Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneous Consensus is Harder than Set Agreement in Message Passing

In the traditional consensus task, processes are required to agree on a common value chosen among the initial values of the participants. It is well known that consensus cannot be solved in crashed-prone, asynchronous distributed systems. Two generalizations of the consensus problem have been introduced: k-set agreement and k-simultaneous consensus. The k-set agreement task has the same requirements as consensus except that processes are allowed to decide up to k distinct values. In the k-simultaneous consensus task, each process participates simultaneously in k instances of consensus and is required to decide in at least one of them; any two processes deciding in the same instance must decide the same value. It is known that both tasks are equivalent in the wait-free shared memory model. Perhaps surprisingly, the paper shows that this is no longer the case in the n-process asynchronous message passing model with at most t process crashes. Specifically, the paper establishes that for parameters t, n, k such that t > (n+k−2)/2 , k-simultaneous consensus is strictly harder than k-set agreement. The proof compares the information on failures necessary to solve each task in the failure detector framework and relies on a result in topological combinatorics, namely, the chromatic number of Kneser graphs. The paper also introduces the new failure detector class V Σk , which is a generalization of the quorum failures detector class Σ suited to k-simultaneous consensus.
Design trade-offs in self-management technology: the HeartMan case

ABSTRACT Health self-management technology has the potential to significantly improve the Quality of Life of patients suffering from chronic diseases. However, designing the technology involves numerous highly context-dependent design decisions. In this paper, we analyse a case study of self-monitoring technology in the field of congestive heart failure. We analyse the design process of the technology from the perspective of design trade-offs. Three important trade-offs related to health self-monitoring technology are described in detail, related to patient autonomy, technology appropriation, and patient well-being. For each of the trade-offs, various mediating factors that influence design decisions are described in detail. On a practical level, this analysis can inform future developments in self-management technology. In addition, this design trade-off analysis provides intermediary knowledge that can contribute to a better theoretical understanding of health self-management technology.
Foundational Language Resource Data on Web Sites

This paper begins with the significance of language information processing technology for the progress of an information society, and then introduces that Institute of Computational Linguistics (ICL) of Peking University and other language resource data on web sites, such as HowNet, WordNet, TreeBank. , achieved the results of these researches and actualities of the language resources at the foundational researches on language information processing. The last section of this paper prints out the tasks for further studies.
A Novel Approach to Measure the Total Antioxidant Power of Wines through Near Infrared Spectroscopy and Its Relevance in Human Nutrition

Wine is the product of the alcoholic fermentation of the grape (Vitis vinifera). As such, it is a hydroalcoholic solution with a variable content of nutritional and functional (polyphenols) molecules, the latter involved in its antioxidant potential. The organoleptic variables of wine, together with its potential positive effects on health of a light-moderate intake, have always been topics of great interest within the cultures. In the label of wine, alcohol is the only declared variable. On the other hand, there is no information about the content of “positive” molecules, such as those associated with the antioxidant power. This value could be very important to classify the wines, helping oenologist and nutritionist in qualifying them as a component of Mediterranean diet. Moreover, one of the most critical aspects in evaluating the role of wine in human health through epidemiological prospective studies is the quality of the products used and their antioxidant potential. This research aimed to optimize and validate an analytical approach based on a portable device (SCIO), using NIRS (near-infrared reflectance spectroscopy). It allows the measure of TAP (total antioxidant power) of wine through the glass. Research findings are promising. To the best of our knowledge, this is the first time that an easy-to-use and cheap hand-held scanner is validated to measure the TAP of a beverage.
Development and Control of Generator-Converter Topology for Direct-Drive Wind Turbines

In this chapter, a new topology for Direct-Drive Wind Turbines (DDWTs) with a low-voltage generator design is presented in order to eliminate the required dc-bus capacitors or dc-link inductors. In the presented topology, the grid-side converter is replaced by a boost Current Source Inverter (CSI) therefore removing the need for the dc-bus electrolytic capacitors which results in increasing the system lifetime. In the developed topology, the synchronous inductance of the generator is utilized. This facilitates the elimination of the intrinsically required dc-link inductor in the CSI which further contributes to a reduction in the overall system weight and size. The boost CSI is capable of converting a low dc voltage to a higher line-to-line voltage. This results in the implementation of a low-voltage generator for DDWTs. The feasibility of the presented low-voltage generator is investigated through Finite Element (FE) computations. In this chapter, a modified 1.5 MW low-voltage generator for the proposed topology is compared with an existing 1.5 MW Permanent Magnet (PM) synchronous generator for DDWTs. The feasibility of the presented topology of generator-converter for DDWTs is verified through simulations and laboratory tests. Furthermore, the controls developed for the developed wind turbine topology is also presented in this chapter.
Ontology Extraction from Question/Answer Sections on Online Marketplaces

