Miracle-dz/UniFAR-FTUs huggingface dataset:
Same Wrong , Different Restitution ? Heightened Sensitivity to Inequitable Treatment in the Context of Apology

Companies often treat customers differently based on their perceived value to the firm. Although average customers may not relish witnessing others receive superior treatment, the present paper examines a context in which differential treatment is particularly galling. We argue that the inequity penalty—the difference in how morally wronged consumers feel when they are treated worse than versus the same as others—is particular acute in the context of apology. When companies are attempting service repair, apologies carry prescriptive norms of restorative justice. Namely, the principle of proportionality holds that restitution should be proportional to harm suffered. Inequitable treatment accompanying an apology can violate this norm. Studies 1-3 demonstrate this heightened inequity penalty and tie it to restorative apologies offered in the service of mending customer relationships (rather than merely expressing empathy). Studies 4-5 explore the role of the principle of proportionality in producing these effects. We examine which harms—those directly inflicted by a company or those that are the indirect result of a company’s actions—factor into such calculations. Guided by insights from our earlier studies, Study 6 tests two ways companies can compensate higher-status customers more handsomely without triggering as much outrage among their broader customer base.
Drivers of Dynamic Learning Mechanism and Dynamic Knowledge Articulation in Alliance Organizations

This study demonstrates that resource-based view (RBV) misidentifies the locus of dynamic knowledge articulation and long-term dynamic competitive capabilities, and focuses on the distinctive role of drivers of dynamic learning mechanism in the evolution of dynamic knowledge articulation and dynamic competitiveness. Five drivers of dynamic learning mechanism such as the integration power of managers, external linkages, previous experience, repeated practice, and codification of experience play important roles on developing dynamic knowledge articulation, and ambiguity is a negative driver impact on developing dynamic knowledge articulation. Dynamic knowledge articulation is a positive impact on dynamic competitiveness in alliance organizations. The purpose of this paper is to contribute to a more complete understanding on developing dynamic knowledge articulation via the dynamic learning mechanism. The paper defines a clear theoretical model for the tautological animadversion of past research on RBV that can be complemented.
IS ARTICULATION-TO-SPEECH SYNTHESIS LANGUAGE INDEPENDENT? A PILOT STUDY

Articulation-to-speech (ATS) synthesis is to directly synthesize speech from articulatory information, which does not require textual input. ATS has recently shown the potential for assistive technologies such as silent speech interfaces (SSIs). ATS is theoretically language-independent, since there is no dictionary involved. However, to our knowledge, there is no data-based experiment has been conducted to answer this question, due to lack of the multi-language, articulatory movement data from the same speakers. In this study, we conducted speaker-dependent ATS experiments using data collected from bilingual speakers, who speak two of the three languages: English, Spanish, and Korean. The experimental results indicated the performance was degraded if ATS was trained with a language and tested with another language. Interestingly, we observed the performance of ATS for one language could be improved if some samples of another language were added to the training dataset.
Unilateral Introduction of Destination-Based Corporate Income Taxation ∗

We consider source-based and destination-based corporate income taxation in a symmetric two-country model with heterogeneous firms. Whereas source-based taxation yields (fiscal) externalities and is thus inefficient, bilateral destination-based taxation is efficient, as proposed by Auerbach & Devereux (2013). This paper shows that the unilateral introduction of a destination-based tax may, first, increase tax revenue in both countries and, second, be Pareto improving if certain conditions are met. Thus, even if a multilateral reform is not feasible, a unilateral reform may be desirable both from the individual and the global point of view. JEL classification: F23, H21, H25
ASSESSING THE EFFECT OF ADAPTIVE MUSIC ON PLAYER NAVIGATION IN VIRTUAL ENVIRONMENTS

Through this research, we develop a study aiming to explore how adaptive music can help in guiding players across virtual environments. A video game consisting of a virtual 3D labyrinth was built, and two groups of subjects played through it, having the goal of retrieving a series of objects in as short a time as possible. Each group played a different version of the prototype in terms of audio: one had the ability to state their preferences by choosing several musical attributes, which would influence the actual spatialised music they listened to during gameplay; the other group played a version of the prototype with a default, non-adaptive, but also spatialised soundtrack. Time elapsed while completing the task was measured as a way to test user performance. Results show a statistically significant correlation between player performance and the inclusion of a soundtrack adapted to each user. We conclude that there is an absence of a firm musical criteria when making sounds be prominent and easy to track for users, and that an adaptive system like the one we propose proves useful and effective when dealing with a complex user base.
TAGAN: Tonality Aligned Generative Adversarial Networks for Realistic Hand Pose Synthesis

