correct solution. By scalable, we mean that the derived solutions deliver robust performance with increasing processor count and problem complexity. For common synchronization problems, join patterns perform as well as specialized algorithms from the literature. • It shows how to express scalable algorithms through reagents, a new monadic abstraction. With reagents, algorithms no longer need to be constructed from "whole cloth," i.e. by using system-level primitives directly. Instead, they are built using a mixture of shared-state and message-passing combinators. Concurrency experts benefit, because they can write libraries at a higher level, with more reuse, without sacrificing scalability. Their clients benefit, because composition empowers them to extend and tailor a library without knowing the details of its underlying algorithms.
Translation discovery using diverse similarity measures

Automatic translation between natural languages using empirical learning methods is an active research area. Much of the work in this area is concentrated on acquiring models of translation from expensive resources, including large databases containing hundreds of thousands of translated sentences, which are available for only a small number of the language pairs between which one might wish to translate. This dissertation describes the automatic acquisition of translations into English for unknown foreign language words given the lack of any high-quality translation learning resources, which is the reality for nearly all of the world's languages. The methods presented in this dissertation require minimal training supervision; the necessary inputs consist of only a few hundred thousand words of raw news text in a language of interest, such as Uzbek, and a translation dictionary into English for some related language, such as Turkish. We show how diverse measures of cross-language word similarity can be combined to learn translations for a considerable subset of the vocabulary of a language for which no translations are initially known. These similarities include statistical models of related-language cognate words, various cross-language comparisons of how words are used in monolingual text, and the distribution of terms in dated news articles. The ultimate result is a functional translation capability for new language pairs using minimal training resources and almost no human supervision.
Utility-Value-Based Data Delivery Scheme for Opportunistic Network and Its Performance Study

This paper proposes a utility-value-based forwarding protocol (UVBFP), which uses the history information of the met between nodes to predict the chance of connection in the future. Based on the prediction, our method builds a utility computation model for setting up the connection between the node and the goal node. In our approach, we first define the node connectivity, node correlation degree, node frequent degree, node intimacy, connection time interval degree, node similarity and node mobile connectivity degree. Then, we calculate the utility value of the node from these factors. Finally, based on the history information of the node, we sort out the network topology information of the neighbors, and select the best nest hop relay node for data forwarding. Simulation experiments show that UVBFP is cost effective and has high delivery ratio.
Differential Effects of Dry vs. Wet Heating of β-Lactoglobulin on Formation of sRAGE Binding Ligands and sIgE Epitope Recognition

The effect of glycation and aggregation of thermally processed β-lactoglobulin (BLG) on binding to sRAGE and specific immunoglobulin E (sIgE) from cow milk allergic (CMA) patients were investigated. BLG was heated under dry conditions (water activity < 0.7) and wet conditions (in phosphate buffer at pH 7.4) at low temperature (<73 °C) and high temperatures (>90 °C) in the presence or absence of the milk sugar lactose. Nε-(carboxymethyl)-l-lysine (CML) western blot and glycation staining were used to directly identify glycation structures on the protein fractions on SDS-PAGE. Western blot was used to specify sRAGE and sIgE binding fractions. sRAGE binding was highest under wet-heated BLG independent of the presence of the milk sugar lactose. Under wet heating, high-molecular-weight aggregates were most potent and did not require the presence of CML to generate sRAGE binding ligands. In the dry system, sRAGE binding was observed only in the presence of lactose. sIgE binding affinity showed large individual differences and revealed four binding profiles. Dependent on the individual, sIgE binding decreased or increased by wet heating independent of the presence of lactose. Dry heating required the presence of lactose to show increased binding to aggregates in most individuals. This study highlights an important role of heating condition-dependent protein aggregation and glycation in changing the immunogenicity and antigenicity of cow’s milk BLG.
Phytoplasma Infection could Affect Chemical Composition of Artemisia sieberi

Artemisia sieberi showing symptoms resembling those caused by phytoplasma were observed in Geno Mountain, Hormozgan Province, Iran, and were examined for phytoplasma presence by PCR assays. In addition, the essential oils hydrodistilled from the aerial parts of phytoplasma-infected and healthy plants have been analyzed and compared by GC and GC/MS. Phylogenetic and virtual RFLP analysis of the 16S rRNA gene sequences revealed that the phytoplasma associated with A. sieberi witches’ broom (AsWB) was a strain of ‘Candidatus Phytoplasma aurantifolia’. The presence of the disease, however, induced a further enrichment (from 4.9 to 45.2%, a relative increase of 90%) of the entire monoterpene class as compared to the abundance in healthy samples. Conversely, a matching decrease in monoterpenoid (from 48.7 to 2%, a relative decrease of 90.2%) was observed in the infected plants. Besides the first report of phytoplasma infection of A. sieberi, the changes of its essential oils are reported.
Anti-proteinase 3-positive Eosinophilic Granulomatosis with Polyangiitis Revealed by Cardiac Tamponade

Eosinophilic granulomatosis with polyangiitis (EGPA) is a rare systemic vasculitis characterized by asthma, eosinophilia, and diffuse eosinophilic infiltration. Although cardiovascular involvement is common and a leading cause of EGPA-related mortality, severe pericarditis-led cardiac tamponade occurs rarely. We herein report a 72-year-old man with anti-proteinase 3 (anti-PR3) anti-neutrophil cytoplasmic antibody (ANCA)-positive EGPA diagnosed by the presence of cardiac tamponade, which responded quickly to pericardiocentesis and a single administration of prednisolone. This is the first case of anti-PR3 ANCA-positive EGPA with cardiac tamponade; the patient displayed clinical features of both ANCA-positive and ANCA-negative cases.
Phase 0 Clinical Trial Strategies for the Neurosurgical Oncologist

