Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric interpretation of a CNN's last layer

Training Convolutional Neural Networks (CNNs) remains a non-trivial task that in many cases relies on the skills and experience of the person conducting the training. Choosing hyper-parameters, knowing when the training should be interrupted, or even when to stop trying training strategies are some difficult decisions that have to be made. These decisions are difficult partly because we still know little about the internal behaviour of CNNs, especially during training. In this work we conduct a methodical experimentation on MNIST public database of handwritten digits to better understand the evolution of the last layer from a geometric perspective: namely the classification vectors and the image embedding vectors. Within this context we present the problem of the variability across equal set-up trainings due to the random component of the initialisation method. We propose a novel approach that guides the initialisation of the parameters in the classification layer. This method reduces 12% the variability across repetitions and leads to accuracies 18% higher on average.
Multi-granularity Generator for Temporal Action Proposal – Supplementary Material

Recall Rates. Ground-truth proposals with short temporal spans are hard to capture, which mainly dues to that short proposals are of less semantic information. In the main paper, we illustrate the improvement of recall rates in short proposals with the U-shape architecture. Here, we demonstrate the short proposal recall rates of different methods including DAP [2], TURN [4], CTAP [3], BSN [6] and our proposed MGG on the testing set of THUMOS-14. The temporal spans ranges from 1 frame to 60 frames, and the recall rates are computed with AN and tIoU set to 100 and 0.75, respectively. As shown in Table 1, the recall rates of MGG outperform the other competitor methods. One reason is that the temporal boundary adjustment (TBA) module is helpful for the proposal to be accurate in boundaries. Thus the generated proposals will have high overlap with ground-truths. Another reason is the U-shape architecture, which provides high-level semantic information for lower layers and helpful for the capture of proposals with short temporal durations.
Coloring gray-scale image using artificial neural networks

This paper presents a novel method on coloring the grayscale images. For this purpose, a combination of artificial neural networks and some image processing algorithms was developed to transfer colors from a user-selected source image to a target grayscale image. According to the results of this combining method, a survey where volunteers were asked to rate the plausibility of the colorings generated automatically was performed for individual images. In most cases automatically-colored images were rated either as totally or mostly acceptable.
Towards the topologically correct drainage network : integration between TAS GIS and GRASS GIS software

In the segment of freely available software (Freeware and FLOSS) there are no tools which individually allow to build topologically corrected drainage network and use it to extended spatial analysis.Some of the useful functions are diffused among different tools; two programs distinguish among other: GRASS GIS: the open source fully featured universal GIS suite and TAS GIS: (free software developed by J. Lindsay) specialized in hydro-geomorphological terrain analysis with lot of procedures unavailable in other programs. The main advantage of GRASS is its interoperability with Linux system, PostgreeSQL database and R statistical environment, which allow easily process data in batch mode in any manner. Unfortunately GRASS GIS hydrological modeling and raster-vector conversion tools do not create correct topological structure. On the other hand TAS GIS has very limited batch procedures and no tools for network analysis but has tools to generate topologically corrected vector data structure of drainage network. Additional information, like Starhler stream order, channel type and many others can be acquired in raster format. It is important to take into account that drainage network is a tree data structure called rooted tree where one node (outlet of the catchment) is designated the root. Segments (channels) of the network have a natural orientation towards the root. Most of the network has internal topology based on topology of the GIS data structure, however the lack of data describing the spatial relationship within the catchment often limits the possibility of advanced spatial analysis. This approach must deal with the specificity of the network considered as a directed tree and allow to model characteristics not only on the network nodes (junctions) but also along the sections between nodes. The procedure of data interoperability between TAS and GRASS GIS use vector-format drainage network and raster data imported to GRASS GIS from TAS. Procedure uses internal GRASS vector topology and SQL statements. Additional information about relationship: ancestor-successor, Strahler stream order of particular channel, Shreve stream magnitude are derived with sampling raster data and build into SQL relationship. Such network, connected with additional SQL tables can store bundle of geomorphological data both for nodes (junctions) and particular vertex of any channel. Storing data in GRASS GIS topological format allow to transfer geometrical data to R environment where geometrical properties of network components like channel directions can be easily calculated without particular skills in programing. keywords: drainage networks, geomorphology, OpenSource GIS, PostgreSQL, hierarchical structures,
Strategic Alignment as a Key Factor of Success for 4 PL Development : A Research Program

