Miracle-dz/UniFAR-FTUs huggingface dataset:
Claims-Based Co-management in Norway's Arctic? Examining Sami Land Governance as a Case of Treaty Federalism

Abstract Around the world, Indigenous peoples seek increased control of traditional lands. In northern Canada, such control may be afforded by claims-based co-management regimes. Such regimes are a common, and sometimes celebrated, component of treaty federalism. In Norway, Europe's only Indigenous people, the Sami, now participate in a land-management regime: the Finnmark Estate (FeFo). We explore whether FeFo is, in effect, claims-based co-management and whether Sami thus enjoy the sort of guaranteed shared rule envisioned in treaty federalism. We compare FeFo to Canadian co-management in three dimensions: novelty, independence and Indigenous influence. We conclude FeFo is indeed claims-based co-management. But FeFo falls short of the treaty-federal ideal, for reasons possibly including bureaucratic capture, fragile legitimacy, conflicting interpretations of the Sami interest and conflicting views on the merits of shared rule. Résumé Partout dans le monde, les peuples autochtones aspirent à exercer un contrôle accru sur les terres ancestrales. Dans le Nord du Canada, ce contrôle peut être assuré par des régimes de cogestion « fondés sur les revendications ». De tels régimes sont une composante courante, et parfois célébrée, du « fédéralisme des traités ». En Norvège, le seul peuple autochtone d'Europe, les Sami participe maintenant à un régime de gestion des terres, le Finnmark Estate (FeFo). Nous examinons si le FeFo est en fait une cogestion « fondée sur les revendications » et si les Samis jouissent ainsi de la sorte de règle partagée garantie envisagée dans le « fédéralisme des traités ». Nous comparons le FeFo à la cogestion canadienne en trois dimensions : nouveauté, indépendance et influence autochtone. Nous concluons que le FeFo est en effet une co-gestion « fondée sur les revendications ». Mais, pour des raisons telles que la capture bureaucratique, une légitimité fragile, les interprétations contradictoires des intérêts des Samis et les opinions contradictoires sur les mérites d'une règle partagée, le FeFo n'atteint pas l'idéal fédéral du traité.
Winning Rate Prediction Model Based on Monte Carlo Tree Search for Computer Dou Dizhu

Poker is the typical game of incomplete information, and remains a longstanding challenge problem in artificial intelligence (AI). The poker game of Dou Dizhu has been viewed as a thorny topic in AI because of its own characteristics. This article introduces a developed Monte Carlo tree search (MCTS) method for Dou Dizhu to solve the decision making effectively. We built the winning rate prediction model (WRPM) to predict the winning rate of moves as the initial situation estimation and improve the model to be more applicable to different player roles. Then, the WRPM is embedded as the core algorithm into MCTS for extension and simulation and named it WRPM-MCTS. In addition, we also train a card distribution prediction model to predict the holding cards of opponents for further improving the performance of WRPM-MCTS on the agent of Dou Dizhu. Experiments show that the WRPM-MCTS has a statistically significant performance better than the pure MCTS and the pure WRPM. In the game with human players from an online game platform, the WRPM-MCTS-based agent had the winning rate of 52.86% in 4 000 000 games and ranked in top 1.22% among 500 000 human players, indicating that this agent had reached the expert level of humans.
The practice of ‘managing as designing’

Recent studies of ‘design thinking’ for management have criticized the current focus on principles and tools of design thinking, for creating an over-simplified view of a complex process. As a response, this paper sets out to study the empirical details of ‘doing designing’ in order to explore what ‘managing as designing’ involves in practice. Adopting a practice-based theoretical orientation, the paper presents findings from the design meetings of three residential refurbishment projects in the UK. The findings suggest that the management of design practices was accomplished through everyday interactions during which the nature and level of uncertainty of various issues were established, and the corresponding adaptive and innovative courses of actions were developed. Based on these insights, it is concluded that ‘managing as designing’ is primarily about facilitation of everyday organizational interactions, and leadership for the reconciliation of various concerns of multiple stakeholders.
Unbiased Semi-Supervised LF-MMI Training Using Dropout