3D hand pose estimation from a single RGB image is important but challenging due to the lack of sufﬁciently large hand pose datasets with accurate 3D hand keypoint annotations for training. In this work, we present an effective method for generating realistic hand poses, and show that existing algorithms for hand pose estimation can be greatly improved by augmenting training data with the generated hand poses, which come naturally with ground-truth annotations. Speciﬁcally, we adopt an augmented reality simulator to synthesize hand poses with accurate 3D hand-keypoint annotations. These synthesized hand poses look unnatural and are not adequate for training. To produce more realistic hand poses, we propose to blend each synthetic hand pose with a real background and develop tonality-alignment generative adversarial networks (TAGAN), which align the tonality and color distributions between synthetic hand poses and real backgrounds, and can generate high-quality hand poses. TAGAN is evaluated on the RHP , STB , and CMU-PS hand pose datasets. With the aid of the synthesized poses, our method performs favorably against the state-of-the-arts in both 2D and 3D hand pose estimation.
Synoptic Climatology of Deep Slab Avalanches in the Western United States

Deep slab avalanches result from season-long weather beginning with the formation of snow stratigraphy conducive to deep slab avalanches. The onset of deep slab activity is commonly preceded by heavy snowfall, wind, rain-on-snow, or rapid warming events. Local slope weather (or surface weather) is mainly driven by atmospheric circulation at higher altitudes. The field of synoptic climatology relates these atmospheric processes to surface observations. Previous work has investigated the meteorology of deep slab avalanches, as well as the synoptic climatology of snowfall and avalanche occurrence in several different locations around the world. However, there is limited research that specifically relates atmospheric circulation to deep slab avalanches. This work aims to bridge a knowledge gap by investigating the synoptic climatology of deep slab avalanches across the western United States. We obtained daily 500 mb geopotential height maps from the NCEP/NCAR Reanalysis dataset for 38 consecutive winter seasons from 1979-2017, resulting in over 6,000 days of data. We have classified these daily 500 mb height maps using Self-Organizing Maps (SOM) to obtain 20 summary map patterns characterizing the observed modes of atmospheric variability over western North America and the eastern Pacific. We used Westwide Avalanche Network (WAN) data from three ski resorts across the western U.S. that represent the three dominant snow climates (maritime, continental, and intermountain) and count the number of deep slab avalanche events for each synoptic type at each location. We investigate which synoptic types tend to occur immediately prior to deep slab avalanche events. Our work improves our understanding of the relationship between atmospheric circulation and deep slab avalanches, thereby enabling practitioners to better anticipate deep slab avalanche events.
Rehabilitation Provision and Practice Patterns across Canada

Purpose: Rehabilitation is increasingly recognized as an important therapeutic intervention for people with cancer. The main objective of this study was to explore the current practice pattern and provision of oncology rehabilitation in Canada. Methods: A descriptive cross-sectional online survey was administered to Canadian facilities offering cancer treatment and/or listed as offering rehabilitation services during or after cancer treatment (cancer centres, rehabilitation hospitals, community centres, and private clinics). Results: Of the 116 sites contacted, 62 completed the questionnaire, 20 of which reported having an oncology rehabilitation programme. The majority of respondents indicated that they are not meeting their clients’ rehabilitation needs. Rehabilitation programmes were provided by multidisciplinary health care teams, the majority of which included a physiotherapist. Funding and availability of resources were identified as the main barriers to the development of oncology rehabilitation programmes. Conclusions: Formal oncology rehabilitation programmes appear to be scarce, despite growing evidence that rehabilitation offers benefits across the cancer survivorship continuum.
SUSTAINING FOREST HABITAT FOR THE NORTHERN GOSHAWK : A QUESTION OF SCALE

The nest area, post-fledging family area, and foraging area are critical home range components for maintaining Northern Goshawk (Accipiter gentilis) populations. Each of these forest components has a specific purpose in the life history of the Northern Goshawk and each contains several important attributes, ranging from forest structure to forest floor characteristics. The way in which home range components in a forest regenerate, develop, and die is highly variable, both temporally and spatially. Therefore, forests need to be regenerated and tended to ensure that a portion of a goshawk’ s home range is in old forests indefinitely. This can be best accomplished by analyzing and managing large tracts of forests as sustainable ecological units rather than managing smaller tracts of forests as individual home ranges.
An Expert Forensic Investigation System for Detecting Malicious Attacks and Identifying Attackers in Cloud Environment

