Miracle-dz/UniFAR-FTUs huggingface dataset:
The MediaMill TRECVID 2010 Semantic Video Search Engine

In this paper we describe our TRECVID 2010 video retrieval experiments. The MediaMill team participated in three tasks: semantic indexing, known-item search, and instance search. The starting point for the MediaMill concept detection approach is our top-performing bag-of-words system of TRECVID 2009, which uses multiple color SIFT descriptors, sparse codebooks with spatial pyramids, kernel-based machine learning, and multi-frame video processing. We improve upon this baseline system by further speeding up its execution times for both training and classification using GPU-optimized algorithms, approximated histogram intersection kernels, and several multi-frame combination methods. Being more efficient allowed us to supplement the Internet video training collection with positively labeled examples from international news broadcasts and Dutch documentary video from the TRECVID 2005-2009 benchmarks. Our experimental setup covered a huge training set of 170 thousand keyframes and a test set of 600 thousand keyframes in total. Ultimately leading to 130 robust concept detectors for video retrieval. For retrieval, a robust but limited set of concept detectors justifies the need to rely on as many auxiliary information channels as possible. For automatic known item search we therefore explore how we can learn to rank various information channels simultaneously to maximize video search results for a given topic. To further improve the video retrieval results, our interactive known item search experiments investigate how to combine metadata search and visualization into a single interface. The 2010 edition of the TRECVID benchmark has again been a fruitful participation for the MediaMill team, resulting in the top ranking for concept detection in the semantic indexing task. Again a lot has been learned during this year’s TRECVID campaign; we highlight the most important lessons at the end of this paper.
Visualization of areas of interest in software architecture diagrams

Understanding complex software systems requires getting insight in how system properties, such as performance, trust, reliability, or structural attributes, correspond to the system architecture. Such properties can be seen as defining several 'areas of interest' over the system architecture. We visualize areas of interest atop of system architecture diagrams using a new technique that minimizes visual clutter for multiple, overlapping areas for large diagrams, yet preserves the diagram layout familiar to designers. We illustrate our proposed techniques on several UML diagrams of complex, real-world systems.
On the Generalization of Deep Reinforcement Learning Methods in the Problem of Local Navigation

In this paper, we study the application of Deep Reinforcement Learning (DRL) algorithms in the context of local navigation problems, in which a robot moves towards a goal location in unknown and cluttered workspaces equipped only with limited-range exteroceptive sensors, such as Light Detection And Ranging (LiDAR). Collision avoidance policies based on DRL present some advantages, but they are quite susceptible to local minima, once their capacity to learn suitable actions is limited to the sensor range. Since most robots perform tasks in unstructured environments, it is of great interest to seek generalized local navigation policies capable of avoiding local minima, especially in untrained scenarios. To do so, we propose a novel reward function that incorporates map information gained in the training stage, increasing the agent’s capacity to deliberate about the best course of action. Also, we use the Soft Actor-Critic (SAC) algorithm for training our Artiﬁcial Neural Network (ANN), which shows to be more eﬀective than others in the state-of-the-art literature. A set of sim-to-sim and sim-to-real experiments illustrate that our proposed reward combined with the SAC outperforms the compared methods in terms of local minima and collision avoidance.
Understanding preferences for walking attributes

Formally developed walking routes can have substantial benefits for individuals as a recreational resource and can be a tool for promoting economic development in marginal rural areas. Moreover, the provision of walking trails can facilitate individuals in meeting health related guidelines for physical activity. The overall aim of this paper is to examine respondents’ attitudes towards the formal development and maintenance of walking trails. Analysis of individuals’ attitudes towards walking related activities can provide information from which policymakers can ascertain if policy measures in relation to the provision of walking trails and public access to farmland are in line with citizens’ views and needs. A principal component factor analysis was performed on respondent’s importance ratings of various walking attributes. Factor scores were then used in a binary logit model formulated to ascertain the major influences on individuals demand for walking trail facilities (such as a route map, information point, car parking etc.). Results suggest that certain cohorts of the population have a much higher demand for the provision of various trail facilities on walking routes. The study also highlighted how the importance respondents place on the provision of trail facilities as well as a variety of background variables play a significant role in influencing respondents’ willingness to make a financial contribution towards the formal development and maintenance of walking trails.
cTBL: Augmenting Large Language Models for Conversational Tables