The lattice-free MMI objective (LF-MMI) with finite-state transducer (FST) supervision lattice has been used in semisupervised training of state-of-the-art neural network acoustic models for automatic speech recognition (ASR). However, the FST based supervision lattice does not sample from the posterior predictive distribution of word-sequences but only contains the decoding hypotheses corresponding to the Maximum Likelihood estimate of weights, so that the training might be biased towards incorrect hypotheses in the supervision lattice even if the best path is perfectly correct. In this paper, we propose a novel framework which uses Dropout at the test time to sample from the posterior predictive distribution of word-sequences to produce unbiased supervision lattices for semi-supervised training. We investigate the dropout sampling from both the acoustic model and the language model to generate supervision. Results on Fisher English show that the proposed approach achieves WER recovery of ∼ 51.6% over regular semi-supervised LFMMI training.
The effect of social capital on the performance of technology transfer

: In this paper, the social capital was measured based on the social network theory to evaluate effects on the performance of technology transfer. Firstly, the hypotheses were established based on literature review. Secondly, the multi agent model of transforming cooperation was constructed by Netlogo. Then, technology transformation performance was analyzed from macro and micro perspectives. Finally, conclusion was drawn and suggests proposed.
Optimization of the Spin-Torque Oscillator Response for Microwave-Assisted Magnetic Recording

Microwave assisted magnetic recording (MAMR) is one of the promising next generation recording technologies that is expected to carry the magnetic recording industry beyond today’s perpendicular magnetic recording (PMR) limit. MAMR involves the introduction of a high frequency oscillating magnetic field in the microwave frequency (GHz) range at the time of writing. This assisting energy at a specific frequency helps to make the magnetic grains more malleable and thereby reverse more easily which permits the usage of smaller, higher coercivity grains in the media, supporting a higher recording density. The generation of a stable oscillating magnetic field is expected to come from a spin-torque oscillator (STO) that has a rotating field-generating layer (FGL) when a current is injected. The STO must be properly designed in order to generate the stable oscillating magnetic field of the desired frequency and with sufficient amplitude to assist in the media reversal. In this work, we optimize the STO to produce a large and stable oscillating field with the desired parameters. Micromagnetic simulations of the STO are used to determine the STO’s behavior and we propose various metrics to be used as a measure of the performance of the STO’s response. Subsequently the response surface methodology (RSM) is employed to optimize our selected performance metric, producing the highest performing STO.
Representation of knowledge for a NASA stakeholder value model

A challenge in the representation of preferences for government stakeholders is the valuation of benefits. In for‐profit companies, benefits are typically calculated as the company's revenue, with centuries of literature supporting different estimations for the benefit of revenue. Government stakeholders are much more difficult to develop value models for because of the lack of benefit‐side valuation research. In its pursuit of understanding the theory of systems engineering, the NASA Systems Engineering Research Consortium has examined value models as mathematical representations of preferences, with a focus on NASA‐related stakeholders. This article leverages the Consortium work and develops an outline for a NASA stakeholder value model, based on NASA documentation, with key benefit attributes of knowledge, prestige, and the avoidance of catastrophes. The attribute of knowledge is specifically explored in this article. Knowledge‐related evidence from previous NASA missions are collected and analyzed. A discussion describes how such knowledge‐related evidence can be used to inform the systems engineer on preliminary, evidence‐based attribute and value relationships useful in value model formation. Future research avenues for knowledge‐focused benefits for value models are also discussed.
Effects of Gain and Index of Difficulty on Mouse Movement Time and Fitts’ Law

The mouse, being the major means of inputting and controlling data on a computer, should be set right for best performance. Mouse gain, defined as the ratio of the movement distance on the computer screen to the movement distance of the mouse, controls the movement time when both speed and accuracy are important. Reported optimum gain values vary widely from about 2 to 15. An experiment having a targeting task was used to test 16 participants using a wide range of gain (1.2, 2.4, 14.5, and 38.9). An optimum gain of 2.4 was found for a display movement amplitude (A) of 100 mm, whereas the optimum gain was 14.5 for amplitudes of 200 and 400 mm. Depending on the gain and the movement amplitude, Fitts’ law should be modified to accurately model movement time (MT) in the form of MT = A + b(ID) − c(A*ID). Only at low gains could a critical index of difficulty be determined, that being about 3.5.
Towards Business Process Digitalization in SMEs: A Case of a Small Optical Retail Business