— In recent years’ complex and high level computations is done in cloud environment to achieve better performance with low cost. Different large and medium organizations are moving towards cloud computing due to its several trending features. This leads to a drastic increase in cloud services. However, shared on demand characteristic of cloud increases the vulnerability of several security threats. Several security mechanisms and intrusion identification techniques are proposed in the recent years to ensure a better quality of services. But ensuring a complete flawless system is very difficult. So, forensic science or investigation helps in identifying the adversary and collecting proper evidence against the intruder. No traditional digital and network forensic methods are applicable in cloud computing due to its different architectural features compared to a client-server network. A generic forensic model is proposed in this paper for cloud environment. Focus is given on the identification phase of the forensic system because a proper identification of the intruder leads to better forensic evidence generation. A strong fuzzy based expert forensic model ―Fuzzy Expert System for Network Log Analysis‖ and ―Expert System for Management Log Analysis‖ is projected which analyses network and management logs from cloud server for identifying the intruder. A ―Forensic Investigation Report‖ is prepared to serve as a forensic report that will help to smoothly continue the forensic investigation as well as serve as evidence. The proposed model is also simulated in a private cloud environment showing improved accuracy up to ~5.6% over known forensic systems.
Using Social Media to Assess Earthquake Impact on People and Infrastructure : Examples from Earthquakes in 2018

Social media activity following the occurrence of an earthquake has the potential to provide valuable data on infrastructure condition and for disaster response and reconnaissance purposes. The challenge however is that the data with valuable content (i.e., the signal) is small compared to the earthquake-related social media activity overall (i.e., the noise). We explore Twitter activity related to earthquakes throughout 2018 and also analyze selected earthquakes with the intent to explore social media dynamics and compare the observed patterns to the characteristics of the earthquake event. We find that earthquake-related social media activity, amounting to more than 4 million tweets in 2018, directly relates to earthquake activity. Social media activity associated with a specific event is affected by the earthquake magnitude, and other social factors. However, the most important factor is the earthquake intensity with respect to population density. Even relatively small events (e.g., 4<Mw<5) in urban areas, had significant social networking response. Damaging earthquakes have a continued social media response compared to events that did not cause significant infrastructure damage. Social media activity was also spatially correlated to earthquake activity. Analyses using machine learning demonstrated that there is significant content related to the impact of the earthquake on infrastructure that can be used to pinpoint expected damage in the immediate aftermath of an earthquake.
Super-Resolution For Speech Recognition

Automatic bandwidth extension (restoring high-frequency information from low sample rate audio) has a number of applications in speech processing. We introduce an end-to-end deep learning based system for speech bandwidth extension for use in a downstream automatic speech recognition (ASR) system. Specifically we propose a conditional generative adversarial network enriched with ASR-specific loss functions designed to upsample the speech audio while maintaining good ASR performance. Evaluations on the speech commands dataset and the LibriSpeech corpus show that our approach outperforms a number of traditional bandwidth extension methods with respect to word error rate.
BURR FORMATION MECHANISMS DURING SLOT-END MILLING OPERATIONS FOR LOW CARBON AND STAINLESS STEELS

Understanding of burr formation mechanism is essential in order to reduce the deburring cost by minimizing the burr formation. And, to avoid or minimize the burr formation during cutting, it is necessary to realize the relationship between the burr formation mechanism and the cutting parameters involved in the machining operations. This paper is first concerned with a general review on the milling burrs, including classification of burr types, mechanisms of milling burr formation and cutting conditions, and types of burr measurement. Therefore, this research is an attempt to investigate experimentally the effect of using a wide range of speeds, feed, and cutting depths on the mechanism of formation of burr in slot-end milling operations of low carbon and stainless steels using HSS cutting tools and cutting fluids. Also, this study was focused on the influence of these cutting parameters on the burr size and type. Thus, burr heights were measured at different machining conditions and the data obtained for low carbon steel were compared with those for stainless steel. The quantitative measurements provide much useful information because of relatively uniform small burrs formed over the used cutting conditions. However, the burr heights in general were minimized over the used cutting conditions for both materials
BUILDING ADAPTIVE TESTS USING BAYESIAN NETWORKS 1