An open challenge in multimodal conversational AI requires augmenting large language models with information from textual and non-textual sources for multi-turn dialogue. To address this problem, this paper introduces Conversational Tables ( C TBL), a three-step encoder-decoder approach to retrieve tabular information and generate dialogue responses grounded on the retrieved information. C TBL uses Transformer encoder embeddings for Dense Table Retrieval and obtains up to 5% relative improvement in Top-1 and Top-3 accuracy over sparse retrieval on the H YRBI D IA - LOGUE dataset. Additionally, C TBL performs tabular knowledge retrieval using encoder and decoder models, resulting in up to 46% relative improvement in ROUGE scores and better human evaluation for response generation on H YRBI D IALOGUE .
Tropical Atlantic seasonal dynamics in the Early Middle Eocene from stable oxygen and carbon isotope profiles of mollusk shells

There are disagreements whether the tropical sea surface temperature during the warm early to early middle Eocene was equal to the present temperature or significantly lower. In order to determine the sea surface temperature of the eastern tropical Atlantic in the early middle Eocene we have established δ18O and δ13C profiles along the transect of growth of excellently preserved shells of Turritella gastropods and a Macoma bivalve from tropical West Africa. This method makes possible a distinction between the winter and the summer temperature. The δ18O and δ13C data indicate sea surface temperatures of 29°–31°C, which declined to 23°–25°C during seasonal upwelling. These conditions are similar to those of today and suggest that significant seasonal dynamics prevailed in the early middle Eocene tropics. The new data further contradict that the tropics cooled during the generally warm early to early middle Eocene, as inferred in studies on foraminifera δ18O.
Malicious-Proof and Fair Credit-Based Resource Allocation Techniques for DSA Systems

We propose a credit-based resource allocation technique for dynamic spectrum access that is robust against malicious and selfish behaviors and ensures good overall system fairness performance while also allowing spectrum users to achieve high amounts of service. We also propose a new objective function that, when combined with the proposed credit-based technique, leads to further improvements of the system fairness performance. Our proposed techniques overcome user misbehavior by masking the impact of the users' pursued private objectives on the overall system performance. They also improve fairness among users by allocating service to users adaptively by accounting for how much service each user has received in the past. Our simulation results show that our proposed techniques maintain high system performance by allowing users to achieve high amounts of service and by ensuring fair allocation of spectrum resources among users even in the presence of misbehaved users. Using simulations, we also show that these high performances are also achievable under various different network scenarios.
Ogallala Oases: Water Use, Population Redistribution, and Policy Implications in the High Plains of Western Kansas, 1980–1990

Abstract Contrary to recent literature that portrays the High Plains as an over-exploited region of economic and demographic decline and as place that should be deprivatized, abandoned, and resotred to a buffalo commons, this study suggests that the irrigated Ogallala region is not dying. It is experiencing, instead, a major redistribution of population into nucleated Ogallala oases sustained by the presence of ample groundwater for irrigation. Although more remote rural areas with irrigated agriculture are losing population, they endure nonetheless as important components of the cultural landscape in their role as hinterlands for the Ogallala oases. This paper examines the relationship between proximity to groundwater use and population change in the Ogallala aquifer region of Kansas during the 1980s. The analysis focuses on three levels; the county, incorporated and census designated places, and rural census county subdivisions. The association between proximity to groundwater and population change is s.
Urban Spatiotemporal Data Synthesis via Neural Disaggregation

The level of granularity of open data often conflicts the benefits it can provide. Less granular data can protect individual privacy, but to certain degrees, sabotage the promise of open data to promote transparency and assist research. Similar in the urban setting, aggregated urban data at high-level geographic units can mask out the underline particularities of city dynamics that may vary at lower areal levels. In this work, we aim to synthesize fine-grained, high resolution urban data, by breaking down aggregated urban data at coarse, low resolution geographic units. The goal is to increase the usability and realize the values as much as possible of highly aggregated urban data. To address the issue of simplicity of some traditional disaggregation methods – 1) we experimented with numerous neural-based models that are capable of modeling intricate non-linear relationships among features. Neural methods can also leverage both spatial and temporal information concurrently. We showed that all neural methods perform better than traditional disaggregation methods. Incorporating the temporal information further enhances the results. 2) We proposed a training approach for disaggregation task, Chain-of-Training (COT), that can be incorporated into any of the training-based models. COT adds transitional disaggregation steps by incorporating intermediate geographic dimensions, which enhances the predictions at low geographic level and boosts the results at higher levels. 3) We adapted the idea of reconstruction (REC) from super-resolution domain in our disaggregation case — after disaggregating from low to high geographic level, we then re-aggregate back to the low level from our generated high level values. Both strategies improved disaggregation results on three datasets and two cities we tested on.
Observations of dugongs at Aldabra Atoll, western Indian Ocean: lagoon habitat mapping and spatial analysis of sighting records

