Miracle-dz/UniFAR-FTUs huggingface dataset:
2. Conservation psychology and climate change

As the project of this book indicates, the major question surrounding environmental change and degradation is no longer, “Is it happening? ”, but “What can people do about it? ” This is frequently followed by the frustrated question, “Why aren’t people doing anything (or doing enough) to address climate change? ”, or framed more positively, “What explains the people who are taking action? ” With this as the foundation, it becomes clear that we need to consider what motivates or deters people from engaging in actions that protect the climate. Only by understanding these positive and negative factors can we create a social context in which barriers are reduced, facilitators are enhanced, and more people act in ways that protect the natural world. The investigation into behavioral motivations is a fundamentally psychological question. Fortunately, psychologists are increasingly contributing to research and policy that is directed toward environmental challenges. Psychological research on sustainable behavior, examining some of the triggers of actions such as recycling or energy efficiency, has existed more or less since the 1950s. More recently, psychologists have emulated conservation biology by developing the field of conservation psychology: psychological research and theory directed toward understanding and promoting a healthy relationship between humans and the natural world (Clayton & Myers, 2015). The relevance of psychology to sustainability is important to examine for what it says about the role of nature in our lives. Psychologists are deeply concerned with human behavior, and with understanding the motivators and facilitators of behavior in order to promote positive behavioral change. But psychologists are also concerned with human health and well-being, and we are beginning to understand the relevance of environmental health to human health. There has been a veritable explosion of research demonstrating not only the threat that climate change poses to human health, but also the positive benefits that can be obtained from exposure to a healthy natural environment. Thus, even for anyone short-sighted enough to claim that they don’t care about the health of the environment, there are reasons for concern about environmental degradation.
Positive emotions enhance adaptability to contextual-cueing learning

Contextual cueing refers to the global properties of a context or scene used to search for specific objects and regions. Chun and Jiang (1998) found that in a visual search, the reaction time to repeated configurations was shorter than the reaction time to newly generated configurations. The benefit of repeated context–target association is widely known as the contextual-cueing effect, which indicates that the subject has learned the contextual association by which attention is guided to facilitate the searching. However, the learning of contextual cueing lacks adaptability. When the subject has learned a set of contexts, it is difficult to update a new target into existing contexts (re-learning) or to learn a new set of contexts (new-learning). Previous studies have shown that restarted learning processes can facilitate the learning of new context–target associations, while updating old contexts is associated with the scope of attention. Notably, positive emotions could broaden the scope of attention and break the cognitive fixation on old processes; therefore, it is possible to improve the adaptability of contextual-cueing learning via positive emotions. This study aimed to explore whether positive emotions could enhance the adaptability of contextual learning. To this end, we recruited a sample of 18 young adults with positive and neutral affective priming as experimental conditions and control conditions, respectively, which allowed us to explore the contextual-cueing effect under the conditions of re-learning and new-learning. It should be noted that contextual cueing was defined in operation as the reaction time to the newly generated configuration minus that to the repeated configuration. The experiment was divided into two phases: the learning phase and the switch phase. In the learning phase, the subjects learned a set of contextual cues. In the switch phase, with the contextual-cueing effect as the dependent variable, a repeated measures ANOVA was conducted with the emotional valence (positive versus neutral), the new contextual-cueing learning type (re-learning versus new-learning), and the time phase (early phase versus late phase).
OntoDSumm: Ontology-Based Tweet Summarization for Disaster Events