One of the most significant upheavals of the logistics industry is the emergence of a new generation of logistics service providers, called fourth party logistics (4PL). Their function is to provide the supply chain members with a customized service, based on a flawless coordination of logistical resources mobilized from the various firms. Considering the importance of IT, the paper suggests a research program on this subject referring to strategic alignment models. Its aim is to know whether the 4PL are able to combine their business strategy, IT deployment and organizational performance efficiently so as to manage interfaces between the supply chain members in the best conditions.
Comparing Pipelined and Integrated Approaches to Dialectal Arabic Neural Machine Translation

When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.
Question Item Quality Analysis in Learning Content Management Systems (LCMS): A Case Study Based Approach

Nowadays Learning Content Management System (LCMS) has taken a major role in e-Learning evolution.Along many proprietory LCMS , Moodle, as an Open Source Software(OSS) based LCMS established itself as a major player in E-Learning deployment.Besides managing many activities under LCMS, assessment module is very important and crucial component. Most of the time we focus on performance issues of the Learners, implicitly ignoring the effectiveness of the assessment questions. Question Item analysis encompasses the process of collecting, summarizing the information from student’s responses to assess the quality of multiple-choice questions (MCQs). This article focuses on available features in LCMS along with a case study based approach towards assessing quality of MCQs in the light of few statistical parameters as Facility index (Fp) and Discriminative Efficiency (DEp), Discrimination efficiency (Dp) etc. So here we are to get interpretation of quality parameters to evaluate overall standard of multiple-choice questions (MCQs). As in near future standardization of web based learning management systems along with question item analysis will significantly influence assessment practices.
Supplement to Infinite Mixture Prototypes for Few-Shot Learning

For all few-shot experiments, we use the same base architecture as prototypical networks for the embedding network. It is composed of four convolutional blocks consisting of a 64-filter 3 × 3 convolution, a batch normalization layer, a ReLU nonlinearity, and a 2 × 2 max-pooling layer per block. This results in a 64-dimensional embedding vector for omniglot, and a 1600 dimensional embedding vector for mini-imagenet. Our models were trained via SGD with RMSProp (Tieleman & Hinton, 2012) with an α parameter of 0.9.
Divergent Last Century Tree Growth along An Altitudinal Gradient in A Pinus sylvestris Dry-edge Population

Research Highlights: This research highlights the importance of environmental gradients in shaping tree growth responses to global change drivers and the difficulty of attributing impacts to a single directional driver. Background and Objectives: Temperature increases associated with climate change might strongly influence tree growth and forest productivity in temperate forest species. However, the direction and intensity of these effects at the dry edge of species range are still unclear, particularly given the interaction between local factors and other global change drivers such as land use change, atmospheric CO2 increase and nitrogen deposition. While recent studies suggest that tree growth in cool temperate forests has accelerated during the last decades of the 20th century, other studies suggest a prevalence of declining growth, especially in dry-edge populations. Materials and Methods: Using historical forest inventories, we analyzed last century tree growth trends (1930–2010) along an elevation gradient (1350–1900 meters above sea level (m a.s.l.)) in a dry edge scots pine (Pinus sylvestris L.) forest in Central Iberian Peninsula. Growth was estimated as decadal volume increments in harvested trees of different size classes from 1930 to 2010 (1930–1940, 1939–1949, 1949–1959, 1959–1968, 1989–1999, 2000–2010). Results: Our results showed opposite growth trends over time depending on elevation. While tree growth has accelerated in the low end of the altitudinal gradient, tree growth slowed down at higher elevations (1624–1895 m a.s.l.). Moreover, the magnitude of growth reduction along the altitudinal gradient increased with tree age. Conclusions: Throughout the last 80 years, growth trends in a rear-edge Pinus. sylvestris forest has shown divergent patterns along an altitudinal gradient. Specifically, environmental conditions have become more adverse for growth at high altitudes and have improved at low altitudes. This suggests that local factors such as topography can modulate the impact of climate change on forest ecosystems.
Representation Learning for Drum Loops with a Variational Autoencoder

A representation is learned for MIDI drum loops, using a variational autoencoder. The aim is to create a representation which will be useful as a component in human-computer interfaces and in music generation systems. A large library of MIDI drum loops is described and used to train an autoencoder neural network in an unsupervised fashion. The result is a low-dimension representation which captures essential dimensions of variation in the data, and can be used to generate new drum loops and interpolate between pairs of loops.
A Methodological Framework for Measuring Social Innovation