As small-medium enterprises (SMEs) produce significant amounts of added value in the market, it is important to ensure that their business processes are optimized. Business Process Management (BPM) is a popular methodology in large enterprises; however, there is a clear lack of explicit information on how it can be adapted by SMEs considering their financial and informational constraints as well as other specific factors. In this research, we concentrated on one of the aspects of BPM, which is a business process improvement, within the scope of a small optical retail business’s order management process. Process improvement is crucial when moving towards business process digitalization by the means of business process management systems (BPMSs) or any other IT technology for that matter. In this paper we presented basic aspects of our developed process improvement approach, which contributes to the better understanding of the specificity of process reengineering task in SMEs and could be used as practical means to prepare for process digitalization using BPMS in a more effective manner. The paper also presents process model simulation-based evaluation of a number of qualitative process analysis methods within the scope of a small optical retail business case, which was one of the founding stones of our approach. The acquired process model with the best execution metrics was then used as a base executable model for the developed business order management system in our target business enterprise.
Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation

As the rate at which scientific work is published continues to increase, so does the need to discern high-impact publications. In recent years, there have been several approaches that seek to rank publications based on their expected citation-based impact. Despite this level of attention, this research area has not been systematically studied. Past literature often fails to distinguish between short-term impact, the current popularity of an article, and long-term impact, the overall influence of an article. Moreover, the evaluation methodologies applied vary widely and are inconsistent. In this work, we aim to fill these gaps, studying impact-based ranking theoretically and experimentally. First, we provide explicit definitions for short-term and long-term impact, and introduce the associated ranking problems. Then, we identify and classify the most important ideas employed by state-of-the-art methods. After studying various evaluation methodologies of the literature, we propose a specific benchmark framework that can help us better differentiate effectiveness across impact aspects. Using this framework we investigate: (1) the practical difference between ranking by short- and long-term impact, and (2) the effectiveness and efficiency of ranking methods in different settings. To avoid reporting results that are discipline-dependent, we perform our experiments using four datasets from different scientific disciplines.
Price Setting Behavior in Israel –An Empirical Analysis Using Microdata

The paper studies pricing behavior in Israel, using a very large dataset containing more than 3 million observations drawn from the Israel Central Bureau of Statistics (CBS) database, covering all CPI components except housing and fruit and vegetables. The mean duration between price changes is about 6 to 9 months, depending on the method of computation. We find considerable heterogeneity in the frequency of price changes within and between sectors. We also find that the inflation environment, outlet characteristics and seasonality affect pricing behavior. Changes in VAT have some impact on duration but generally only affect the size of price changes of the most flexible prices. According to our analysis, the size of the average price change is 3.5%. Price reductions constitute about a third of price changes, and their average size, about 10%, is similar to that of price increases.
Categorizing the stages of lung cancer using Multi SVM Classifier

Detection of cancer is the utmost fascinating analysis space for scientists in the early period. The projected method is meant to identify cancer in the beginning phase. The projected method comprises several phases, such as image acquisition, pre-processing, segmentation, feature extraction, and classi ication. In our proposed work, segmentation is done to fragment the CT image. We use solid feature extraction (GLCM) technique to extract certain essential features from the segmented images. Further extracted features are considered for classi ication (Multi SVM)process to checkwhether cancerousornoncancerous.
Automated Estimation of Oral Reading Fluency During Summer Camp e-Book Reading with MyTurnToRead

Use of speech technologies in the classroom is often limited by the inferior acoustic conditions as well as other factors that might affect the quality of the recordings. We describe My-TurnToRead, an e-book-based app designed to support an inter-leaved listening and reading experience, where the child takes turns reading aloud with a virtual partner. The child’s reading turns are recorded, and processed by an automated speech analysis system in order to provide feedback or track improvement in reading skill. We describe the architecture of the speech processing back-end and evaluate system performance on the data collected in several summer camps where children used the app on consumer-grade devices as part of the camp programming. We show that while the quality of the audio recordings varies greatly, our estimates of student oral reading ﬂuency are very good: for example, the correlation between ASR-based and transcription-based estimates of reading ﬂuency at the speaker level is r=0.93. These are also highly correlated with an external measure of reading comprehension.
Securing Internet of Things (IoT) with machine learning

Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet‐connected devices are part of the IoT ecosystem. IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host‐based and network‐based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices.
Development of the Usage of E‑services in Households, by Voivodship: a Cluster Analysis