The huge popularity of social media platforms like Twitter attracts a large fraction of users to share real-time information and short situational messages during disasters. A summary of these tweets is required by the government organizations, agencies, and volunteers for efficient and quick disaster response. However, the huge influx of tweets makes it difficult to manually get a precise overview of ongoing events. To handle this challenge, several tweet summarization approaches have been proposed. In most of the existing literature, tweet summarization is broken into a two-step process where in the first step, it categorizes tweets, and in the second step, it chooses representative tweets from each category. There are both supervised as well as unsupervised approaches found in literature to solve the problem of first step. Supervised approaches requires huge amount of labelled data which incurs cost as well as time. On the other hand, unsupervised approaches could not clusters tweet properly due to the overlapping keywords, vocabulary size, lack of understanding of semantic meaning etc. While, for the second step of summarization, existing approaches applied different ranking methods where those ranking methods are very generic which fail to compute proper importance of a tweet respect to a disaster. Both the problems can be handled far better with proper domain knowledge. In this paper, we exploited already existing domain knowledge by the means of ontology in both the steps and proposed a novel disaster summarization method OntoDSumm. We evaluate this proposed method with 4 state-of-the-art methods using 10 disaster datasets. Evaluation results reveal that OntoDSumm outperforms existing methods by approximately 2-66% in terms of ROUGE-1 F1 score.
An adaptive sub-space filter model

A tunable approximate piecewise linear regression model [A. Zaknich, Y. Attikiouzel, December 2000] has been previously developed, which can be adjusted by a single smoothing parameter continuously from the best piecewise linear model in each sub-space to the best approximate piecewise linear model over the whole data space. A suitable value in between ensures that all neighbouring piecewise linear models merge together smoothly at their boundaries. This model was developed by making relatively minor changes to the form of the modified probabilistic neural network [A. Zaknich, July 1998], a network used for general nonlinear regression. The special modified probabilistic neural network (MPNN) structure allows it to be easily used to model a process by appropriately weighting piecewise linear models associated with each piecewise linear models associated with each of the network's radial basis functions, which together cover the data space. The model has now bee further extended to allow each piecewise linear model section to be adapted separately as new data flows through it. By doing this, the proposed adaptive sub-space filter (ASF) model represents a learning/filtering method for nonlinear processes that provides a solution to the stability/plasticity dilemma associated with standard adaptive filters.
A remark on the continuous variation of secondary characteristic classes for foliations

(see \S 3). The foliations which we are going to construct to realize some characteristic classes are product foliations. The reason why these classes vary continuously is that they are in some sense ”decomposable. ” The GodbillonVey class is ”indecomposable” in the sense that it vanishes on any product foliation. In \S 3, we shall remark that there are infinitely many series of indecomposable classes, first of which are those of Godbillon-Vey. To evaluate the characteristic classes on product foliations, we need the ”Cartan formula” for the secondary classes. This will be done in \S 2. The author would like to express his hearty thanks to Dr. Liang for many conversations and to Dr. Lazarov for helpful suggestions. Actually the formulation of the Cartan formula in \S 2 is due to him.
ASSESSING THE RELIABILITY OF ALVARADO SCORING SYSTEM IN PREDICTION OF ACUTE APPENDICITIS.

and Dr. Abdulaziz Altwejri 3 . 1. Undergraduate students at College of medicine, Qassim University. 2. MBBs, MD, SBGS, General surgery consultant, head of general surgery department king fahad specialist hospital-Buraidah. 3. MBBS, general surgery senior specialist, head of A&E department at king fahad specialist hospital-Buraidah. Manuscript Info Abstract . Manuscript History
A Hybrid Approach for the Identification of Multiword Expressions

According to Bannard (2007), “A multiword expression is usually taken to be any word combination that has some feature (syntactic, semantic or purely statistic) that cannot be predicted on the basis of its component words and/or the combinatorial process of language. ”. We present a procedure for the identification of German multiword expressions (MWES) by making use of the morpho-syntactic restrictions of MWEs and of their semantic opacity which we approximate by their translational behaviour. As morpho-syntactic and translational features are independent, we combine their benefits assuming that they complement each other (hybrid approach). Based on the different features, we compute scores indicating a degree of idiomaticity. The procedure can be divided into two parts: candidate extraction from preprocessed text and feature evaluation, cf. Figure 1. We concentrate on preposition-noun-verb (PNV) triples (e.g. ’unter (den) Teppich kehren’ – lit. ’to sweep under the rug’, idiom. ’to hide sth. ’), as this is a high-frequent pattern covering many idiomatic MWEs.
Extended Sammon Projection and Wavelet Kernel Extreme Learning Machine for Gait-Based Legitimate User Identification on Smartphones

Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored password/s. As a result of this potential scenario, this work demonstrates the possibilities of legitimate user identification in a semi-controlled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process: sub-activity recognition followed by user/impostor identification. Prior to the identification; Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrate that together time and frequency domain features optimized by ESP to train the wavelet kernel based extreme learning machine classifier is an effective system to identify the legitimate user or an impostor with \(97\%\) accuracy.
A Pattern Language: Towns, Buildings, Construction

You can use this book to design a house for yourself with your family; you can use it to work with your neighbors to improve your town and neighborhood; you can use it to design an office, or a workshop, or a public building. And you can use it to guide you in the actual process of construction. After a ten-year silence, Christopher Alexander and his colleagues at the Center for Environmental Structure are now publishing a major statement in the form of three books which will, in their words, "lay the basis for an entirely new approach to architecture, building and planning, which will we hope replace existing ideas and practices entirely. " The three books are The Timeless Way of Building, The Oregon Experiment, and this book, A Pattern Language. At the core of these books is the idea that people should design for themselves their own houses, streets, and communities. This idea may be radical (it implies a radical transformation of the architectural profession) but it comes simply from the observation that most of the wonderful places of the world were not made by architects but by the people. At the core of the books, too, is the point that in designing their environments people always rely on certain "languages," which, like the languages we speak, allow them to articulate and communicate an infinite variety of designs within a forma system which gives them coherence. This book provides a language of this kind. It will enable a person to make a design for almost any kind of building, or any part of the built environment. "Patterns," the units of this language, are answers to design problems (How high should a window sill be? How many stories should a building have? How much space in a neighborhood should be devoted to grass and trees? ). More than 250 of the patterns in this pattern language are given: each consists of a problem statement, a discussion of the problem with an illustration, and a solution. As the authors say in their introduction, many of the patterns are archetypal, so deeply rooted in the nature of things that it seemly likely that they will be a part of human nature, and human action, as much in five hundred years as they are today.
Self-Calibrated Cross Attention Network for Few-Shot Segmentation

The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize support samples. Most solutions compress support foreground (FG) features into prototypes, but lose some spatial details. Instead, others use cross attention to fuse query features with uncompressed support FG. Query FG could be fused with support FG, however, query background (BG) cannot find matched BG features in support FG, yet inevitably integrates dissimilar features. Besides, as both query FG and BG are combined with support FG, they get entangled, thereby leading to ineffective segmentation. To cope with these issues, we design a self-calibrated cross attention (SCCA) block. For efficient patch-based attention, query and support features are firstly split into patches. Then, we design a patch alignment module to align each query patch with its most similar support patch for better cross attention. Specifically, SCCA takes a query patch as Q, and groups the patches from the same query image and the aligned patches from the support image as K&V. In this way, the query BG features are fused with matched BG features (from query patches), and thus the aforementioned issues will be mitigated. Moreover, when calculating SCCA, we design a scaled-cosine mechanism to better utilize the support features for similarity calculation. Extensive experiments conducted on PASCAL-5i and COCO-20i demonstrate the superiority of our model, e.g., the mIoU score under 5-shot setting on COCO-20i is 5.6%+ better than previous state-of-the-arts. The code is available at https://github.com/Sam1224/SCCAN.
Space-time super-resolution

We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By "temporal super-resolution," we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else are observed incorrectly) in any of the input sequences, even if these are played in "slow-motion. " The spatial and temporal dimensions are very different in nature, yet are interrelated. This leads to interesting visual trade-offs in time and space and to new video applications. These include: 1) treatment of spatial artifacts (e.g., motion-blur) by increasing the temporal resolution and 2) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. We further analyze and compare characteristics of temporal super-resolution to those of spatial super-resolution. These include: the video cameras needed to obtain increased resolution; the upper bound on resolution improvement via super-resolution; and, the temporal analogue to the spatial "ringing" effect.
A Subspace-Based Non-Dominated Subset Selection Method