Online marketplaces are a significant part of the global market.They aggregate an enormous amount of data about the products that they are selling, in the form of product descriptions and reviews. Users form their opinions about an item according to other users' opinions, expressed in the form of reviews. A number of marketplaces are also starting to use a question/answer system along reviews, in which any user can post a question and another user can answer to that same question. There is a wealth of relevant data, though this information is expressed in natural language, meaning that it's understandable for a human being but difficult to translate into meaningful data for a computer program. We addressed the problems involved with the programmatic analysis of the language contained in question/answer sections with the purpose of extracting information to build and populate an expressive knowledge model. The approach we choose relies on the use of named entity recognition and part-of-speech tagging to identify concepts to populate the ontology, and learn relations between the elements of the ontology.
Forward guidance as a tool of the European Central Bank’s monetary policy

The purpose of this article is to take a closer look at one of the most unconventional instruments of monetary policy which is the Forward Guidance. This instrument is increasingly more often adopted by central banks. What is more, it is also used by the European Central Bank which implemented this approach for the first time in 2013. The first part of the article will be focused on presenting a theoretical approach of forward guidance, while the following part will describe examples of implementation of this instrument by the European Central Bank as the new way of communication with the market. The example of forward guidance use by the European Central Bank covers the way of communication of future monetary policy regarding the interest rates. The data presented in this paper shows a positive effect of the forward guidance approach. This method of communication has brought a clearer view of monetary policy adopted by the European Central Bank. Moreover it limited some uncertainty showed by the market participants regarding the level of future interest rates. The positive effects of the forward guidance lead not only to the continuous but also widespread use of this approach by the European Central Bank.
Dynamic Optimization in Hardware

Dynamic instruction count and instruction-level parallelism (ILP) are two limiting factors for high performance computing. Processor designers typically attack these limiting factors through processor frequency scaling, and by creating superscalar processors to leverage the inherent ILP in applications. This dissertation adopts a complementary approach of attacking these two factors by using integrated dynamic optimization hardware to directly reduce dynamic instruction count and increase ILP. On a broad level, we describe the relationship between well-known compiler optimization concepts and hardware-implemented optimizations. We define and explore two hardware-centric dynamic optimization paradigms: continuous optimization and discrete optimization. Continuous optimization optimizes instructions in the processor pipeline prior to execution. It is primarily useful for reducing processor resource contention and increasing ILP. Discrete optimization captures reusable chunks of the dynamic instruction stream, optimizes them, and then sequences them back into execution. Its primary benefits come from reducing instruction count and increasing ILP. The benefits of these two models are mostly orthogonal, and, therefore, they can be combined to provide larger improvements than either approach could provide by itself.
Analysis of Semi-Open Queueing Network with Customer Retrials

A semi-open queueing network is considered. The network of an arbitrary topology consists of a finite number of nodes operation of which is modeled by single-server queueing systems with a buffer and an exponential service time distribution. Customers arrive at the network according to a Markovian arrival process. The total number of customers, which can be processed simultaneously, is restricted by a fixed in advance threshold. If the number of customers in the network at a new customer arrival moment is less than the threshold, this customer is admitted for service. Otherwise, the customer temporarily leaves the system (it is said that the customer joins the orbit) and retries to get access to the network after the random time intervals duration of which has an exponential distribution. The capacity of the orbit is infinite. After the admission to the network, the customer starts sequential service in a random number of nodes. The choice of the first and the subsequent nodes for service is made stochastically according to a fixed stochastic vector and a transition probability matrix. The behavior of the network is described by the continuous-time Markov chain that belongs to the class of asymptotically quasi-Toeplitz Markov chains. This allows to derive a transparent condition for the ergodicity of the chain and compute its steady-state distribution. Expressions for computation of various performance measures of the network are derived. Numerical results are presented. The model can be applied, e.g., for analysis and optimization of the operation of logistics and manufacturing systems, and the wireless telecommunication networks.
Differential Tolerance of Mammalian Myelinated and Unmyelinated Nerve Fibers to Oxygen Lack