»Ein methodischer Ansatz zur Messung sozialer Innovation«. This paper introduces a methodological framework to develop dimensions for measuring social innovation capacities of spatial units. The framework is designed to ensure the compatibility of these dimensions with theoretical concepts as well as innovative practices. Therefore, theoretical top-down strategies have been combined with an empirical bottom-up strategy. From the top-down perspective, we assess opportunities and limits of existing metrics of technologicaleconomic innovation in the light of theoretical requirements of social innovation. As an interim result we present measurement dimensions for social innovation at the national level. Within the framework of the bottom-up strategy, we start our analysis from the local level. In qualitative case studies in four German cities, we investigate whether these dimensions are of significance for the innovative practice. In order to study the innovation process in the context of innovative practice we look at the example of the integration of migrants and discuss systemic determinants of social innovativeness. Finally, these systemic implications have been used to modify the measurement dimensions and to adjust them to the local level.
Validation of a 5-minute VPC Test to Assess and Compare Cognitively Intact Individuals and Individuals with Cognitive Impairments

The prevalence of cognitive impairments in the older adult population is growing. Finding treatment solutions to impede a cognitive decline can possibly lead to fewer cases of mild cognitive impairment, dementia, and Alzheimer’s disease. A Visual Paired Comparison (VPC) could serve as a tool to predict, monitor, and regulate people who are susceptible to a cognitive decline. The purpose of this study was to 1) to determine the validity of the Neurotrack 5-minute VPC test with the Neurotrack 30-minute VPC test, 2) to determine the test-retest reliability of the Neurotrack 5-minute VPC test, 3) to compare Neurotrack 5-minute VPC scores between individuals with cognitive impairment (Mild Cognitive Impairment and/or Alzheimer's Disease) to cognitively intact adults, 4) lastly to compare Neurotrack VPC results with other cognitive tasks (MoCA, NIH toolbox, Dual task) performed within the study. This study included older adults age 60+ split into cognitively intact individuals and cognitively impaired individuals based from the MoCA. Analysis was ran on 28 subject in which 11 were cognitively impaired (mean=.687; Std=.137) and 17 were cognitively intact (mean=.851; Std=.044). The relationship between 5-minute VPC and the 30-minute VPC revealed a positive associations for both the first (r=.504; p=.006) and second (r=.420; p=.019) time points/trials. No significant differences between the 2 time points/trials (p=.212) which indicates a reliable 5-minute VPC test. A significant difference was found between the groups (p=.000). Domain-specific cognitive functions were examined through other assessments, in which the 5-minute VPC test was correlated to each of these tests. This study suggests that VPC to be a potentially reliable tool to assess cognitive function. Table of
Mobility Prediction in MANETs

As mobile devices, such as laptops, PDAs or mobile phones, are getting more and more ubiquitous and are able to communicate with one another using technologies like wireless LAN (WLAN), the paradigm of wireless mobile ad hoc networks (MANETs) is gaining popularity. MANETs impose new challenges to the design of applications and network protocols because of their self-organizing, mobile and error-prone nature. Mobility prediction is a tool to deal with the problems emerging from the nodes’ mobility by predicting future changes in the network topology. This is crucial for different tasks such as routing and distributed server selection. This thesis presents an approach to mobility prediction based on pattern matching. Each node monitors the Signal to Noise Ratio (SNR) of its links to obtain a time series of past measurements. When a prediction is requested, the node tries to detect situations similar to the current one in the history of its links by computing the normalized cross-correlation function of the recent past with the collected training data. The found matches are then used as a base of the prediction. As an application of the SNR prediction, an extension to the formerly developed Priority Based Selection (PBS) algorithm was defined. PBS is used for distributed server selection in mobile ad hoc networks by computing and maintaining a Dominating Set of the network graph. The extension introduces a link stability criterion, which requires that a Client accepts only a node as Server to which it has a link predicted to be stable for a certain time. In order to evaluate the developed prediction algorithm, it has been implemented in the network simulator ns-2. Simulations have shown that the predictions are highly accurate. Furthermore, the application to server selection has been proved successfully, as the stability of the computed Dominating Sets increased significantly using the link stability criterion.
A Pilot Study on Detecting Violence in Videos Fusing Proxy Models