Abstract In an era of escalating drug discovery costs, shifting priorities within the pharmaceutical industry, and longstanding challenges in central nervous system drug delivery, surgical trials offer an avenue to identify promising agents with demonstrable tumor penetration and molecular effects. The rise of pharmacodynamic- and pharmacokinetic-driven clinical trials, including phase 0 study designs, creates an opportunity for the neurosurgical oncologist to engage drug development for brain tumor patients directly. Here, we review the phase 0 clinical trial mechanism as well as its current and future applications within neurosurgical oncology.
MOF-Derived Isolated Fe Atoms Implanted in N-Doped 3D Hierarchical Carbon as Efficient ORR Electrocatalyst in Both Alkaline and Acidic Media.

In order to improve the catalytic performance of oxygen reduction reaction (ORR), it is pivotal to increase the density and accessibility of the active sites. Herein, we have developed a template-free melamine-assisted co-calcined strategy to afford Fe-embedded and N-doped carbons (Fe-N-C) with not only high density of atomically dispersed Fe-Nx active sites but also abundant 3D interconnected mesopores by directly pyrolyzing Fe-ZIF-8 covered with a controllable melamine layer. It is demonstrated that the introduction of melamine in the precursor play a key role in constructing various carbonized products with controllable morphology, porosity and components. With an optimal mass ratio 1:1 of melamine to Fe-ZIF-8, the resultant Fe@MNC-1 exhibits excellent ORR activity and stability, which exceeds 20 wt. % commercial Pt/C catalyst (with half-wave potential of 0.88 V vs. 0.85 V) in alkaline electrolyte, and is even comparable to the commercial Pt/C catalyst (with half-wave potential of 0.78 V vs. 0.80 V) in acidic electrolyte. To the best of our knowledge, Fe@MNC-1 can be ranked among the best non-precious metal electrocatalysts for ORR in both alkaline and acidic media. The present synthetic strategy may provide a new opportunity for the design and construction of MOF-derived nanomaterials with rational composition and desired porous structure to boost their electrocatalytic performance.
Towards healthy settings for people with intellectual disabilities

Abstract People with intellectual disabilities (ID) depend on their environment for support to live healthily. The characteristics of healthy settings for people with ID are unknown. This study aims to conceptualize healthy settings for people with ID by conducting an international and multidisciplinary concept mapping study. As theoretical framework the settings approach, an ecological model with a whole system focus toward health promotion, was used. The integrative mixed-methods approach of this study involved concept mapping with researchers specialized in healthcare for people with ID and researchers specialized in healthy settings. The 41 participants generated statements that were later sorted and rated. Findings encompass 13 clusters relating to the social environment, the physical environment and societal preconditions. Specific factors of healthy settings for people with ID include: (i) universal design of the physical environment, (ii) the role of care professionals in the social environment to empower people with ID, (iii) possibilities for care providers to contribute to a health-promoting setting and (iv) preconditions that allow people to engage in society. These factors can be used in strategies to apply the approach in practice and give directions to put in place policies on developing enabling environments and decreasing health inequities.
How does the public understand recovery from severe mental illness versus substance use disorder?

OBJECTIVE Recovery from severe mental illnesses (SMI) has been described as an outcome (end state where persons are symptom free) or as a process (despite symptoms, people can pursue life goals). Less clear is whether recovery as a process has credibility in the substance use disorders (SUD) community. We examined how public perceptions and expectations of outcome and process between SMI and SUD differed. A severity effect within SMI and SUD categories was also examined. METHOD Participants (N = 195) read definitions of SMI and SUD and completed an online survey of their agreement on: perceptions of recovery from SMI and SUD as outcome and process, and expectations of recovery as outcome and process. Participants were then given more and less severe SMI (i.e., schizophrenia vs. depression) and SUD (opiate vs. alcohol use) definitions. They then responded to recovery items SMI and SUD conditions with low and high severity. RESULTS For SMI, perceptions and expectations of recovery as process were endorsed more than outcome. Severity effect led to greater increases in perceptions and expectations about recovery as process. Specifically, differences between outcome and process for schizophrenia were significantly larger than for depression. For SUD, expectations of process were significantly lower than outcome ratings. One negative interaction was found for SUD expectations; difference scores for opiate users were smaller than for alcohol. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE We discussed implications for interventions that enhance recovery for people with SMI and SUD. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Product modeling of buildings for data exchange standards: from IGES to PDES/STEP and beyond

Digital data exchange problems are severe in the building community because of its organizational and operational complexity and the vast scope of its information needs. Early CAD data exchange standards, such as the Initial Graphics Exchange Specification (IGES), dealt exhaustively with the syntax or arrangement of the data to be exchanged. Most of the semantics or meaning of the data must be imposed by the user of the standard. Current work to develop the first international standard, called both the Standard for the Exchange of Product Model Data (STEP---the IS0 nomenclature) and the Product Data Exchange Specification (PDES---the USA nomenclature), is based on an explicit definition of semantics at the user level. The response of the building community to these two different approaches to standardization is reviewed in terms of product modeling of buildings. A number of challenges are discussed in the context of building data.