Ischemic neuropathy has a well-recognized but unexplained tendency to spare unmyelinated (C) fibers such as those that subserve pain. We investigated whether low energy requirement plays a part in this selectivity, by comparing the abilities of A&bgr;, A&dgr;, and C fibers in cervical vagus nerve of rabbit at 38°C to maintain excitability in the presence of severe hypoxia. We found that, given enough glucose, many C fibers and some A&dgr; fibers, but not A&bgr; fibers, can withstand complete anoxia for an apparently unlimited period.
Automatic Speech Recognition Bahasa Indonesia Menggunakan Bidirectional Long Short-Term Memory dan Connectionist Temporal Classification

Automatic speech recognition refers to a process of converting an input in form of audio signal into text for better human-computer interaction activity. Automatic speech recognition is typically done by using Hidden Markov Model. Despite of its common usage on automatic speech recognition, a processing duration problem is found on Hidden Markov Model implementation, especially for the segmentation and tuning process. Therefore, a method has to be implemented in order to resolve the processing duration problem on Hidden Markov Model. In this research, automatic speech recognition process is done by using bidirectional Long Short-Term Memory for audio signal segmentation and Connectionist Temporal Classification to convert the result of bidirectional Long Short-Term Memory into readable text. Tokyo Institute of Technology Multilingual (TITML) speech corpus and additional dataset was used to train the network. The result shows that the number of neuron and learning rate used in the process could affect the error rate of the speech recognition process. The experiment returns Word Error Rate (WER) value of 3.01 % on testing dataset, 24.49 % on speech recorded on quiet environment with different microphone, and 75.08 % on speech recorded on noisy environment.
Agent based simulation for 'Choice of Seats': a Study on the Human Space Usage Pattern

Agent simulation in the context of architecture is a great practical tool for analysing space and examining its influence from the perspective of users. On the other hand, the theories and concepts that deal with human cognition as it relates to environments are typically studied within the field of environmental psychology. Research on the intersection between the two fields has focused mainly on the analysis of the built environment but not necessarily the agents themselves. The present research applies the theories in environmental psychology as rules in an agent simulation model validated by comparing the results with actual mapped human behavioural patterns in space. It aims to develop a method and a tool for analysing space from the perspective of the users and thus allow for the design of built environments that take that perspective into account. We specifically used visibility graphs to map the visitors’ field of view on spatial configurations as a proxy for the concepts of “prospect and refuge” in environmental psychology. We then applied an agent model to the same spatial configurations that simulated the tendency to prefer sitting areas that are a certain distance from others and as well as other elements of human cognition such as the perception of crowdedness of a space and uncertainty. We validated the simulation model against onsite observations to examine how the hypothetical patterns it creates relate to the observed patterns in real-world settings in the context of the theories implemented from environmental psychology. No statistically significant correlations emerged between the theoretical patterns and observed behaviour pointing to the complexity of human usage patterns in space as well as the difficulties of measuring it. Nevertheless, the proposed research provides a framework for investigating people’s possible staying patterns over spatial configurations and seat arrangements with different concordant rates according to crowdedness conditions and group type.
Chemo-Enzymatic Metathesis/Aromatization Cascades for the Synthesis of Furans: Disclosing the Aromatizing Activity of Laccase/TEMPO in Oxygen-Containing Heterocycles

The unprecedented Trametes versicolor laccase/TEMPO-catalyzed aromatization of 2,5-dihydrofurans to furans is described. A variety of furan derivatives have been synthesized in moderate to high conversions (21–99%) and yields (20–76%) under mild reaction conditions. This work reveals the aromatization ability of the Trametes versicolor laccase/TEMPO system in synthesizing oxygen-containing heterocycles. Moreover, the direct synthesis of furans from aliphatic diallyl ethers through a chemo-enzymatic metathesis/aromatization cascade, which combines Grubbs-catalyzed ring-closing metathesis and laccase/TEMPO-catalyzed aromatization in the same reaction medium, has been successfully developed.
Antidepressant effects of the hydro-alcoholic extract and fractions of Anchomanes difformis (Blume) Engler rhizome (family Araceae) in mice