We propose an approach to detect violence in CCTV feeds that is robust to new datasets and situations. This approach breaks with the traditional assumption of having large amounts of training data that are representative samples. Detecting violence in CCTV feeds is an objectively hard problem that is of paramount importance to solve for effective situational understanding. Violence comprises a large spectrum of activities that can go from abuse, to fighting, to road accidents, that can therefore take place in completely different environments, from public buildings, to underground stations, to roads during the day or the night. This large spectrum of activities and environments makes this a hard classification task for machines. We show that there are specific, detectable, and measurable features of video feeds that correlate with—among other things—violence and, by fusing such features with semantic knowledge, we can in principle provide estimates of sequences of videos that correlate with violence.
Unsupervised clustering based understanding of CNN

Convolutional Neural networks have been very successful for most computer vision tasks such as image recognition, classification, object detection and segmentation. Even though CNNs are very successful and give superior results as compared to traditional image processing algorithms, interpretability of their results remains an important issue to be solved. Indeed, lack of interpretability and explainability of how CNN work at their various levels, caused a certain skepticism among their potential users, as for example those working in medical diagnosis or autonomous driving cars. The current study aims to answer some of the issues related to interpretability by the use unsupervised methods to discern the features learned by the CNN in different layers.
User Association and Small-Cell Base Station On/Off Strategies for Energy Efficiency of Ultradense Networks

The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.
Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives Multiple Representations for Pattern Exploration with the Graphing Calculator and Manipulatives

T o teach mathematics as a connected system of concepts, we must have a “shift in emphasis from a curriculum dominated by memorization of isolated facts and procedures and by proficiency with paper-and-pencil skills to one that emphasizes conceptual understandings, multiple representations and connections, mathematical modeling, and mathematical problem solving,” according to the NCTM’s Curriculum and Evaluation Standards for School Mathematics (1989, 125). Too often, educators spend time teaching skills. As a result, little time remains to concentrate on concepts that are essential to understanding mathematics. Students should not be forced to play with symbols on a piece of paper; rather, they should be allowed to play with ideas that lead to conceptual understanding. Manipulatives and technology encourage discovery. Later, some of the desirable skills can be addressed in a remarkably shorter time frame than traditional instruction usually requires (Heid 1992). Many times, as a first introduction to proof by induction, students are asked to prove
Case Study Role Play for Risk Analysis Research and Training

Typically, a risk analysis may identify and document sensitive and confidential information regarding threats, vulnerabilities, assets and their valuation, etc. The intrusive nature of the risk analysis process makes it difficult for researchers (or students) to gain access to scenarios from operational organizations for evaluating (or training on) risk analysis methods. In order to resolve these issues, we propose Case Study Role Play (CSRP). We elaborate the use of CSRP in combination with the Conflicting Incentives Risk Analysis (CIRA) method to analyze privacy risks to an end-user from using the eGovernment service. This paper contributes by demonstrating how CSRP helps to establish a platform for doing risk management related research and training in a ‘reasonably’ realistic environment, where confidentiality, sensitivity issues, red tape and the need for permissions do not create roadblocks. Furthermore, CSRP ensures that the time and resources needed to set up the required environment is low and predictable.
ASSIMILATING TIPPING BUCKET RAIN GAUGE DATA INTO THE PACRAIN DATABASE

The Pacific Rainfall Project (PACRAIN) is part of the Environmental Verification and Analysis Center at the University of Oklahoma. One of the core components of PACRAIN is a comprehensive rainfall database (Greene et al. 2008). The database currently spans a period of 1874 to the present and contains more than 2 million observations from more than 800 sites. The data are compiled from various national agencies as well as sources unique to PACRAIN. Data are publicly available online at . The goal of the PACRAIN database is to be a starting point for any research requiring tropical Pacific rainfall data. To that end, it presents data in a format that is intended to be flexible enough for a wide range of applications while being robust enough to preserve the information content of a variety of rainfall measurement techniques. Data interpretation is minimal because users are in the best position to choose the most appropriate techniques for their needs. PACRAIN is a partner in the Pacific Islands Global Climate Observing System (PI-GCOS) initiative to expand and enhance climate observation networks in the region. To date, a total of 50 tipping bucket rain gauges have been sent to Cook Islands, Guam, Kiribati, Niue,