Until recently, it was thought that dugongs (Dugong dugon) were extinct in the Seychelles. However, a collection of sightings at Aldabra Atoll, a World Heritage Site in the Seychelles, has renewed interest in dugong distribution in the western Indian Ocean. This article consolidates the records of dugong sightings held in the Aldabra Research Station library and explores their spatial patterning. The locations of sightings (2001–2009) are plotted onto a high-resolution benthic habitat map of the Aldabra lagoon created by classifying a QuickBird satellite remote-sensing image in January 2009. A spatial cluster detection procedure is applied to point records of sightings to reveal a statistically significant cluster of sightings in the north-west of the lagoon, at Bras Monsieur Clairemont, suggesting a mutual co-existence of dugongs and seagrass beds. A habitat suitability model combines the point data set of dugong sightings within the continuous benthic habitat map and identifies the central western area as containing the most suitable habitat for dugong inside the Aldabra lagoon.
Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback

Programing robots to perform tasks is difficult in the real world because of its richness and uncertainty. For robots and agents to be more useful, they must be able to learn quickly from ordinary people via natural interactions. In this paper, we investigate how an agent can learn from demonstration and positive and negative evaluative feedback provided by a human teacher. Specifically, we proposed a model-based method-IRL-TAMER-by combining learning from demonstration via inverse reinforcement learning (IRL) and learning from human reward via the TAMER framework. We tested our method in the Grid World domain and compared with the TAMER framework using different discount factors on human reward. Our results suggest that although an agent learning via IRL can learn a useful value function indicating which states are good based on the demonstration, it cannot obtain an effective policy navigating to the goal state with one demonstration. However, learning from demonstration can reduce the number of human reward needed to obtain an optimal policy, especially the number of negative feedback. That is to say, learning from demonstration can be a jump-start for agent's learning from human reward and reduce the number of mistakes-incorrect actions. Furthermore, our results show that learning from demonstration can only be useful for agent's learning from human reward when the discount factor is small, i.e., learning from myopic human reward.
Anisotropy of schists: Contribution of crustal anisotropy to active source seismic experiments and shear wave splitting observations

We have made sets of five independent compressional and shear wave velocity measurements, which with density, allow us to completely characterize the transverse isotropy of samples from five metamorphic belts: the Haast schist terrane (South Island, New Zealand), Poultney slate, Chugach phyllite, Coldfoot schist, and Pelona schist (United States). These velocity measurements include compressional wave velocities for propagation parallel, perpendicular, and at 45° to the symmetry axis, shear wave velocity for propagation and particle motion perpendicular to the symmetry axis, and shear wave velocity for propagation parallel to the symmetry axis. Velocity measurements were made up to pressures of 1 GPa (∼35-km depth) where microcracks are closed and anisotropy is due to preferred mineral orientation. Our samples exhibit compressional wave anisotropy of 9–20% as well as significant shear wave splitting. Metamorphic terranes that are anisotropic to ultrasonic waves may also be anisotropic at the scale of active and passive seismic experiments. Our data suggest that a significant thickness (10–20 km) of appropriately oriented (steeply dipping foliation) schist in the crust could contribute as much as 45% of observed shear wave splitting. Our data set can also be used to model the effects of crustal anisotropy for active source seismic experiments in order to determine if the anisotropy of the terrane is significant and needs to be taken into account during processing and modeling of the data.
New evidences of hydrothermal fluids circulation at the Devonian Kess Kess mounds, Hamar Laghdad (eastern Anti‐Atlas, Morocco)

New evidences based on a combination of field and laboratory investigations reinforce the hypotheses that the circulation of warm fluids has remarkably contributed to the origin and development of the Devonian Kess Kess mounds of the Hamar Laghdad Ridge (eastern Anti‐Atlas, Morocco). The limestones of the Hamar Laghdad Ridge were deposited above a structural high generated by calc‐alkaline volcanic activity that has probably fuelled the circulation of warm fluids throughout the overlying carbonate units. The geological and palaeontological attributes described throughout the succession of the Hamar Laghdad Ridge (from the Lochkovian to Frasnian intervals) are interpreted as the result of hydrothermal processes related to a volcanic system. In particular, these attributes seem consistent with a chemo‐physical environment fuelled by the circulation of warm and late magmatic fluids. These attributes include a very low oxygen stable isotope signature (δ18O ~ −10‰) for carbonates. Evidences for a late magmatic fluid circulation consist of volcanic glass and pyroclasts replacement with hydrothermal minerals such as quartz, anatase and clinochlore. Fluids circulating through veins and pores into sediments, and venting to the seafloor, probably induced the formation of cavities where monospecific trilobite communities were detected. The partially silicified trilobite remains are associated with traces of goethite. This iron‐bearing oxide mineral is also present in the upper part of the Hamar Laghdad Ridge. All these attributes are here interpreted as possible evidences for a low‐temperature hydrothermal venting system active during the Lochkovian–Frasnian time span. This study combines an updated revision with new petrographic, geological and geochemical results aimed at providing an overall framework on the origin and early diagenesis of the Devonian succession of Hamar Laghdad. Copyright © 2014 John Wiley & Sons, Ltd.
Place branding strategies and urban transformation in ’emerging world class’ cities