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front.
The DaanMatch System: Matching NGOs with CSRs using the UN Sustainable Development Goals

India's legions of hard-working non-profit organizations strive to reduce absolute and relative poverty and improve the capabilities of communities and individuals but struggle for resources. With the introduction of a legal mandate in a 2013 revision to her Companies Act, India became the first country to mandate Corporate Social Responsibility (CSR) expenditure by large firms. Both corporations and Non-Governmental Organizations (NGOs) still struggle to meet the act's requirements. CSR funding is unevenly distributed or unspent, neglecting many issues and regions in need. In 2021, 45% of mandated corporations remained non-compliant and less than 1% of India's NGOs had received funding through the CSR mandate. Small, local NGOs are often extremely effective in terms of impact and outcome. They have the connections, local knowledge and agility to generate and implement sustainable solutions but are excluded from consideration if they struggle to meet transparency requirements. The goal of DaanMatch is to use technology to overcome these and other challenges in development. We are developing a system to facilitate localization of the United Nations Sustainable Development Goals (SDGs) and improve outcomes for Corporate Social Responsibility. This paper describes the DaanMatch system and the innovative technology it utilizes to match CSR with NGOs in a way that showcases small NGOs and helps level the playing field for them. Although the initial emphasis is on India, our goal is to reinvent the process of NGO evaluation, monitoring, auditing, and reporting for charitable giving and global development.
Images, Pre-trained Networks, and Information

This research explores a workflow for describing the relationship between machine learned task complexity and image quality using an image quality metric. The outcome provides strong evidence for the feasibility of an automated analytic scale. To perform these critical experiments, we leverage the current state-of-the-art machine learning detection and classification algorithms that utilize convolutional neural networks (CNNs). The CNN generation of machine learning algorithms has two attractive attributes over previous generations that we leverage in this exploration: 1. Ingest raw imagery rather than hand crafted problem specific features; and 2. Contain a huge volume of computational neurons that produce fine grained discriminations. Previous studies relating task complexity to image quality focused on human perception. These studies do not effectively transfer to automation because 1. human perception can address any task solved at coarser image quality can also be solved with higher image quality and 2. human perception measures do not incorporate false-positive rates into their scale.For this research, we generate image features from pre-trained networks across different image classification tasks with increasing complexity. Those image features are images inferred through a VGG16 CNN model trained with ImageNet weights. We analyze CosmiQ Rare Planes dataset with 2, 4, 8, and 16 classes. Then, we apply the Euclidean distance metric across class means to characterize task complexity. Though we expect better metrics to exist, the workflow shows that an automated analytic scale is feasible for image detection and discrimination tasks.
Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks

In many applications of wireless sensor networks, a sensor node senses the environment to get data and delivers them to the sink via a single hop or multi-hop path. Many systems use a tree rooted at the sink as the underlying routing structure. Since the sensor node is energy constrained, how to construct a good tree to prolong the lifetime of the network is an important problem. We consider this problem under the scenario where nodes have different initial energy, and they can do in-network aggregation. In previous works, it has been proved that finding a maximum lifetime tree from all feasible spanning trees is NP-complete. Since delay is also an important element in time-critical applications, and shortest path trees intuitively have short delay, it is imperative to find a shortest path tree with long lifetime. This paper studies the problem of maximizing the lifetime of data aggregation trees, which are limited to shortest path trees. We find that when it is restricted to shortest path trees, the original problem is in P. We transform the problem into a general version of semi-matching problem, and show that the problem can be solved by min-cost max-flow approach in polynomial time. Also we design a distributed solution. Simulation results show that our approach greatly improves the lifetime of the network and is more competitive when it is applied in a dense network.