Bayesian networks are probabilistic graphical models that are capable of modelling domains comprising uncertainty. They were introduced to the field of expert systems by Pearl [17] and Spiegelhalter and R. P. Knill-Jones [21]. The first applications were an expert system for electromyography Munin [2] and the Pathfinder system [8]. Since then Bayesian networks were successfully applied in several areas. Strength of graphical models is not only that they enable efficient uncertainty reasoning with hundreds of variables (e. g. using the method of Lauritzen and Spiegelhalter [13]), but also they help humans to understand better the modelled domain. This is mainly due to their comprehensible representation by use of directed acyclic graphs representing dependencies between domain variables. See [14] where some recent applications of Bayesian networks are discussed. Bayesian network consists of an directed acyclic graph (DAG) G = (V, E), to each node i ∈ V corresponds one random variable Xi with a finite set Xi of mutually exclusive states and a conditional probability table (CPT) P (Xi | (Xj)j∈pa(i)), where pa(i) denotes the set of parents of node i in graph G. See Figure 1 for an example of Bayesian network. Bayesian network encodes qualitative and quantitative knowledge. Quantitative knowledge is represented by CPTs, while qualitative is encoded by use of a DAG. The DAG implies certain conditional independence relations between variables (Xi)i∈V .
Responses of Successful Latina Students to Academic Challenges of Prerequisite Nursing Courses

Responses of Successful Latina Students to Academic Challenges of Prerequisite Nursing Courses by Julie Wiggins Nadeau MSN, California State University, Sacramento, 1996 BA, Gustavus Adolphus College, 1981 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Education Teacher Leadership
Proactive Message Passing on Memory Factor Networks

We introduce a new type of graphical model that we call a "memory factor network" (MFN). We show how to use MFNs to model the structure inherent in many types of data sets. We also introduce an associated message-passing style algorithm called "proactive message passing"' (PMP) that performs inference on MFNs. PMP comes with convergence guarantees and is efficient in comparison to competing algorithms such as variants of belief propagation. We specialize MFNs and PMP to a number of distinct types of data (discrete, continuous, labelled) and inference problems (interpolation, hypothesis testing), provide examples, and discuss approaches for efficient implementation.
TALLA: Large-scale TDoA Localization with Ultra-wideband Radios

Time difference of arrival (TDoA) localization with ultra-wideband (UWB) radios is rapidly gaining interest. Nevertheless, TDoA requires tightly time-synchronized anchors, either via wireless synchronization in small-scale setups with all anchors in range or via expensive wired backbones, both ultimately hampering the use of TDoA in large-scale areas.In this paper we present TALLA, a novel wireless-only TDoA approach able to scale over large operational areas without sacrificing positioning accuracy. TALLA relies on a TDMA schedule enabling continuous, multi-hop operation of the anchor infrastructure. We evaluate TALLA in our 12-node UWB testbed and in much larger (>100 anchors) simulated areas, empowered by a technique that generates synthetic timing information faithfully reproducing the trends of the real one. Our real and simulated results show that TALLA achieves decimeter-level accuracy while tracking a moving target across several hops.
Social organization , distribution and movements of a migratory guanaco population in the Chilean Patagonia Organizaci

Herein we document the distribution, movement, and social organization of a guanaco (Lama guanicoe) population in a 25km2 area at Torres del Paine National Park in southern Chile. In 1980, the population was censused 28 times. Four socioecological periods were recognized: summer territorial, fall transitional, winter aggregational, and spring transitional. Family groups, male groups, solo males, mixed groups, and female groups were the major social units recognized. Guanacos spent the summer in the east region, migrating 12 km to the west region during winter. Family groups (53% of all animals), male groups (35%) and solo male (8%) were the main social units in summer, and mixed groups (80%) in winter. Snow cover and availability of forage were the suspected causes of seasonal migratory movements, that in turn greatly influenced social organization, group size, and composition.
1 Location , Market Orientation and Organizational Performance in the Colombian Health Industry

(maximum 300 words) This work analyses for service companies (in the health industry) the dynamics and relationships between the variables of Location (i.e. within an industrial cluster), Market Orientation (hereinafter MO) and Organisational/Business Performance (hereinafter OP). A mixed research methodology (quantitative and qualitative) was applied. For the quantitative analysis, 134 managers of companies in the health sector of the city of Cali (Colombia) were surveyed by telephone. For the qualitative analysis, 6 managers of companies from the same sample were interviewed. For the measurement of MO and OP reliable scales found in the literature were used. The results obtained validate that health industry firms located in a cluster have higher performance. On the other hand, it was verified that MO generates a positive and significant effect on OP, with Competitor Orientation being the sole component of MO with a significant influence. These results provide empirical evidence to support health firm’s managers in their strategic decision making in terms of locating in a cluster and/or in strengthening their MO level, mainly customer orientation, with a view to maintain a sustainable good performance.