Countries in the Persian Gulf and Pacific Asia have in a short time transformed themselves into economic powerhouses. The urban environment in these countries has been highly impacted by a tremendous economic growth and an emphasis on megaprojects containing modernistic urban design as well as the hosting of prestigious events. This has evidently been a part of well-defined strategies in order to maintain growth and to secure future development. Although same or at least similar strategies are used by many ‘emerging world class’ cities, they act in very different environments and with different preconditions. These strategies are mainly thought to originate from theories about global competitiveness where cities act and compete in a global hierarchy.One of the ‘emerging world class’ cities is Kuala Lumpur, the capital of Malaysia. In the case study provided in this thesis, evidence of a very tight and defined strategy containing the usage of world-class urban projects as a facilitator of future growth is presented. This strategy is also a product of a powerful government with a highly motivated Prime minister with almost unrestricted possibilities. Not many other actors have played a significant role in the recent urban transformation of Kuala Lumpur. A different perspective is also investigated which seeks to explain the development using a more domestic approach rather than the global perspective which is emphasized through this study.
An Evaluation of Naive Bayesian Anti-spam Filtering

It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail (" spam "). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter's performance, issues that had not been previously explored. After introducing appropriate cost-sensitive evaluation measures, we reach the conclusion that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide

Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active-learner, which is initialized by X-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. A multi-phase potential is generated for a canonical example of the archetypal refractory oxide, HfO2, by drawing a minimum number of training configurations from room temperature to the liquid state at ~2900oC. The method significantly reduces model development time and human effort.
Phone Recognition Using a Non-Linear Manifold with Broad Phone Class Dependent DNNs

Although it is generally accepted that different broad phone classes (BPCs) have different production mechanisms and are better described by different types of features, most automatic speech recognition (ASR) systems use the same features and decision criteria for all phones. Motivated by this observation, this paper proposes a two-level DNN structure, referred to as a BPC-DNN, inspired by the notion of a topological manifold. In the ﬁrst level, several small separate BPC-dependent DNNs are applied to different broad phonetic classes, and in the second level the outputs of these DNNs are fused to obtain senone-dependent posterior probabilities, which can be used for frame level classiﬁcation or integrated into Viterbi decoding for phone recognition. In a previous paper using this approach we reported improved frame classiﬁcation accuracy on the TIMIT corpus compared with a conventional DNN. The contribution of the present paper is to demonstrate that this advantage extends to full phone recognition. Our most recent results show that the BPC-DNN achieves reductions in error rate relative to a conventional DNN of 16% and 8% for frame classiﬁcation and phone recognition, respectively.
Community-Based Quality of Life Indicators for Urban Areas as Derived in Galway City, Ireland

This paper describes how promoting the quality of life and well-being of citizens is increasingly recognized as an essential component of sustainable urban development. The use of indicators is considered by many to be a key element in giving practical effect to the concept of sustainable cities. However, an emerging body of literature acknowledges that indicators are unlikely to be acceptable or particularly useful unless they are developed in close consultation with their target populations. Community-derived indicators allow individual citizens and communities to express and measure the most important determinants of quality of life for them. This participatory approach raises awareness of elements of the urban environment that are highly valued by citizens and has the potential to contribute to improving local governance. The derivation of quality of life indicators and their observation in urban settings, together with the compilation, analysis, and interpretation of related databases, are highly problematic, not least because the indicators tend to be qualitative in nature and difficult to measure objectively. The use of community-derived quality of life indicators in sustainable urban planning is reviewed in this paper. The process of developing such indicators in Galway (Ireland), one of the fastest growing cities in the European Union (EU) is examined in detail. The manner in which the municipal authority may utilize these indicators to monitor quality of life trends and as a practical tool for bolstering participatory democracy is discussed. This enhanced governance is demonstrated to be a potentially influential process of promoting sustainability practices in cities.