The aim of this work is to assess the development of voivodships in terms of the usage of e‑services (e‑government, e‑commerce, e‑health, etc.) in households, in comparison with the research on the overall ICT usage in households, in voivodships. The results of both of these studies enabled the verification of the thesis that the higher the level of overall ICT usage in households, in a given voivodship, the higher the level of e‑services usage in these households. In the theoretical part of the work, the rationale for the research was presented. Therefore, the importance of the usage of e‑services in households, for building an information society and consequently knowledge‑based economy, has been described. The research methodology also included: linear ordering methods (Hellwig’s method, methods that are non‑based on the pattern of development), agglomerative hierarchical clustering method (Ward’s method), and optimisation clustering method (the k‑means method) have been discussed. The empirical part of the work involves presentation of the research results. Data from the year 2017, provided by the Central Statistical Office of Poland, was used. Within the framework of the usage of e‑services in households (26 variables), the rankings of voivodships were created and the clusters of voivodships were detected. The obtained rankings and clusters of voivodships served to compare and assess voivodships in the analysed year. In the light of the results relating to the usage of the e‑services, the results of ordering and clustering of voivodships within the framework of overall ICT usage in households (27 variables) were obtained. Then, the verification of the assumed thesis was conducted.
Sustainability and Real Estate Curriculum: A Review of Estate Management Curriculum in Nigerian Federal Universities

Climate change and global warming are issues that continue to take centre stage in many global discussions presently. Thus, various programs and schemes are geared towards the mitigation of these pressing problems. In 2005, the United Nations declared the Decade of Education for Sustainable Development (DESD) to integrate sustainable development values with various aspects of learning. The ultimate goal was to induce sustainability behavioural changes through education. One of the objectives of the DESD was to embed sustainable development issues in various curricula. It is however unclear if the real estate curriculum in Nigeria has since reflected any such changes. This study uses a content analysis to assess the current curricula of the estate management departments of selected federal universities in Nigeria, in a bid to ascertain the level of inclusion of environmental sustainability issues in the teaching – learning process. The findings of the study are important for curriculum modification and sustainable development goals implementation in Nigeria.
On life satisfaction and vocational rehabilitation outcome in Sweden

OBJECTIVE: The main objective of this investigation was to identify a set of variables usable in early outcome prediction of vocational rehabilitation.DESIGN: On commencement of rehabilitation at an employability institute, data were sampled using structured interviews and checklists. Two years later the subjects were followed up by telephone interviews.SUBJECTS: The study sample was all unemployed with a somatic disorder as the cause of vocational disability, admitted from October 1995 to December 1996 (n = 149). Of these 109 (73%) agreed to participate.METHODS: Initially, data on demography, symptoms, expectations/beliefs about future capacity for gainful employment and sense of coherence (as an indicator of coping resources) were sampled. At follow-up the subjects' vocational situations were recorded.RESULTS: At follow-up, 40% were working or employable. Among the about 30 variables included, logistic regression showed that having a relatively high belief in vocational return (odds ratio, OR: 4.6, CI: 1.4-15.4), having a relatively high sense of coherence (OR: 3.5, CI: 1.5-8.4) and having a relatively high educational level (OR: 2.6, CI: 1.1-6.3) were significant predictors of positive outcome.CONCLUSION: In this sample, socio-psychological and educational aspects were far more predictive of outcome than were medical circumstances.
Uncovering Local Hierarchical Overlapping Communities at Scale

Real-life systems involving interacting objects are typically modeled as graphs and can often grow very large in size. Revealing the community structure of such systems is crucial in helping us better understand their complex nature. However, the ever-increasing size of real-world graphs and our evolving perception of what a community is, make the task of community detection very challenging. A critical relevant challenge is the discovery of the possibly overlapping communities of a given node in a billion-node graph. This problem is very common in modern large social networks like Facebook and LinkedIn. In this work, we propose a scalable local community detection approach to efficiently unfold the communities of individual target nodes in a given network. Our goal is to reveal the clusters formed around nodes (e.g., users) by leveraging the relations within all different contexts these nodes participate in. Our approach, termed Local Dispersion-aware Link Communities or LDLC, considers the similarity of pairs of links in the graph as well as the extent of their participation in multiple contexts. Then, we determine the order in which we should group the pairs of links so that we form meaningful hierarchical communities. We are not affected by constraints existing in previous techniques such as the need for several seed nodes or the need to collapse multiple overlapping communities to a single community. Our experimental evaluation using ground-truth communities for a wide range of large real-world networks shows that our LDLC algorithm significantly outperforms state-of-the-art methods on both accuracy and efficiency. Moreover, we show that LDLC uncovers very effectively the hierarchical structure of overlapping communities by producing detailed dendrograms.