In ethno-medicinal practice in the South-western part of Nigeria, the rhizome of Anchomanes difformis is claimed to be effective in the management of mental illness. This study determined the antidepressant effects of hydro-alcoholic extract and fractions of Anchomanes difformis rhizome and the likely mechanisms of action of the extract and the most active fraction in mice. The extract was partitioned into n-hexane, ethylacetate, butanol and distilled water to obtain n-hexane, ethylacetate, butanol and residual aqueous fractions, respectively. The extract and fractions were administered to mice of both sexes (18 to 25 g) at 60, 125, 250, 500, and 1000 mg/kg, orally. Tail suspension and forced swimming anti-depressant animal models were used. The likely mechanism of anti-depressant action was assessed using yohimbine and cyproheptadine. The data were analyzed using one-way ANOVA followed by Student-Newman-Keul’s post hoc test with the level of significance taken as p<0.05. The extract and ethylacetate fraction at 60 to 250 mg/kg significantly (p<0.05) reduced the period of immobility in both the tail suspension and forced swimming tests without significantly impairing the locomotor activity, suggesting an anti-depressant effect. This effect was reversed by yohimbine (1 mg/kg, i.p.), an α2-adrenergic receptor antagonist, yohimbine and also by cyproheptadine (0.5 mg/kg, i.p.), a serotonergic receptor antagonist. The study concluded that the extract and ethylacetate fraction of A. difformis rhizome possessed an anti-depressant effect in mice which was probably mediated through α2-adrenergic and serotonergic pathways.
Embedding runtime verification post-deployment for real-time health management of safety-critical systems

As cyber-physical systems increase in both complexity and criticality, formal methods have gained traction for design-time verification of safety properties. A lightweight formal method, runtime verification (RV), embeds checks necessary for safety-critical system health management; however, these techniques have been slow to appear in practice despite repeated calls by both industry and academia to leverage them. Additionally, the state-of-the-art in RV lacks a best practice approach when a deployed system requires increased flexibility due to a change in mission, or in response to an emergent condition not accounted for at design time. Human-robot interaction necessitates stringent safety guarantees to protect humans sharing the workspace, particularly in hazardous environments. For example, Robonaut2 (R2) developed an emergent fault while deployed to the International Space Station. Possibly-inaccurate actuator readings trigger the R2 safety system, preventing further motion of a joint until a ground-control operator determines the root-cause and initiates proper corrective action. Operator time is scarce and expensive; when waiting, R2 is an obstacle instead of an asset. We adapt the Realizable, Responsive, Unobtrusive Unit (R2U2) RV framework for resourceconstrained environments. We retrofit the R2 motor controller, embedding R2U2 within the remaining resources of the Field-Programmable Gate Array (FPGA) controlling the joint actuator. We add online, stream-based, real-time system health monitoring in a provably unobtrusive way that does not interfere with the control of the joint. We design and embed formal temporal logic specifications that disambiguate the emergent faults and enable automated corrective actions. We overview the challenges and techniques for formally specifying behaviors of an existing command and data bus. We present our specification debugging, validation, and refinement steps. We demonstrate success in the Robonaut2 case study, then detail effective techniques and lessons learned from adding RV with real-time fault disambiguation under the constraints of a deployed system.
A distributed computational model for Reo.

The work described in this document aims at producing a formal computational model for the Reo coordination language, that can facilitate the implementation of Reo circuits in a distributed computing environment. The model introduced here partially covers what Reo requires - it implements a less strict form of the merge behavior of mixed nodes. While this already allows computing of a large class of useful circuits, it does not properly deal with some synchronous circuits that contain LossySync channels. This work has lead to a new and more powerful approach to computing the behavior of Reo circuits, called Connector Coloring
Threats Detection using Big Data Analytics

Social networking sites (SNS) are being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The ubiquitous use of social media has generated unparalleled amounts of social data. Data may be in the form of text, audio, video, images. It is necessary to analyze this massive amount of data and extracting useful information from it. Information can be spread across social networks quickly and effectively, hence have now become prone to different types of undesired and malicious spammer/hacker actions. Spam’s can be in the form of images, text, videos, audio etc. However, Social Networking Sites is providing opportunities for cybercrime activities. Therefore, there is a pivotal need for security in social media and industry. The aim of this paper is to detect threats in a social media networks using Big Data analytics. Keywords: Threat classification, Security Threats, Big data Analytics